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 Machine Learning

Jul 1

ISM:Self-Improving Strategy Memory for Continual Mathematical Reasoning

arXiv:2606. 31191v1 Announce Type: new Abstract: We propose Intelligent Schema Memory (ISM), a self-evolving memory-augmented system that improves mathematical reasoning for a frozen LLM under continual learning with hard episodic resets.

By Prakhar Dixit, Tim Oates

llms

arXiv AI

Jul 1

Long-term Traffic Simulation via Structured Autoregressive Modeling

arXiv:2606. 31209v1 Announce Type: new Abstract: Interactive traffic simulation is a vital world model for autonomous driving.

By Lingyu Xiao, Zexin Feng, Xintao Yan

llms agents robotics benchmarks safety

arXiv AI

Jul 1

HealthAgentBench: A Unified Benchmark Suite of Realistic Agentic Healthcare Environments for Challenging Frontier AI Agents

arXiv:2606. 31179v1 Announce Type: new Abstract: As AI agents become increasingly capable of complex, long-horizon reasoning, rigorous and holistic evaluation is essential for measuring progress toward real-world healthcare applications.

By Qianchu Liu, Sheng Zhang, Guanghui Qin, Jeya Maria Jose Valanarasu, Maximilian Rokuss, Mingyu Lu, Timothy Ossowski, Juan Manuel Zambrano Chaves, Cliff Wong, Peniel Argaw, Yashna Hasija, Mu Wei, Wen-wai Yim, Qin Liu, Zilin Jing, Jason Entenmann, Naoto Usuyama, Tristan Naumann, Hoifung Poon

llms agents benchmarks

arXiv AI

Jul 1

Revealing Safety-Critical Scenarios for UTM via Transformer

arXiv:2606. 31114v1 Announce Type: new Abstract: Unmanned Traffic Management (UTM) systems are cloud-based platforms designed to manage and coordinate multiple aerial vehicles remotely.

By Huaze Tang, Bill Zeng, Chao Wang, Zhenpeng Shi, Qian Zhang, Wenbo Ding

llms reinforcement-learning safety

arXiv AI

Jul 1

MECoBench: A Systematic Study of Multimodal Agent Collaboration in Embodied Environments

arXiv:2606. 31966v1 Announce Type: cross Abstract: Recent multimodal large language models (MLLMs) have strong potential as embodied agents, but their ability to collaborate in visually grounded environments remains underexplored.

By Qingyun Liu, Jiwen Zhang, Jingyi Hu, Siyuan Wang, Zhongyu Wei

llms agents robotics multimodal benchmarks

arXiv AI

Jul 1

QVal: Cheaply Evaluating Dense Supervision Signals for Long-Horizon LLM Agents

arXiv:2606. 32034v1 Announce Type: cross Abstract: LLM agents increasingly act over long horizons, where a single trajectory can contain hundreds or thousands of actions.

By Sergio Hern\'andez-Guti\'errez, Matteo Merler, Ilze Amanda Auzina, Joschka Str\"uber, Ameya Prabhu, Matthias Bethge

llms rag agents efficiency benchmarks

arXiv AI

Jul 1

DDIAgents: Mechanism-Conditioned Context Flow for Drug-Drug Interaction Prediction

arXiv:2606. 31085v1 Announce Type: new Abstract: Drug-drug interaction (DDI) prediction is essential for medication safety, yet it requires reasoning over heterogeneous biomedical evidence whose relevance changes across interaction mechanisms.

By Zhenqian Shen, Yu Liu, Xiaoyi Fu, Quanming Yao

llms agents benchmarks safety

arXiv AI

Jul 1

ADAPT: Attention Dynamics Alignment with Preference Tuning for Faithful MLLMs

arXiv:2606. 31054v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) are critically hampered by hallucination, generating content inconsistent with the provided image.

By Zhiyuan Yao, Zheren Fu, Zhixiao Zheng, Jiajun Li, Yi Tu, Zhendong Mao

llms diffusion reinforcement-learning multimodal benchmarks safety

arXiv AI

Jul 1

Emergent Culture in Minimal LLM Systems

arXiv:2606. 30668v1 Announce Type: cross Abstract: What happens when LLM agents operate with no context outside a turn, minimal prompting, and simple tools?

By Simon Jones, Sabine Hauert

llms agents

arXiv Machine Learning

Jul 1

Multipole Semantic Attention: A Fast Approximation of Softmax Attention for Pretraining

arXiv:2509. 10406v4 Announce Type: replace Abstract: Pretraining transformers on long sequences (entire code repositories, collections of related documents) is bottlenecked by quadratic attention costs.

