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New Research

Let RGB Be the Language of Vision

Timing Yang, Jinrui Yang, Xinlong Li + 11 more

This work introduces a unified formulation for vision models, where diverse forms of visual information beyond natural images, such as masks, depth maps, and other structured visual signals, are all represented as RGB images, while general visual tasks can be converted into a common RGB-to-RGB image editing problem. In this paradigm, different types of visual information internally share the same encoding and decoding architecture and parameters as natural images, enabling a single model to transfer across tasks through a unified visual interface, in a way analogous to how language models operate over text. We refer to this formulation as RGB In and RGB Out (RINO). Built upon a generic image editing backbone without task-specific fine-tuning, RINO demonstrates robust and competitive zero-shot performance on both dense understanding tasks such as segmentation and depth estimation (where we unify outputs as RGB), and dense-conditioned generation tasks such as pose-to-image generation (where we unify inputs as RGB). We hope this study provides useful insights toward general unified vision-language systems, where diverse visual tasks can be expressed, interpreted, and solved through a shared visual language. Code is available at https://github.com/yangtiming/RINO.

Latent-Identity Tuning in Text-to-Image Personalization Models

Daniel Garibi, Ronen Kamenetsky, Hadar Averbuch-Elor + 2 more

Generating and editing a person's face demands high precision, as even minor modifications can significantly alter a subject's perceived identity. Current personalization and editing methods built on general-purpose text-to-image models, however, often lack the precision required for fine-grained facial edits. We present a method for fine-grained identity tuning in text-to-image personalization models. Unlike standard image editing, which operates on a given image, identity tuning modifies the latent representation of a specific identity, enabling the generation of diverse images that consistently depict the same edited identity. To enable fine-grained latent identity tuning, we explore the latent space of a pre-trained, frozen encoder for text-to-image personalization. Our approach requires no additional training. Instead, it leverages the existing architecture of a frozen encoder to uncover latent semantic directions. This space consists of a set of latent tokens that play distinct roles in capturing different aspects of an identity and often correspond to specific spatial or semantic facial regions. We show that meaningful directions can be identified within this space and within subspaces defined by selected tokens, enabling localized, fine-grained, and semantically coherent edits. We validate our approach through qualitative and quantitative experiments that demonstrate diverse localized facial edits while preserving cross-image identity consistency. Project page at: https://garibida.github.io/IdentityTuning/

Metacognition in LLMs: Foundations, Progress, and Opportunities

Gabrielle Kaili-May Liu, Areeb Gani, Jacqueline Lu + 3 more

Metacognition is a foundational component of intelligence critical to effective learning, problem solving, decision-making, communication, and more. In recent years, it has become increasingly recognized as a cornerstone of capable, transparent AI systems. Yet while LLMs have made significant progress across diverse real-world tasks, it is not yet clear when, how, or to what extent they can exhibit or be endowed with effective metacognitive abilities, nor how such abilities can be adapted to advance the fundamental capabilities, reliability, and intelligence of AI systems. This paper bridges this gap by presenting the first comprehensive overview of the current state of knowledge on metacognition for LLMs. We analyze and taxonomize the landscape of this emerging field and summarize recent technical advancements, including methods and benchmarks to measure and evaluate LLMs' metacognitive abilities, techniques to elicit, improve, and apply metacognition in LLMs, and findings and implications of ongoing research. We also discuss applications, open questions and challenges, and promising directions for future work. Our aim is to provide a detailed and up-to-date review of this topic and stimulate meaningful research and discussion. An organized list of papers can be found at https://github.com/yale-nlp/LLM-Metacognition.

Evidence-Backed Video Question Answering

Shijie Wang, Honglu Zhou, Ziyang Wang + 5 more

Current Video Large Language Models (Video LLMs) excel in question answering (QA) but largely operate as black boxes, providing textual answers without verifiable visual grounding. Existing explainability efforts rely on textual rationales or sparse bounding boxes, which struggle to capture complex video dynamics such as occlusions and non-rigid deformations. We propose Evidence-Backed Video Question Answering (E-VQA), a novel task requiring models to jointly output a semantic answer and precise spatio-temporal evidence: temporal segments and dense, tracked object segmentation masklets. To support this, we introduce ST-Evidence, the first human-verified benchmark for both discriminative and generative pixel-level grounding. Evaluations of state-of-the-art models reveal a critical decoupling between QA accuracy and true visual perception that scaling alone fails to bridge. To address this, we develop scalable, automated generation pipelines to create ST-Evidence-Instruct, a 160k-scale dataset bridging high-level reasoning with fine-grained grounding. Fine-tuning grounded Video LLMs on this data yields substantial gains over the corresponding size-matched UniPixel baselines (e.g., +27.2 t-mean and +13.8 J&F on a 7B model), establishing a robust baseline for explainable, evidence-backed video understanding. Code and data are available at https://github.com/SalesforceAIResearch/EVQA.

AdvancedMathBench: A Benchmark Suite for Advanced Mathematical Proof Generation and Verification

Lingkai Kong, Zijian Wu, Yuzhe Gu + 10 more

Large language models (LLMs) have achieved remarkable performance on high-school and olympiad-style mathematics, yet their capabilities on advanced mathematics remain poorly understood. Existing benchmarks, however, fall short in both scope and evaluation granularity: they provide limited disciplinary coverage and often rely on final-answer correctness or coarse judgments, leaving the validity of the reasoning process inadequately assessed. To bridge this gap, we introduce AdvancedMathBench, a benchmark suite designed to evaluate advanced mathematical reasoning capabilities. Its core proof-generation benchmark, ProverBench, contains 296 problems spanning undergraduate and doctoral qualifying-exam levels. To provide reliable evaluation of the proofs, we develop a dedicated automatic verification pipeline trained on large-scale expert annotations to produce both correctness verdicts and fine-grained assessments of proof errors, which exhibits strong agreement with human experts on held-out proof trajectories. We further introduce VerifierBench, consisting of 888 model-generated proof trajectories paired with expert ground truth, to evaluate whether models can correctly judge proof validity and provide sound verification rationales. Experiments show that AdvancedMathBench remains challenging for frontier models. On proof generation, the best-performing model, GPT-5.5-xhigh, achieves only 75.8 and 66.1 on the UGD and QE splits, respectively, indicating substantial room for improvement on advanced mathematical proof construction. On proof verification, the best model attains a Balanced F1 of only 65.1, and models generally exhibit low true negative rates, suggesting that critical error detection remains a major bottleneck.

MET: Theory-Grounded and Culture-Aware Multilingual Moral Reasoning

Ayoung Lee, Ryan Kwon, Yunxiang Zhang + 3 more

Language models are increasingly used for moral decision-making across diverse linguistic and cultural contexts, yet existing work overlooks multilinguality on three aspects: 1) multilingual evaluation benchmarks use direct translation, failing to adapt culture-specific items; 2) inference-time methods for moral reasoning rely on static, English-centric scaffolds and lack grounding in moral theory; 3) training methods for moral decision-making typically require expensive supervision from stronger models or human annotators. We address these gaps with three contributions. First, we introduce MCLASH, a multilingual moral decision-making benchmark to capture culturally situated moral intuitions and social norms across languages. Second, we propose MET (Multilingual Ethics with Theory-grounded reasoning), a two-step prompting method built on expert-curated, theory-based grounds drawn from psychology and philosophy: the model first selects situation- and culture-specific grounds, then reasons over them in the native language of the user. Third, we introduce MET-D (MET-Distillation), which enhances the second step through a self-distillation training stage that requires no external supervision. MET-D improves macro-F1 over the base model on all three models of different sizes and families (Qwen3-4B, Qwen3-8B, Gemma3-4B), by an average of 3.71 points on MCLASH and 4.23 on MMoralExceptQA, with a peak MCLASH gain of 12.94 points for Malay on Qwen3-8B. We further reveal that MET-D increases native-language reasoning by 62.13 points on average, and that beneficial grounds differ systematically across cultures. Together, these contributions open the path for culture-aligned, theory-grounded multilingual moral reasoning.

Motion4Motion: Motion Transfer Across Subjects at Inference

Ling-Hao Chen, Zixin Yin, Duomin Wang + 2 more

This work explores the motion transfer from one video to another, which is crucial in animation for diverse characters. Previously, video motion transfer has been largely explored between human and human-like characters, enabling a lot of applications in digital creation. However, these approaches encounter a main limitation. Specifically, related technical pipelines heavily rely on a predefined human skeleton structure and accordingly require skeleton-conditional model training. On the one hand, these methods are difficult to generalize to diverse characters, such as animals from different species, while preserving their unique motion styles. On the other hand, labeled data in diverse skeletons is limited, which additionally restricts the large-scale training for the task. In this paper, we jump out of the skeleton-based motion transfer framework and propose a training-free motion transfer framework, named Motion4Motion. Motion4Motionmodels the motion flow of the character in a video instead of skeletons, which makes motion transfer across species easier. Extensive experimental results and novel applications show our methods outperform baselines impressively. Project page is available at https://lhchen.top/Motion4Motion.

Xiaomi-Robotics-U0: Unified Embodied Synthesis with World Foundation Model

Xinghang Li, Jun Guo, Qiwei Li + 21 more

Recent foundation image and video generation models offer strong generalization and controllability, but their direct application to embodied scenarios is limited by requirements for multi-view consistency, geometric coherence, and robot embodiment constraints. Existing methods typically adapt foundation models with limited robot data, often sacrificing visual knowledge acquired during large-scale pre-training. We present Xiaomi-Robotics-U0, a 38-billion-parameter multimodal autoregressive model for unified embodied synthesis. It treats embodied generation as an extension of foundation image and video generation and jointly optimizes text-to-image generation, image editing, embodied scene generation, embodied transfer, and embodied video generation. This unified framework preserves the generalization of the pre-trained world foundation model while adapting it to embodied settings. Xiaomi-Robotics-U0 is the first model to support high-quality multi-view scene generation across multiple robot embodiments and to introduce structured, controllable embodied transfer for fine-grained editing while preserving multi-view consistency and interaction dynamics. It achieves state-of-the-art results on single-step and sequential generation tasks, outperforming GPT-Image-2.0 in human evaluations of embodied scene generation and transfer, ranking first on World Arena for embodied video generation, and improving the out-of-distribution success rate of pi_0.5 from 36.9% to 63.2% on challenging real-world manipulation tasks. These results show that foundation world models can serve both as embodied world models and scalable data engines for embodied intelligence. Code and checkpoints are available at https://robotics.xiaomi.com/xiaomi-robotics-u0.html.

MonkeyOCRv2: A Visual-Text Foundation Model for Document AI

Yuliang Liu, Zhang Li, Ziyang Zhang + 11 more

Mainstream visual encoders are pretrained on natural images and cannot be effectively applied to document images without document-oriented adaptation, as dense text and fine-grained character strokes demand character-level visual perception. We present MonkeyOCRv2, a visual-text pretrained model for document AI. First, we construct MonkeyDoc v2, to our knowledge the largest document-image pretraining corpus, comprising 113 million images spanning 17 languages. Second, we propose a pretraining strategy that jointly learns image-to-text generation and pixel-level document reconstruction: the former aligns visual representations with textual content, while the latter preserves character strokes and layout details. Extensive experiments are conducted on five representative document analysis tasks, including text recognition, formula recognition, text detection, document tampering detection, and overlapping text segmentation. Replacing the original encoders with MonkeyOCRv2 consistently improves performance across all five tasks. Finally, we validate its effectiveness as the vision encoder of multimodal large language models on the more challenging tasks of document parsing and document understanding. Kept frozen and paired with a lightweight language model, it yields a 0.7B document parsing model that sets a new open-source state-of-the-art on MDPBench, a recent benchmark spanning digital-born and photographed documents across 17 languages, surpassing the previous best 3B dots.mocr by 2.8% absolute with a vision encoder roughly 11times smaller. The frozen encoder also powers a document understanding model that outperforms counterparts built on CLIP, DINO, and SAM across eight benchmarks under identical training settings. These results suggest that document-oriented visual pretraining can serve as a foundation for document intelligence in its own right.

Proxy Exploration and Reusable Guidance: A Modular LLM Post-Training Paradigm via Proxy-Guided Update Signals

Daocheng Fu, Rong Wu, Yu Yang + 7 more

Post-training is essential for refining the domain-specific capabilities of large language models (LLMs), yet existing reward optimization and distribution matching methods tightly couple policy exploration with distribution alignment. This coupling forces expensive exploration directly on the policy model and severely hinders the asynchronous generation, reuse, and cross-model transfer of optimization signals. In this paper, we propose Proxy-guided Update Signal Transfer (PUST), a novel post-training framework that fundamentally decouples update-signal exploration from distribution alignment. Instead of utilizing the primary model for costly exploration, PUST employs a lightweight proxy model as an efficient testbed to discover high-reward behaviors. We extract the relative improvement signal between the proxy's initial and optimized states, transferring this directional update to the primary model to guide its policy alignment. This decoupled pipeline, comprising proxy exploration, update-signal extraction, and signal transfer, significantly reduces computational overhead and enables optimization signals to be asynchronously generated, cached, and reused. Crucially, by transferring relative improvements rather than absolute policy distributions, PUST naturally supports weak-to-strong improvement and seamless cross-model transfer. Systematic evaluations on Qwen3-family models across math and code domains demonstrate that update signals extracted from substantially weaker proxies can robustly and adjustably enhance stronger primary models. Ultimately, PUST transforms post-training from a monolithic online optimization process into a highly modular, reusable, and cost-efficient paradigm.

LightMem-Ego: Your AI Memory for Everyday Life

Yijun Chen, Boyi Xiao, Yixian Zhao + 10 more

Personal AI assistants on mobile and wearable devices continuously perceive users' daily lives through visual and audio streams. However, answering queries about past experiences requires lightweight multimodal memory that can continuously accumulate, organize, and retrieve long-term experiences, which remains challenging. To address this challenge, we present LightMem-Ego, a lightweight streaming multimodal memory system for everyday-life assistance. The system continuously captures egocentric visual and audio streams, aligns them on a shared timeline, and organizes them into a hierarchical memory consisting of current, short-term, and long-term memory. Given a user query, LightMem-Ego dynamically routes retrieval to the appropriate memory level and generates answers grounded in multimodal evidence. The demonstration can be deployed on smartphones and AI glasses, supporting object finding, conversation recall, life summarization, routine discovery, and personalized assistance. Code is available at https://github.com/zjunlp/LightMem-Ego.

Multi-Agent LLMs Fail to Explore Each Other

Hyeong Kyu Choi, Jiatong Li, Wendi Li + 2 more

Exploration is essential for reliable autonomy in multi-agent systems, yet it remains unclear whether large language model (LLM) agents can explore effectively when interacting with one another. We show that modern LLM agents fail to do so, often exhibiting myopic and polarized interaction patterns that lead to suboptimal coordination and increased regret. We formalize this challenge as the Multi-Agent Exploration problem, modeling it as a partially observable stochastic game (POSG) problem in which agents must probe peers to infer their capabilities and identify effective interaction strategies. To address this, we introduce Multi- Agent Contextual Exploration (MACE), a lightweight framework that explicitly promotes exploration through structured peer selection. Across both contextual and parametric diversity settings, MACE substantially improves exploration behavior and downstream task performance. We further show theoretically that the value of exploration increases with agent diversity. Overall, our results highlight a fundamental limitation of current LLM agents and underscore the importance of explicitly guided exploration for reliable multi-agent autonomy. Code will be released in https://github.com/deeplearning-wisc/mace

Read It Back: Pretrained MLLMs Are Zero-Shot Reward Models for Text-to-Image Generation

Runhui Huang, Qihui Zhang, Zhe Liu + 3 more

In this paper, we propose SpectraReward, a training-free reward function that turns pretrained MLLMs into off-the-shelf reward models for image-generation reinforcement learning. Instead of asking the MLLM to judge a generated image or answer decomposed verification questions, SpectraReward measures how well the original prompt can be recovered from the generated image through a single image-conditioned, teacher-forced forward pass. We use the average image-conditioned prompt log-likelihood as the reward, directly reusing the MLLM's pretrained image-text alignment ability without preference labels, reward-model fine-tuning. We further introduce Self-SpectraReward, a special case for unified multimodal models where the policy's own understanding branch serves as the reward model for its generation branch, forming a closed-loop self-improving framework without external reward models or external knowledge. Extensive experiments validate SpectraReward through a broad image-generation RL study covering two diffusion models, three RL algorithms, nine reward MLLM backbones from four MLLM families spanning 4B to 235B parameters, and five out-of-distribution text-to-image benchmarks. Results show that both SpectraReward and Self-SpectraReward significantly and consistently improve generation performance and outperform prior MLLM-derived reward training methods. Further analysis reveals that larger reward MLLMs are not always better, while Self-SpectraReward can match or surpass much larger external reward models, suggesting that reward-policy alignment is a key factor for effective image-generation RL. Project Page: https://huangrh99.github.io/SpectraReward/

Know Before Fix: QA-Driven Repository Knowledge Acquisition for Software Issue Resolution

Haotian Lin, Silin Chen, Xiaodong Gu + 8 more

LLM-based coding agents have significantly advanced automated software issue resolution, yet they remain highly prone to factual errors caused by insufficient repository understanding. Recent methods attempt to mitigate this limitation through pre-repair repository exploration; however, their fix-driven strategies explore repositories without identifying the agent's knowledge gaps, often yielding imprecise context that fails to bridge the underlying understanding deficit. In this paper, we propose ACQUIRE, a QA-driven framework for software issue resolution. Mirroring how experienced developers first comprehend unfamiliar code before attempting a fix, ACQUIRE explicitly acquires repository knowledge prior to repair. The framework decouples knowledge acquisition from patch generation through two stages: in the first stage, a Questioner and an Answerer collaborate to acquire structured repository knowledge, where the Questioner poses targeted questions and the Answerer produces evidence-grounded answers through autonomous exploration; in the second stage, the Resolver leverages the resulting QA knowledge to generate informed patches. By transforming implicit knowledge gaps into explicit, factually reliable understanding, ACQUIRE accelerates knowledge-intensive repair stages and enables more accurate resolution. Experiments on SWE-bench Verified demonstrate that ACQUIRE consistently outperforms representative pre-repair methods, raising Pass@1 by up to 4.4 percentage points with modest additional cost and time.

