model releases

Goal-driven Bayesian Optimal Experimental Design for Robust Decision-Making Under Model Uncertainty

arXiv:2605.26093v1 Announce Type: new Abstract: Bayesian optimal experimental design (BOED) selects experiments to maximize information gain about model parameters. However, in decision-critical setti

Model Releases

Grammatically-Guided Sparse Attention for Efficient and Interpretable Transformers

DGX agent

arXiv:2605.24518v1 Announce Type: cross Abstract: The quadratic complexity of self-attention in Transformer models remains a significant bottleneck for processing long sequences and deploying large la

model-releasesarxiv-cs-ai

26 May 2026

GreenSeg: Ground Segmentation Algorithm for Agricultural Robots in Mediterranean Greenhouses using RGB-D Point Clouds

model-releasesarxiv-cs-ro

arXiv:2605.25279v1 Announce Type: new Abstract: Greenhouse agriculture in the Mediterranean region faces significant automation challenges due to its unique structural and environmental constraints. T

GroupTravelBench: Benchmarking LLM Agents on Multi-Person Travel Planning

arXiv:2605.25200v1 Announce Type: new Abstract: Travel planning is a realistic task for evaluating the planning and tool-use abilities of LLM agents. However, existing benchmarks typically assume only

Guided Flow Matching for Forward and Inverse PDE Problems with Sparse Observations: Algorithm and Theory

arXiv:2605.25509v1 Announce Type: cross Abstract: Reconstructing PDE solutions from sparse observations is a core challenge in scientific computing. We present FM4PDE, a flow-matching generative frame

Hadamard Representation: Scaffolding Performance Across Model-free RL

arXiv:2406.09079v5 Announce Type: replace Abstract: Deep reinforcement learning agents progressively lose representational capacity during training: neurons become dormant, removing active capacity fr

HEAPr: Hessian-based Efficient Atomic Expert Pruning in Output Space

arXiv:2509.22299v3 Announce Type: replace-cross Abstract: Mixture-of-Experts (MoE) architectures in large language models (LLMs) deliver exceptional performance and reduced inference costs compared to

HiGraph: A Large-Scale Hierarchical Graph Dataset for Malware Analysis

arXiv:2509.02113v2 Announce Type: replace-cross Abstract: The advancement of graph-based malware analysis is critically limited by the absence of large-scale datasets that capture the inherent hierarc

HiMed: Incentivizing Hindi Reasoning in Medical LLMs

arXiv:2605.24635v1 Announce Type: new Abstract: Medical large language models hold promise for reducing healthcare disparities, yet Hindi remains severely underrepresented. While medical LLMs excel in

HoloFair: Unified T2I Fairness Evaluation and Fair-GRPO Debiasing

arXiv:2605.24687v1 Announce Type: cross Abstract: Text-to-Image (T2I) models have made significant strides in visual realism and semantic consistency, yet they often perpetuate and amplify societal bi

How Many Tools Should an LLM Agent See? A Chance-Corrected Answer

arXiv:2605.24660v1 Announce Type: cross Abstract: Before an LLM agent can use a tool, a retrieval system must decide which candidate tools to show to the agent. How long should that shortlist be? Show

How Much Do Large Language Model Cheat on Evaluation? Benchmarking Overestimation under the One-Time-Pad-Based Framework

arXiv:2507.19219v2 Announce Type: replace Abstract: Overestimation in evaluating large language models (LLMs) has become an increasing concern. Due to the contamination of public benchmarks or imbalan

How Much Thinking is Enough? Quantifying and Understanding Redundancy in LLM Reasoning

arXiv:2605.23926v1 Announce Type: new Abstract: Reasoning-capable large language models solve hard problems by emitting long chains of thought, paying heavily in latency, GPU time, and energy. Casual

How we evolved Google’s global and data center networks for the AI era

model-releasesgoogle-cloud-ai

Over the last 25 years of building Google’s global network, we’ve navigated major architectural eras — from the Internet, to streaming, and the cloud. Today, we are squarely in the midst of a fourth:

How Well Do Models Follow Their Constitutions?

arXiv:2605.24229v1 Announce Type: new Abstract: Frontier AI developers now train models against long written behavioral specifications, such as Anthropic's constitution (Anthropic, 2025a) and OpenAI's

Hylos: Operability Contracts for Model-Native Spatial Intelligence

arXiv:2605.24728v1 Announce Type: new Abstract: Foundation models can increasingly describe, reconstruct, and generate 3D objects, assemblies, scenes, and environments, but visually plausible spatial

