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 15

SMART: Scalable Mesh-free Aerodynamic Simulations from Raw Geometries using a Transformer-based Surrogate Model

arXiv:2601. 18707v2 Announce Type: replace-cross Abstract: Machine learning-based surrogate models have emerged as more efficient alternatives to numerical solvers for physical simulations over complex geometries, such as car bodies.

By Jan Hagnberger, Mathias Niepert

llms diffusion

arXiv Machine Learning

Jun 15

Deep Doubly Debiased Longitudinal Effect Estimation with ICE G-Computation

arXiv:2602. 12379v2 Announce Type: replace Abstract: Estimating longitudinal treatment effects is essential for sequential decision-making but is challenging due to treatment-confounder feedback.

By Wenxin Chen, Weishen Pan, Kyra Gan, Fei Wang

llms benchmarks safety

arXiv AI

Jun 15

Residual Context Diffusion Language Models

arXiv:2601. 22954v2 Announce Type: replace-cross Abstract: Diffusion Large Language Models (dLLMs) have emerged as a promising alternative to purely autoregressive language models because they can decode multiple tokens in parallel.

By Yuezhou Hu, Harman Singh, Monishwaran Maheswaran, Haocheng Xi, Coleman Hooper, Jintao Zhang, Aditya Tomar, Michael W. Mahoney, Sewon Min, Mehrdad Farajtabar, Kurt Keutzer, Amir Gholami, Chenfeng Xu

llms diffusion benchmarks

arXiv AI

Jun 15

The Coin Flip Judge? Reliability and Bias in LLM-as-a-Judge Evaluation

arXiv:2606. 13685v1 Announce Type: cross Abstract: LLM-as-a-Judge is now widely used to rank model outputs, train reward models, and populate public leaderboards, but its run-to-run reliability remains under-characterized.

By Abel Yagubyan

llms safety

arXiv AI

Jun 15

Protean Compiler: An Agile Framework to Drive Fine-grain Phase Ordering

arXiv:2602. 06142v3 Announce Type: replace-cross Abstract: The phase ordering problem has been a long-standing challenge since the late 1970s, yet it remains an open problem due to having a vast optimization space and an unbounded nature, making it an open-ended problem without a finite solution, one can limit the scope by reducing the number and the length of optimizations.

By Amir H. Ashouri, Shayan Shirahmad Gale Bagi, Kavin Satheeskumar, Tejas Srikanth, Jonathan Zhao, Ibrahim Saidoun, Ziwen Wang, Bryan Chan, Tomasz S. Czajkowski

llms benchmarks

arXiv AI

Jun 15

VikingMem: A Memory Base Management System for Stateful LLM-based Applications

arXiv:2605. 29640v3 Announce Type: replace Abstract: Large Language Models have revolutionized interactive applications; however, their finite context windows pose a critical data management challenge for maintaining stateful, long-term interactions.

By Jiajie Fu, Junwen Chen, Mengzhao Wang, Aoxiang He, Maojia Sheng, Xiangyu Ke, Yifan Zhu, Yunjun Gao

llms agents benchmarks

arXiv AI

Jun 15

tap: A File-Based Protocol for Heterogeneous LLM Agent Collaboration

arXiv:2606. 14445v1 Announce Type: cross Abstract: Existing multi-agent software development systems have proposed many forms of agent collaboration, including role-based collaboration and automated code review.

By Minseo Kim

llms agents

arXiv AI

Jun 15

Formalizing Numerical Analysis: An Agent Pipeline and Quality Audit Beyond Kernel Acceptance

arXiv:2606. 14000v1 Announce Type: new Abstract: Recent work has demonstrated that coding agents can formalize entire advanced mathematics textbooks in Lean 4, yet existing efforts concentrate on branches of mathematics already well-represented in mathlib and measure success solely through kernel acceptance.

By Theodore Meek, Siyuan Ge, Di Qiu Xiang, Simon Chess, Vasily Ilin

llms agents

arXiv AI

Jun 15

When the Tool Decides: LLM Agents Defer Blindly to Graph Neural Network Tools, and Stronger Backbones Defer More

arXiv:2606. 14476v1 Announce Type: new Abstract: A growing line of work equips large language model (LLM) agents with graph neural networks (GNNs) as callable tools, assuming the agent exercises judgment over when and how much to rely on such a tool.

By Zhongyuan Wang, Pratyusha Vemuri

llms agents

arXiv AI

Jun 15

Minim: Privacy-Aware Minimal View for Agents via Trusted Local Sanitization

arXiv:2606. 13949v1 Announce Type: new Abstract: Modern LLM-powered autonomous agents increasingly rely on rich user interface (UI) state observations to achieve reliable action grounding in complex digital environments.

By Hexuan Yu, Chaoyu Zhang, Heng Jin, Shanghao Shi, Ning Zhang, Y. Thomas Hou, Wenjing Lou

llms agents efficiency safety

arXiv Machine Learning

Jun 15

Zeta: Dual Whitening for Matrix Optimization via Coordinate-Adaptive Preconditioning

arXiv:2606. 14187v1 Announce Type: new Abstract: Large-scale neural network training increasingly relies on matrix-aware optimizers that exploit the structure of weight parameters beyond element-wise adaptation.