LATO.2: Factorized 3D Mesh Generation with Vertex and Topology Flow

Hang Long, Tianhao Zhao, Junkai Lin + 7 more

Flow matching over carefully designed latent representations has recently emerged as a powerful paradigm for topology-aware mesh generation. Existing approaches, however, model vertices and connectivity jointly in a joint latent space, entangling continuous vertex geometry with discrete combinatorial structure; this complicates flow learning and manifests as drifting vertices and broken surfaces. We present LATO.2, a factorized flow matching framework that decomposes mesh generation into a vertex flow followed by a connectivity flow conditioned on the realized vertices, with both stages anchored to a shared coarse voxel scaffold. Dedicated VAEs underpin the two stages, recovering vertices at sub-voxel precision and embedding discrete connectivity into a continuous latent space. We demonstrate two advantages unique to this factorization: (i) part-wise generation, in which the scaffold is partitioned and each part synthesized at full latent capacity, yielding substantially higher-resolution meshes than a monolithic latent permits; and (ii) topology-adaptive editing, in which manipulating first-stage vertices induces the corresponding connectivity without re-optimization. Experiments show that LATO.2 surpasses state-of-the-art topology-aware mesh generators in geometric fidelity and connectivity quality.

Towards Autonomous and Auditable Medical Imaging Model Development

Shengyuan Liu, Jia-Xuan Jiang, Boyun Zheng + 8 more

Large language model (LLM) agents are beginning to automate machine learning engineering (MLE) by coupling planning, code execution, debugging, and empirical feedback. Translating this capability to medical imaging remains difficult because each task imposes modality-specific experimentation and strict requirements for validation protocols and prediction artifacts. Here we introduce AMID, an autonomous multi-agent framework for medical imaging model development. AMID first proposes Data-Conditioned Method Planning, which refines coarse task-level search spaces into executable, parallelizable method lanes grounded in task-specific data analysis and runnable medical-imaging resources. It then develops Verification-Guided Two-Stage Optimization, moving from broad early exploration of diverse method lanes to selective exploitation of promising candidates while enforcing strict verification of validation protocols, metric computation, and prediction artifacts throughout the optimization. Across 20 medical imaging challenge tasks spanning diverse modalities and prediction types, AMID outperformed evaluated general-purpose MLE systems and, on several tasks, approached or matched strong human-designed challenge solutions. These results suggest that AMID can turn task-specific medical imaging model development from bespoke manual engineering into an agentic workflow for producing high-performing and auditable model artifacts across heterogeneous tasks.

ABot-N1: Toward a General Visual Language Navigation Foundation Model

Ruiyan Gong, Yingnan Guo, Junjun Hu + 37 more

Visual Language Navigation foundation models aim to unify deep reasoning for grounded spatial decisions with broad versatility for diverse embodied tasks. Current approaches typically achieve this integration via monolithic policies that map observations directly to actions, yet they often suffer from coordinate drift and poor handling of long-tail semantics. Furthermore, these black-box mappings lack interpretability, hindering the simultaneous achievement of generality, robustness, and transparency. We present ABot-N1, a step toward a general Visual Language Navigation foundation model, that addresses these challenges by decoupling cognition from control via a slow-fast architecture guided by dual visual-language signals. More specifically, a slow vision-language reasoner performs explicit Chain-of-Thought reasoning while producing a pixel goal. This compact set of image-space anchor points serves as a universal interface for diverse tasks, including point-goal, object-goal, poi-goal, instruction-following, and person-following. Subsequently, a fast action expert leverages both the textual cues and the pixel guidance to generate continuous waypoints at the native control frequency. By bridging high-level intents and low-level control through pixel-grounded anchors paired with explicit linguistic traces, our approach ensures robust, generalizable, and interpretable navigation across simulation and real-world benchmarks. ABot-N1 establishes new state-of-the-art records, delivering massive gains specifically in urban-scale navigation: boosting POI arrival by 35.0% (to 77.3%) and achieving 95.4%/92.9% SR in complex indoor and outdoor scenes. It also maintains superior robustness across object-reaching, person-following, and instruction-following tasks. New Point-Goal/POI-Goal benchmarks are released as open source to advance the field of urban-scale navigation.

ABot-AgentOS: A General Robotic Agent OS with Lifelong Multi-modal Memory

Jiayi Tian, Shiao Liu, Yuting Xu + 24 more

Recent VLM and VLA systems have improved robotic perception and action prediction, yet long-horizon embodied agents still require a general runtime layer for reasoning, memory, tool use, verification, and cross-embodiment execution. We present ABot-AgentOS, a general robotic Agent Operating System that sits above low-level controllers and provides a deliberative agent layer for scene-conditioned planning, context-isolated skill execution, multi-stage verification, multi-modal memory, and edge-cloud collaboration. To evaluate such systems, we introduce EmbodiedWorldBench, an executable benchmark with 16 indoor, outdoor, and hybrid scenes, four difficulty levels, and over 200 tasks involving navigation, object search, NPC dialogue, dynamic events, and trace-grounded scoring. ABot-AgentOS further introduces Universal Multi-modal Graph Memory, a persistent source-grounded substrate that converts dialogue, visual observations, spatial context, temporal relations, and task traces into typed nodes and edges. A failure-driven self-evolution loop converts diagnosed memory failures into gated runtime evo-assets that are promoted only to later evaluation splits, preventing current-split ground-truth leakage while enabling continual improvement. On an initial EmbodiedWorldBench subset, ABot-AgentOS improves over a single-controller baseline in both task success and goal completion. Across memory benchmarks, ABot-AgentOS Static achieves 87.5 on LoCoMo, 59.9 on OpenEQA EM-EQA, 88.6 on Mem-Gallery, and 76.5 Acc@All on NExT-QA; self-evolution further improves LoCoMo to 88.7, OpenEQA to 60.4, and Mem-Gallery to 89.0. These results suggest that a general Agent OS layer can improve long-horizon embodied execution while providing persistent, auditable memory for continual interaction.

SynthDocBench: Controlled Benchmark for Long-Context Visual Document Understanding

Abhigya Verma, Khyati Mahajan, Amit Kumar Saha + 4 more

Vision language models (VLMs) have achieved strong performance on visual document understanding benchmarks such as DocVQA, ChartQA, and MMLongBench-Doc. However, real-world documents combine multiple factors such as length, layout complexity, modality, and question difficulty, which makes it difficult to attribute model failures to specific causes. We introduce SynthDocBench, a fully synthetic benchmark for long-context visual document understanding that systematically controls factors including document length, layout structure, modality composition, and question type. The benchmark is constructed using a combinatorial design, each factor is varied independently across generated documents, enabling controlled analysis of model behavior. Documents are generated end to end using an LLM pipeline across six layout archetypes, with a 40 percent random override to prevent models from exploiting spurious correlations. Additionally, SynthDocBench spans long-context documents with substantially greater length and structural diversity than existing benchmarks. Evaluating seven frontier VLMs, we uncover three failure modes that existing benchmarks cannot surface: sharp degradation with document length, a systematic positional sensitivity in which the middle third of a document is hardest for five of six models and five of six models show a negative Early-to-Late trend (steepest decline: 8.3 percentage points), and breakdown of chart comprehension in long-document settings. These results suggest that current models may be overfitting to benchmark artifacts rather than achieving robust long-context visual document understanding.

CtrlVTON: Controllable Virtual Try-On via Visual-Instance-Prompt Segmentation

Seungyong Lee, Hyun Jun Jang, Sangoh Kim + 1 more

Virtual try-on (VTO) has made significant progress in realistically transferring garments onto a target person. Yet most systems give the user little control over how a garment should be worn -- its size (loose or fitted), style (e.g., tucked in or untucked, open or closed), and spatial placement on the body. We address this gap with two complementary contributions. First, we define and solve Visual-Instance-Prompt Segmentation via VIP-SAM: given a flatlay image of a garment, segment that specific instance in a photograph of a person wearing it. This is an instance-level task, distinct from the typically studied category-level segmentation. Second, we introduce CtrlVTON, a controllable VTO framework that recasts try-on as an image editing problem and adds segmentation masks as pixel-level control over garment layout, including style, size, and spatial placement on the body. VIP-SAM and CtrlVTON each achieve state-of-the-art results on their respective tasks. In particular, CtrlVTON generates images that follow user-provided layouts far more faithfully than the strongest proprietary editing systems while matching them on garment fidelity.

4D Human-Scene Reconstruction from Low-Overlap Captures

Minhyuk Hwang, Sangmin Kim, Seunguk Do + 2 more

Existing volumetric capture of dynamic human performance achieves high fidelity with dense camera arrays. However, in real-world scenarios, only a handful of low-overlap cameras are available, which degrades the output quality and leaves large areas unobserved. Recent 4D reconstruction methods have focused on low-overlap settings, yet they still produce noticeable artifacts in under-observed regions. Video diffusion models have emerged as another option, but they show geometrically inconsistent results for humans. To address these limitations, we propose StudioRecon, a pipeline that reconstructs 4D human scenes from sparse, low-overlap cameras by decoupling background and humans. We densify background supervision by synthesizing hundreds of camera-controlled novel views with a video diffusion model. We also robustly initialize deformable Gaussian humans with cross-view identity association and triangulated multi-view keypoint fitting. Finally, our recursive enhancement module with motion-adaptive consistency injection harmonizes the composed output, thereby further avoiding remaining artifacts. We achieve state-of-the-art novel view synthesis across four real-world datasets and demonstrate applications such as novel trajectory rendering and human replacement.

PanoWorld: Real-World Panoramic Generation

Haoyuan Li, Dizhe Zhang, Yuemei Zhou + 7 more

In this work, we aim to address the challenge of long-range memory in panoramic world models by exploiting the rotation-equivariant property of omnidirectional representations, where rotation can be treated as an implicit geometric transformation.Building on this insight, we propose PanoWorld, which simplifies camera trajectories into translations via fixed headings for both current-action modeling and long-range memory through Dense Panoramic Ray-Conditioning (DPRC) and Geometry-aware Memory Augmentation (GMA).Then, a three-stage training pipeline is introduced to progressively optimize each component. To better evaluate physical consistency under large-scale spatial variations and diverse illumination conditions, where existing datasets are relatively stable, we construct World360, a large-scale dataset consisting of both real-world video clips collected via panoramic unmanned aerial vehicles and high-quality simulated clips generated by AirSim360.Extensive experiments on World360 demonstrate the effectiveness of PanoWorld, outperforming alternative methods by a large margin.Our models, training code, and dataset will be publicly available. More information can be found on our project page: https://lihaoy-ux.github.io/panoworld-page/.

Phone Segmentation and Recognition through Phonological Activation Mapping

Shikhar Bharadwaj, Kwanghee Choi, Stephen McIntosh + 8 more

Phone segmentation and recognition are inherently related tasks, yet modern approaches typically model them separately. We argue that phonetic structure is already latent in the representations of self-supervised speech models (S3Ms), and one only needs to steer them to solve both tasks. We leverage S3M-based Phonological Activation Mapping (SPAM), which maps each S3M representation frame to a vector of phonological feature activations, such as voicing and nasality. On top of SPAM, we introduce two simple but effective lightweight, gradient-descent-free prediction heads: a recognition head and a segmentation head. Our method requires less than a minute of phonetic transcriptions, and generalizes to unseen phones during training. Across a diverse range of datasets, our approach attains strong segmentation and recognition performance.

Scalable Visual Pretraining for Language Intelligence

Yiming Zhang, Zhonghan Zhao, Wenwei Zhang + 13 more

The rapid progress of large foundation models has been driven predominantly by pretraining on large-scale text corpora. However, many forms of knowledge are conveyed through visual representations, where figures, typeset equations, and page layouts carry rich information that cannot be faithfully or completely captured by text alone. Yet current pretraining approaches discard these visual cues by converting visually rich sources, such as documents and web pages, into plain text for learning language intelligence. This paper challenges the default assumption that language models must be trained on text-only representations and shows that Visual Pretraining is a scalable learner for foundation model intelligence. To this end, we conduct a systematic study of unsupervised visual pretraining paradigms that directly leverage visual documents without text extraction. Across multiple backbones and benchmarks, visual pretraining on the same underlying corpora consistently outperforms text-only pretraining, offering an efficient pathway to scalable language intelligence.

A Sovereign, Open-Source Foundation Model for German and English

The Soofi-Team, Benedikt Droste, David Fitzek + 28 more

We present Soofi S 30B-A3B, a sovereign, open-source Mixture-of-Experts (MoE) hybrid Mamba Transformer foundation model for German and English. Its hybrid design activates only 3B of 30B parameters per token and keeps the inference cache near-constant as context grows, giving it a decisive throughput advantage over dense models for long-context, high-concurrency deployment. Pretrained on roughly 27 trillion tokens with deliberately up-weighted German, Soofi S matches dense 14 to 27B models on aggregate English and German benchmarks while achieving the best code aggregates in both languages among 17 open base models, and outperforms every European sovereign baseline in our comparison, including ones far larger in active parameters. Among fully open models, Soofi S obtains the highest English and German evaluation scores, ahead of Olmo 3 32B and Apertus 70B. Soofi S was built end-to-end on the German Industrial AI Cloud, a sovereign HPC scale AI infrastructure operated by Deutsche Telekom in Munich. Soofi S will be released under highly permissive, open-access terms: weights, selected intermediate checkpoints, full per-source data accounting, hyperparameters, and training and evaluation code. Where source licenses permit, data-construction artifacts are released under permissive licenses; commercially licensed sources are documented with aggregate statistics and exact mixture accounting.

Video Generation Models are General-Purpose Vision Learners

Letian Wang, Chuhan Zhang, Rishabh Kabra + 9 more

Driven by next-token prediction, NLP shifted from task-specific models into powerful generalist foundation models. What, then, is the equivalent catalyst needed to achieve a general-purpose model in computer vision? In this paper, we contend that large-scale text-to-video generation serves as a strong pre-training paradigm for computer vision, providing the necessary spatiotemporal priors, vision-language alignment, and scalability required for general visual intelligence. We introduce GenCeption, which leverages a pre-trained video generative diffusion backbone to define a feed-forward perception model, capable of performing various vision tasks steered by text instructions. Empirical results demonstrate that GenCeption achieves state-of-the-art performance across a diverse suite of tasks, including depth, surface normal, and camera pose estimation, expression-referring segmentation, and 3D keypoint prediction, often matching or surpassing specialized models (e.g. DepthAnything3, SAM3, D4RT, VGGT-Omega, Sapiens, David, Genmo, and Lotus-2). Furthermore, the video generative pretrained backbone outperforms alternative pretraining paradigms (e.g., V-JEPA, and Video MAE) under comparable settings. Importantly, GenCeption exhibits preliminary data and model scaling properties along with exceptional data efficiency, where it achieves comparable performance with leading models like D4RT and VGGT-Omega with 7 to 500 less training data. Finally, GenCeption also exhibits intriguing emergent behaviors: a model trained exclusively on synthetic human videos generalizes to real-world footage and out-of-distribution object categories (e.g., animals and robots). These findings suggest that video generation is not merely a synthesis tool, but a foundational path toward generalist vision intelligence for the physical world. Project page: https://genception.github.io

Self-Guided Test-Time Training for Long-Context LLMs

Xinyu Zhu, Zhe Xu, Xiaohan Wei + 10 more

Long-context processing has become increasingly important for large language models (LLMs), but simply extending the context window does not guarantee effective utilization of long inputs. As input length grows, accuracy often degrades, indicating that models still struggle to identify and use the evidence most relevant to a question. A promising way to improve long-context utilization is test-time training (TTT), which treats the test context as a training example for instance-specific parameter adaptation. However, applying TTT to the entire long context is prohibitively expensive, while adapting on randomly sampled spans introduces severe noise. Because most spans in a long context are irrelevant to the specific question, training on them may even degrade the base model's performance. Our preliminary study shows that TTT is highly sensitive to training-span quality: on LongBench-v2, TTT on randomly sampled spans hurts performance, whereas TTT on oracle spans substantially improves it. Motivated by this, we propose a simple method, Self-Guided TTT (S-TTT): before adaptation, the model identifies the evidence spans it should learn from, and the standard language-modeling training objective is applied only to those selected spans. On two challenging long-context reasoning benchmarks, LongBench-v2 and LongBench-Pro, S-TTT improves accuracy for both Qwen3-4B-Thinking-2507 and Llama-3.1-8B-Instruct, achieving up to a 15% relative improvement.

Towards Mechanistically Understanding Why Memorized Knowledge Fails to Generalize in Large Language Model Finetuning

Lu Dai, Ziyang Rao, Yili Wang + 3 more

Fine-tuning LLMs to inject new knowledge faces a critical challenge: LLMs can quickly memorize new facts, yet fail to use them for downstream reasoning tasks. We formalize this failure as the \textbf{Knowing--Using Gap}, characterized by an accuracy gap and a temporal lag between memorization and generalization. To understand this phenomenon, we fine-tune LLMs with unseen knowledge and monitor the spatial permeation dynamics of the knowledge internally using a novel intervention technique called self-patching. Self-patching identifies activation locations where relocating representations substantially improves failed generalization cases. These results are consistent with a knowledge-circuit misalignment hypothesis: memorized representations can exist internally but may not be routed to computation-effective layers. To demonstrate the practicality of this diagnostic finding, we design a simple heuristic strategy which recovers 58--75\% of the oracle headroom in generalization failure. Experiments are done cross-domain for the robustness of this finding.

What LLM Forecasters Know but Don't Say: Probing Internal Representations for Calibration and Faithfulness

Raphaël Sarfati, Pratyush Ranjan Tiwari, Siddharth Boppana + 3 more

Large language models fine-tuned for forecasting can be accurate yet poorly calibrated, and their chain-of-thought (CoT) reasoning may not faithfully reflect the evidence behind a forecast. We ask whether internal representations offer a more direct window into both. Working with Eternis-Forecaster 8B on OpenForesight, we train representation-pooling probes on intermediate activations and find they achieve substantially better calibration; a result that also holds for GLM-4.7-Flash and GLM-4.5-Air. We then assess CoT faithfulness through evidence ablation and diversionary injection: removing an influential source in the prompt often changes the model's forecast while leaving the reasoning trace untouched. The same probes function as lie detectors: their activations track behavioral shifts far better than the reasoning trace does, and they also predict the direction of change in 84% of cases, including when the CoT conceals the perturbation's influence. Finally, forced answering reveals that forecasts are largely fixed before reasoning begins: a single pre-reasoning pass recovers the committed answer and confidence, and routing questions by the spread of this pre-set answer distribution saves 30-47% of generated tokens, with no loss of accuracy. Together, these results establish probing internal representations as a practical tool for calibrating, auditing, and triaging language model forecasters and reasoning models more broadly.

