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 10

PatchSTG: Scalable Spatiotemporal Graph Transformers for Traffic Forecasting on Irregular Sensor Networks

arXiv:2606. 09872v1 Announce Type: cross Abstract: Traffic forecasting is a fundamental component of intelligent transportation systems, yet remains challenging in real-world settings due to irregular sensor distributions and the high computational cost of modeling large-scale spatiotemporal dependencies.

By Jichao Li, Xuanming Shi

llms benchmarks

arXiv AI

Jun 10

Adoption of Generative Artificial Intelligence in the German Software Engineering Industry: An Empirical Study

arXiv:2601. 16700v2 Announce Type: replace-cross Abstract: Generative artificial intelligence (GenAI) tools have seen rapid adoption among software developers.

By Ludwig Felder, Tobias Eisenreich, Mahsa Fischer, Stefan Wagner, Chunyang Chen

llms agents

arXiv AI

Jun 10

A Comprehensive Survey of Direct Preference Optimization: Datasets, Theories, Variants, and Applications

arXiv:2410. 15595v4 Announce Type: replace Abstract: With the rapid advancement of large language models (LLMs), aligning policy models with human preferences has become increasingly critical.

By Wenyi Xiao, Zechuan Wang, Leilei Gan, Shuai Zhao, Zongrui Li, Ruirui Lei, Wanggui He, Luu Anh Tuan, Long Chen, Hao Jiang, Zhou Zhao, Fei Wu

llms reinforcement-learning safety

arXiv AI

Jun 10

Null-Space Constrained Low-Rank Adaptation for Response-Specified Large Language Model Unlearning

arXiv:2606. 10989v1 Announce Type: new Abstract: Large language model unlearning aims to suppress designated undesirable knowledge while preserving benign capabilities.

By Bocheng Ju, Jianhua Wang, Chengliang Liu, Xiaolin Chang

llms fine-tuning benchmarks safety

arXiv AI

Jun 10

ComBench: A Benchmark for Rigorous Proof Reasoning and Constructive Realization in Olympiad-Level Combinatorics

arXiv:2606. 10479v1 Announce Type: new Abstract: Combinatorics is central to Olympiad-level mathematical problem solving, requiring deep discrete reasoning, creative constructions, and rigorous structural insight.

By Shunkai Zhang, Haoran Zhang, Yun Luo, Qianjia Cheng, Haodi Lei, Yizhuo Li, Runzhe Zhan, Zhilin Wang, Bangjie Xu, Yucheng Su, Xinmiao Han, Xiaoye Qu, Dongrui Liu, Zhouchen Lin, Yu Qiao, Ning Ding, Yafu Li, Yu Cheng

llms benchmarks

arXiv AI

Jun 10

Frontier Coding Agents Use Metaprogramming to Adapt to Unfamiliar Programming Languages

arXiv:2606. 10933v1 Announce Type: new Abstract: LLM-based coding agents are usually evaluated in familiar software settings: mainstream languages, common libraries, and public repositories.

By Aman Sharma, Sushrut Thorat, Paras Chopra

llms agents benchmarks

arXiv Machine Learning

Jun 10

Spatiotemporal Graph Transformer for 3D Neighborhood Interaction and Quality Prediction in Metal Additive Manufacturing

arXiv:2606. 10227v1 Announce Type: new Abstract: Metal additive manufacturing enables the fabrication of complex parts, but achieving consistent build quality remains challenging due to interactions induced by repeated layer-wise melting, solidification, and reheating across the 3D build.

By Joyce Karen Pelaez, Siqi Zhang, Hoo Sang Ko

llms multimodal

arXiv AI

Jun 10

Infini Memory: Maintainable Topic Documents for Long-Term LLM Agent Memory

arXiv:2606. 10677v1 Announce Type: new Abstract: Long-term LLM agents need persistent memory that can track changing facts and provide relevant evidence across sessions.

