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

Atomix: Timely, Transactional Tool Use for Reliable Agentic Workflows

arXiv:2602. 14849v2 Announce Type: replace-cross Abstract: LLM agents execute multi-step workflows that mutate external state through tools.

By Bardia Mohammadi, Nearchos Potamitis, Lars Klein, Akhil Arora, Laurent Bindschaedler

llms agents

arXiv Machine Learning

Jun 2

ReFLEX: Length-Generalizable CSI Denoising for MIMO-OFDM via Relative-Frequency Bias

arXiv:2606. 00263v1 Announce Type: cross Abstract: This letter studies CSI denoising for MIMO--OFDM with variable NR resource block (RB) allocations.

By Zhibin Zhang, Robert Potekhin, Ziwei Wan, Vladimir Lyashev, Zhen Gao

llms safety

arXiv AI

Jun 2

LC-ERD: Mining Latent Logic for Self-Evolving Reasoning via Consistency-Regulated Reward Decomposition

arXiv:2605. 24005v2 Announce Type: replace Abstract: The evolution of Large Language Model (LLM) reasoning is bottlenecked by the scarcity of high-quality process data.

By Yanyu Chen, Jiyue Jiang, Dianzhi Yu, Zheng Wu, Jiahong Liu, Jiaming Han, Xiao Guo, Jinhu Qi, Yu Li, Yifei Zhang, Irwin King

llms agents safety

arXiv AI

Jun 2

TuneAgent: Agentic Operating System Kernel Tuning with Reinforcement Learning

arXiv:2508. 12551v2 Announce Type: replace-cross Abstract: Linux kernel tuning is essential for optimizing operating system (OS) performance, yet remains challenging due to the complex kernel space, sparse performance feedback, and strong workload sensitivity.

By Hongyu Lin, Yuchen Li, Haoran Luo, Zhenghong Lin, Libo Zhang, Mingjie Xing, Yanjun Wu

llms agents reinforcement-learning

arXiv AI

Jun 2

A Structured Benchmark for Text-Guided Anomaly Detection: When Language Stops Conditioning the Decision

arXiv:2606. 01992v1 Announce Type: cross Abstract: Industrial anomaly detection has historically been a unimodal task.

By Stefano Samele, Eugenio Lomurno, Teodora Jovanovic, Sanjay Shivakumar Manohar, Alberto Crivellaro, Matteo Matteucci

llms rag multimodal benchmarks

arXiv AI

Jun 2

Multi-Contrast MRI Motion Correction via Parameter-Informed Disentanglement and Adaptive Experts

arXiv:2606. 00146v1 Announce Type: cross Abstract: Motion artifacts in magnetic resonance imaging (MRI) degrade diagnostic reliability.

By Honglin Xiong, Yuxian Tang, Feng Li, Yulin Wang, Lei Xiang, Dinggang Shen, Qian Wang

llms rag computer-vision benchmarks

arXiv AI

Jun 2

FeynmanBench: Benchmarking Multimodal LLMs on Diagrammatic Physics Reasoning

arXiv:2604. 03893v2 Announce Type: replace Abstract: Current multimodal benchmarks for scientific reasoning primarily evaluate local information extraction -- models recognize symbols and values and then perform textual inference.

By Zeyu Wang, Jingye Xu, Xiaogang Li, Peiyao Xiao, Qinhao Kong, Ben Wang, Chengliang Xu, Zichao Chen, Bing Zhao, Hu Wei

llms multimodal benchmarks

arXiv AI

Jun 2

TCAR-Gen: Temporal Graph Retrieval with Evidence Fusion for Knowledge-Grounded Generation

arXiv:2606. 00029v1 Announce Type: cross Abstract: Retrieval-augmented generation systems struggle with temporal reasoning and evidence fusion when answering complex questions over historical criminal case narratives.

By Sidra Nasir, Muhammad Noman Zahid, Rizwan Ahmed Khan

llms rag nlp benchmarks

arXiv AI

Jun 2

AEyeDE: An Attention-Based Attribution Framework for AI-Generated Text Detection

arXiv:2606. 00016v1 Announce Type: cross Abstract: Detecting AI-generated text is becoming increasingly challenging as modern language models approach human-level fluency and can evade detectors that rely on surface statistics or likelihood-based signals.

By Aria Nourbakhsh, Adelaide Danilov, Christoph Schommer, Salima Lamsiyah

llms benchmarks

arXiv Machine Learning

Jun 2

Position: the Stochastic Parrot in the Coal Mine. Model Collapse is a Threat to Low-Resource Communities

arXiv:2605. 04127v2 Announce Type: replace Abstract: Model collapse, the degradation in performance that arises when generative models are trained on the outputs of prior models, is an increasing concern as artificially generated content proliferates.