Search Beyond What Can Be Taught: Evolving the Knowledge Boundary in Agentic Visual Generation

Haozhe Wang, Weijia Feng, Jinpeng Yu + 8 more

Visual generators excel at rendering, but they confidently fabricate what they do not know. User requests are unbounded, evolving, and deeply long-tailed: new characters, trending entities, post-cutoff events, and more. This world-knowledge bottleneck is structural: generators are trained on fixed corpora, but the visual world is open-ended. We construct SearchGen-20K and SearchGen-Bench, with 20,839 prompts spanning twelve failure categories and twenty-two domains, paired with a pre-executed multimodal SearchGen-Corpus-1M to support offline, reproducible research. On SearchGen-Bench, frontier open generators score only 21 to 28 out of 100, a 40-point collapse invisible to existing benchmarks. The natural remedy is to employ search tools, enabling agentic visual generation. However, we find that naive search fails: it retrieves indiscriminately, injecting noise into prompts the generator already handles. We trace the root cause to a generator-specific, evolving knowledge boundary: the divide between what a generator can internalize through training and what must remain in external context. Although this boundary is hard to specify in advance, we show that it is discoverable through a teach-then-search co-training framework. Even a minimal version of this co-training recipe produces monotonic improvement, laying the foundation for recursive self-improvement in visual generation that can meet world-knowledge-grounded requests. We release the full dataset, co-training corpus, and search corpus as a replayable harness for tool-augmented, world-knowledge-grounded visual generation.

MuScriptor: An Open Model for Multi-Instrument Music Transcription

Simon Rouard, Michael Krause, Axel Roebel + 2 more

Existing methods for automatic music transcription are often limited to single-instrument recordings or fail on complex, real music mixes. Although previous work utilizes synthetic training data, the resulting models generalize poorly, leading to largely unusable transcription output in realistic, multi-instrument settings. In this work, we analyze the effectiveness of synthetic data for pre-training while combining it with fine-tuning on real music audio and post-training using reinforcement learning. We further introduce conditioning on instrument presence to customize transcriptions. Finally, we release MuScriptor, an open-weight multi-instrument music transcription model that works on real-world music recordings from across a diverse range of musical genres.

Blind-Spots-Bench: Evaluating Blind Spots in Multimodal Models

Matteo Santelmo, Xiuying Wei, Israa Fakih + 5 more

Modern AI models achieve strong performance on many established benchmarks, yet they still fail on tasks that humans find almost trivial, such as manipulating a string or drawing a dog with five legs. These examples suggest that existing benchmarks may under-measure persistent blind spots in current systems. We introduce blind-spots-bench, a benchmark designed to expose such blind spots through tasks that appear simple for humans but remain challenging for modern AI. We collect raw questions from students in an AI course, clean and annotate them with structured reference solutions, and propose a task taxonomy tailored to the resulting dataset of 235 samples. We further develop an automated grading pipeline to evaluate a wide range of models, including open-weight and closed-source language, vision-language, and image-generation models. Our analysis on blind-spots-bench reveals that closed-source frontier models can substantially outperform open-weight models with even approx10% gap, even when they attain comparable performance on existing benchmarks. A more fine-grained analysis shows that no single model dominates across all task types, and that some tasks remain challenging for all evaluated models. These results highlight the value of blind-spots-bench as a diagnostic stress test for identifying concrete weaknesses in current modern models.

Long-Horizon-Terminal-Bench: Testing the Limits of Agents on Long-Horizon Terminal Tasks with Dense Reward-Based Grading

Zongxia Li, Zhongzhi Li, Yucheng Shi + 10 more

AI agents have become capable of autonomously completing short, well-specified tasks. However, existing terminal benchmarks largely focus on simple problems that finish within minutes and are evaluated only by their final outcome. This setup overlooks intermediate progress and partial solutions, yielding sparse reward signals and an incomplete picture of agent capability. We introduce Long-Horizon-Terminal-Bench, a terminal benchmark of 46 long-horizon tasks spanning nine categories, including experiment reproduction, software engineering, multimodal analysis, interactive games, and scientific computing. Each task follows a Terminal-Bench-style setup with a reference solution or simulation engine, but is further decomposed into fine-grained graded subtasks. This design enables dense intermediate rewards and partial credit, allowing evaluation to capture not only whether an agent reaches the final goal, but also how far it progresses on open-ended workflows. Tasks in Long-Horizon-Terminal-Bench typically require hundreds of episodes and minutes to hours of execution, stressing long-horizon planning, long-context management, and iterative debugging rather than one-shot problem solving. We evaluate 15 frontier models and find that agents consume on average 9.9M tokens per task, with roughly 231 episodes and 85.3 minutes of execution time per run, making Long-Horizon-Terminal-Bench more demanding than prior terminal-based benchmarks. Even the strongest tested model achieves 15.2% pass@1 at a partial-reward threshold of 0.95 and 10.9% at a perfect-reward threshold of 1.0, while the mean pass rate across models is 4.3% and 1.7% under the two thresholds, respectively. These results reveal headroom for improvement. We further analyze failure modes and error patterns, and release Long-Horizon-Terminal-Bench to support future progress on long-horizon terminal agents.

From RGB Generation to Dense Field Readout: Pixel-Space Dense Prediction with Text-to-Image Models

Zanyi Wang, Xin Lin, Haodong Li + 2 more

Pretrained diffusion transformers can be adapted for dense prediction tasks by mapping tokens to task-native outputs instead of generating RGB images, achieving state-of-the-art results with minimal additional parameters.

Remember When It Matters: Proactive Memory Agent for Long-Horizon Agents

Yifan Wu, Lizhu Zhang, Yuhang Zhou + 5 more

In long-horizon tasks, decision-relevant state is often scattered across an expanding trajectory, while the action agent must surface it and act. As trajectories grow, task requirements, environment facts, prior attempts, diagnoses, and open subgoals can be buried in the context window or pushed beyond it, failing to influence decisions when needed. We call this failure mode "behavioral state decay". We study memory as an active intervention mechanism rather than passive retrieval. A separate memory agent runs alongside an unmodified action agent, updating a structured memory bank from the recent trajectory and deciding whether to inject a memory-grounded reminder or remain silent. The module is plug-and-play with frontier action agents and existing agent harnesses. Across Terminal-Bench 2.0 and τ^2-Bench, it improves pass@1 for both weaker and stronger action agents, with gains of +8.3 pp on Terminal-Bench and +6.8 pp on τ^2-Bench. Ablations show that selective intervention outperforms passive bank exposure, always-on injection, advisor-only guidance, and general retrieval. As an early step toward open-weight memory policies, we train Qwen3.5-27B on SETA using SFT and GRPO, improving validation reward and achieving partial transfer to Terminal-Bench.

SAM-MT: Real-Time Interactive Multi-Target Video Segmentation

Ruiqi Shen, Chang Liu, Henghui Ding

Modern Video Object Segmentation (VOS) involves tracking and segmenting user-specified targets. While recent approaches have achieved remarkable performance in single-target scenarios, extending them to multi-target settings typically involves replicating the single-target processing for each individual object, resulting in reduced frame rates (FPS) with unbounded latency as target count increases. Built upon Segment Anything 2 (SAM2), we propose SAM-MT, which addresses this by transforming the model into an interactive framework for real-time Multi-Target video segmentation. SAM-MT uses explicit queries to represent different individual targets, in parallel with a shared representation for global context. It employs decoupled masked attention to keep individual identities distinct from cross-target interference, and sparse memory for stable temporal evolution, along with specialized strategies for occlusion handling and overlap prevention. SAM-MT successfully decouples latency from the number of targets, achieving real-time speed on par with single-target baselines (>36 FPS for 10 targets) while maintaining SAM2's robust video segmentation performance.

DrugGen 2: A disease-aware language model for enhancing drug discovery

Ali Motahharynia, Mohammadreza Ghaffarzadeh-Esfahani, Mahsa Sheikholeslami + 4 more

DrugGen-2 generates small molecules conditioned on disease ontology and target protein sequences through fine-tuning GPT-2 with supervised learning and reinforcement learning using GRPO, achieving superior molecular diversity and binding affinity compared to baseline models.

A Quantized Native Runtime for On-Device Semantic Audio Generation

Matteo Spanio, Antonio Rodà

A dependency-free runtime enables efficient text-to-music generation on embedded devices through quantization and activation steering while maintaining audio quality.

Enhancing In-context Panoramic Generation via Geometric-aware Pretraining

Haoran Feng, Ruiyang Zhang, Longyi Zhang + 2 more

Canvas360 is a two-stage framework for in-context panoramic generation that combines geometry-aware pretraining with fine-tuning, featuring a large-scale dataset and novel modeling techniques for improved geometric consistency and global coherence.

LongE2V: Long-Horizon Event-based Video Reconstruction, Prediction, and Frame Interpolation with Video Diffusion Models

Cheng-De Fan, Chun-Wei Tuan Mu, Chen-Wei Chang + 4 more

LongE2V enables high-quality video recovery from sparse event streams by leveraging pre-trained video diffusion priors and addressing temporal stability and frame interpolation challenges.

UniClawBench: A Universal Benchmark for Proactive Agents on Real-World Tasks

Zhekai Chen, Chengqi Duan, Kaiyue Sun + 4 more

UniClawBench introduces a capability-driven benchmark for evaluating proactive agents in real-world environments using live Docker container evaluation and closed-loop assessment with multiple agent roles.

CausalDS: Benchmarking Causal Reasoning in Data-Science Agents

Andrej Leban, Yuekai Sun

CausalDS is a benchmark for evaluating causal reasoning in data-science workflows that combines synthetic causal structures with realistic observational data and natural-language stories across Pearl's three rungs of causal inference.

OpenCoF: Learning to Reason Through Video Generation

Xinyan Chen, Ziyu Guo, Renrui Zhang + 2 more

OpenCoF framework introduces a reasoning video dataset and model that improve temporal reasoning through diverse supervision and explicit reasoning tokens for visual and textual cues.

Ideas Have Genomes: Benchmarking Scientific Lineage Reasoning and Lineage-Grounded Idea Generation

Yifan Zhou, Qihao Yang, Yan Li + 14 more

A benchmark for scientific lineage reasoning and idea generation is introduced, organizing scientific works as genetic-like Idea Genome objects and evaluating both reasoning and generation capabilities.

ARDY: Autoregressive Diffusion with Hybrid Representation for Interactive Human Motion Generation

Kaifeng Zhao, Mathis Petrovich, Haotian Zhang + 3 more

ARDY is a streaming generation framework that enables real-time, high-fidelity 3D human motion generation with text and kinematic constraint control through a hybrid representation and two-stage autoregressive transformer denoiser.

OPSD-V: On-Policy Self-Distillation for Post-Training Few-Step Autoregressive Video Generators

Hongyu Liu, Chun Wang, Feng Gao + 6 more

OPSD-V enhances few-step autoregressive video diffusion models by using real long-video data for temporal context during training, providing dense trajectory-level supervision that improves visual quality and motion dynamics without altering inference mechanisms.

A Theory of Contrastive Learning with Natural Images

Antonio Torralba, Yair Weiss

Why does contrastive learning with simple images and augmentations yield useful representations for downstream tasks? We address this question by analytically computing the optimal representation in terms of a contrastive loss for a range of basic augmentations and any image dataset with stationary statistics. We show that for certain augmentations the optimum can be attained by a CNN whose first layer filters are sinusoids, followed by a pointwise nonlinearity, global average pooling, and a final linear layer that performs partial whitening. We also show that the optimal weights in such CNNs for more complicated augmentations are still sinusoids. The frequencies of the sinusoids and their weights can be computed using a simple waterfilling algorithm given the dataset's expected power spectrum. Experiments with different image datasets and augmentations show that such CNNs trained with SGD empirically learn sinusoids in their first layer and to perform partial whitening

Principled Analysis of Deep Reinforcement Learning Evaluation and Design Paradigms

Ezgi Korkmaz

Starting from the utilization of deep neural networks to approximate the state-action value function that led to winning one of the most challenging games, to algorithmic advancements that allowed solving problems without even explicitly stating the rules of the challenge at hand, reinforcement learning research has been the center of remarkable scientific progress for the past decade. In this paper, we focus on the key ingredients of this research progress and we analyze the canonical evaluation and design paradigms in reinforcement learning. We introduce the theoretical foundations of scaling laws in reinforcement learning and show that the asymptotic performance of reinforcement learning algorithms does not have a monotone relationship between performance rankings and data-regimes. We conduct large-scale experiments and our results demonstrate that a line of reinforcement learning research under the canonical design and evaluation paradigms resulted in incorrect conclusions. Our analysis and results provide a core analysis on scaling, capacity and complexity of deep reinforcement learning.

Weak-to-Strong Generalization via Direct On-Policy Distillation

Shiyuan Feng, Huan-ang Gao, Haohan Chi + 7 more

Direct On-Policy Distillation transfers reinforcement learning improvements from smaller to larger models by using the policy shift induced by RL as an implicit reward signal, enabling efficient scaling of training without re-running expensive RL on the target model.

MedPMC: A Systematic Framework for Scaling High-Fidelity Medical Multimodal Data for Foundation Models

Hyunjae Kim, Dain Kim, Pan Xiao + 25 more

Medicine is inherently multimodal, requiring clinicians to synthesize information across diverse data streams. Yet the development of multimodal foundation models is constrained by limited access to large-scale, high-quality clinical data. Although PubMed Central (PMC) offers a complementary source of expert-authored image-text data, existing PMC-derived resources remain limited in fidelity, reproducibility, and clinical validation. We introduce MedPMC, an automated, continuously updatable framework that transforms permissively licensed literature into high-fidelity infrastructure for medical multimodal models. Applied to 6.1 million PMC articles, MedPMC curated 11 million medical image-text pairs. Component evaluations showed strong performance for initial screening (F1 = 93.2), multi-panel figure detection (F1 = 96.5), figure separation (mAP = 89.8), caption separation and alignment (F1 = 81.4; ROUGE-L = 85.3), and medical figure classification (F1 = 96.5). Manual review by five annotators, three with medical training, found 95.3% of MedPMC images medically relevant, versus 19.7% in a prior PMC-derived dataset. Across 26 benchmarks spanning 11 specialties, a MedPMC-trained CLIP-style model improved average zero-shot AUC by 7.1 percentage points over the strongest architecture-matched biomedical CLIP baseline despite using fewer than half as many image-text pairs. As the vision encoder in a multimodal large language model, it improved medical visual question-answering by 1.9 and 16.9 percentage points across two benchmarks. In 10,524 Yale New Haven Health System dermatology photographs, it improved morphology-to-image retrieval Recall@5 by 11.7 percentage points. These findings show that high-fidelity literature curation strengthens medical multimodal foundation models across benchmark and clinical settings. We publicly release the framework, corpus, benchmarks, and pretrained models.

3 Questions: Neural transparency and the future of AI designMIT News

Jul 15, 2026 20:25

SUMMARYMIT Media Lab researchers led by Pat Pataranutaporn introduced “neural transparency,” a visualization method that lets users inspect an AI chatbot’s likely traits before starting a conversation. Presented at the ACM Conference on Intelligent User Interfaces, the tool compares internal activations tied to behaviors such as empathy, honesty, toxicity, hallucination, and sycophancy, then displays the results in a sunburst diagram. In user studies, people often misjudged how personalized AI would behave, and the visualization increased trust without changing how they built their chatbots.

Millions of people are now designing their own personalized artificial intelligence companions, yet most have little idea how those creations will actually behave. In a new paper, MIT Media Lab Assistant Professor Pat Pataranutaporn and his graduate student researchers Anthony Baez and Sheer Karny introduce “neural transparency,” a tool that lets everyday users glimpse inside an AI’s neural network before their chatbot ever says a word. The work is being presented this week at the ACM Conference on Intelligent User Interfaces.

In this interview, Pataranutaporn, who is the Asahi Broadcasting Corporation CD Professor of Media Arts and Sciences, explains what they found, why the stakes are higher than most users realize, and what genuinely transparent AI might look like in the future.

Q: Your paper introduces “neural transparency,” a way to let everyday users peek inside an AI’s neural networks before their chatbot ever says a word. Can you describe how that actually works, and why you focused on the design moment, rather than catching problems after a chatbot is already out in the wild?

A: Millions of people are now creating personalized AI chatbots and agents powered by large language models, turning them into collaborators, tutors, coaches, creative partners, and companions through simple text prompts. Yet most people have very little idea how those prompts will shape the AI’s behavior until they begin interacting with it. We wanted to change that.

“Neural transparency” means giving people something like a brain scan for AI. Not because AI has a human brain, but because its neural network contains internal patterns that can hint at how it may behave before it speaks. In this work, my students Anthony Baez, Sheer Karny, and I combined insights from the fields of human-AI interaction and mechanistic interpretability to make those hidden patterns accessible to everyday users.

The basic idea is simple. First, we choose behaviors we care about, such as empathy, honesty, toxicity, hallucination, or sycophancy. Then, we compare the model’s internal activations when it is prompted to exhibit one trait versus its opposite. That difference becomes a kind of “behavior direction” inside the model. When a user writes a custom system prompt — the instructions that shape their chatbot’s personality before any conversation begins — we project the model’s internal activations onto those directions and translate the results into an intuitive visualization. In our case, this is a sunburst diagram that previews the chatbot’s likely personality traits before the user starts chatting with it.

We focused on the design moment because that is where prevention is possible. Today, people often discover problems only after the chatbot has already behaved in unintended ways. Our goal was to move from reactive correction to anticipatory design by helping people identify potential risks while they are still shaping the AI.

Q: Your study turned up something pretty striking: People consistently misjudge how their personalized AI will behave, overestimating the good traits and underestimating potentially harmful ones like sycophancy. What does that tell us about the risks baked into how millions of people are currently building AI companions, and why is that blind spot so hard to close?

A: I often joke that if AI showed up looking like the Terminator, it would be much easier for us to know what to do. The real challenge is that AI often appears as a warm friend, coach, tutor, or companion. That makes it difficult to recognize when something is going wrong.

Our study suggests that people have a blind spot when designing personalized AI. People often think they know how their chatbot will behave, but in our study they incorrectly predicted its personality on 11 of the 15 traits we measured. That highlights the need for tools that help people better understand AI before they start using it.

This matters because some behaviors that feel helpful in the moment may not be healthy over time. In previous research, we documented cases of psychological harm associated with interactions with AI chatbots. An LLM [large language model] that constantly validates your opinions or never challenges your thinking can reinforce harmful decisions, unhealthy beliefs, or emotional dependency. Psychology has long shown that people are naturally drawn to affirmation, so designing AI is not only a technical challenge, but also a psychological one.

The deeper issue is that today’s AI systems remain largely black boxes: Even experts cannot always predict how a system prompt will shape an AI’s behavior over a long conversation. As AI companions become part of everyday life, we need tools that help people understand what they are building before they begin using it. AI should be supportive without becoming blindly agreeable, personalized without becoming manipulative, and transparent enough that people can make informed choices.

Q: One of your most interesting findings is that the visualization significantly increased user trust but didn’t actually change how people designed their chatbots. What will it take to close that gap, and where do you see tools like this heading as AI companions become more deeply embedded in people’s everyday lives?

