Large language models

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

25,737 stories · RSS feed

arXiv Machine Learning

Jul 1

Capturing Context-Aware Route Choice Semantics for Trajectory Representation Learning

arXiv:2510. 14819v3 Announce Type: replace-cross Abstract: Trajectory representation learning (TRL) aims to encode raw trajectory data into low-dimensional embeddings for downstream tasks such as travel time estimation, mobility prediction, and trajectory similarity analysis.

By Ji Cao, Yu Wang, Tongya Zheng, Jie Song, Qinghong Guo, Zujie Ren, Canghong Jin, Gang Chen, Mingli Song

llms rag benchmarks

arXiv AI

Jul 1

LUMOS: A Semantic Operating-System Layer for Accessibility-Grounded AI Agents

arXiv:2606. 30697v1 Announce Type: cross Abstract: Current operating systems expose interfaces optimized for human users but not for AI agents.

By Yogeswar Reddy Thota

llms agents computer-vision

arXiv AI

Jul 1

Teaching LLMs String Matching, Backtracking, and Error Recovery to Deduce Bases and Truth Tables for the Combinatorially Exploding Bit Manipulation Puzzles

arXiv:2606. 23672v2 Announce Type: replace Abstract: This paper presents our algorithmic innovations for the NVIDIA Nemotron Model Reasoning Challenge, focusing on Bit Manipulation Puzzles.

By Prateek Agnihotri, Sanchit Jain, Prabhat Agnihotri, Aditya Prasad, Shubham Jain

llms agents nlp robotics fine-tuning safety

arXiv Machine Learning

Jul 1

Flow-Opt: Scalable Centralized Multi-Robot Trajectory Optimization with Flow Matching and Differentiable Optimization

arXiv:2510. 09204v4 Announce Type: replace-cross Abstract: Centralized trajectory optimization in the joint space of multiple robots allows access to a larger feasible space that can result in smoother trajectories, especially while planning in tight spaces.

By Simon Idoko, Prajyot Jadhav, Arun Kumar Singh

llms diffusion robotics benchmarks safety

arXiv AI

Jul 1

The Consistency Dilemma in LLMs: Generator-Evaluator Agreement and Vulnerability to Mistakes

arXiv:2606. 30653v1 Announce Type: cross Abstract: Large language models are increasingly deployed in agentic pipelines that depend on the model evaluating its own outputs without external verification.

By Marina Mancoridis, Zo\"e Hitzig

llms agents

arXiv AI

Jul 1

Position: Vision-Language-Action Models Cannot Be Verified to Perform Physical Reasoning

arXiv:2606. 30686v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) systems, built on pretrained vision-language models (VLMs), have shown rapidly improving performance on robot manipulation benchmarks.

By Taozhao Chen, Ian Manchester, Huaming Chen

llms robotics multimodal benchmarks

arXiv Machine Learning

Jul 1

An Empirical Study of Security Calibration in Large Language Models for Code

arXiv:2606. 31159v1 Announce Type: cross Abstract: Large Language Models (LLMs) are rapidly transforming software development, yet their use in security-critical contexts raises a key question: do models know when their generated code is insecure?

By Mohammed Latif Siddiq, Md. Nafiu Rahman, Joanna C. S. Santos

llms benchmarks

arXiv AI

Jul 1

CLOUDADV: Decision-Aligned Instance Sizing with Zero-Shot Foundation Models under Drift

arXiv:2606. 31470v1 Announce Type: new Abstract: Cloud virtual machines are often overprovisioned, creating avoidable cost and operational inefficiency.

By Jack Bell, Giacomo Carfi, Gerlando Gramaglia, Andrea Simioni, Daniele Fontani, Vincenzo Lomonaco

llms safety

arXiv Machine Learning

Jul 1

When Embedding-Based Defenses Fail: Rethinking Safety in LLM-Based Multi-Agent Systems

arXiv:2605. 01133v3 Announce Type: replace-cross Abstract: Large language model (LLM)-powered multi-agent systems (MAS) enable agents to communicate and share information, achieving strong performance on complex tasks.

By Lingxi Zhang, Guangtao Zheng, Hanjie Chen

llms rag agents safety

arXiv Machine Learning

Jul 1

A Systematic Approach to Multi-Agent AI from Advanced Regulatory Control Theory: Safe and Auditable LLM Operator Agents for Process Control

arXiv:2606. 30877v1 Announce Type: cross Abstract: Recent literature shows that large language models (LLMs) are useful for general-purpose tasks yet perform poorly on specific domain ones.

