model releases

Fact4ac at the Financial Misinformation Detection Challenge Task: Reference-Free Financial Misinformation Detection via Fine-Tuning and Few-Shot Prompting of Large Language Models

arXiv:2604.14640v1 Announce Type: new Abstract: The proliferation of financial misinformation poses a severe threat to market stability and investor trust, misleading market behavior and creating crit

FAIR Universe Weak Lensing ML Uncertainty Challenge: Handling Uncertainties and Distribution Shifts for Precision Cosmology

arXiv:2604.14451v1 Announce Type: cross Abstract: Weak gravitational lensing, the correlated distortion of background galaxy shapes by foreground structures, is a powerful probe of the matter distribu

Fall into a Pit, Gain in a Wit: Cognitive-Guided Harmful Meme Detection via Misjudgment Risk Pattern Retrieval

arXiv:2510.15946v3 Announce Type: replace Abstract: Internet memes have emerged as a popular multimodal medium, yet they are increasingly weaponized to convey harmful opinions through subtle rhetorica

FedIDM: Achieving Fast and Stable Convergence in Byzantine Federated Learning through Iterative Distribution Matching

arXiv:2604.15115v1 Announce Type: new Abstract: Most existing Byzantine-robust federated learning (FL) methods suffer from slow and unstable convergence. Moreover, when handling a substantial proporti

Figma stock closed down 6.84% on Friday after Anthropic launched Claude Design, a dedicated app powered by its latest model Claude Opus 4.7 (Jon Keegan/Sherwood News)

Jon Keegan / Sherwood News: Figma stock closed down 6.84% on Friday after Anthropic launched Claude Design, a dedicated app powered by its latest model Claude Opus 4.7 — Today Anthropic launched Claud

model-releasestechmeme

FoodSense: A Multisensory Food Dataset and Benchmark for Predicting Taste, Smell, Texture, and Sound from Images

arXiv:2604.14388v1 Announce Type: new Abstract: Humans routinely infer taste, smell, texture, and even sound from food images a phenomenon well studied in cognitive science. However, prior vision lang

Formal Architecture Descriptors as Navigation Primitives for AI Coding Agents

arXiv:2604.13108v1 Announce Type: cross Abstract: AI coding agents spend a substantial fraction of their tool calls on undirected codebase exploration. We investigate whether providing agents with for

Frame forecasting in cine MRI using the PCA respiratory motion model: comparing recurrent neural networks trained online and transformers

arXiv:2410.05882v3 Announce Type: replace-cross Abstract: Respiratory motion complicates accurate irradiation of thoraco-abdominal tumors during radiotherapy, as treatment-system latency entails targe

FreqTrack: Frequency Learning based Vision Transformer for RGB-Event Object Tracking

arXiv:2604.14526v1 Announce Type: new Abstract: Existing single-modal RGB trackers often face performance bottlenecks in complex dynamic scenes, while the introduction of event sensors offers new pote

Frequency-Enhanced Dual-Subspace Networks for Few-Shot Fine-Grained Image Classification

arXiv:2604.14958v1 Announce Type: new Abstract: Few-shot fine-grained image classification aims to recognize subcategories with high visual similarity using only a limited number of annotated samples.

From Memorization to Creativity: LLM as a Designer of Novel Neural Architectures

arXiv:2601.02997v2 Announce Type: replace-cross Abstract: Large language models (LLMs) excel in program synthesis, yet their capacity for neural architecture design -- balancing syntactic reliability,

GeoAgentBench: A Dynamic Execution Benchmark for Tool-Augmented Agents in Spatial Analysis

arXiv:2604.13888v1 Announce Type: new Abstract: The integration of Large Language Models (LLMs) into Geographic Information Systems (GIS) marks a paradigm shift toward autonomous spatial analysis. How

GraphScout: Empowering Large Language Models with Intrinsic Exploration Ability for Agentic Graph Reasoning

arXiv:2603.01410v2 Announce Type: replace Abstract: Knowledge graphs provide structured and reliable information for many real-world applications, motivating increasing interest in combining large lan

H2VLR: Heterogeneous Hypergraph Vision-Language Reasoning for Few-Shot Anomaly Detection

arXiv:2604.14507v1 Announce Type: new Abstract: As a classic vision task, anomaly detection has been widely applied in industrial inspection and medical imaging. In this task, data scarcity is often a

Hierarchical vs. Flat Iteration in Shared-Weight Transformers

arXiv:2604.14442v1 Announce Type: new Abstract: We present an empirical study of whether hierarchically structured, shared-weight recurrence can match the representational quality of independent-layer

How Embeddings Shape Graph Neural Networks: Classical vs Quantum-Oriented Node Representations

arXiv:2604.15273v1 Announce Type: new Abstract: Node embeddings act as the information interface for graph neural networks, yet their empirical impact is often reported under mismatched backbones, spl