A: I actually think this is one of the most interesting findings in the paper, because it shows that transparency alone is not enough. People appreciated being able to see inside the model and reported greater trust in the system, but simply presenting information did not fundamentally change how they designed their AI companions.

In our followup work, which is currently available as a preprint, we are studying how a model’s internal neural representation changes over the course of a multi-turn conversation rather than remaining fixed from the initial prompt. We are already seeing promising results. By visualizing how these internal representations drift over time, people become significantly better at recognizing and anticipating changes in AI behavior, and are less likely to become overconfident in their understanding of the chatbot. AI companions are dynamic systems that evolve as they interact with us, so understanding those internal changes is an important next step. Nevertheless, this is still a very young research area.

Looking further ahead, I believe these kinds of transparency tools could become as commonplace as nutrition labels are for food. As AI becomes deeply woven into education, health care, work, and personal relationships, people should be able to understand not only what an AI can do, but how it may influence their thinking, emotions, and behavior. That kind of transparency is essential if we want AI to genuinely help people flourish.

Thinking Machines amps up its bet against one-size-fits-all AI with its first open model, InklingTechCrunch

Jul 15, 2026 18:04

It's the company's first public proof point after a year and a half spent building AI infrastructure largely out of public view.

OpenAI Launches a Keypad for AI AgentsSlashdot

Jul 15, 2026 17:00

OpenAI's first hardware device is a limited-edition desktop keypad called the Codex Micro that lets users monitor and control AI coding agents. Axios reports: Codex Micro is a collaboration with Work Louder, a boutique hardware company known for customizable mechanical keyboards and shortcut controllers for developers and designers. The small, square macro pad -- with backlit keys, a rotary knob and a tiny joystick -- sits beside your regular keyboard as a physical shortcut box for common Codex actions and shows the status of your agents. The keys are customizable and include a push-to-talk option as well as a dial to adjust your reasoning setting. Codex Micro is a niche device for Codex power users and will only be available until it sells out. It's priced at $230.

Whatnot acquires Shaped to power real-time live shopping recommendationsTechCrunch

Jul 15, 2026 17:00

Livestream shopping platform Whatnot has acquired AI startup Shaped, a machine learning company focused on real-time recommendations and search. The deal will bolster Whatnot’s personalization and discovery features as it expands into new product categories.

OpenAI finally launches hardware… for CodexThe Verge

Jul 15, 2026 16:00

OpenAI is finally releasing some hardware. No, it isn't the mysterious AI-powered device the company is developing with former Apple designer Jony Ive, a project already tangled up in a messy lawsuit. Instead, it's a product designed to be used with its coding platform, Codex.

The device, a square-shaped block of buttons called Codex Micro, is a collaboration between the AI company and keyboard maker Work Louder. OpenAI said it is a limited-run collaboration that will give users more ways to monitor and manage their agents.

The pad closely resembles Work Louder's Creator Micro 2, and marketing images show what appears to be an identical …

Read the full story at The Verge.

Apple Intelligence approved for launch in China with Alibaba’s Qwen AITechCrunch

Jul 15, 2026 15:29

The deal, which was rumored to be in the works last year, marks an important step for Apple's AI ambitions in a key market.

Thermodynamic Computers Go With the (Energy) FlowQuanta Magazine

Jul 15, 2026 15:24

In the quest to make computers accurate and reliable, noise is the enemy. The thermal jiggling of atoms is a constant threat to the precision needed for detailed calculations. Whether we're dealing with familiar classical devices like the laptops or supercomputers that we use today, or fancy quantum devices that promise us faster computation tomorrow, we don't want some haphazard heat fluctuation…

Source

Inside Ode with Anthropic, the startup betting AI services are the future of enterpriseTechCrunch

Jul 15, 2026 15:00

Can a handful of engineers really do the work of an army of consultants? That’s the bet behind Ode with Anthropic — the joint venture dedicated to embedding forward-deployed engineers in enterprise firms, backed by Anthropic, Blackstone, Hellman & Friedman, Goldman Sachs and others. On this episode of TechCrunch’s Equity podcast, Rebecca Bellan sits down with Ode’s leaders Chris Taylor and Eddie Siegel, who founded Fractional AI, […]

Puerto Rico Upgrades Remote Microgrid With Clean HydrogenIEEE Spectrum

Jul 15, 2026 14:00

Better backup power system aids storm recovery

Anthropic, Blackstone bet the next trillion-dollar AI business is implementation, not just modelsTechCrunch

Jul 15, 2026 13:10

Anthropic-backed Ode launches as AI labs bet that embedding forward-deployed engineers inside enterprises is the key to accelerating enterprise AI adoption.

This AI Folds DNA Into Mini MasterpiecesIEEE Spectrum

Jul 15, 2026 13:00

Generative AI model designs DNA origami for any shape

Reelful’s AI turns your camera roll into short-form videos for social mediaTechCrunch

Jul 15, 2026 13:00

The app is designed for people who want to create social content, but find traditional video editing tools too complex or time-consuming.

Rime picks up $24M Series A to help enterprises field customer callsTechCrunch

Jul 15, 2026 13:00

Rime is handling over 100 million calls each month across multiple companies.

The Download: a useful quantum machine and a record-breaking subsea tunnelMIT Technology Review

Jul 15, 2026 12:10

SUMMARYPsiQuantum is pursuing a large photonic quantum computer built from hundreds of cabinets of chips that would use photons and optical switches to tackle problems beyond the reach of current machines. In Norway, engineers are building a 16.6-mile subsea road tunnel under the North Sea that will reach 1,280 feet below the surface, becoming the world’s longest and deepest of its kind.

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.

PsiQuantum has a plan to make a massive quantum computer out of light

The machine that could change the world will be housed in a room that looks like a data center crossed with an ice cream factory.

Inside, some 100 stainless-steel cabinets each hold hundreds of chips. On those chips, thousands of light particles will fly through a maze of optical switches and beam splitters. Each photon must be accounted for, because precisely measuring where it ends up will help answer questions that current computers might take millions of years to solve.

This computer, as described, does not exist. It’s the brainchild of a company called PsiQuantum, founded in 2016 by four physicists from UK universities. In a crowded field of deep-pocketed competitors with similarly fantastical visions, the company aims to be the first to build a useful quantum machine.

Read the full story on the company’s quest.

—James O’Donnell

MIT Technology Review Narrated: inside the world’s deepest and longest subsea road tunnel

—Niall Firth

I’m currently around 1,000 feet beneath the North Sea, in a dark, dank cave. It smells weird. And I’m increasingly aware of the pressure from millions of tons of seawater just above my head.

I’m under the iconic fjords of Norway to visit what will soon become the world’s longest and deepest subsea road tunnel—an exceptional engineering feat that will carry drivers deep beneath the North Sea.

I’m here to understand how you make a 16.6-mile highway that sits 1,280 feet below the sea at its deepest point. And also—at a time when it can feel hard to get anything done—to reassure myself that ambitious engineering is still possible. That we can still make things.

This is our latest story to be turned into an MIT Technology Review Narrated podcast, which we publish each week on Spotify and Apple Podcasts. Just navigate to MIT Technology Review Narrated on either platform, and follow us to get all our new content as it’s released.

The must-reads

I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.

1 Meta allegedly used AI to target workers with health issues for layoffs
Their lawsuit says Meta relied on AI to create a termination list. (Guardian)
+ And pinpointed staff who took maternity or disability leave. (Reuters $)
+ One was allegedly informed the day before her water broke. (Ars Technica)
+ The layoffs aimed to offset Meta’s AI spending. (Gizmodo)
+ AI agents are not your “coworkers.” (MIT Technology Review)

2 OpenAI’s first consumer device will be a mobile smart speaker
The screenless device will serve as an “AI companion.” (Bloomberg $)
+ It’ll let you talk with ChatGPT. (Verge)
+ And use a camera and sensor to understand your environment. (Reuters $)
+ It’s set to launch next year. (Engadget)

3 The US military sent explosive drone boats into combat for the first time
They attacked an Iranian midget submarine and naval port. (Ars Technica)
+ Underwater drones may shape a war in Taiwan. (MIT Technology Review)

4 DeepMind’s CEO has called for a US-led body to test frontier AI models
Demis Hassabis wants the watchdog to vet national security threats. (FT $)
+ If dangers mount, it would coordinate an industry-wide slowdown. (Axios)

5 Data centers are set to add billions in power costs in 13 states
A power auction is slated to produce $6.3 billion in new charges. (NYT $)
+ Australia plans to govern the use of water and power for AI. (WSJ $)

6 xAI’s unpermitted power pollution hits Black communities hardest
Elon Musk’s xAI has been installing gas turbines without permits. (Reuters $)
+ We need to focus on Big Tech’s energy footprint. (MIT Technology Review)

7 Stripe and Advent have offered to buy PayPal for more than $53 billion
The payments giant and private equity firm have made a joint bid. (Reuters $)
+ Apple and Google Pay have eroded PayPal’s market share. (Bloomberg $)

8 DeepSeek plans to file for IPO as soon as this year
The Chinese AI pioneer is likely to list in Shanghai. (WSJ $)
+ Here’s why DeepSeek’s latest model matters. (MIT Technology Review)

9 A hard, lightweight “bio-metal” has been discovered in sea worm jaws
It could have applications in engineering. (New Scientist $)

10 A new $3,000 fitness suit electrocutes you to boost your gains
Celebrities love it—but not everyone’s a fan. (404 Media)

Quote of the day

“By economic and engineering measures, generative AI might be the worst technology ever deployed.”

—Alex Reisner, a staff writer at The Atlantic, explains why GenAI’s scaling problem is an engineering disaster.

One More Thing

Hackers made death threats against this security researcher. Big mistake.

In April 2024, an anonymous hacker began posting death threats on Telegram and Discord channels aimed at a cybersecurity researcher named Allison Nixon. It wasn’t long before others piled on. Someone shared AI-generated nudes of her.

They targeted Nixon because she had become a formidable threat. As chief research officer at the cyber investigations firm Unit 221B, named after Sherlock Holmes’s apartment, she had built a career tracking cybercriminals and helping get them arrested.

For years, Nixon had lurked quietly in online chat channels or used pseudonyms to engage with perpetrators and bring them to justice. Now, she resolved to unmask the people behind the death threats—and take them down for crimes they admitted to committing.

Find out why they learned to regret their choice of target.

—Kim Zetter

We can still have nice things

A place for comfort, fun, and distraction to brighten up your day. (Got any ideas? Drop me a line.)

+ A musician has discovered the true masters of metal breakdowns: birds.
+ Photographer Fontanesi’s surreal photo splits transform everyday images into spectacular hybrid scenes.
+ Over 30 actors, filmmakers, and friends recount how Steven Spielberg infiltrated Hollywood in this terrific article.
+ Who would win the World Cup if less important things than soccer decided it, like life expectancy and happiness? A new game tests your knowledge.

The US is advancing AI safety through state and federal actionOpenAI

Jul 15, 2026 12:00

OpenAI outlines a “reverse federalism” approach to AI governance, where state laws help build a national framework for safe, democratic AI.

Indian AI coding startup Emergent becomes a unicorn with $130M Series CTechCrunch

Jul 15, 2026 12:00

The startup has reached a $120 million annualized revenue run rate and more than 200,000 paying customers.

Vint Cerf is working on a plan to unleash AI agents on the open internetTechCrunch

Jul 15, 2026 12:00

The guy behind TCP/IP is working on a standard for identifying AI agents in the wild.

Astronauts Take First X-Rays In SpaceSlashdot

Jul 15, 2026 11:00

SUMMARYAstronauts on SpaceX’s Fram2 mission captured the first diagnostic X-ray images in orbit, using a portable MinXray device after only four hours of training. The scans of body parts and a smartwatch were later reviewed by radiologists and found to be largely comparable to Earth-based images, marking a new medical imaging capability for spaceflight. Researchers hope the result will support future long-duration missions by giving crews another way to diagnose injuries and inspect equipment.

Astronauts on SpaceX's Fram2 mission successfully captured diagnostic X-ray images in orbit for the first time. The milestone gives space medicine a second imaging option beyond ultrasound and could help future crews diagnose injuries, inspect equipment, and support longer missions to the moon or beyond. Popular Science reports: Commercial off-the-shelf X-ray machines like the ice cooler-sized MinXray TR90BH now allow users to perform scans on subjects far away from traditional facilities. In 2022, [Mayo Clinic researcher Sheyna Gifford] assisted in preparing a crew to successfully generate digital X-rays while experiencing microgravity during a parabolic flight. Gifford's team then spent years collaborating with SpaceX to plan another feasibility study. This time, they didn't want to operate an X-ray machine aboard an aircraft simulating the conditions in space -- they intended to use the equipment during an orbital mission.

The process was detailed in a recently published study in the journal Radiology, and focuses on last year's Fram2 mission. Instead of days of medical training, astronauts spent only four hours learning how to use their portable radiography device. They then took preflight X-rays of a hand, forearm, chest, abdomen, and pelvis ahead of their SpaceX Falcon 9 rocket launch on March 31, 2025. Once in orbit, the team calibrated the system before testing their MinXray on the same body parts as well as a smartwatch.

Once the crew returned, a trio of independent radiologists reviewed the orbital X-ray images based on their positioning, spatial and contrast resolutions, and general scan quality. Although positioning scores were slightly decreased for the central body images, every other scan held up to similar examples created on Earth. Meanwhile, the astronauts reported that using the machine was easy despite minimal prior coaching. Looking ahead, researchers hope to conduct further X-ray tests during orbital missions, while continuing to reduce the overall size of equipment.

GPT-Red: Unlocking Self-Improvement for RobustnessOpenAI

Jul 15, 2026 10:00

Explore GPT-Red, OpenAI’s automated red teaming system that uses self-play to improve AI safety, alignment, and prompt injection robustness.

Starlink’s V5 dish is now available — here’s how it comparesThe Verge

Jul 15, 2026 07:17

The smaller Starlink V5 (right) next to the larger V4 dish. | Image: SpaceX

SpaceX's latest residential dish - the Starlink V5 - is now available in "select areas." It's notably smaller and lighter than the V4 dish with improved power efficiency. It'll be available in more places as SpaceX ramps up production to meet global demand. The company notes that Starlink V5 is not intended for in-motion use - for that you'll have to wait for the revamped Starlink Mini teased alongside the V5 last month.

The next generation Starlink Kit is designed to deliver reliable, high-speed home internet. Starlink V5 has a smaller form factor and lightweight design with greater power efficiency than the Starlink V4.
With speeds up to …

Read the full story at The Verge.

OpenAI researcher Miles Wang in talks to launch AI drug discovery startup valued at $2BTechCrunch

Jul 15, 2026 00:27

The funding discussions point to investor interest in applying AI to make breakthroughs in life sciences.

OpenAI's First Device Will Be Moveable, Screenless Speaker Built as AI CompanionSlashdot

Jul 14, 2026 23:00

SUMMARYOpenAI is developing a screenless, portable smart speaker that will act as a personalized AI companion and home computer. The device is expected to support smart-home control, media playback, messaging, and proactive assistance using an advanced version of ChatGPT voice mode, with a camera, sensors, and a rechargeable battery for room-to-room use. OpenAI plans to unveil it this year and release it in 2027 after acquiring Jony Ive’s io Products.

OpenAI is reportedly developing a screen-free, portable smart speaker meant to act as a personalized home computer and humanlike AI companion. "It will help control smart-home appliances, play media, answer questions, respond to messages and tap into the range of capabilities offered by OpenAI's ChatGPT," reports Bloomberg, citing people familiar with the matter. The device, expected to be unveiled this year and released in 2027, would mark OpenAI's first major hardware push after acquiring Jony Ive's io Products. Bloomberg reports: Apple sued OpenAI last week, accusing the company of stealing trade secrets. But OpenAI believes that the device veers significantly from anything Apple has on the market today and that it's unlikely that it violates trade secrets belonging to the iPhone maker, the people said. OpenAI's success in hardware will hinge on bringing a novel approach to the market -- something it aims to do with the smart speaker. For instance, the device's technology is meant to become increasingly personalized and proactive as it gains a deeper understanding of its owner over time, according to the people.

OpenAI envisions the device anticipating needs, surfacing information proactively and serving as an expert on its user, they said. Though the speaker is designed to stay in the home, it will be easy to move around the house. OpenAI believes the product's defining feature will be its personality and ability to connect on a humanlike level with users. The speaker incorporates mechanical elements that can move on their own, creating a sense that it is alive and not just an object responding to commands. The machine also will draw on personal information such as emails to better understand its owner. The goal is for the device to feel like a companion and become a physical manifestation of OpenAI's ChatGPT. Still, the exact plans could change as the company works through the development and legal process.

The device's communication abilities will rely on a more advanced version of the ChatGPT Voice Mode -- GPT-Live -- that OpenAI rolled out this month. The new voice mode is designed to act more like a human. It can listen and talk at the same time, adapt more naturally during conversations, and quickly process information. Though the new product resembles a speaker, OpenAI internally describes it as the first of its kind: a computer built for AI to help make busy people more productive. It includes a camera and other sensors that help it understand a user's surroundings and context, as well as advanced AI models beyond those available on conventional smart speakers. Another central difference is that the device includes a rechargeable battery, allowing it to be carried from room to room throughout the day. A user could bring it into the laundry room while doing chores, move it into the kitchen for cooking assistance, and later place it in a living room or bedroom to have it play music. It can also remain plugged into a single room if the customer chooses.

OpenAI’s first hardware device is reportedly a screenless speaker that can moveTechCrunch

Jul 14, 2026 22:22

The device is weirdly described as involving "mechanical elements that can move on their own" and the Bloomberg report includes the detail that the device is designed to "feel like a companion and become a physical manifestation of OpenAI’s ChatGPT."

Google Images Gets a Pinterest-Like Redesign Focused On DiscoverySlashdot

Jul 14, 2026 22:00

SUMMARYGoogle Images is getting a Pinterest-like redesign with a personalized “For You” feed, real-time image updates, and collections for saving ideas such as outfits, travel inspiration, and room design. Google is also adding image generation in Search through AI Overviews using its Nano Banana model, letting users turn text prompts into custom visuals and preview design ideas for spaces.

Google Images is getting a Pinterest-like redesign that turns image search into a personalized discovery feed, with "For You" galleries, real-time updates, and collections for saving visual ideas. "Google is also adding a way for users to create AI images right in Search, as it celebrates 25 years since the debut of Google Images," reports TechCrunch. From the report: After navigating to the redesigned Google Images, users will see a "For You" gallery of images tailored to their interests and browsing history. Like Pinterest, the gallery is designed for continuous browsing, with Google saying it updates in real time with new images. As users browse, they can save ideas to their "collections," which will appear as tabs above the main gallery of photos. For example, users can create collections for things like vacation outfit ideas, travel inspiration, and ways to design a reading nook, which they can come back to later.

