Search: “model-releases”

Live blog: Code w/ Claude 2026

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Simon Willison's live blog covers Anthropic's Code w/ Claude 2026 event, documenting the morning keynote sessions with real-time updates. The event featured announcements including updates to Claude m

LiveFMBench: Unveiling the Power and Limits of Agentic Workflows in Specification Generation

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arXiv:2605.01394v1 Announce Type: cross Abstract: Formal specification is essential for rigorous program verification, yet writing correct specifications remains costly and difficult to automate. Alth

model-releasesarxiv-cs-ai

6 May 2026

LLM-ADAM: A Generalizable LLM Agent Framework for Pre-Print Anomaly Detection in Additive Manufacturing

arXiv:2605.03328v1 Announce Type: new Abstract: Additive manufacturing (AM) continues to transform modern manufacturing by enabling flexible, on-demand production of complex geometries across diverse

Low Rank Tensor Completion via Adaptive ADMM

arXiv:2605.03736v1 Announce Type: cross Abstract: We consider a novel algorithm, for the completion of partially observed low-rank tensors, as a generalization of matrix completion. The proposed low-r

Mantis: Mamba-native Tuning is Efficient for 3D Point Cloud Foundation Models

arXiv:2605.03438v1 Announce Type: new Abstract: Pre-trained 3D point cloud foundation models (PFMs) have demonstrated strong transferability across diverse downstream tasks. However, full fine-tuning

MAP-Law: Coverage-Driven Retrieval Control for Multi-Turn Legal Consultation

arXiv:2605.01486v1 Announce Type: new Abstract: Legal consultation is a high-stakes, knowledge-intensive task that requires agents to identify relevant legal issues, retrieve authoritative support, an

Maximizing mutual information between prompts and responses improve LLM personalization with no additional data or human oversight

arXiv:2603.19294v2 Announce Type: replace-cross Abstract: While post-training has successfully improved large language models (LLMs) across a variety of domains, these gains heavily rely on human-labe

MCJudgeBench: A Benchmark for Constraint-Level Judge Evaluation in Multi-Constraint Instruction Following

arXiv:2605.03858v1 Announce Type: new Abstract: Multi-constraint instruction following requires verifying whether a response satisfies multiple individual requirements, yet LLM judges are often assess

MCP-Atlas: A Large-Scale Benchmark for Tool-Use Competency with Real MCP Servers

arXiv:2602.00933v2 Announce Type: replace-cross Abstract: The Model Context Protocol (MCP) is rapidly becoming the standard interface for Large Language Models (LLMs) to discover and invoke external t

MedStruct-S: A Benchmark for Key Discovery, Key-Conditioned QA and Semi-Structured Extraction from OCR Clinical Reports

arXiv:2605.03103v1 Announce Type: new Abstract: Semi-structured information extraction (IE) from OCR-derived clinical reports is crucial for efficiently reconstructing patients' longitudinal medical h

Meta-Inverse Physics-Informed Neural Networks for High-Dimensional Ordinary Differential Equations

arXiv:2605.03511v1 Announce Type: new Abstract: Solving inverse problems in dynamical systems governed by high-dimensional coupled ordinary differential equations (ODEs) is a ubiquitous challenge in s

MHPR: Multidimensional Human Perception and Reasoning Benchmark for Large Vision-Languate Models

arXiv:2605.03485v1 Announce Type: new Abstract: Multidimensional human understanding is essential for real-world applications such as film analysis and virtual digital humans, yet current LVLM benchma

MILE: Mixture of Incremental LoRA Experts for Continual Semantic Segmentation across Domains and Modalities

arXiv:2605.03555v1 Announce Type: new Abstract: Continual semantic segmentation requires models to adapt to new domains or modalities without sacrificing performance on previously learned tasks. Exper

Mitigating Frequency Learning Bias in Quantum Models via Multi-Stage Residual Learning

arXiv:2603.10083v2 Announce Type: replace-cross Abstract: Quantum machine learning models based on parameterized circuits can be viewed as Fourier series approximators. However, they often struggle to

Mixed-Precision Information Bottlenecks for On-Device Trait-State Disentanglement in Bipolar Agitation Detection

arXiv:2605.03039v1 Announce Type: new Abstract: Continuous monitoring of bipolar disorder agitation via voice biomarkers requires disentangling stable speaker traits from volatile affective states on

Moral Sensitivity in LLMs: A Tiered Evaluation of Contextual Bias via Behavioral Profiling and Mechanistic Interpretability

arXiv:2605.03217v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed in settings that require nuanced ethical reasoning, yet existing bias evaluations treat model out

