Large language models

Model releases, architecture work and prompting research on large language models — from frontier-lab announcements to the arXiv papers behind them.

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arXiv AI

Jun 2

PR2: Predictive Routing Replay for MoE-Based LLM Reinforcement Learning

arXiv:2606. 00395v1 Announce Type: cross Abstract: Mixture of Experts (MoE) Large Language Models (LLMs) achieve strong performance at scale.

By Daize Dong, Junlin Chen, Haolong Jia, Jiawei Wu, Huanwei Di, Jiang Liu, Jialian Wu, Zhengzhong Liu, Zicheng Liu, Emad Barsoum, Dimitris N. Metaxas, Hongyi Wang

llms reinforcement-learning benchmarks

arXiv AI

Jun 2

MOC: Multi-Order Communication in LLM-based Multi-Agent Systems

arXiv:2606. 02359v1 Announce Type: new Abstract: Despite the remarkable progress of Large Language Model (LLM) based Multi-Agent Systems, most research focuses on optimizing coordination topology while largely underexploring the equally critical problem: how to transmit and optimize messages among agents effectively?

By Yao Guan, Lin Wang, Zhihu Lu, Ziyi Wang, Wenzhu Yan, Qiang Duan

llms agents

arXiv AI

Jun 2

Evaluating Deep Research Agents on Expert Consulting Work: A Benchmark with Verifiers, Rubrics, and Cognitive Traps

arXiv:2605. 17554v2 Announce Type: replace Abstract: Frontier deep research agents (DRAs) plan a research task, synthesize across documents, and return a structured deliverable on demand.

By Tanmay Asthana, Aman Saksena, Divyansh Sahu

llms agents benchmarks

arXiv Machine Learning

Jun 2

Beyond Procedure: Substantive Fairness in Conformal Prediction

arXiv:2602. 16794v2 Announce Type: replace-cross Abstract: Conformal prediction (CP) offers distribution-free uncertainty quantification for machine learning models, yet its interplay with fairness in downstream decision-making remains underexplored.

By Pengqi Liu, Zijun Yu, Mouloud Belbahri, Arthur Charpentier, Masoud Asgharian, Jesse C. Cresswell

llms safety

arXiv Machine Learning

Jun 2

Quantized Reasoning Models Think They Need to Think Longer, but They Do Not

arXiv:2606. 00206v1 Announce Type: new Abstract: Post-training quantization (PTQ) is widely used to deploy large language models efficiently, but its effect on reasoning models is not well understood.

By Sanae Lotfi, Polina Kirichenko, Steven Li, Zechun Liu

llms efficiency benchmarks

arXiv AI

Jun 2

SkillRevise: Improving LLM-Authored Agent Skills via Trace-Conditioned Skill Revision

arXiv:2606. 01139v1 Announce Type: new Abstract: Agent skills are procedural artifacts that enable LLM agents to execute workflows, verify constraints, and recover from failures.

By Yuxuan Liu, Zhaochen Su, Lingyun Xie, Yuhao Zhang, Qing Zong, Jiahe Guo, Zhongwei Xie, Yiyan Ji, Yauwai Yim, Hongyu Luo, Xiyu Ren, Ruan Chenyu, Haoran Li, Yangqiu Song

llms agents benchmarks

arXiv AI

Jun 2

Food Noise & False Safety: A Systematic Evaluation of How LLMs Fail to Adapt to Eating Disorder Queries with Clinician Feedback

arXiv:2606. 02444v1 Announce Type: new Abstract: Recent evidence shows that people with eating disorders (EDs) are increasingly seeking guidance, advice, and emotional support from Large Language Model (LLM)-based chat systems.

By Giulia Pucci, Emily Hemendinger, Ruizhe Li, Gavin Abercrombie, Tanvi Dinkar, Arabella Sinclair

llms safety

arXiv AI

Jun 2

Beyond One-shot: AI Agents for Learning in Field Experiments

arXiv:2606. 02458v1 Announce Type: new Abstract: Organizations routinely run experiments for A/B testing, yet the data generated from one experiment is underutilized to inform subsequent intervention design.

By Junjie Luo, Ritu Agarwal, Gordon Gao

llms agents

arXiv AI

Jun 2

POIROT: Interrogating Agents for Failure Detection in Multi-Agent Systems

arXiv:2606. 02282v1 Announce Type: new Abstract: Orchestrating Large Language Models into Multi-Agent Systems (LLM-MAS) has unlocked remarkable reasoning capabilities, yet emergent failures and hallucinations that resist characterisation block their deployment in safety-critical domains -- a gap made legally untenable by emerging AI regulation.

By I\~naki Dellibarda Varela, R. Sendra-Arranz, Pablo Romero-Sorozabal, J. M. Valverde-Garc\'ia, Annemarie F. Laudanski, \'Alvaro Guti\'errez, Eduardo Rocon, Manuel Cebrian

llms agents benchmarks safety

arXiv AI

Jun 2

ANDES: Agent Native Data Evolving Synthesis Tool for Autonomous Instruction Alignment

arXiv:2606. 01279v1 Announce Type: new Abstract: AI agents are increasingly being tasked with automating AI research itself, particularly the critical post-training phase that transforms base LLMs into aligned assistants.