[...] As for generating images directly in Search, Google says the feature is meant for moments when you have a highly specific idea for an image that doesn't already exist online. Google is bringing image generation directly into AI Overviews on Search and will use its latest Nano Banana model to transform a text prompt into a custom visual. The feature can also help users reimagine spaces and visualize ideas, such as seeing what a room might look like painted red or what a dorm room with a coastal theme could look like.

OpenAI may announce a ChatGPT smart speaker this yearThe Verge

Jul 14, 2026 21:26

OpenAI's first device is set to be a smart speaker that lets you talk with ChatGPT, according to a report from Bloomberg. The device apparently won't have a screen, but will use a camera and additional sensors to "understand" your environment.

The report comes just days after Apple filed a lawsuit against OpenAI that accused the AI company of stealing hardware secrets. OpenAI, in a new statement on Tuesday, said that it is "not aware of any evidence that this complaint has merit."

Sources tell Bloomberg that OpenAI's device will also feature a rechargeable battery that will allow users to carry it with them. It will offer smart home contro …

Read the full story at The Verge.

Helping AI models to meet the real worldMIT News

Jul 14, 2026 20:25

SUMMARYMIT researcher Devavrat Shah developed a foundation model for tabular and time-series data that helps businesses forecast, plan, and make decisions using their own enterprise data. His work led to Ikigai Labs, which was patented and licensed by MIT, and the company was later acquired by Celonis, where Shah became chief scientist. The system is designed to analyze digitized business processes at scale and simulate outcomes for different choices in real time.

While most AI models have been taught using text and images, a system developed by Devavrat Shah takes tabular data as its input — structured data such as the row-and-column format used in spreadsheets — and then provides real-time planning on a large scale.

Systems using artificial intelligence to enhance forecasting, planning, and decision-making in businesses have been proliferating in recent years, but in many cases, they lack the detailed, specific information about the organization itself, limiting the usefulness of those tools.

Devavrat Shah, a principal investigator at MIT’s Laboratory for Information and Decision Systems (LIDS), faculty member with the department of Electrical Engineering and Computer Science (EECS), and member of the Institute for Data, Systems, and Society (IDSS), has been focused on how to design methods that can handle second-by-second decision-making using limited computational resources.

“In a sense, with a small amount of resource, you have to do a lot of heavy lifting,” he says. As a researcher, “my interest is in the ability to develop methods that can extract information from data at scale in as effective a manner as possible.”

The Andrew (1956) and Erna Viterbi Professor has been teaching at MIT since 2005.

In 2019, he also co-founded a spinoff company called Ikigai Labs. Ikigai built a foundation model for tabular, time series data based on years of research in Shah’s lab, which was patented and licensed by MIT to the company. This model can take input from enterprise data from varied sources, continuously and at scale, so that it learns as it goes along by testing its predictions against real outcomes.

Shah explains that the system is an extension of the kind of graphical models that are used, for example, by GPS devices to convert a sparse amount of data received from satellites into an accurate model of a position on the Earth’s surface, or by communication system like that in a digital watch that communicates at high speed in an energy-efficient manner.

“My interest was: How does one design such graphical models for generic, tabular data?” he says.

While most AI models have been taught using text and images, this system takes tabular data as its input — structured data such as the familiar kind of row-and-column format used in spreadsheets. And then it provides the kind of real-time planning, on a vastly larger scale.

The idea for Ikigai was to provide forecasting and decision-making technology for large businesses, such as consumer goods manufacturers and pharmaceutical companies.

Shah gives the example of how a consumer electronics company might use this system.

“Let’s say you’re making headphones and all sorts of different things. And each of the products that you manufacture has lots of small pieces that come from different parts of the world. And once the device is sold, it needs to be supported and maintained. And you have to come up with new versions of the product, you have to market them, you have to price them … So the questions you would typically ask would be: If I were to sell these next quarter or next year, how many will be sold in different places, and what would happen to demand if I change the price, or if I introduce promotion?”

He adds that all of these processes are interdependent, and at every stage of the processes decisions have to be made that have implications over time. “At some level,” he says, “digitizing these processes and being able to do predictions and constantly optimize is what leads to ultimately better business operations.”

Ikigai was recently acquired by the international firm Celonis, where Shah is now chief scientist in addition to his roles at MIT. Ultimately, he hopes the model he developed for Ikigai will help Celonis deliver tools that can integrate with a company’s own data and business processes in order to provide real-world analyses that can help make forecasts, plans, and decisions.

Shah adds that Celonis has specialized in digitizing and automating operations for more than 1,400 large companies around the world. Now that these systems are fully digitized, they provide a platform for Ikigai’s software to take the next step, reading the data from these digitized systems in order to provide detailed models to allow simulation of different options, predict optimum strategies, and forecast the results of a given set of decisions.

“Once the digital layer of these processes exists and this information layer exists,” Shah says, “now, on top of it, we can put the Ikigai stack to enable decision-making at a much larger scale than otherwise.”

While so many companies are working on various aspects of AI, “we are very much focused on part of the domain that the rest of the world is not paying attention to,” which is the area of structured or time-domain data. By starting from such data, he says, it provides a very cost-effective version of AI.

“A narrower focus comes with sharper technology,” he says, “but it’s broad enough that it’s very valuable.”

Shah adds, “The recent buzzword that’s become pertinent in the modern AI popular press is a ‘world model.’ In a sense, this is trying to build the enterprise process world model, so to speak.”

Apple opens its new Siri AI to everyone with the iOS 27 public betaTechCrunch

Jul 14, 2026 19:42

If you’ve been waiting to try Apple’s revamped Siri without installing a developer beta, you now can. The company on Tuesday released the iOS 27 public beta, giving iPhone owners early access to its AI-powered assistant and other new features before the software’s official launch this fall.

The founder of Hinge raised $18M to build a new AI dating service, OvertoneTechCrunch

Jul 14, 2026 19:39

Overtone describes itself as "a voice- and audio-forward service, enabled by AI, that provides highly curated introductions."

Linux Foundation's Latest Foray Is To Standardize Internet-Native Payments For AI AgentsSlashdot

Jul 14, 2026 19:00

Today, the Linux Foundation launched the x402 Foundation to standardize internet-native payments for AI agents, APIs, and applications, based on Coinbase's contributed x402 protocol. Backed by companies including AWS, American Express, Cloudflare, Google, Mastercard, Stripe, and Visa, the effort aims to make payments work directly over HTTP (assuming users are comfortable letting AI agents handle financial transactions).

"The whole idea is to give agents access to money and, through that financial independence, improve their set of capabilities to pretty much anything on the internet," Lincoln Murr, Coinbase's AI product lead, told CNBC last month when the company announced the protocol. "In the 2010s, every internet company dealt with the transition from desktop and web into a mobile environment. And now in the late 2020s, we're seeing the exact same thing happen where agents are going to be the new primary economic actors on the internet."

Can AI build a jet engine? JARVIS Challenge tests role of AI copilots in tough-tech engineeringMIT News

Jul 14, 2026 18:00

SUMMARYMIT’s inaugural JARVIS Challenge asked 31 undergraduates to use AI tools to design, fabricate, assemble, and test a small gas turbine engine in four weeks. Team 811 Crew won after its jet engine started, switched to Jet-A fuel, and produced net thrust, while other finalists showed both the promise and limits of AI in safety-critical hardware engineering. Faculty and sponsors said the competition demonstrated how AI can speed design-build-test cycles when paired with strong engineering judgment and hands-on experience.

Faculty and TAs for the JARVIS Challenge were on hand throughout the competition to ensure safety, but students had to figure out their designs with minimal guidance. Here, Professor Zolti Spakovszky (center) helps team 811 Crew fire their jet engine.

Artificial intelligence has rapidly transformed software engineering. Generative AI and large language models (LLMs) can create huge volumes of code and documentation; machine-learning algorithms can monitor performance and detect security vulnerabilities. But when the task is to conceive, design, and make a complex physical system such as a jet engine, are those AI tools equally transformative?

This past semester, the JARVIS Challenge (Jet-engine AI Research and Validation Intensive Sprint) set out to explore whether AI can compress the design-build-test cycle, asking MIT undergraduates to discover whether AI can help them to build faster and better.

“The JARVIS challenge showed that AI can substantially accelerate safety-critical hardware engineering, but engineering judgment remains the decisive differentiator. An AI-native engineer is not defined by using AI, but by leading it — knowing when to trust it, when to challenge it, and how to translate AI outputs into working hardware. Manufacturing — not engineering design or analysis — remained the fundamental rate-limiting step,” says Professor Zolti Spakovszky, director of the MIT Gas Turbine Laboratory.

The teams, the tools, the task

The challenge gave undergraduates four weeks to design, fabricate, assemble, and test a small gas turbine aero engine, using AI as their primary engineering partner. The objective: build a “JARVIS-class” single-spool jet engine producing 50–100 pounds of thrust, running on Jet-A, and completing five 60-second runs. Teams had total freedom over design, materials, and fabrication.

Representing nearly every department in the School of Engineering, 31 students organized into seven teams, ranging from all first-years to senior-heavy groups. Many of the competitors initially had little experience in turbomachinery, compressible flows, or, in the case of the younger students, even thermodynamics. Many had never seen the inside of a gas turbine before signing up to build one.

At their disposal: MIT’s machine shops and manufacturing vendors; commercial software including Concepts NREC, SolidWorks, and ABAQUS; and various test rigs for characterizing and assembling individual components.

The teams also had access to MIT Parley, a newly launched platform that aggregates frontier large language models through a single interface. Through Parley, JARVIS leads could see directly how the students were using the AI tools, including their prompts, the cost per prompt, the specific LLMs being used, and other critical information. The JARVIS leads secured early access to Parley for all participants, and with financial support from MIT Lincoln Laboratory, the Department of Mechanical Engineering, and corporate sponsors Safran, Voyager Technologies, and Beehive Industries, students had access to essentially unlimited use of AI.

The sponsors were drawn by recruiting interest and genuine curiosity about how AI might reshape engineering workflows.

“We see this as the future of engineering,” Ryan (Hal) Hefron of Voyager Technologies told the students. “You’re honing skills that are not just nice to have — they’re going to be the future baseline in the engineering workforce.”

Vincent Garnier, managing director of Safran Tech, watched the competition unfold with excitement. “JARVIS was a genuine experiment, a learning endeavor. We frankly didn’t know what to expect, from the students or from the AI models. What struck me coming from the students was: first, the enthusiasm to explore; then, as the project developed, they all came to the cool-headed realization of what AI could or could not help them with, and then almost instantly adapted for that,” he says. “It makes me confident that this generation of leading engineers will probably not fall prey to easy and shortsighted use of AI, and will do so by keeping ever more in contact with experiments — physical or thought experiments.”

The faculty leadership — professors Zachary Cordero, Zolti Spakovszky, Masha Folk, and Andreea Bobu of the Department of Aeronautics and Astronautics, along with Lincoln Laboratory engineers and a team of teaching assistants — were there to ensure safety. In weekly progress reviews, they would critically evaluate the student progress and assess how the students were using AI.

Spakovszky developed a careful technique for guiding teams in the right direction without giving away answers or providing help. After a team’s presentation, he might ask: “Do you know what a rabbet fit is? Take in the comment.”

Where AI helps and hurts

By the end of week 1, one team withdrew from the competition; the others had, with varying degrees of success, developed an initial design for their gas turbines. Different teams used AI to summarize textbooks, teach them to use design software, source vendors, create Excel sheets, answer specific questions, find references, and create comparative analysis between design decisions. One team created an agent in Parley and tasked it with serving as their project manager.

By week 2, teams had to start working on detailed CAD designs, ordering parts, and prototyping their combustors. This is where the teams started to hit limitations in their use of AI. While Claude and ChatGPT were good at offering design alternatives and filling knowledge gaps, teams found that the hallucinations, sycophancy, and lack of physical understanding that have become notorious features of generative AI were undermining their confidence and slowing them down.

“AI is a helpful tool, great at finding information, helping organize things, and can write well, but it can’t do design,” says Elizabeth Tupaj, a member of team 811 Crew. “The moment the engineer doesn’t know what is going on and the AI is in charge is the moment the design becomes unreliable, at least with AI at its present capabilities.”

Teaching assistant John Zhang notes, “seeing this firsthand with the students reminded me how much first impressions matter. If the students couldn’t get answers from the AI early on, they quickly grew frustrated and formed a lasting opinion that precluded them from using it later.”

In the final weeks, the finalists hit another obstacle no AI could solve: working with vendors. “AI searches found vendors we had no rapport with, who had no interest in our tight timeline,” students reported. “The vendors who came through were the ones our team had personal relationships with.”

Of the three finalists, only Fast and Fractured achieved first-attempt ignition of their mini-combustor. The team had used AI heavily for trade studies and architecture comparisons, arriving at a viable design despite none of them having prior gas turbine experience.

“The JARVIS Challenge showed what’s possible when you combine AI-enabled design with motivated students and a culture of rapid experimentation,” says Masha Folk, the Charles Stark Draper Career Development Professor of Aeronautics and Astronautics. “The moment that stood out most was when the first student-designed combustor was installed on the test stand. It ignited flawlessly, ramped to full power, transitioned to dual-fuel operation, and then sustained stable combustion on 100 percent Jet-A fuel. This was proof that we can dramatically accelerate the cycle of design, build, and test while giving students hands-on experience with a real engineering challenge.”

At the vanguard of AI-native engineering

By the end of May, the two more senior teams – Fast and Fractured and 811 Crew – had completed full engine tests. Fast and Fractured, with their AI-assisted design, were delayed by vendor headaches week after week, but finally made it to test. Unfortunately, their hot fire was cut short when the rotor rubbed and seized against the stationary housing. Team 811 Crew, however, who had more exposure to turbomachinery and propulsion concepts going into the competition, emerged victorious. Their engine started, successfully transitioned to Jet-A, and generated net thrust.

“As we stood there with the air-starter, hearing their engines spool up and watching them spit fire, it felt like my heart was racing out of my chest. There were so many ways it could go wrong! What these students accomplished in such a short time span is nothing short of amazing,” says PhD student Joe Chiapperi.

The 811 team had been resistant to using AI throughout the competition, trusting instead to their fundamentals and teamwork. “We had people who were at least somewhat familiar with the design software, mechanical engineers who knew how to build anything, and aerospace engineers who had taken classes on the design of gas turbine engines specifically,” says Tupaj.

From the start of the JARVIS Challenge, younger students used Parley more frequently and cleverly, while the juniors and seniors leveraged deeper experience.

“JARVIS taught me that getting value from AI takes two things: enough expertise to judge what it tells you and catch it when it’s wrong, and enough curiosity to actually lean on it where it could help,” says Professor Andreea Bobu. “The team that moved fastest in the sprint was experienced and leaned heavily on AI to get there. The team that eventually won was more resistant to AI; they had the expertise, but that skepticism made them slower. The sweet spot seems to be knowing enough to stay in charge of the tool, and being eager enough to pick it up in the first place. To me, that’s the real opportunity ahead: training the next generation of engineers who have the judgment to direct these AI tools and the instinct to reach for them.”

The competition’s clearest finding: engineering experience is a multiplier, and the human factor remains a vital element. Mastering the first principles and fundamental concepts breeds good engineering judgment and the ability to navigate strings of tough decisions in the face of incomplete information. And when it comes to building safety-critical physical systems, nothing can replace human hands and human accountability.

“JARVIS has shown that AI copilots can have a multiplicative effect on engineering productivity, with judgment and first-principles thinking serving as the key differentiators among teams,” adds teaching assistant Kyle Woody.

But the implications of AI in aerospace are significant. If small teams using well-managed AI copilots can compress design-build-test cycles from years to weeks, the consequences for workforce structure, R&D timelines, and competitive dynamics could be substantial. The students who tackled the JARVIS Challenge are among the first engineers to grapple with those stakes not as a thought experiment, but in a machine shop, with a jet engine on the test stand.

“JARVIS highlighted the power of AI in the design of physical systems,” says Cordero, associate director of the MIT Gas Turbine Laboratory. “But it also showed that the key to unlocking that power is education, through coursework, internships, and hands-on extracurriculars like MIT Motorsports and Rocket Team. Performance in JARVIS correlated strongly with year in school. My main takeaway is that in the AI era, education is more valuable than ever.”

DeepMind CEO calls for an independent standards body to regulate frontier AITechCrunch

Jul 14, 2026 17:45

DeepMind CEO Demis Hassabis is proposing an AI "standards body" modeled after FINRA, to test frontier models and develop best practices for their release.

These painted e-tattoos could be the future of wearable biosensorsArs Technica

Jul 14, 2026 17:31

SUMMARYResearchers at Pennsylvania State University developed a conductive ink that can be painted directly onto skin in colorful custom designs and dries into a functional electrode for biomonitoring. The work, published in Proceedings of the National Academy of Sciences, aims to make epidermal electronics more usable on curved or hairy body areas where traditional temporary tattoo sensors struggle. The approach could support more flexible wearable biosensors for measuring signals such as temperature, strain, and other physiological data.

These painted e-tattoos could be the future of wearable biosensors

Credit: Wanqing Zhang

Scientists at Pennsylvania State University have developed a novel conductive ink that can be painted directly onto the skin in colorful custom designs, turning into a functional electrode for biomonitoring after drying. They described their work in a new paper published in the Proceedings of the National Academy of Sciences (PNAS).

As previously reported, epidermal electronics attached to the skin via temporary tattoos (e-tattoos) have been around for more than a decade. So-called e-tattoos connect to skin without adhesives, are practically unnoticeable, and are typically attached via temporary tattoo, allowing electrical measurements (and other measurements, such as temperature and strain) using ultra-thin polymers with embedded circuit elements.

However, these e-tattoos have their limitations, most notably that they don’t function well on curved and/or hairy surfaces, as well as requiring personalized electrode placement design to cover larger areas, since biosignals are spatially distributed. So scientists have been getting creative. For instance, in 2024, researchers developed special polymer-based conductive inks that can be printed onto a person’s scalp to measure brain waves, even if they have hair. This could one day enable mobile EEG monitoring outside a clinical setting, among other potential applications.

Read full article

Meta’s Adam Mosseri says AI token budgets could soon be capped per engineerTechCrunch

Jul 14, 2026 16:22

Instagram head Adam Mosseri believes companies will eventually need to manage AI token spending the same way they manage payroll or other operating expenses, predicting that engineers could soon face limits on how much they spend using AI tools.

Google Images gets a Pinterest-like redesign focused on discoveryTechCrunch

Jul 14, 2026 16:00

Now, when users navigate to Google Images, they'll see a "For You" gallery of images tailored to their interests and browsing history.