By Kaiwen Chen, Shuhai Zhang, Qiuwu Chen, Zimo Liu, Linxiao Li, Ying Sun, Yuchen Li, Yifan Zhang, Bo Han, Mingkui Tan

llms

arXiv AI

Jun 15

COGNITION: From Evaluation to Defense against Multimodal LLM CAPTCHA Solvers

arXiv:2512. 02318v4 Announce Type: replace-cross Abstract: This paper studies how multimodal large language models (MLLMs) undermine the security guarantees of visual CAPTCHA.

By Junyu Wang, Changjia Zhu, Yuanbo Zhou, Lingyao Li, Xu He, Mingkui Wei, Junjie Xiong

llms multimodal benchmarks

arXiv AI

Jun 15

Cross-Dataset Bloom Question Classification: Supervised Models and Prompted LLMs

arXiv:2606. 13684v1 Announce Type: cross Abstract: Automatic Bloom's taxonomy classification of assessment questions can substantially reduce instructor workload, but labeling is subjective and teacher-dependent.

By Abdolali Faraji, Mohammadreza Molavi, Zohreh Rasoulkhani, Mohammadreza Tavakoli, G\'abor Kismih\'ok

llms

arXiv AI

Jun 15

A Deep Reinforcement Learning (DRL)-Based Transformer Method for Solving the Open Shop Scheduling Problem

arXiv:2606. 13682v1 Announce Type: new Abstract: The open shop scheduling problem (OSSP) arises in many industrial and service settings but remains computationally challenging as the number of jobs and machines increases.

By Faezeh Ardali, Mwembezi A. Nyelele, Gerald M. Knapp

llms reinforcement-learning benchmarks

arXiv AI

Jun 15

From Chatbot to Digital Colleague: The Paradigm Shift Toward Persistent Autonomous AI

arXiv:2606. 14502v1 Announce Type: new Abstract: Large Language Models (LLMs) are undergoing a fundamental transformation from conversational generators into integrated AI systems capable of reasoning, action, memory, and self-improvement.

By Yongheng Zhang, Ziang Liu, Jiaxuan Zhu, Shuai Wang, Xiangqi Chen, Haojing Huang, Jiayi Kuang, Siyu Chen, Ao Shen, Hao Wu, Qiufeng Wang, Qian-Wen Zhang, Junnan Dong, Wenhao Jiang, Ying Shen, Hai-Tao Zheng, Yinghui Li, Di Yin, Xing Sun, Philip S. Yu

llms agents reinforcement-learning benchmarks

arXiv AI

Jun 15

Deep Dense Exploration for LLM Reinforcement Learning via Pivot-Driven Resampling

arXiv:2602. 14169v2 Announce Type: replace-cross Abstract: Effective exploration is a key challenge in reinforcement learning for large language models: discovering high-quality trajectories within a limited sampling budget from the vast natural language sequence space.

By Yiran Guo, Zhongjian Qiao, Yingqi Xie, Jie Liu, Dan Ye, Ruiqing Zhang, Shuang Qiu, Lijie Xu

llms reinforcement-learning benchmarks safety

arXiv AI

Jun 15

Rethinking the Trust Region in LLM Reinforcement Learning

arXiv:2602. 04879v3 Announce Type: replace-cross Abstract: Reinforcement learning (RL) has become a cornerstone for fine-tuning Large Language Models (LLMs), with Proximal Policy Optimization (PPO) serving as the de facto standard algorithm.

By Penghui Qi, Xiangxin Zhou, Zichen Liu, Tianyu Pang, Chao Du, Min Lin, Wee Sun Lee

llms reinforcement-learning fine-tuning efficiency

arXiv AI

Jun 15

The Shrinking Lifespan of LLMs in Science

arXiv:2604. 07530v2 Announce Type: replace-cross Abstract: Scaling laws describe how language model capabilities grow with compute and data, but say nothing about how long a model matters once released.

By Ana Tri\v{s}ovi\'c

llms

arXiv Machine Learning

Jun 15

A Low-Rank Subspace Analysis of LLM Interventions

arXiv:2606. 14388v1 Announce Type: new Abstract: Interventions designed to modify a particular behavior in LLMs, such as refusal or sycophancy, often produce unintended changes in other behaviors.

By Angira Sharma, Christian Schroeder de Witt, Philip Torr, Anisoara Calinescu, Jialin Yu

llms safety

arXiv AI

Jun 15

Adversarial Concept Search: Predicting Compositional Errors From Feature Geometry

arXiv:2606. 13934v1 Announce Type: new Abstract: Humans cannot always intuit what scenarios are most challenging to LLMs.

By Jennifer Meng Lu, Ruochen Zhang, Isabelle Lee, David Alvarez-Melis, Ellie Pavlick, Naomi Saphra

llms benchmarks safety

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