By Devon Jarvis, Richard Klein, Benjamin Rosman, Steven James, Stefano Sarao Mannelli

llms

arXiv AI

Jun 2

JenBridge: Adaptive Long-Form Video Soundtracking across Scene Transitions

arXiv:2606. 01703v1 Announce Type: cross Abstract: We address the challenge of generating high-fidelity, long-form soundtracks that remain coherent across scene transitions.

By Jiashuo Yu, Yao Yao, Boyu Chen, Alex Wang

llms agents multimodal benchmarks safety

arXiv AI

Jun 2

Beyond Offline A/B Testing: Context-Aware Agent Simulation for Recommender System Evaluation

arXiv:2604. 09549v2 Announce Type: replace-cross Abstract: Recommender systems are central to online services, enabling users to navigate through massive amounts of content across various domains.

By Nicolas Bougie, Gian Maria Marconi, Xiaotong Ye, Narimasa Watanabe

llms agents

arXiv AI

Jun 2

Who Annotates in NLP? A Large-scale Assessment of Human Annotation Reporting between 2018 and 2025

arXiv:2606. 02255v1 Announce Type: cross Abstract: Human annotation is the empirical foundation of much NLP research, from dataset construction to model evaluation, but papers often leave unclear who produced the annotations and how the annotation process was controlled.

By Maria Kunilovskaya, Gagan Bhatia, Lisa Sophie Albertelli, Yanran Chen, Christian Greisinger, Lotta Kiefer, Christoph Leiter, Subhadeep Roy, Tewodros Achamaleh, Muhammad Arslan Manzoor, Sebastian Pohl, Yufang Hou, Steffen Eger

llms nlp

arXiv AI

Jun 2

OPD+: Rethinking the Advantage Design for On-Policy Distillation

arXiv:2606. 01039v1 Announce Type: cross Abstract: On-policy distillation (OPD) is a widely used technique to transfer capabilities from capable teacher language models to the base student models, and can be formulated in a reinforcement learning style objective using student generated rollouts.

By Hanyang Zhao, Haoxian Chen, Han Lin, Genta Indra Winata, David Yao, Wenpin Tang

llms reinforcement-learning efficiency benchmarks

arXiv AI

Jun 2

On Effectiveness and Efficiency of Agentic Tool-calling and RL Training

arXiv:2606. 00135v1 Announce Type: cross Abstract: Tool-calling is a central component of modern large language model (LLM) agents, equipping them with skills beyond their parametric knowledge.

By Tong Liu, Cheng Qian, Matej Cief, Yuan He, Daniele Dan, Nikolaos Aletras, Gabriella Kazai

llms agents reinforcement-learning benchmarks

arXiv Machine Learning

Jun 2

ChWDTA: Channel-wise Wavelet-Domain Transformer Attention and Entropy Modeling for Learned Image Compression

arXiv:2606. 00111v1 Announce Type: cross Abstract: State-of-the-art learned image compression (LIC) schemes are increasingly based on hybrid CNN-transformer architectures.

By Haisheng Fu, Runyu Yang, Feng Ding, Siyu Zhu, Jie Liang, Xiaoxiao Li, Zhenman Fang, Jingning Han

llms nlp benchmarks

arXiv AI

Jun 2

Short-form Text Rewriting with Phi Silica

arXiv:2606. 00462v1 Announce Type: cross Abstract: Short-form text rewriting is a constrained variant of paraphrasing in which limited context and high semantic density leave little room for variation.

By Divya Tadimeti, Shawn Pan, Sameera Lanka, Chenghui Zhou, Sadid Hasan

llms fine-tuning efficiency safety

arXiv AI

Jun 2

Bridging Reasoning Trajectories in On-Policy Distillation via Near-Future Guidance

arXiv:2606. 00305v1 Announce Type: cross Abstract: On-Policy Distillation (OPD) improves large language model reasoning by training a student model on trajectories sampled from its own policy under teacher supervision.

By Yuxuan Jiang, Francis Ferraro

llms efficiency

arXiv AI

Jun 2

Dynamic Trust-Aware Sparse Communication Topology for LLM-Based Multi-Agent Consensus

arXiv:2606. 01828v1 Announce Type: cross Abstract: Large language model-driven multi-agent systems enhance the reliability of complex reasoning tasks through multi-round deliberation, role specialization, and cross-validation.

By Wanshuang Gou, Zihan Liu

llms agents

arXiv AI

Jun 2

RA-LWLM: Retrieval-Augmented In-Context Localization with Wireless Foundation Models

arXiv:2606. 01899v1 Announce Type: cross Abstract: Wireless localization is a fundamental capability of sixth-generation (6G) networks.

By Guangjin Pan, Hui Chen, Hei Victor Cheng, Henk Wymeersch

llms rag

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