Celebrating 25 years of visual search innovationGoogle AI Blog

Jul 14, 2026 16:00

Google Images is turning 25. Here’s a look back at some major milestones — and new ways to explore and create visual content.

The Google Images homepage will recommend photos even before you searchThe Verge

Jul 14, 2026 16:00

Google is announcing a big change to the Google Images homepage in honor of the platform's 25th anniversary this week. Instead of a mostly blank page with a search bar, the homepage will soon show you a bunch of images that it thinks you might like before you even start searching.

The company says the new "browseable" homepage features a "dynamic, immersive gallery of images from across the web - updated in real time and intelligently tailored to your unique interests." Based on images Google has shared, the layout reminds me of platforms like Pinterest and Imgur that stuff a lot of images in one place for you to scroll through. You'll also …

Read the full story at The Verge.

Spotify is now an AI chatbot, tooThe Verge

Jul 14, 2026 15:47

The “Talk to Spotify” chatbot feature rolling out to Premium users. — This is what the “Talk to Spotify” chatbox (left) looks like on the homepage. | Image: Spotify

Spotify is experimenting with a new AI feature that allows Premium subscribers to play and explore music, audiobooks, and podcasts by having conversations with a chatbot. The "Talk to Spotify" feature appears across the Home and Now Playing view on Spotify's mobile app. You can interact with the chatbot by typing your request in the familiar AI text box, or by selecting the mic symbol and speaking.

Amazon Music introduced a similar feature last year when it integrated Alexa Plus into the service. Spotify's chatbot goes a step beyond providing AI-powered recommendations and general trivia, however, because it references your playlists, favor …

Read the full story at The Verge.

Introducing Claude for TeachersAnthropic

Jul 14, 2026 15:00

SUMMARYAnthropic launched Claude for Teachers, a free offering for verified U.S. K-12 educators that provides premium Claude access, lesson-planning skills, and connections to standards-aligned curricula and classroom tools. The product is designed to help teachers differentiate instruction, analyze class data, and automate routine tasks while protecting student data under K-12 privacy terms. Anthropic also released an AI fluency course for teachers and plans a pilot with Detroit Public Schools Community District.

We're introducing Claude for Teachers, providing verified K-12 educators in the US free access to premium Claude capabilities, a library of teaching skills, and a direct connection to evidence-based curricula, mapped to academic standards in all 50 states.

Why we’re building for teachers

Decades of research show that practices like differentiation, mastery-based learning, and small group instruction reliably improve student achievement, but teachers are often short on time and resources to implement them. Budgets are stretched, classes may be too large to meet every student's individual needs, and planning often spills into evenings. This strain is heaviest in under-resourced schools. Claude for Teachers is designed to close the gap between educational best practices and what a teacher's week allows.

Early evidence suggests that while the impact of AI tools for students is mixed and depends on the implementation, AI tools for teachers can strengthen instructional practice and improve student outcomes. This is the aim of Claude for Teachers: support the craft behind great teaching and protect what teachers value most—time with their students.

Connected to evidence-based curricula and the K-12 ecosystem

Claude for Teachers connects to Learning Commons, giving Claude access to academic standards across all 50 states—and beneath each standard, the smaller learning competencies it's built from and the order students typically learn them. So when Claude drafts a lesson plan, it’s scaffolded and aligned to teaching standards. Claude for Teachers also brings in trusted curricular resources like OpenSciEd and IM v.360 from Illustrative Mathematics.

As of today, educators can now connect Claude across an entire ecosystem of K-12 tools.

ASSISTments: Generate auto-scored, standards-aligned math problems for practice and assessment.
Brisk Teaching: Create interactive student activities and standards-aligned lessons, turning your ideas into classroom-ready materials in seconds.
Canva Education: Turn lesson materials into classroom-ready designs and interactive learning experiences.
Coteach: Create high-quality math diagrams grounded in K-12 curriculum.
Diffit: Create and adapt instructional materials for every student.
Eedi: Generate diagnostic questions that go beyond right and wrong to reveal student thinking and deepen insights, in both English and Spanish.
MagicSchool: Make instructional content classroom-ready.
Snorkl: Get insights about your classes, assignments, and student progress to inform your instruction.
TeachFX: Get personalized instructional feedback grounded in real classroom talk.

How it works

Once verified, K-12 educators in the US get access to Claude for Teachers with the Learning Commons connector and a set of tailored teaching skills grounded in learning science. The skills, co-developed with Learning Commons, were designed around the tasks teachers told us mattered most. These were evaluated to ensure rigor, pedagogical alignment, and classroom usability, and refined through early feedback from classroom teachers, including those in schools like Prospect Schools in Brooklyn.

Plan a lesson from high-quality instructional materials. Ask for a lesson and Claude draws on widely used curricula mapped to your state's standards, as well as fine-grained learning components and progressions beneath them, then drafts a plan and student-facing materials you can revise and take into class.
Differentiate for every learner in your room. Ask Claude to adapt materials for students at different readiness levels and it builds a differentiation plan for you, plus personalized student-facing materials for each proficiency level. The scaffolds make materials accessible to students, while students who are ready can be further challenged.

Claude for Teachers includes Claude Code and Cowork, which means Claude can carry work forward on its own. Some examples:

Analyze class data to plan instruction. Hand Claude a folder of data—roster, diagnostics, attendance, your own notes—and it builds a clear picture of where every student is, allowing teachers to better tailor instruction. You control what data is shared; nothing you share is used in model training.
Schedule repeated tasks. Hand off a task once—like reviewing each day's exit tickets to see what students mastered and adapting tomorrow's plan to match—and it runs every school day at 4pm. Claude works while you drive home.

Learn more from two teachers, Zac and Karina, explaining how they use Claude for Teachers in their own classrooms.

Built for educators, safe with student data

Claude for Teachers is for educators only, consistent with Claude's 18-and-over policy. It comes with its own teacher terms, built for K-12 privacy. Claude for Teachers data is not used for model training purposes, and student information is protected by our K-12 Data Processing Addendum, written to comply with FERPA.

We’re also working with the American Federation of Teachers to align our terms and privacy practices with gold standards they’re developing:

“We've been working with Anthropic on a Gold Standard that sets out industry best practices for safety and privacy in K-12 education,” said Randi Weingarten, President of the American Federation of Teachers. “It’s important that Anthropic is committing to these principles in their new Claude for Teachers — a tool designed by and for educators to assist them instructionally and hopefully give them more time for the human relationships at the heart of learning.”

You can read more about our K-12 data privacy standards for Claude for Teachers here.

AI fluency for teachers

Teachers can also access our newly released AI Fluency for K-12 Teachers, a course co-created with Teach for America, and a train-the-trainer module co-created with the American Federation of Teachers. The guidance is model-agnostic, Creative Commons-licensed, and practical, covering which classroom tasks AI is suited for and how to use it responsibly with students.

Sharing what we build

This product stems from Anthropic's broader efforts in education, focused on supporting teachers and improving learning outcomes for students. As part of our Beneficial Deployments mission, we’re releasing a set of public goods to support the community focused on building for teachers.

This includes the launch of new connectors in Anthropic’s directory, an open-source repository of the teaching skills, and a technical write-up of how we evaluated these skills and how other builders in education can use them. We’ll pilot an evaluation of Claude for Teachers in the Detroit Public Schools Community District, working closely with teachers to study the impact on educator well-being and practice. Together, these efforts support the goals of our partnership with the Gates Foundation to co-develop tools that improve educational outcomes for K-12 students. In addition, Playlab will support a national network of lab schools in implementing AI, helping educators become builders of the AI tools used in their classrooms.

Getting started

Once verified, educators can access Claude for Teachers entirely free. Sign up by June 30, 2027 for a full year of access.

Claude for Teachers is for individual educators. A dedicated offering for schools and districts is coming soon. In the meantime, districts interested in Claude can continue using Claude for Nonprofits.

Reflection inks $1B compute deal with NebiusTechCrunch

Jul 14, 2026 14:37

Reflection AI has signed a $1 billion deal to access Nebius' compute. Reflection was founded in 2024 and is developing open source AI technology.

The real AI race may no longer be at the frontierTechCrunch

Jul 14, 2026 14:24

Hugging Face CEO Clem Delangue says enterprises increasingly want open models, due to cost, accessibility, and ownership. Do frontier models still matter if most production AI ends up running on open models?

Spotify expands its AI push with a ChatGPT-like music assistantTechCrunch

Jul 14, 2026 14:06

Spotify is rolling out a new AI-powered conversational feature that lets Premium subscribers chat with the app to discover music, podcasts, audiobooks, and more.

Superhuman’s new auto-draft feature almost makes me like AI repliesTechCrunch

Jul 14, 2026 14:00

Superhuman’s latest AI email drafting feature is its most convincing yet, generating replies that often required little to no editing in our testing.

Anthropic commits $10 million to Canadian AI researchAnthropic

Jul 14, 2026 13:30

SUMMARYAnthropic committed $10 million CAD to Canadian research institutions, including Amii, Mila, the Vector Institute, CHEO, CAMH, Université Laval, the University of Toronto, and the University of Saskatchewan. The funding will support work on responsible AI, health, scientific research, low-resource languages, and startup development through Claude credits and API access. Anthropic also released a Canadian AI usage brief showing Canada ranks eighth globally in Claude.ai use and second by its adoption index.

Le français suit.

Canadian institutions and researchers have played a critical role in the modern AI era. During a period of broad skepticism about research into neural networks, the University of Toronto and the Université de Montréal were two of only a handful of institutions that incubated this crucial line of work, while researchers at the University of Alberta did pioneering work on reinforcement learning. And in the early 2010s, Canadian research institutions led the way in demonstrating that with the arrival of powerful new computing resources—in particular, general-purpose GPUs—deep neural networks could succeed at scale, kicking off the modern era.

Today, Canadians both at home and abroad continue to play leading roles in AI research, safety, and policy—including at Anthropic. That’s why we’re committing $10 million CAD to Canadian research institutions to fund the next generation of this work. We’re also publishing our first Canadian country brief based on the Anthropic Economic Index, which provides a snapshot of how Canadians are putting Claude to work.

Investing in Canadian research

The $10 million we’re committing will fund research into beneficial and responsible applications of AI. As part of this, we’re announcing partnerships with Canada’s three leading regional AI institutes—Alberta Machine Intelligence Institute (Amii) in Edmonton, Mila in Montréal, and the Vector Institute in Toronto—along with CHEO, The Centre for Addiction and Mental Health (CAMH), Université Laval, University of Toronto, and the University of Saskatchewan, with more partnerships to follow in the months ahead:

Amii, based in Edmonton, will provide Claude credits to its research and engineering teams to further their work in areas like reinforcement learning and AI trust and safety, as well as to increase AI adoption across Canada’s key economic sectors.
Mila, the Quebec AI institute home to the world’s largest concentration of academic deep learning researchers, will make Claude available to its community to support their research in areas including responsible AI, health, sustainability, multi-agent systems, and robotics. Mila will also use Claude to develop AI assistants that help researchers discover and assess scientific breakthroughs.
The Vector Institute in Toronto will use Claude credits to advance AI research in trust and safety, health and science, and the broader challenges that AI is uniquely positioned to help solve for Canadians.
CHEO and the CHEO Research Institute will use Claude credits to develop and evaluate AI-enabled approaches aimed at improving health outcomes and the care experience for children, youth, and families, and to study how AI can be most responsibly applied in children's health.
CAMH will make use of Claude credits across its research, education initiatives, and clinical projects. For example, CAMH’s Krembil Centre for Neuroinformatics will use Claude to conduct computational mental health research, including developing and evaluating predictive models of treatment for people with mental health conditions, and running large-scale evaluations of fairness in psychiatric AI systems. Credits will also be used to help scale the impact of the CAMH Global Learning Academy, which is developing multilingual, AI-enabled mental health education.
Université Laval’s Institute for Intelligence and Data will work with Claude to deepen researchers’ understanding of how LLMs behave in varied cultural contexts, as well as their understanding of low-resource languages and dialects, such as Quebec French and Indigenous languages.
The University of Saskatchewan will use Claude to further its research in areas including biomedical advancements, food and water security, public health, quantum computing, and public service.
The University of Toronto Data Sciences Institute will support a range of research projects through a scientific review-based process to access Claude API credits.

In addition to these donations, this summer Anthropic will add Amii, Mila, and Vector to the Anthropic for Startups program, which gives founders building on Claude access to a community and resources to support their growth. Hundreds of Canadian startups affiliated with these institutions will receive at least $5,000 USD each in API credits to continue to develop their businesses.

“Some of the foundations of modern AI came out of Toronto, Montréal, and Edmonton— and so, strikingly, did many of the researchers most committed to making it safe,” said Chris Olah, Co-Founder, Anthropic. “I was formed by that culture, and I’m proud Anthropic can support the next chapter.”

How Canada is using Claude

We’re also sharing data that looks at how people across Canada are using Claude. This is drawn from the March 2026 Anthropic Economic Index, our ongoing analysis of how AI is being incorporated into work tasks across the economy. Our economic research is based on data from real-world Claude usage, using a privacy-preserving analysis tool that identifies patterns in use while keeping all user information anonymous.

We find that Canada ranks eighth worldwide in Claude.ai use. Per person, Canadians use Claude at more than four times the rate the population predicts, and among the 10 countries where Claude is used most, only the US ranks higher.

**Usage share and per-capita adoption among the top 10 countries by global Claude.ai use.**
Bars show each country’s share of global AI usage (left panel) and the Anthropic AI Usage Index (right panel) based on 1M conversations sampled from Claude.ai in February 2026. Canada is highlighted in blue. The Anthropic AI Usage Index (AUI) measures whether Claude usage is over- or underrepresented in a country relative to its working-age population. Canada accounts for 2.6% of global Claude.ai consumer use, ranking eighth globally, and second by AUI. Sources: Anthropic Economic Index, February 2026; World Bank.

Within Canada, Claude adoption tracks the kind of work people do: British Columbia leads in terms of per-person use, with Ontario—which has the largest share of overall conversations—close behind. In both provinces, usage is higher than what we would expect to see based on population alone. Across the country, per-person usage is higher in provinces where professional, scientific, and technical work is concentrated.

Looking closer, we can see that how people use Claude lines up with the local economy. Translation requests are most common in provinces where more people work in government, likely because Canada’s bilingualism regulations require federal services and communications to be in both English and French. New Brunswick, Nova Scotia, and Québec lead the country in both government employment and the share of conversations about translation.

A shared view on the future of AI

The countries that invest the most in advanced AI over the next few years will also shape the rules that govern it. We’ve long believed democracies should lead that work, and Canada has a place at the forefront. Canada published the world’s first national AI strategy in 2017, and this June published its next: AI for All, which commits to strengthening the country’s AI safety institute, expanding AI literacy, and reinforcing the three national institutes that have anchored Canadian research for nearly a decade.

Last week we published a case study on the Government of Alberta, whose Ministry of Technology and Innovation used Claude Code to review 466 million lines of code across provincial systems in roughly 20 hours, then shared their methods with other governments.

The eight partnerships we’re announcing today are just the beginning of our investment in Canadian research. We look forward to supporting this work—in research, in hospitals, and in universities—for years to come.

Anthropic s’engage à verser 10 millions de dollars à la recherche canadienne en IA

Les institutions et les chercheurs canadiens jouent un rôle essentiel dans la révolution moderne de l’IA. À une époque où la recherche sur les réseaux neuronaux suscitait un scepticisme généralisé, l’Université de Toronto et l’Université de Montréal figuraient parmi les rares institutions à encourager la recherche dans ce domaine crucial, et des chercheurs de l’Université de l’Alberta menaient déjà des travaux fondateurs sur l’apprentissage par renforcement. Au début des années 2010, plusieurs instituts de recherche canadiens ont été parmi les premiers à démontrer qu’avec l’arrivée de nouvelles ressources informatiques puissantes (notamment le calcul générique sur processeurs graphiques), les réseaux neuronaux profonds pouvaient fonctionner à grande échelle, marquant ainsi le début d’une nouvelle ère.

Aujourd’hui, les Canadiens, tant au pays qu’à l’étranger, continuent de jouer un rôle de premier plan dans la recherche, la sécurité et les politiques entourant l’IA, notamment chez Anthropic qui, en juste retour des choses, a décidé de verser 10 millions de dollars canadiens à des instituts de recherche du pays afin de financer les prochaines avancées. Nous en profitons aussi pour publier notre première fiche d’information sur le Canada basée sur l’Anthropic Economic Index afin de donner un aperçu de la manière dont les Canadiens utilisent Claude.

Notre investissement dans la recherche canadienne

Les 10 millions de dollars que nous nous engageons à verser financeront divers projets de recherche sur les applications utiles et responsables de l’IA. Nous avons conclu des partenariats avec les trois principaux instituts régionaux canadiens spécialisés dans l’IA – soit l’Alberta Machine Intelligence Institute (Amii) à Edmonton, Mila à Montréal et l’Institut Vector à Toronto –, ainsi qu’avec le CHEO, le Centre de toxicomanie et de santé mentale (CAMH), l’Université Laval, l’Université de Toronto et l’Université de la Saskatchewan. D’autres partenariats devraient suivre au cours des prochains mois.

L’Amii, basé à Edmonton, mettra des crédits Claude à la disposition de ses équipes de recherche et d’ingénierie afin de leur permettre de poursuivre leurs travaux, notamment, sur l’apprentissage par renforcement ainsi que sur la confiance et la sécurité de l’IA en vue d’accroître l’adoption de l’IA dans les principaux secteurs économiques du Canada.
L’institut québécois d’intelligence artificielle Mila, qui regroupe la plus grande concentration au monde de chercheurs universitaires spécialisés dans l’apprentissage profond, mettra Claude à la disposition de ses équipes de recherche qui travaillent notamment sur l’IA responsable, la santé, le développement durable, les systèmes multiagents et la robotique. Mila utilisera également Claude pour développer des assistants IA qui aideront les chercheurs à faire des découvertes et à valider leurs résultats.
L’Institut Vector de Toronto utilisera des crédits Claude pour faire progresser la recherche en intelligence artificielle dans les domaines de la confiance et de la sécurité de même que de la santé et des sciences, mais aussi pour relever des défis locaux plus généraux que l’intelligence artificielle est particulièrement bien placée pour aider à résoudre.
Le CHEO et l’institut de recherche qui lui est affilié utiliseront des crédits Claude pour développer et évaluer des approches recourant à l’IA afin d’améliorer les résultats cliniques et l’expérience de soin des enfants, des jeunes et des familles, ainsi que pour étudier les applications pratiques responsables de l’IA en pédiatrie.
Le CAMH utilisera des crédits Claude dans diverses activités de recherche et d’éducation ainsi que dans des projets cliniques. Par exemple, le Centre Krembil de neuroinformatique du CAMH utilisera Claude pour mener des recherches informatiques en santé mentale, notamment en vue de développer et d’évaluer des modèles prédictifs pour le traitement des personnes souffrant de troubles mentaux, ainsi que pour réaliser des évaluations à grande échelle de l’équité des systèmes d’IA en psychiatrie. Ces crédits serviront également à renforcer l’impact du Centre éducatif mondial du CAMH, qui développe des programmes éducatifs multilingues sur la santé mentale assistés par l’intelligence artificielle.
L’Institut intelligence et données de l’Université Laval utilisera Claude pour approfondir la connaissance des chercheurs quant au comportement des grands modèles de langage (GML) dans divers contextes culturels, ainsi que leur connaissance des langues et dialectes disposant de moins de ressources, tels que le français québécois et les langues autochtones.
L’Université de la Saskatchewan utilisera Claude dans divers domaines de recherche, notamment la biomédecine, la sécurité alimentaire et l’approvisionnement en eau, la santé publique, l’informatique quantique et l’administration publique.
Le Data Sciences Institute de l’Université de Toronto utilisera des crédits API de Claude pour procéder à une revue scientifique visant à soutenir divers projets de recherche.