By Idelfonso B. R. Nogueira, Sigurd Skogestad

llms agents safety

arXiv Machine Learning

Jul 1

SemRF: A Semantic Reference Frame for Residual-Stream Dynamics in Language Models

arXiv:2606. 32022v1 Announce Type: new Abstract: Residual-stream analysis asks how language-model computation evolves across depth, but intermediate decoding requires comparable readout coordinates across layers.

By Jian Gu, Aldeida Aleti, Chunyang Chen, Hongyu Zhang

llms rag

arXiv AI

Jul 1

Containment Verification: AI Safety Guarantees Independent of Alignment

arXiv:2605. 09045v2 Announce Type: replace Abstract: Agentic frameworks are the software layer through which AI agents act in the world.

By Royce Moon, Lav R. Varshney

llms agents safety

arXiv AI

Jul 1

ASR-Agnostic Multimodal Spectrotemporal Modeling for Early Dementia Detection

arXiv:2606. 30646v1 Announce Type: cross Abstract: Speech recruits the same executive, attentional, and working memory processes underlying instrumental activities of daily living, or IADLs, providing a non-invasive proxy for cognitive assessment.

By Chukwuemeka Ugwu, Oluwafemi Richard Oyeleke

llms rag multimodal benchmarks

arXiv AI

Jul 1

MultiUAV-Plat: An LLM-Oriented Platform, Benchmark and Framework for Multi-UAV Collaborative Task Planning

arXiv:2606. 31073v1 Announce Type: new Abstract: Large language models (LLMs) provide a promising interface for high-level robotic task planning, but their use in multi-UAV collaboration remains difficult to evaluate systematically.

By Sheng Zhang, Qinglin Li, Yuechao Zang, Xueqin Huang, Yijia Fu, Cheng Zhu

llms agents robotics benchmarks

arXiv AI

Jul 1

Breaking Failure Cascades: Step-Aware Reinforcement Learning for Medical Multimodal Reasoning

arXiv:2606. 31825v1 Announce Type: cross Abstract: Recent multimodal large language models have shown great promise in clinical image reasoning, but existing post-training pipelines remain predominantly outcome-centric, relying on final answer correctness or sequence-level preferences.

By Junha Jung, Minbyul Jeong, Suhyeon Lim, Sungwook Jung, Jaehoon Yun, Taeyun Roh, Mujeen Sung, Jaewoo Kang

llms nlp reinforcement-learning multimodal benchmarks

arXiv Machine Learning

Jul 1

Contextual Slate GLM Bandits with Limited Adaptivity

arXiv:2606. 31449v1 Announce Type: new Abstract: We investigate the contextual slate bandit problem with generalized linear rewards under limited adaptivity.

By Tanmay Goyal, Sukruta Prakash Midigeshi, Gaurav Sinha

llms reinforcement-learning benchmarks

arXiv Machine Learning

Jul 1

Revocable Learned State via Process Sidecars

arXiv:2606. 30788v1 Announce Type: new Abstract: Language models are often adapted in stages: a public skill phase, a private memory phase, and a later safety phase that learns to refuse outputs tied to the remembered entities.

By John Sweeney

llms safety

arXiv AI

Jul 1

Token-Sparse Medical Multimodal Reasoning via Dual-Stream Reinforcement Learning

arXiv:2606. 31599v1 Announce Type: cross Abstract: Vision-language models (VLMs) combining reinforcement learning (RL) ignite remarkable progress in multimodal reasoning, yet still struggle with medical images, which typically exhibit extremely sparse visual evidence to inform clinical decision-making.

By Kaitao Chen, Weiqian Zhao, Jiamin Wu, Qihao Zheng, Shangquan Sun, Chunfeng Song, Xiaosong Wang, Mu Zhou, Mianxin Liu

llms nlp reinforcement-learning efficiency multimodal benchmarks

arXiv AI

Jul 1

Cross-lingual Relation Extraction with Large Language Models: Zero-Shot, Few-Shot, and Fine-Tuned Evaluation on Romanian

arXiv:2606. 31718v1 Announce Type: cross Abstract: Relation extraction (RE) for low-resource languages is typically constrained by the lack of annotated corpora.

By Dragos-Mitrut Vasile, Elena-Simona Apostol, Stefan-Adrian Toma, Adrian Paschke, Ciprian-Octavian Truica

llms fine-tuning benchmarks

arXiv AI

Jul 1

LiteResearcher: A Scalable Agentic RL Training Framework for Deep Research Agent

arXiv:2604. 17931v3 Announce Type: replace Abstract: Reinforcement Learning (RL) has emerged as a powerful training paradigm for LLM-based agents.

By Wanli Li, Bince Qu, Bo Pan, Jianyu Zhang, Zheng Liu, Pan Zhang, Wei Chen, Bo Zhang

llms agents reinforcement-learning benchmarks

Get the daily briefing