Most ReLU Networks Admit Identifiable Parameters

arXiv:2605.03601v1 Announce Type: new Abstract: We study the realization map of deep ReLU networks, focusing on when a function determines its parameters up to scaling and permutation. To analyze hidd

MRC is already deployed across all of OpenAI’s largest supercomputers that we use to train frontier models, including our site with @Oracle …

MRC is already deployed across all of OpenAI’s largest supercomputers that we use to train frontier models, including our site with @Oracle Cloud Infrastructure (OCI) in Abilene, Texas, and in @Micros

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MSEarth: A Multimodal Benchmark for Earth Science Phenomenon Discovery with MLLMs

arXiv:2505.20740v3 Announce Type: replace Abstract: The rapid advancement of multimodal large language models (MLLMs) offers new opportunities for complex scientific challenges, yet their application

Multi-Agent Reasoning Improves Compute Efficiency: Pareto-Optimal Test-Time Scaling

arXiv:2605.01566v1 Announce Type: new Abstract: Advances in inference methods have enabled language models to improve their predictions without additional training. These methods often prioritize raw

Multimodal Learning on Low-Quality Data with Conformal Predictive Self-Calibration

arXiv:2605.03820v1 Announce Type: new Abstract: Multimodal learning often grapples with the challenge of low-quality data, which predominantly manifests as two facets: modality imbalance and noisy cor

Neuro-Symbolic Agents for Hallucination-Free Requirements Reuse

arXiv:2605.01562v1 Announce Type: cross Abstract: The Object-Oriented Method for Requirements Authoring and Management (OOMRAM) is a requirements reuse framework that relies on exact identifier matchi

NeuroState-Bench: A Human-Calibrated Benchmark for Commitment Integrity in LLM Agent Profiles

arXiv:2605.01847v1 Announce Type: new Abstract: Outcome-only evaluation under-specifies whether an evaluated agent profile preserves the commitments required to solve a multi-turn task coherently. Neu

New Bounds for Zarankiewicz Numbers via Reinforced LLM Evolutionary Search

arXiv:2605.01120v1 Announce Type: new Abstract: The Zarankiewicz number extbf{Z}(m, n, s, t) is the maximum number of edges in a bipartite graph G_{m, n} such that there is no complete K_{s, t} bipart

NEW paper from Microsoft Research. (bookmark it) The entire interpretability literature is built around human readers. As more analysis gets…

NEW paper from Microsoft Research. (bookmark it) The entire interpretability literature is built around human readers. As more analysis gets delegated to agents, the right target of interpretability s

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Not that Groove: Zero-Shot Symbolic Music Editing

arXiv:2505.08203v2 Announce Type: replace-cross Abstract: While recent advancements in AI music generation have predominantly focused on direct audio synthesis, these systems suffer from inherent rigi

OCRR: A Benchmark for Online Correction Recovery under Distribution Shift

arXiv:2605.03153v1 Announce Type: cross Abstract: Static benchmarks measure a model frozen at training time. Real systems face distribution shift: new categories, paraphrased queries, drift: and must

omg @bcherny with banger quotes “the future is more async agents… this is why we emphasize verification” “if you’re familiar with higher ord…

omg @bcherny with banger quotes “the future is more async agents… this is why we emphasize verification” “if you’re familiar with higher order functions, routines are higher order prompts” “default is

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On the Spectral Structure and Objective Equivalence of Orthogonal Multilabel Fisher Discriminants

arXiv:2605.03283v1 Announce Type: cross Abstract: We provide a unified theoretical analysis of Linear Discriminant Analysis with simultaneous multilabel scatter matrix formulations and Stiefel orthogo

On Verbalized Confidence Scores for LLMs

arXiv:2412.14737v2 Announce Type: replace Abstract: The rise of large language models (LLMs) and their tight integration into our daily life make it essential to dedicate efforts towards their trustwo

OpenAI’s new GPT-5.5 Instant makes ChatGPT smarter, with more concise and reliable responses

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OpenAI Group PBC is replacing the default model in ChatGPT with the launch of GPT-5.5 Instant, claiming users will notice fewer hallucinations when it’s discussing “sensitive topics” such as finance,

Optimal control of the future via prospective learning with control

arXiv:2511.08717v4 Announce Type: replace-cross Abstract: Optimal control of the future is the next frontier for AI. Current approaches to this problem are typically rooted in reinforcement learning (