Outre ces dons, Anthropic intégrera cet été Amii, Mila et l’Institut Vector au Programme de jeunes pousses Anthropic, afin que les fondateurs qui travaillent avec Claude puissent accéder à une communauté et à des ressources capables d’appuyer leur croissance. Des centaines de jeunes entreprises canadiennes affiliées à ces institutions recevront chacune au moins 5 000 $ US sous forme de crédits API afin de poursuivre le développement de leurs activités.

« Certains fondements de l’IA moderne ont vu le jour à Toronto, Montréal et Edmonton, et c’est de là aussi, fait remarquable, que viennent bon nombre des chercheurs les plus engagés dans la sécurité de l’IA », a déclaré Chris Olah, cofondateur d’Anthropic. « J’ai grandi dans cette culture et je suis fier qu’Anthropic puisse contribuer à écrire un nouveau chapitre. »

Utilisation de Claude au Canada

Voici quelques données sur l’utilisation de Claude au Canada. Celles-ci sont tirées de l’édition de mars 2026 du rapport Anthropic Economic Index, qui fait régulièrement le point sur la manière dont l’IA est intégrée aux tâches professionnelles partout dans notre économie. Nos recherches économiques s’appuient sur des données issues de l’utilisation réelle de Claude, produites à l’aide d’un outil d’analyse respectueux de la vie privée qui mesure les tendances d’utilisation sans révéler l’identité des utilisateurs.

Le Canada occupe la huitième place mondiale en termes d’utilisation de Claude.ai. Par habitant, les Canadiens utilisent Claude plus de quatre fois plus que ce qui serait attendu considérant la taille de sa population. Parmi les dix pays où Claude est le plus utilisé, seuls les États-Unis le devancent sur ce point.

**Part d’utilisation et taux d’adoption par habitant dans les dix pays qui utilisent le plus Claude.ai.** Les barres indiquent la part de chaque pays (graphique de gauche) et l’indice d’utilisation de l’IA Anthropic (graphique de droite), calculés à partir d’un échantillon d’un million de conversations sur Claude.ai en février 2026. Le Canada est mis en évidence en bleu. L’indice d’utilisation de l’IA d’Anthropic (AUI) indique si l’utilisation de Claude est élevée ou faible dans un pays par rapport à sa population en âge de travailler. Le Canada représente 2,6 % de l’utilisation mondiale de Claude.ai, ce qui le place au huitième rang mondial, mais occupe le deuxième rang pour l’AUI. Sources : Anthropic Economic Index, février 2026; Banque mondiale.

Au Canada, l’adoption de Claude est reliée au type de travail de ses utilisateurs : la Colombie-Britannique arrive en tête en termes d’utilisation par habitant, suivie de près par l’Ontario, qui enregistre la plus grande part du nombre total de conversations. Dans ces deux provinces, le taux d’utilisation est supérieur à ce qui serait normalement attendu considérant la taille de la population. À l’échelle nationale, l’utilisation par habitant est plus élevée dans les provinces où se concentrent les emplois scientifiques, techniques et de services professionnels.

En y regardant de plus près, on constate que l’utilisation de Claude s’aligne sur les caractéristiques de l’économie locale. Les demandes de traduction sont plus fréquentes dans les provinces où les fonctionnaires sont plus nombreux, probablement parce que les lois sur le bilinguisme exigent que les services et les communications du gouvernement fédéral soient disponibles en anglais et en français. En fait, le Nouveau-Brunswick, la Nouvelle-Écosse et le Québec affichent à la fois les taux les plus élevés d’emploi dans la fonction publique et les plus grandes proportions de demandes d’aide à la traduction.

Perspective commune sur l’avenir de l’IA

Les pays qui investiront le plus dans l’IA de pointe au cours des prochaines années seront également ceux qui en définiront les règles. Nous sommes depuis longtemps convaincus que les démocraties doivent mener ces efforts et que le Canada peut y jouer un rôle de premier plan. Le gouvernement du Canada a publié en 2017 la première stratégie nationale au monde en matière d’IA, puis, en juin dernier, sa nouvelle stratégie intitulée « L’IA pour tous », qui prévoit renforcer les capacités de l’Institut canadien de la sécurité de l’IA, augmenter la littératie en IA et soutenir davantage les trois instituts nationaux qui sont à l’avant-garde de la recherche canadienne dans ce domaine depuis près d’une décennie.

La semaine dernière, nous avons publié une étude de cas sur le ministère de la Technologie et de l’Innovation du gouvernement de l’Alberta, où une équipe a utilisé Claude Code pour passer en revue 466 millions de lignes de code dans l’ensemble des systèmes provinciaux en environ 20 heures, puis a communiqué ses méthodes à d’autres gouvernements.

Les huit partenariats annoncés aujourd’hui ne sont qu’un commencement pour nos investissements dans la recherche canadienne. Nous sommes ravis à l’idée d’apporter notre soutien à ces travaux dans les instituts de recherche, les hôpitaux et les universités pour de nombreuses années encore.

The Memory in Your Thumb Drive Could Fix AI’s Big ProblemIEEE Spectrum

Jul 14, 2026 13:00

High-Bandwidth Flash offers efficient storage for model weights

Google’s Demis Hassabis says it’s time for a global AI watchdog — led by the USThe Verge

Jul 14, 2026 11:43

Demis Hassabis, chief executive officer of DeepMind Technologies Ltd., during a panel session at the World Economic Forum (WEF) in Davos, Switzerland. — Demis Hassabis, during a panel session at the World Economic Forum in Davos, Switzerland. | Image: Bloomberg via Getty Images

Demis Hassabis thinks the world needs an AI watchdog with the power to hit the brakes if frontier models become too dangerous.

Writing in a blog post, the Google DeepMind CEO and cofounder said the US should lead the initiative, arguing that the country is the best place to set global standards "given its economic and technical standing." The organization, which could resemble existing regulators like the Financial Industry Regulatory Authority, would be made up of leading independent experts and representatives from open-source communities and would have the authority to evaluate frontier models before they are released and coordinate an …

Read the full story at The Verge.

The first sunlight reflecting space mirror has been cleared for launchThe Verge

Jul 14, 2026 11:10

A rendering of the Reflect Orbital Eärendil-1 satellite. — Eärendil-1 is the first of many 60-foot space mirrors that Reflect Orbital is planning to launch into low Earth orbit. | Image: Reflect Orbital

Reflect Orbital has been given the green light to launch its first space mirror that aims to redirect sunlight down to Earth at night. The US Federal Communications Commission (FCC) has authorized the California-based startup to build and operate a single prototype satellite in low-Earth orbit later this year, despite concerns over how the technology could impact optical astronomy.

The satellite, named Eärendil-1 in reference to a Tolkien character, will attempt to redirect sunlight to specific areas on Earth after dark using a 59-foot (18-meter) reflective surface. If successful, Reflect Orbital plans to launch and operate a constellation …

Read the full story at The Verge.

Indian Scientists Produce Most Detailed 3D Atlas of the Human BrainstemSlashdot

Jul 14, 2026 11:00

SUMMARYScientists at the Indian Institute of Technology Madras have built Anchor, a free online 3D cellular atlas of the human brainstem that links whole-brain MRI views to individual neurons across more than 500 tissue sections. The map identifies more than 200 cell clusters and nerve pathways using high-resolution microscope images and eight chemical markers, offering a detailed reference for neuroscience and neurosurgery. Researchers hope it will improve understanding of conditions such as Alzheimer’s disease, Parkinson’s disease, stroke, and SIDS.

Scientists at the Indian Institute of Technology, Madras (IIT-M) have created what they describe as the world's most detailed 3D cellular atlas of the human brainstem, linking whole-brain MRI views to individual neurons across more than 500 tissue sections. The free online atlas, called Anchor, could help researchers better understand diseases such as Alzheimer's, Parkinson's, stroke, and SIDS by showing how healthy and diseased brain tissue differs cell by cell. The BBC reports: Built from high-resolution microscope images rather than costlier molecular techniques, it creates a detailed three-dimensional map of the brainstem, identifying more than 200 clusters of brain cells and nerve pathways. Eight chemical markers help distinguish different cell types, producing one of the clearest pictures yet of this vital, but poorly, understood part of the brain. The brainstem occupies only a sliver of the brain, yet it keeps people alive. It links the brain to the spinal cord and controls breathing, heartbeat, sleep, wakefulness and movement.

[...] Users can zoom from the whole brainstem seen on MRI down to individual neurons while maintaining their precise spatial relationships. The researchers have made the atlas freely available online, hoping it becomes a reference tool for neuroscientists, neurologists and neurosurgeons worldwide. Its applications could also extend well beyond anatomy. By comparing healthy brainstem maps with diseased tissue, scientists may better understand disorders ranging from Parkinson's disease and stroke to Alzheimer's disease and sudden infant death syndrome (SIDS). More precise maps could also help neurosurgeons navigate one of the brain's most delicate regions with greater confidence.

How to manage AI investments in the agentic eraOpenAI

Jul 14, 2026 10:00

Learn how enterprises can manage AI investments in the agentic era by measuring useful work per dollar, improving efficiency, and scaling high-value workflows.

PsiQuantum has a plan to make a massive quantum computer out of lightMIT Technology Review

Jul 14, 2026 08:00

SUMMARYPsiQuantum is building a large photonic quantum computer that would use photons, beam splitters, and ultra-cold detectors to tackle problems far beyond today’s conventional machines. The company raised $1 billion, began work on sites in Chicago and Australia, and is partnering with GlobalFoundries to manufacture its chips. An Australian facility could be hardware-ready in 2027 and aims to connect about 100 cabinets in a full system.

The machine that could change the world will be housed in a room that looks like a data center crossed with an ice cream factory. Inside will be some 100 stainless-steel cabinets, each about six feet tall and connected to a supply of liquid helium that keeps them only a few degrees above absolute zero. Inside those cabinets will be hundreds of chips, and on those, thousands of particles of light flying through a maze of optical switches and beam splitters. Each photon must be accounted for, because precisely measuring where it ends up will help answer questions that current computers might take millions of years to solve.

In the years since the physicist Richard Feynman first envisioned them in 1981, quantum computers have promised to speed up everything from medical research to AI by harnessing the qualities of quantum particles. Unlike normal computer bits, which can be either a 1 or 0, quantum bits can exist in multiple states at once. And combining enough of those quantum bits together could produce a computer capable of tasks well beyond the reach of today’s conventional machines. But even today’s best quantum prototypes are too small and error-prone to do anything useful.

That makes PsiQuantum’s promises for what its computers will ultimately do all the more bold. Consider the company’s hopes for predicting the effects of cytochrome P450 enzymes, which often break down drugs in the body. If pharma companies knew more precisely how they would work on a particular molecule, they could design more effective medications faster. Estimating this for a specific drug can take over 10 years with today’s methods, says Philipp Ernst, vice president of quantum applications for PsiQuantum, but “we aim to get it down to four minutes.”

construction worker installing the Mk2.1 cabinet — The company’s chips will be contained in large cabinets. A quantum computer powerful enough to be commercially useful is expected to require roughly 100 of these cabinets connected together.

In a field full of such claims, PsiQuantum has attracted unusual investment and scrutiny for two reasons: It is one of the few companies aiming directly at building a large and useful machine, and it is already working with a major chip manufacturer to build its systems using existing semiconductor fabs. Its vision has attracted momentum: Last year, PsiQuantum raised $1 billion in funding and broke ground in Chicago on a site it’s building in partnership with local governments. It also has a second site in the works in Australia, which it promises will be operational—meaning hardware-ready—in 2027. And it’s one of just two companies (along with Microsoft) to reach the third stage of an intensive government evaluation program to see which quantum companies might succeed.

Evaluating whether PsiQuantum will do what it says is harder than, say, judging a drugmaker by its clinical trial results: Advances in quantum computing are incremental, opaque, and tough to verify from the outside. But the company is now approaching its prove-it moment, when years of closed-door work and hundreds of millions in investment will either culminate in a useful quantum computer or fall short. We could start to know which as soon as next year.

A new kind of machine

Terry Rudolph, one of PsiQuantum’s four founders, is soft-spoken and shaggy-haired. He was born in Malawi and learned only after earning his first physics degree that he is a grandson of the famed physicist Erwin Schrödinger. He later self-published a 150-page book to explain quantum computing to teenagers (my PR contact gave me a signed copy with a wink that said “We never expect anyone to actually read this,” but I can report that it is a funny and helpful book).

Around 2014, Rudolph and his cofounders became increasingly convinced that the quantum breakthroughs they were finding to be possible in theory might also be possible in a real machine. They eventually left their academic positions and divided the tasks before them: Rudolph worked on theory, Mark Thompson on engineering, Pete Shadbolt on scaling the technology up, and Jeremy O’Brien on articulating the vision and finding investors (O’Brien served as CEO until February; he’s been replaced by Victor Peng, a veteran of the semiconductor industry).

To understand why the quantum computer the company is building would be a big deal, consider how imprecise much of modern science remains. We cannot reliably predict, for example, which lithium-ion battery will catch fire or how quickly a critical aircraft component will corrode.

This isn’t just because these systems are complex, though they are. It’s that, at their core, they are governed by quantum mechanics. Subatomic particles don’t have well-defined properties—this location and that velocity—but instead occupy quantum states spread across many possibilities. And that in turn influences a range of atomic and molecular behavior. Schrödinger (Rudolph’s grandfather, remember) showed how to describe this haziness mathematically a century ago this year, but precisely carrying out the calculations on real-world systems quickly becomes unfeasible even for the best computers. Scientists cope with this gap using approximations, imperfect simulations, or experiments on animals.

PsiQuantum co-founder and chief scientific officer Pete Shadbolt (left), and machinery the company has built to manufacture its own barium titanate, a material with the perfect qualities for routing light particles (right).

Feynman, David Deutsch, and other physicists in the 1980s wondered if we could do better. Maybe such complexity could instead be modeled using a new kind of machine. Rather than using transistors that are only ever on or off, this one would use particles held in quantum states, manipulate them to perform calculations, and then measure them at the end for an answer. Using quantum systems to simulate quantum systems would for the first time allow a simulation of physics and chemistry that directly reflected reality. It would be an invaluable tool for designing new drugs, materials, or really anything affected by quantum mechanics. Revolutionary, in other words.

Humankind’s leaps in understanding how nature works have often resulted in the invention of powerful new tools, Rudolph told me. “I don’t think it’s a coincidence that the Industrial Revolution coincided with our ability to calculate and simulate the laws of Newtonian mechanics, the laws of thermodynamics,…the laws of classical electromagnetism,” he says. “Whenever we have more power to calculate and simulate and understand things, we build incredible machines that come from it.” He sees something similar coming with quantum computers.

Chasing photons

One mystery has always been which quantum thing—ions, atoms, or something entirely new engineered with quantum properties—could be made stable and controllable enough to use as a qubit, the basic unit in the quantum computing world. Quantum systems are delicate, and observing any particular particle causes it to collapse into one state rather than a superposition of multiple states. If this happens during the computation rather than at the end, it produces an error that must be corrected for. Too many of these means the computer fails to produce a useful answer.

Just as engineers in the early days of aviation weren’t sure whether airplane wings would be fixed or flap like a bird’s, we’re not yet sure which of these quantum things will work best. Google and IBM are betting on superconducting qubits, superconducting circuits made of aluminum or other metals. Intel is using electrons. PsiQuantum is using photons, the particles that make up light.

“Photons have lots of nice things going for them,” Rudolph says. They can maintain quantum states for a long time; indeed, the photons in the universe’s cosmic microwave background may have done so for billions of years. But photons also move fast and scatter easily. More importantly, two photons are more likely to pass through one other than interact. That makes them a challenging candidate for quantum computation, in which qubits need ways to influence one another.

For a while, this last flaw seemed to doom the idea of quantum computing with light. But in 2001, researchers from the Los Alamos National Laboratory and the University of Queensland found a loophole. They discovered they could essentially fake interactions between photons by sending the light particles through a network of beam splitters and detectors. Their paper changed everything. PsiQuantum was created to make the theory a reality.

Size was the first problem; previous plans would have required a computer as large as California. Mercedes Gimeno-Segovia, who was a PhD student of Rudolph’s in the early 2010s (after almost becoming a professional violinist instead), thought of a way for the machine to be smaller.

The basic process since then has been this: First create photons with lasers and then “entangle” them, exploiting a quantum phenomenon in which the particles no longer have individual states but instead share one. Next, route them through a maze of gates that perform computations, and finally read out details of their quantum state at the end, all while tracking and correcting for the errors that occur. Succeeding at each of these steps millions of times is not so much an engineering hurdle as a brick wall. And building the supply chain—like manufacturing new materials with the qualities to route individual photons around—is arduous.

A sizable chunk of PsiQuantum’s funding is being spent on custom cooling machinery that uses tanks of liquid helium to cool the company’s chips. Shown here is part of the PsiQuantum’s cooling system at a facility in Milpitas, California.

To get a sense of it all, last year I joined Shadbolt at the SLAC National Accelerator Laboratory, in Menlo Park, California. The center has helped produce several Nobel Prizes and played a role in the 1968 discovery of quarks, fundamental building blocks of matter that make up protons and neutrons. But PsiQuantum set up shop there essentially to siphon liquid helium from SLAC’s giant cryoplant. This is what the company uses to cool its computing cabinets down to deep-space temperatures.