By Zhengyang Zhao, Shengjie Ye, Lu Ma, Hao Liang, Hengyi Feng, Wentao Zhang

llms agents benchmarks safety

arXiv AI

Jun 2

Don't Ask the LLM to Track Freshness: A Deterministic Recipe for Memory Conflict Resolution

arXiv:2606. 01435v1 Announce Type: new Abstract: LLM-based memory systems increasingly maintain facts that evolve over time, where a recurring failure is conflict resolution: when a fact has multiple contradictory values, which should the agent return?

By Vikas Reddy, Sumanth Challaram

llms rag agents

arXiv AI

Jun 2

TrafficClaw: A Generalizable LLM Agent in the Unified Physical Environment for Urban Traffic Control

arXiv:2604. 17456v2 Announce Type: replace Abstract: Large language model (LLM) agents have shown strong capabilities in long-horizon reasoning, tool use, and decision-making in digital environments, yet extending them to physically grounded systems remains challenging.

By Siqi Lai, Pan Zhang, Yuping Zhou, Jindong Han, Yansong Ning, Hao Liu

llms agents reinforcement-learning

arXiv AI

Jun 2

Lying Is Just a Phase: The Hidden Alignment Transition in Language Model Scaling

arXiv:2605. 18838v3 Announce Type: replace-cross Abstract: Scaling laws predict loss from compute but not how capabilities interact.

By Adil Amin

llms efficiency benchmarks safety

arXiv AI

Jun 2

CA-BED: Conversation-Aware Bayesian Experimental Design

arXiv:2606. 01182v1 Announce Type: cross Abstract: Large Language Models (LLMs) excel at static reasoning tasks, yet their performance often degrades in interactive scenarios where information must be actively acquired through questioning.

By Daniel Arnould, Rashad Aziz, Zixuan Kang, Tanav Changal, Kevin Zhu, Sunishchal Dev, Gabriel Grand, Shreyas Sunil Kulkarni

llms benchmarks

arXiv AI

Jun 2

Mitigating Perceptual Judgment Bias in Multimodal LLM-as-a-Judge via Perceptual Perturbation and Reward Modeling

arXiv:2606. 02578v1 Announce Type: cross Abstract: Recent multimodal large language models have demonstrated strong reasoning ability, yet their reliability as automated evaluators remains limited by a critical weakness: when visual evidence conflicts with textual cues, MLLM judges tend to reward plausible narratives over perceptually correct answers.

By Seojeong Park, Jiho Choi, Junyong Kang, Seonho Lee, Jaeyo Shin, Hyunjung Shim

llms reinforcement-learning multimodal benchmarks safety

arXiv AI

Jun 2

MESA: Improving MoE Safety Alignment via Decentralized Expertise

arXiv:2606. 00651v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) architectures scale Large Language Models (LLMs) efficiently, enabling greater capacity with reduced computational cost by dynamically routing inputs to relevant experts, yet introduce a critical vulnerability: Safety Sparsity, where safety capabilities concentrate in few experts, making them susceptible to adversarial bypassing.

By Yitong Sun, Yao Huang, Teng Li, Ranjie Duan, Yichi Zhang, Xingjun Ma, Hui Xue, Xingxing Wei

llms efficiency benchmarks safety

arXiv AI

Jun 2

Benchmarking at the Edge of Comprehension

arXiv:2602. 14307v4 Announce Type: replace Abstract: As frontier Large Language Models (LLMs) increasingly saturate new benchmarks shortly after they are published, benchmarking itself is at a juncture: if frontier models keep improving, it will become increasingly hard for humans to generate discriminative tasks, provide accurate ground-truth answers, or evaluate complex solutions.

By Samuele Marro, Jialin Yu, Emanuele La Malfa, Oishi Deb, Jiawei Li, Yibo Yang, Ebey Abraham, Sunando Sengupta, Eric Sommerlade, Michael Wooldridge, Philip Torr

llms benchmarks safety

arXiv AI

Jun 2

Behavior-Invariant Task Representation Learning with Transformer-based World Models for Offline Meta-Reinforcement Learning

arXiv:2606. 00780v1 Announce Type: cross Abstract: Offline meta-reinforcement learning leverages static datasets to enable agents to generalize to unseen environments by combining offline efficiency with meta-learning adaptability, yet it faces key challenges from context and policy distribution shifts.

By Fuyuan Qian, Menglong Zhang, Song Wang, Quanying Liu

llms agents reinforcement-learning benchmarks

arXiv Machine Learning

Jun 2

CAREF: Calibration-Aware Regularization for Explanation Faithfulness Without Rationale Supervision

arXiv:2605. 27835v2 Announce Type: replace Abstract: We introduce CAREF, a parameter-efficient fine-tuning framework that jointly optimizes predictive accuracy and explanation faithfulness via calibration-aware regularization.

By Naphat Nithisopa, Teerapong Panboonyuen

llms fine-tuning efficiency benchmarks safety

arXiv AI

Jun 2

Zamba2-VL Technical Report

arXiv:2606. 00390v1 Announce Type: cross Abstract: We present Zamba2-VL, a suite of vision-language models built on Zamba2, a hybrid language-model architecture combining Mamba2 state-space layers with a small number of shared transformer blocks.

By Hassan Shapourian, Kasra Hejazi, Olabode M. Sule, Beren Millidge

llms computer-vision efficiency multimodal benchmarks

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