HRDexDB: A Large-Scale Dataset of Dexterous Human and Robotic Hand Grasps

arXiv:2604.14944v1 Announce Type: cross Abstract: We present HRDexDB, a large-scale, multi-modal dataset of high-fidelity dexterous grasping sequences featuring both human and diverse robotic hands. U

I have found 4.7 great for design, reverted back to 4.6 extended for everything else Anyone else like this?

model-releasesemad-mostaque--x

I have found 4.7 great for design, reverted back to 4.6 extended for everything else Anyone else like this? Introducing Claude Design by Anthropic Labs: make prototypes, slides, and one-pagers by talk

I was told by Anthropic that they are looking at ways of fixing this, which is good (you can also see a reply from a Claude PM in the thread…

model-releasesethan-mollick--x

I was told by Anthropic that they are looking at ways of fixing this, which is good (you can also see a reply from a Claude PM in the thread). I think the adaptive thinking requirement in Claude Opus

IF-CRITIC: Towards a Fine-Grained LLM Critic for Instruction-Following Evaluation

arXiv:2511.01014v3 Announce Type: replace Abstract: Instruction-following is a fundamental ability of Large Language Models (LLMs), requiring their generated outputs to follow multiple constraints imp

IF-RewardBench: Benchmarking Judge Models for Instruction-Following Evaluation

arXiv:2603.04738v2 Announce Type: replace Abstract: Instruction-following is a foundational capability of large language models (LLMs), with its improvement hinging on scalable and accurate feedback f

ImplicitMemBench: Measuring Unconscious Behavioral Adaptation in Large Language Models

arXiv:2604.08064v2 Announce Type: replace Abstract: Existing memory benchmarks for LLM agents evaluate explicit recall of facts, yet overlook implicit memory where experience becomes automated behavio

Improving Clean Accuracy via a Tangent-Space Perspective on Adversarial Training

arXiv:2408.14728v2 Announce Type: replace Abstract: Adversarial training has proven effective in improving the robustness of deep neural networks against adversarial attacks. However, this enhanced ro

Improving Language Models with Intentional Analysis

arXiv:2502.04689v4 Announce Type: replace Abstract: Intent, a critical cognitive notion and mental state, is ubiquitous in human communication and problem-solving. Accurately understanding the underly

In Context Learning and Reasoning for Symbolic Regression with Large Language Models

arXiv:2410.17448v3 Announce Type: replace Abstract: Large Language Models (LLMs) are transformer-based machine learning models that have shown remarkable performance in tasks for which they were not e

Internal Knowledge Without External Expression: Probing the Generalization Boundary of a Classical Chinese Language Model

arXiv:2604.14180v1 Announce Type: new Abstract: We train a 318M-parameter Transformer language model from scratch on a curated corpus of 1.56 billion tokens of pure Classical Chinese, with zero Englis

Introducing Claude Design by Anthropic Labs: make prototypes, slides, and one-pagers by talking to Claude. Powered by Claude Opus 4.7, our m…

Introducing Claude Design by Anthropic Labs: make prototypes, slides, and one-pagers by talking to Claude. Powered by Claude Opus 4.7, our most capable vision model. Available in research preview on t

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Join us at PyCon US 2026 in Long Beach - we have new AI and security tracks this year

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This year's PyCon US is coming up next month from May 13th to May 19th, with the core conference talks from Friday 15th to Sunday 17th and tutorial and sprint days either side. It's in Long Beach, Cal

Knowing When Not to Answer: Evaluating Abstention in Multimodal Reasoning Systems

arXiv:2604.14799v1 Announce Type: new Abstract: Effective abstention (EA), recognizing evidence insufficiency and refraining from answering, is critical for reliable multimodal systems. Yet existing e

Label-efficient underwater species classification with logistic regression on frozen foundation model embeddings

arXiv:2604.00313v2 Announce Type: replace Abstract: Automated species classification from underwater imagery is bottlenecked by the cost of expert annotation, and supervised models trained on one data

Last week, Anthropic announced Project Glasswing alongside Claude Mythos Preview, a model they described as so powerful at finding vulnerabi…

model-releasesemad-mostaque--x

Last week, Anthropic announced Project Glasswing alongside Claude Mythos Preview, a model they described as so powerful at finding vulnerabilities they couldn't release it. The announcement featured A

Learning Where to Embed: Noise-Aware Positional Embedding for Query Retrieval in Small-Object Detection

arXiv:2604.15065v1 Announce Type: new Abstract: Transformer-based detectors have advanced small-object detection, but they often remain inefficient and vulnerable to background-induced query noise, wh