ORPilot: A Production-Oriented Agentic LLM-for-OR Tool for Optimization Modeling

arXiv:2605.02728v1 Announce Type: new Abstract: This paper presents ORPilot, an open-source agentic AI system that translates real-world business problems into solver-ready optimization models. Unlike

Pairwise matrices for sparse autoencoders: single-feature inspection mislabels causal axes

arXiv:2605.03160v1 Announce Type: new Abstract: The standard sparse-autoencoder (SAE) interpretability protocol labels each feature from its top-activating contexts and validates by single-feature ste

Parameter-Efficient Distributional RL via Normalizing Flows and a Geometry-Aware Cramer Surrogate

arXiv:2505.04310v2 Announce Type: replace-cross Abstract: Distributional Reinforcement Learning (DistRL) improves upon expectation-based methods by modeling full return distributions, but standard app

Parameter-Efficient Multi-View Proficiency Estimation: From Discriminative Classification to Generative Feedback

arXiv:2605.03848v1 Announce Type: new Abstract: Estimating how well a person performs an action, rather than which action is performed, is central to coaching, rehabilitation, and talent identificatio

PatRe: A Full-Stage Office Action and Rebuttal Generation Benchmark for Patent Examination

arXiv:2605.03571v1 Announce Type: new Abstract: Patent examination is a complex, multi-stage process requiring both technical expertise and legal reasoning, increasingly challenged by rising applicati

PERSA: Reinforcement Learning for Professor-Style Personalized Feedback with LLMs

arXiv:2605.01123v1 Announce Type: new Abstract: Large language models (LLMs) can provide automated feedback in educational settings, but aligning an LLMs style with a specific instructors tone while m

PHBench: A Benchmark for Predicting Startup Series A Funding from Product Hunt Launch Signals

arXiv:2605.02974v1 Announce Type: cross Abstract: Structured launch signals on Product Hunt contain statistically significant predictive information for Series A funding outcomes. We construct PHBench

PhysicianBench: Evaluating LLM Agents in Real-World EHR Environments

arXiv:2605.02240v1 Announce Type: new Abstract: We introduce PhysicianBench, a benchmark for evaluating LLM agents on physician tasks grounded in real clinical setting within electronic health record

PIIGuard: Mitigating PII Harvesting under Adversarial Sanitization

arXiv:2605.03129v1 Announce Type: cross Abstract: Browsing-enabled LLM assistants can fetch webpages and answer contact-seeking queries, creating a practical channel for scraping contact-style persona

Pioneering AI-assisted code migration: How Google achieved 6x faster migration from TensorFlow to JAX

model-releasesgoogle-cloud-ai

AI coding agents are rapidly becoming ubiquitous across the software industry, fundamentally changing how developers write, test, and debug daily code. While these tools excel at localized, self-conta

PODiff: Latent Diffusion in Proper Orthogonal Decomposition Space for Scientific Super-Resolution

arXiv:2605.03399v1 Announce Type: new Abstract: Probabilistic super-resolution of high-dimensional spatial fields using diffusion models is often computationally prohibitive due to the cost of operati

Preview in Claude Code on desktop makes it easy to give Claude context. You can attach DOM elements or use the pencil tool to draw directly …

Claude Code on desktop includes a preview feature that allows users to provide context to Claude by attaching DOM elements or drawing directly with a pencil tool. This functionality streamlines the pr

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PriorNet: Prior-Guided Engagement Estimation from Face Video

arXiv:2605.03615v1 Announce Type: new Abstract: Engagement estimation from face video remains challenging because facial evidence is often incomplete, labeled data are limited, and engagement annotati

Prism: Efficient Test-Time Scaling via Hierarchical Search and Self-Verification for Discrete Diffusion Language Models

arXiv:2602.01842v3 Announce Type: replace Abstract: Inference-time compute has re-emerged as a practical way to improve LLM reasoning. Most test-time scaling (TTS) algorithms rely on autoregressive de

PROBE: Probabilistic Occupancy BEV Encoding with Analytical Translation Robustness for 3D Place Recognition

arXiv:2603.05965v2 Announce Type: replace-cross Abstract: We present PROBE (PRobabilistic Occupancy BEV Encoding), a learning-free LiDAR place recognition descriptor that models each BEV cell's occupa

Quantum Hierarchical Reinforcement Learning via Variational Quantum Circuits

arXiv:2605.03434v1 Announce Type: new Abstract: Reinforcement learning is one of the most challenging learning paradigms where efficacy and efficiency gains are extremely valuable. Hierarchical reinfo