By Rupert Mitchell, Kristian Kersting

llms efficiency

arXiv AI

Jul 1

RigorBench: Benchmarking Engineering Process Discipline in Autonomous AI Coding Agents

arXiv:2606. 22678v2 Announce Type: replace-cross Abstract: Agentic coding harnesses - such as Agent-Skills, Superpowers, and Agent-Rigor - are increasingly deployed to augment underlying LLMs for real-world software engineering tasks.

By Meher Bhaskar Madiraju, Meher Sai Preetam Madiraju

llms agents benchmarks

arXiv AI

Jul 1

CoReLIN: Constraint-based Reasoning for Zero-shot Lifelong Interactive Navigation

arXiv:2602. 20055v2 Announce Type: replace-cross Abstract: Robot navigation typically assumes an obstacle-free path exists between start and goal.

By Apoorva Vashisth (Purdue University), Manav Kulshrestha (Purdue University), Pranav Bakshi (IIT Kharagpur), Damon Conover (DEVCOM Army Research Lab), Guillaume Sartoretti (National University of Singapore), Aniket Bera (Purdue University)

llms robotics

arXiv AI

Jul 1

PPT-Eval: A Benchmark for Computer-Use Agents on PowerPoint Tasks

arXiv:2606. 31154v1 Announce Type: cross Abstract: Creating and editing slides is a rich, multimodal activity that is ubiquitous in professional and educational settings, making it an ideal testbed for real-world computer-use agents.

By Apurva Gandhi, Vishwas Suryanarayanan, Raja Hasnain Anwar, Firoz Shaik, Shubhang Desai, Thong Q. Nguyen, Muhammad Taqi Raza, Vishal Chowdhary, Graham Neubig

llms agents multimodal benchmarks

arXiv AI

Jul 1

Optimal Self-Consistency for Efficient Reasoning with Large Language Models

arXiv:2511. 12309v2 Announce Type: replace-cross Abstract: Self-consistency (SC) is a widely used test-time inference technique for improving performance in chain-of-thought reasoning.

By Austin Feng, Marius Alonso, Ambroise Odonnat, Vasilii Feofanov, Ievgen Redko

llms benchmarks

arXiv AI

Jul 1

Geometry-Preserving Orthonormal Initialization for Low-Rank Adaptation in RLVR

arXiv:2606. 31813v1 Announce Type: cross Abstract: Low-rank adaptation (LoRA) and its variants enable parameter-efficient fine-tuning of large language models under the supervised fine-tuning (SFT) paradigm.

By Ruijia Zhang, Jiacheng Zhu, Hanqing Zhu, Laixi Shi

llms reinforcement-learning fine-tuning benchmarks

arXiv Machine Learning

Jul 1

Patch-PODiff-ViT: Structured Latent Diffusion with Patchwise POD for Super-Resolution and Uncertainty Quantification

arXiv:2606. 31290v1 Announce Type: new Abstract: Diffusion models enable probabilistic super-resolution and conditional generation, but pixel-space methods are computationally expensive and learned latent spaces often lack interpretable uncertainty quantification.

By Onkar Jadhav, Tim French, Matthew Rayson, Nicole L. Jones

llms diffusion computer-vision

arXiv AI

Jul 1

TSHA: A Benchmark for Visual Language Models in Trustworthy Safety Hazard Assessment Scenarios

arXiv:2603. 29759v2 Announce Type: replace-cross Abstract: Recent advances in vision-language models (VLMs) have accelerated their application to indoor safety hazards assessment.

By Qiucheng Yu, Ruijie Xu, Mingang Chen Jianfeng Dong, Xin Tan

llms multimodal benchmarks safety

arXiv AI

Jul 1

Transformers as Bayesian In-Context Experimenters: Smoothness-Adaptive Efficient ATE Estimation

arXiv:2606. 31184v1 Announce Type: cross Abstract: Adaptive experiments for average treatment effects (ATE) require randomized allocations balancing valid inference with statistical efficiency.

By Jiachun Li, David Simchi-Levi

llms

arXiv AI

Jul 1

Learning to Select, Not Relearn: Hard-Routed Mixtures of Reasoning LoRAs

arXiv:2606. 31413v1 Announce Type: new Abstract: Composing independently trained LoRA adapters into a single large language model is useful for multi-domain adaptation, especially when the original training data cannot be shared.

By Seyed Alireza Molavi, Zhan Su, Yan Hu, Peyman Sheikholharam Mashhadi, Stefan Byttner, Prayag Tiwari

llms reinforcement-learning fine-tuning 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

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