I don't comment on every article that uses out-of-date measures on AI ability, but I felt (probably wrongly) that the article was a response…

model-releasesethan-mollick--x

I don't comment on every article that uses out-of-date measures on AI ability, but I felt (probably wrongly) that the article was a response to my viral tweet, so I felt I needed to say something! htt

I found this Wired article on AI fact-checking frustrating. It could have been about why we continue to need human fact checkers (talk to pe…

model-releasesethan-mollick--x

I found this Wired article on AI fact-checking frustrating. It could have been about why we continue to need human fact checkers (talk to people, use judgement, resolve conflict). Instead it is full o

IndexMem: Learned KV-Cache Eviction with Latent Memory for Long-Context LLM Inference

arXiv:2605.25475v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly expected to operate over long contexts, yet standard softmax attention incurs a KV cache that grows line

INDUCTION: Finite-Structure Concept Synthesis in First-Order Logic

arXiv:2602.18956v3 Announce Type: replace Abstract: We introduce INDUCTION, a benchmark for finite structure concept synthesis in first order logic. Given small finite relational worlds with extension

Inference Time Optimization with Confidence Dynamics

arXiv:2605.25244v1 Announce Type: new Abstract: Inference time optimization techniques, such as repeated sampling, have significantly advanced the reasoning capabilities of Large Language Models (LLMs

InfiFPO: Implicit Model Fusion via Preference Optimization in Large Language Models

arXiv:2505.13878v3 Announce Type: replace-cross Abstract: Model fusion combines multiple Large Language Models (LLMs) with different strengths into a more powerful, integrated model through lightweigh

Insuring Every Action: An Authority Frontier Framework for Runtime Actuarial Control of Autonomous AI Agents

arXiv:2605.25632v1 Announce Type: new Abstract: Autonomous AI agents increasingly issue side-effect-bearing actions: database mutations, refunds, payments, external commitments. We propose the Actuari

Introducing CHI-Bench on @huggingface: the world’s first long-horizon healthcare benchmark for AI agents. 75 real healthcare workflows + 20 …

model-releasesclem-delangue--x

Introducing CHI-Bench on @huggingface: the world’s first long-horizon healthcare benchmark for AI agents. 75 real healthcare workflows + 20 apps + 200+ MCP tools + 1,290 skills + process / outcome rew

IterInject: Indirect Prompt Injection Against LLM Agents via Feedback-Guided Iterative Optimization

arXiv:2605.24659v1 Announce Type: new Abstract: LLM-based agents are increasingly deployed for complex tasks requiring planning, tool use, and interaction with external services. Their reliance on unt

JacQuant: STE-Free Quantization-Aware Training via Learned Jacobian Surrogates

arXiv:2605.25469v1 Announce Type: new Abstract: Quantization-aware training (QAT) is widely deployed but typically relies on the Straight-Through Estimator (STE), which passes gradients through non-di

JAEGER: Joint 3D Audio-Visual Grounding and Reasoning in Simulated Physical Environments

arXiv:2602.18527v2 Announce Type: replace-cross Abstract: Current audio-visual large language models (AV-LLMs) are predominantly restricted to 2D perception, relying on RGB video and monaural audio. T

JEPA-DNA: Grounding Genomic Foundation Models through Joint-Embedding Predictive Architectures

arXiv:2602.17162v2 Announce Type: replace Abstract: Genomic Foundation Models (GFMs) typically rely on Masked Language Modeling (MLM) or Next-Token Prediction (NTP) to learn the 'Laws of Nature'. Whil

JudgmentBench: Comparing Rubric and Preference Evaluation for Quality Assessment

arXiv:2605.25240v1 Announce Type: cross Abstract: Two methodologies dominate current practices of benchmarking: rubric-based scoring evaluates items against predefined criteria, whereas comparative ju

KAME: Tandem Architecture for Enhancing Knowledge in Real-Time Speech-to-Speech Conversational AI

arXiv:2510.02327v2 Announce Type: replace-cross Abstract: Real-time speech-to-speech (S2S) models excel at generating natural, low-latency conversational responses but often lack deep knowledge and se

Keep the Proof State Live: Snapshotting for Efficient Tactic Search in Lean 4

arXiv:2605.25556v1 Announce Type: cross Abstract: Automated theorem proving systems built on Lean 4 increasingly rely on parallel tactic search over partially specified proofs, such as those generated