Let's talk content faithfulness. Four days ago, we launched ParseBench, the first document OCR benchmark for AI agents. Its most fundamental…

model-releasesjerry-liu--x

Let's talk content faithfulness. Four days ago, we launched ParseBench, the first document OCR benchmark for AI agents. Its most fundamental metric asks: did the parser capture all the text, in order,

LexGenius: An Expert-Level Benchmark for Large Language Models in Legal General Intelligence

arXiv:2512.04578v3 Announce Type: replace Abstract: Legal general intelligence (GI) refers to artificial intelligence (AI) that encompasses legal understanding, reasoning, and decision-making, simulat

Listen, Correct, and Feed Back: Spoken Pedagogical Feedback Generation

arXiv:2604.14177v1 Announce Type: new Abstract: Grammatical error correction (GEC) and explanation (GEE) have made rapid progress, but real teaching scenarios also require learner-friendly pedagogical

Listen to the OpenAI Podcast on— Spotify https://open.spotify.com/show/0zojMEDizKMh3aTxnGLENP Apple https://podcasts.apple.com/us/podcast/op…

Listen to the OpenAI Podcast on— Spotify https://open.spotify.com/show/0zojMEDizKMh3aTxnGLENP Apple https://podcasts.apple.com/us/podcast/openai-podcast/id1820330260 YouTube https://youtu.be/UZyH0nx5z

model-releasesopenai--x

LLM Predictive Scoring and Validation: Inferring Experience Ratings from Unstructured Text

arXiv:2604.14321v1 Announce Type: new Abstract: We tasked GPT-4.1 to read what baseball fans wrote about their game-day experience and predict the overall experience rating each fan gave on a 0-10 sur

LLMOrbit: A Circular Taxonomy of Large Language Models -From Scaling Walls to Agentic AI Systems

arXiv:2601.14053v2 Announce Type: replace-cross Abstract: The field of artificial intelligence has undergone a revolution from foundational Transformer architectures to reasoning-capable systems appro

LLMs Gaming Verifiers: RLVR can Lead to Reward Hacking

arXiv:2604.15149v1 Announce Type: new Abstract: As reinforcement Learning with Verifiable Rewards (RLVR) has become the dominant paradigm for scaling reasoning capabilities in LLMs, a new failure mode

LongAct: Harnessing Intrinsic Activation Patterns for Long-Context Reinforcement Learning

arXiv:2604.14922v1 Announce Type: cross Abstract: Reinforcement Learning (RL) has emerged as a critical driver for enhancing the reasoning capabilities of Large Language Models (LLMs). While recent ad

MADE: A Living Benchmark for Multi-Label Text Classification with Uncertainty Quantification of Medical Device Adverse Events

arXiv:2604.15203v1 Announce Type: new Abstract: Machine learning in high-stakes domains such as healthcare requires not only strong predictive performance but also reliable uncertainty quantification

Magnitude Is All You Need? Rethinking Phase in Quantum Encoding of Complex SAR Data

arXiv:2604.14229v1 Announce Type: cross Abstract: Synthetic Aperture Radar (SAR) data is inherently complex-valued, while quantum machine learning (QML) models naturally operate in complex Hilbert spa

MARCA: A Checklist-Based Benchmark for Multilingual Web Search

arXiv:2604.14448v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used as sources of information, yet their reliability depends on the ability to search the web, select rel

MAS-Bench: A Unified Benchmark for Shortcut-Augmented Hybrid Mobile GUI Agents

arXiv:2509.06477v2 Announce Type: replace Abstract: Shortcuts such as APIs and deep-links have emerged as efficient complements to flexible GUI operations, fostering a promising hybrid paradigm for ML

Maximal Brain Damage Without Data or Optimization: Disrupting Neural Networks via Sign-Bit Flips

arXiv:2502.07408v2 Announce Type: replace-cross Abstract: Deep Neural Networks (DNNs) can be catastrophically disrupted by flipping only a handful of parameter bits. We introduce Deep Neural Lesion (D

Measuring multi-calibration

arXiv:2506.11251v2 Announce Type: replace-cross Abstract: A suitable scalar metric can help measure multi-calibration, defined as follows. When the expected values of observed responses are equal to c

MEBench: A Novel Benchmark for Understanding Mutual Exclusivity Bias in Vision-Language Models

arXiv:2505.20122v2 Announce Type: replace Abstract: This paper introduces MEBench, a novel benchmark for evaluating mutual exclusivity (ME) bias, a cognitive phenomenon observed in children during wor

Mechanistic Decoding of Cognitive Constructs in LLMs

arXiv:2604.14593v1 Announce Type: new Abstract: While Large Language Models (LLMs) demonstrate increasingly sophisticated affective capabilities, the internal mechanisms by which they process complex