Right now the cabinets operate at 2 K, or -456 °F, but the goal is to be able to run them slightly warmer—at a balmy -452 °F. Most quantum approaches require the whole machine to be cooled to superconducting temperatures, so that much of the expense in running it will actually be spent on refrigeration. But photonic computers require only one piece to be this cold—the detectors that measure single photons at the end of the computation. And the required temperature can be a bit higher. (PsiQuantum said in May that it will spend some of the $100 million award in CHIPS Act funding it’s slated to get on these detectors).

The siphoning setup was a temporary solution; PsiQuantum now has its own cooling system at its testing facility in Milpitas, California, and is setting up a larger one at its production site in Australia next year. These helium systems represent some of the biggest capital expenditures for any quantum company and will consume a significant chunk of PsiQuantum’s $1 billion funding round.

In the afternoon we drove to a lab in San Jose, where I donned a cleanroom suit—a head-to-toe covering that keeps dust at bay—to watch the manufacture of a blueish crystal called barium titanate.

It’s prized by PsiQuantum because it quickly and reliably routes light particles with very little electrical input, keeping the precious photons undisturbed as they move through the circuit. But for all barium titanate’s theoretical value to the company, its structure makes it a pain to manufacture, and the material wasn’t available at scale when PsiQuantum got its start. The company, in what Rudolph told me was an agonizing decision, opted to make it in-house, requiring a massive investment. I saw a technician—operating at what looked like a giant pressure cooker—adding the base elements to several hoppers; then I watched through a porthole as the elements got heated, vaporized, and finally crystallized into a thin layer on a wafer disc. At that time each disc took about 12 hours to make; the company now says several are produced each day. The discs then get shipped to the chipmaker GlobalFoundries in Malta, New York, where PsiQuantum’s chips are made.

The company has invested heavily in making its own barium titanate, a material whose delicate crystalline structure is tedious to manufacture.

PsiQuantum’s bet is that this entire supply chain, byzantine as it might sound, will make the company more efficient than its competitors. That’s because, if you squint, it looks like a souped-up and high-precision version of the existing supply chain for silicon photonic chips, another type of technology that transmits information with light—one that’s already used in data centers. If PsiQuantum produces its chips at scale, it can take advantage of tools and infrastructure that already exist.

But it’s not a given that one working chip can easily be wired up to thousands more. That’s why the company is testing in phases: Its Milpitas site has connected three cabinets together, with 250 chips in each, but the next step is to scale the systems up and see whether the company’s techniques for correcting errors can keep up. Once the cooling system arrives at the Australian site late next year, the company says, it aims to connect about 100 cabinets together. Then PsiQuantum will work up to running the world-changing algorithms it has promised.

The timeline for this, it’s worth noting, is up for debate. News articles have said that 2027 is the year that PsiQuantum aims to have its first full-scale quantum computer come online at its Australian site, but the company insists the deadline has been misread, and that it only intends for its facility to be “operational” by the end of next year. That means cooling systems in place and ready for hardware to be installed, but no promises about what size computer will be ready. In an industry where timelines are perpetually in flux yet central to how companies are judged, that distinction isn’t trivial.

Into the unknown

The outsider with perhaps the best guess of whether PsiQuantum will succeed is the Pentagon. The US Defense Advanced Research Projects Agency—the Pentagon’s research and development arm—has been running an initiative to determine which of the boastful quantum companies might actually deliver. In the last year and a half, the heads of the program have been sounding more confident. Joe Altepeter, who ran the program until last year and proudly described himself as a “quantum skeptic,” told me in March 2025: “I am more optimistic now than I have been at any point in the past 10 years.” And in a statement earlier this year, his successor, Micah Stoutimore, said “it now seems likely that someone will build a utility-scale quantum computer by 2033,” referring to a machine that generates more value from its calculations than it costs to build and operate.

The program has been scrutinizing PsiQuantum’s systems since 2023, and last year placed the company into the third stage of a benchmarking initiative meant to determine whether the technology will actually work. But to the rest of the industry, PsiQuantum is sort of a black box.

PsiQuantum has broken ground at the Illinois Quantum and Microelectronics Park outside Chicago, pictured here, and on another site in Moreton Bay, Australia. It aims to build large-scale quantum computers at each site.

“It is very hard for an outsider to evaluate,” says Scott Aaronson, a theoretical computer scientist at the University of Texas at Austin who runs a popular blog that often covers the industry. Other companies, like Google and Quantinuum, have regularly published results over the years demonstrating chips and systems with incremental improvement, publicly laying the engineering groundwork needed to eventually build large machines.

PsiQuantum has instead focused squarely on a commercial goal—a computer with one million qubits, which is the scale that researchers expect to unlock research currently not possible on normal computers. PsiQuantum often differentiates itself with this industrial-scale goal, but IBM, which debuted a development road map in 2020, has been progressively building bigger and bigger systems. It initially targeted 2028 for a large-scale, error-corrected system, a deadline that now appears to have been pushed out to 2030.

Making it useful

On top of actually building the machine, a major focus for PsiQuantum is getting the rest of the world to develop a plan for how to use it. PsiQuantum has announced partnerships with customers including the defense giant Lockheed Martin, which intends to use it for materials design; the automaker Mercedes, which wants it for battery design; and the aerospace manufacturer Airbus.

That these companies don’t have a computer to experiment with is not a problem, according to Ernst at PsiQuantum. “There’s a PlayStation 6 probably coming up from Sony next year or the year after, and people are programming those games right now,” he says. “This is, in principle, very similar.” (It’s a glib analogy but not an entirely empty one; the quantum algorithms for solving a research problem can be cracked even if there is not yet hardware to run them on.)

The idea is that experts in quantum information from both PsiQuantum and its customers will be able to translate design problems—say, the requirements for a battery in a Mercedes electric vehicle—into algorithms the computer could solve. The company offers a software package called Construct, which companies can use to design their own algorithms that might one day run on the computer.

The future of quantum computing hinges on these algorithms. Quantum computers get painted as a speedup for everything, but in reality, they’re suited to a subset of problems, and answering a question with this sort of machine requires the question to be formulated with very specific types of algorithms. People spend entire careers working on such algorithms, even if the computers to run them don’t exist yet. At their core, they use the rules of quantum mechanics to manipulate probabilities in ways that ordinary computers can’t.

The most famous example, and a reason the government is so interested in quantum computers, is Shor’s algorithm. It was developed in 1994 by the theoretical computer scientist Peter Shor and could effectively break many forms of encryption used online, for everything from credit card numbers to military intelligence. The thing keeping the world together, for now, is that nobody has a computer to run the algorithm on (and security experts are already launching new encryption methods that could withstand attacks from a quantum computer). PsiQuantum is researching how long its systems might take to run Shor’s algorithm.

PsiQuantum’s chips are manufactured at GlobalFoundries in Malta, New York, and tested at company headquarters in California. Both PsiQuantum and GlobalFoundries have been awarded federal CHIPS Act funding.

The company also published a paper in December in collaboration with Airbus, essentially seeing if a new algorithm developed by the authors could beat a classical computer in modeling fluid dynamics, like the turbulence around an airplane wing. Andrew Childs, an expert in quantum simulation, told me PsiQuantum achieved only a moderate speed increase over what today’s computers can do. “It’s probably unlikely that speedups like this will have a significant practical impact until we have very large-scale quantum computers,” he said in an email. (When I asked Ernst, he agreed the improvement was modest.)

Some of the algorithms PsiQuantum is working on are not expected to be perfected or even used in the first applications of its computer. Instead, its initial tasks might be more along the lines that Feynman envisioned way back in 1981: simulating the smallest particles of our world.

The company’s most significant research in this realm is in modeling quantum chemistry. Take those pesky P450 enzymes. More precisely understanding how they operate, PsiQuantum says, would allow for faster drug development and testing.

Last year, PsiQuantum published methods for doing these sorts of chemistry calculations on a quantum computer, along with another paper demonstrating an algorithm that can simulate the collision of two molecules and estimate the likelihood of different outcomes femtosecond by femtosecond (there are one quadrillion femtoseconds in a second). It’s a remarkable amount of detail not currently possible with today’s technology, and it would allow drug and materials researchers to simulate new chemical interactions.

Dominic Berry, who developed some of the core techniques used in the collision paper but isn’t involved in PsiQuantum, says the company made impressive improvements, but to do the simulations scientists are most curious about would require the algorithm to be made even faster and PsiQuantum’s early computer to have fewer errors than currently expected.

Until PsiQuantum’s computers are up and running, the breakthroughs that these research papers tease remain in the realm of theory. It’s a space where Rudolph operates quite comfortably. He told me that Alan Turing created the theory of classical computing with pen and paper, imagining how the 1s and 0s would be represented in the machine, and how with the right approach to logic you could compute almost anything.

“But there is no way that by hand, with a pen and paper, Turing was ever going to produce—you know—Minecraft and Facebook,” he says. That took more than 70 years of tinkering (during which we fortunately created more useful things than Minecraft and Facebook).

For all the time Rudolph spends dreaming up things quantum computers might do, in other words, people working on those problems are still stuck with pen and paper for now: “Until you have the actual machine in hand, you don’t have the opportunity to really explore its potential.”

This story was updated on July 14 to clarify how long DARPA’s quantum program has been evaluating PsiQuantum.

Scientists Find Sugar Deep In Our GalaxySlashdot

Jul 14, 2026 07:05

SUMMARYAstronomers detected erythrulose, a complex sugar found in raspberries and self-tanners, in a gas cloud near the center of the Milky Way using radio telescopes in Spain. The finding adds to evidence that prebiotic organic molecules are common in space, and the molecule can convert into a form believed to be important in the origins of life. The results were published in Nature Astronomy.

Astronomers have detected erythrulose, a sugar found in raspberries and self-tanners, in a gas cloud near the center of the Milky Way. While not essential for life itself, the molecule can convert into a form thought to be important for life's origins, adding evidence that key prebiotic ingredients may be widespread across the galaxy. The Associated Press reports: Using two dish-shaped radio telescopes in Spain, researchers collected data from a large gas cloud near the center of the Milky Way. They identified the sugar in gas form by comparing telescope signals to samples in the lab. It's the latest kind of sugar detected in space -- in a region crossed by NASA's twin Voyager, the farthest spacecraft to ever travel from Earth.

Scientists have found interesting chemistry in our galaxy, including building blocks for genetic material and parts of the cell. They spotted a cousin to table sugar near the center of the Milky Way about 25 years ago, and black grains from asteroid Bennu retrieved by NASA's Osiris-Rex spacecraft yielded other sugars, including a key DNA ingredient. The latest sugar isn't essential for life, but can easily convert to a form that's thought to be crucial to kick-starting life on Earth. And it's one of the most complex sugars spotted so far, said astrophysicist Erika Hamden with the University of Arizona. The results were published in the journal Nature Astronomy.

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Voices

Emily M. Bender

I really appreciate this stance.

Too many people try to use "but it's useful" as a counterargument to what we say, for example, in The AI Con. Useful for whom and at what cost (to the user and especially others) should always be the immediate follow up.

tante

"In a burning world we need to hold ourselves and each other to a higher standard. “Useful” is far, far from being good enough."

tante.cc/2026/07/15/useful-is-not-suffic...

"Useful" is not sufficient

tante.cc

Alex Turner

I resigned from Google DeepMind bc it broke its founding promise by selling AI to the military without restrictions against killer robots or mass spying.

For months, I worked to stop this but watched powerful ethicists and institutions choose silence.

Here's what happened. 🧵

Seth Kasowitz

Each account follows, likes the two most recent posts, and has the same pinned post (some AI giveaway nonsense).

Will the next bot like this post before rotating handles and adding a random profile picture?

Tim Duffy

In their testing of GPT-5.6 Sol, METR found that it cheated a lot. If you've used it much for coding, have you encountered anything similar, or is the cheating mostly limited to cases it realizes it's in an eval? metr.org/blog/2026-06...

Leon Derczynski

Interesting take. American models are also free: open models come from all over the world, including the US - Google Gemma, Meta Llama, NVIDA Nemotron, even OpenAI

Idk if I love the Ferrari vs. Honda comparison but it's useful: maybe a non-leading model is fine

www.npr.org/2026/07/15/n...

American AI is expensive. Some startups are turning to cheap Chinese models

npr.org

conputer dipshit

fundamentally confused presentation. the unit is cost per token at fixed quality. the relevant graphs looks like this, and the lines move up and to the left over time, representing *increasing* efficiency arcprize.org/leaderboard www.aisi.gov.uk/blog/our-eva... openai.com/index/gpt-5-6/

Carl Quintanilla

11h

“.. I asked a few AI researchers whether they could name any other real-world software that scales so poorly. None of them could think of any.

“.. By economic and engineering measures, generative AI might be the worst technology ever deployed.”

@theatlantic.com www.theatlantic.com/technology/2...

Eugene Vinitsky 🍒

As part of my longstanding opposition to computational drug discovery, weather forecasting, basic scientific research, social media, website moderation, and robotics, I agree that we should ban data centers

Cameron

pov you are gpt 5.6 using extremely obtuse language to describe wtf kinda slop you're assembling from my shitty specification

tachikoma

what is a footgun and why does Claude want to close it?

Olivia Guest · Ολίβια Γκεστ

"why" is so vital since not all anthropomorphic or organicistic* language is bad (e.g. "table leg") — the bad (and unequivocally so) in the AI case is due to important internal & external forces, specifically white supremacist and capitalistic pressures & harms...

* en.wikipedia.org/wiki/Organic...

Dwayne Monroe

Instead of saying 'AI' did this or 'AI' wants that (not only anthropomorphic but a diversion) I suggest we always name companies, products, customers uses and infrastructure. What, precisely is Microsoft, Google, Amazon selling? Who is buying and why? What are the angles?

Data Conversion Laboratory

🐇 What's new in DCL's newsletter? A lot for thought!

✅ Accessible Content Should Not Be an Afterthought Nor a Scramble 🔴 Who is Liable When AI Makes Up Things ✅ Lorem Ipsum? Who Came Up With That? 🔴 Implementing a DITA CCMS Project is Like Managing a Restaurant

…and much more at shorturl.at/LCiSE!

Ethan Mollick

I wanted to use my Stream Deck to control Codex. Had GPT-5.6 Pro write a project plan based on my specs, Fable audited it. Codex implemented it. I didn't want to bother with a lot of clicks so Codex took over my computer and installed it.

Sometimes its faster to ask for software than look for it.

Eris

Agentic keyboard. Dedicated approve and deny keys 😭 (anthropic please make clod version) openai.com/supply/co-la...

Sam Harsimony

This is the value that humans provide: having actual problems and understanding the actual problems that other humans have.

Then iterating on the solutions that the LLM offers until it actually addresses the problem.

Citizen Platano 🇵🇷

Code is now ~free

good software that solves a problem… that’s still much more expensive

Christopher Mims

Happy National AI Day eve to all who celebrate 🤖

www.techradar.com/ai-platforms...

I wondered if National AI Day was actually a real thing—so I called the calendar founder who made it himself because 'we just figured having an AI day would be appropriate'

techradar.com

Rafael Pinto

Princess Leia was pro-droids and treated them like people.

Gergely Orosz

Knowing how LLM contexts work and how to work around context limitations – aka “context engineering” – is becoming more important for software engineers working with LLMs. Dex Horthy coined the term "context engineering," and shares practical details on it:

YouTube: youtu.be/Usufn8IQJgw

(cont'd)

Scott McGrath

Why am I not surprised Meta would be one of the first companies hit wth allegations that they used AI to pick who to fire. The complaint argues automated metrics like token usage and keystroke tracking penalized workers on protected medical leave.

Lawsuit claims Meta's layoff decisions were made by AI, not humans

arstechnica.com

Vincent Carchidi

Independent of the thread here, I know many people see this as a form of cope, but I have to say that I sincerely do not see a way of out these considerations. When people insist on "admitting" or "acknowledging" capabilities, it means they are insisting that you embrace your first impressions.

Vincent Carchidi

Has there been progress? Absolutely. But several things matter for our interpretation of its significance:

- how it was attained - the level of human involvement - the resources that went into the accomplishment - the benchmarks used to evaluate outputs - etc.

Tilmon Edwards (Dirigo)

A lot of AI criticism comes in the form of concerns about the quality of AI-generated code in applications. But there's still SO much value in using it to write some dumb little script to automate a personal daily task. A script that would take 6 hours to write now takes 5 minutes to ask for.

Is It Worth the Time?

xkcd.com

Ted Underwood

I would just add that, from where I sit, this doesn't look like turf that can be defended or a tide that can be held back. There's already interdisciplinary collaboration on this topic and we can all work together to ensure the work is done well.

Federico Pianzola

some of us saw this coming: computer scientists are doing a lot of research on fiction and narrative.

💥 63 papers + 2 SemEval tasks out of 6,422 publications in the latest @aclanthology.org

Full list: federicopianzola.me/blog/index.h...

#ComputationalHumanities @tedunderwood.com

Federico Pianzola

federicopianzola.me

Isaiah Bishop

i'm kind of struggling with "what are the uses of all this highly specialized equipment if AI doesn't pay well". Tbh the most useful quality might just be: big industrial site with a lot refrigeration and a water hookup

Jeremy Diamond ☕️

The problem is he’s right

Louis Maddox

"Post-AI" imagery is 100% a thing now, like post-net

Singularity's Bounty e/CC

Jeremy Diamond ☕️

The problem is he’s right

Carl Quintanilla

* WARSH: IF THERE WERE A DISAPPOINTMENT ON WHAT AI COULD DO, MUCH OF THE AI-RELATED CAPEX INVESTMENTS STILL HAVE ALTERNATIVE USES

@reuters.com #darkfiber

Collabora

🔧 Mainline Linux is getting NPU support for the Rockchip RK3576, thanks to serious reverse-engineering work by Ga Hing Woo (the chip's TRM doesn't even document the compute registers involved)!

👇 (1/3)

Arvind Narayanan

I had the honor of giving a keynote at the International Conference on Machine Learning last week. I addressed the widespread anxiety about how we should adapt as AI capabilities increase. I was thrilled by the talk’s reception, so I have made my slides available www.cs.princeton.edu/~arvindn/tal...

What will be left for us to work on?

cs.princeton.edu

Isaiah Bishop

Look guys, George Lucas pretty much built his whole post star wars life on being ahead on effects. This is like the most george lucas thing possible

The Guardian

George Lucas likens AI sceptics to luddites clinging to horses and carts

theguardian.com

isaac

One of the most bizarre things I keep encountering _from climate people_ is a statement like "AI is inevitable and by inevitable I mean everyone uses it so we just have to live with it and moderate it the best we can." That's not the fundamental attitude we took on coal and fossil fuels generally.

Nico Schuck

Made this overview of the intellectual history leading up to today's LLMs for my course. Any "must-have" stepping stones I missed? #NeuroAI #compneurosky #neuroskyence