Kolmogorov-Arnold Fourier Networks

arXiv:2502.06018v3 Announce Type: replace-cross Abstract: Although Kolmogorov-Arnold-based interpretable networks (KANs) possess strong theoretical expressiveness, they suffer from severe parameter ex

Latent Q-Barrier Shielding for Safe In-Context Reinforcement Learning

arXiv:2605.25267v1 Announce Type: cross Abstract: Safe in-context reinforcement learning (ICRL) adapts online from interaction history without test-time parameter updates while controlling episode cos

Learning Fine-grained Parameter Sharing via Sparse Tensor Decomposition

arXiv:2411.09816v4 Announce Type: replace Abstract: Large neural networks achieve state-of-the-art performance on many tasks, yet their sheer size hinders deployment on resource-constrained devices. A

Learning Sparse Compositional Functions with Norm-Constrained Neural Networks

arXiv:2605.25608v1 Announce Type: cross Abstract: The ability of deep neural networks to learn hierarchical features is widely regarded as a key mechanism underlying their success in high-dimensional

Learning to Reason Efficiently with A* Post-Training

arXiv:2605.24597v1 Announce Type: new Abstract: Many applications of large language models (LLMs) require deductive reasoning, yet models frequently produce incorrect or redundant inference steps. We

LIBERO-PRO: Towards Robust and Fair Evaluation of Vision-Language-Action Models Beyond Memorization

model-releasesarxiv-cs-ro

arXiv:2510.03827v2 Announce Type: replace-cross Abstract: LIBERO has emerged as a widely adopted benchmark for evaluating Vision-Language-Action (VLA) models; however, its current training and evaluat

LiveMCP-101: Stress Testing and Diagnosing MCP-enabled Agents on Challenging Queries

arXiv:2508.15760v2 Announce Type: replace-cross Abstract: Tool calling has emerged as a critical capability for AI agents. In contrast to conventional tool calling frameworks that rely on static, prov

Llamion Technical Report

arXiv:2605.25676v1 Announce Type: new Abstract: We release Llamion, a family of 14B-parameter open-weight language models obtained by transforming Orion-14B into the standardized Llama-family architec

LLM-as-a-Reviewer: Benchmarking Their Ability, Divergence, and Prompt Injection Resistance as Paper Reviewers

arXiv:2605.25415v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used in academic peer review, yet their reliability, alignment with human judgment, and robustness to adve

LLMs on prem. but the prem is your wardrobe. http://shop.cohere.com The design team cooked on this one.

Cohere has announced a creative marketing initiative featuring LLM-themed merchandise available through their shop, with the tagline playing on the phrase 'on premises' to humorously suggest their lan

model-releasescohere--x

LLMTabBench: Evaluating LLMs on Binary Tabular Classification From Zero to Few Shots

arXiv:2605.24417v1 Announce Type: new Abstract: Supervised classification for tabular data remains a core machine learning task, yet its reliance on large labeled datasets limits applicability in data

Looped Diffusion Language Models

arXiv:2605.26106v1 Announce Type: new Abstract: Masked diffusion models (MDMs) have emerged as a promising alternative to autoregressive models for language modeling, yet the effective design of trans

LWiAI Podcast #246 - Gemini 3.5 + Omni, Musk Loses, OpenAI vs Erdős

model-releaseslast-week-in-ai

This podcast episode discusses Google's release of Gemini 3.5 and its Omni multimodal capabilities, covers recent developments in Elon Musk's AI ventures, and examines tensions or competition between

M^3-Verse: A 'Spot the Difference' Challenge for Large Multimodal Models

arXiv:2512.18735v2 Announce Type: replace-cross Abstract: Modern Large Multimodal Models (LMMs) have demonstrated extraordinary ability in static image and single-state spatial-temporal understanding.

MDIA: A Multi-Agent Diagnostic Intelligence Pipeline on HealthBench Professional

arXiv:2605.24699v1 Announce Type: new Abstract: Most reported gains on agentic-LLM clinical benchmarks are often attributed to prompt engineering, yet our results suggest that larger improvements can

Measuring Reasoning Quality in LLMs: A Multi-Dimensional Behavioral Framework

arXiv:2605.24661v1 Announce Type: new Abstract: LLMs have achieved remarkable success in complex reasoning tasks, yet current evaluation approaches predominantly rely on final-answer correctness, offe

MEDAL: Manifold Embedding Distillation via Autoencoder Learning

arXiv:2605.24244v1 Announce Type: cross Abstract: Low-dimensional embeddings are widely used as visual summaries of high-dimensional data and to enable downstream scientific discoveries. Yet, popular