By Suozhao Ji, Baodong Wu, Zehao Wang, Lei Xia, Qingping Li, Ruisong Wang, Wenbo Ding, Zhenhua Zhu, Boxun Li, Guohao Dai, Yu Wang

llms agents

arXiv Machine Learning

Jun 10

Hasse Diagrams for Attention: A Partial Order Framework for Designing Transformer Masks

arXiv:2606. 09951v1 Announce Type: new Abstract: During the training of large Transformer models, attention masks regulate the scope and direction of information flow across a sequence.

By Chentao Li, Han Guo

llms

arXiv AI

Jun 10

Unsupervised Style Representation Learning for AI-Text Detection via Paraphrase Inversion

arXiv:2606. 10099v1 Announce Type: cross Abstract: The rapid development of large language models (LLMs) has raised concerns about misuse such as plagiarism, misinformation, and automated influence operations, motivating the need for robust detectors.

By Rafael Rivera Soto, Barry Chen, Nicholas Andrews

llms benchmarks safety

arXiv AI

Jun 10

Towards Robust Arabic Speech Emotion Recognition with Deep Learning

arXiv:2606. 10278v1 Announce Type: cross Abstract: Speech Emotion Recognition (SER) aims to identify a speaker's emotional state from audio signals.

By Youcef Soufiane Gheffari, Samiya Silarbi

llms fine-tuning

arXiv AI

Jun 10

Modeling Complex Behaviors: Multi-Personality Composition and Dynamic Switching in Vision-Language Models

arXiv:2606. 11074v1 Announce Type: cross Abstract: With the widespread deployment of Multimodal Large Language Models (MLLMs) in social interaction, understanding and controlling their behavior under complex personality conditions is essential.

By Peiqi Jia (Xi'an Jiaotong University), Haonan Jia (Beihang University), Ziqi Miao (Beihang University), Linkang Du (Xi'an Jiaotong University), Yuntao Wang (Xi'an Jiaotong University), Zhou Su (Xi'an Jiaotong University)

llms nlp multimodal

arXiv Machine Learning

Jun 10

Influence Dynamics and Stagewise Data Attribution

arXiv:2510. 12071v2 Announce Type: replace Abstract: Current training data attribution (TDA) methods treat the influence one sample has on another as static, but neural networks learn in distinct stages that exhibit changing patterns of influence.

By Jin Hwa Lee, Matthew Smith, Maxwell Adam, Jesse Hoogland

llms

arXiv AI

Jun 10

Structure from Reasoning, Numbers from Search: On-Premise Open LLMs as Structural Priors for Coupled MIMO Controller Tuning

arXiv:2606. 11015v1 Announce Type: new Abstract: Tuning controllers for strongly coupled multi-input multi-output (MIMO) industrial processes is hard: decentralized classical auto-tuning ignores loop interaction, and local numerical optimization from natural initializations stalls in the resulting non-convex cost landscape.

By Jiaxuan Chen, Haonan Li, Yang Shu

llms benchmarks safety

arXiv AI

Jun 10

Integrating Local and Global Entropy for Uncertainty Quantification in LLMs

arXiv:2606. 09875v1 Announce Type: cross Abstract: Large language models hallucinate confidently, making uncertainty quantification (UQ) essential for reliable deployment.

By Johanne Medina, Tianyi Zhou, Keivin Isufaj, Aristides Gionis, Sanjay Chawla

llms benchmarks

arXiv AI

Jun 10

Toward Secure LLM Agents: Threat Surfaces, Attacks, Defenses, and Evaluation

arXiv:2606. 10749v1 Announce Type: cross Abstract: Large language model (LLM) agents are rapidly moving from conversational interfaces to software components that plan, invoke tools, maintain memory, and act on external environments.

By Yuchen Ling, Shengcheng Yu, Zhenyu Chen, Chunrong Fang

llms agents benchmarks

arXiv AI

Jun 10

Supervised Fine-tuning with Synthetic Rationale Data Hurts Real-World Disease Prediction

arXiv:2606. 10279v1 Announce Type: new Abstract: Supervised fine-tuning with synthetic rationale data is widely assumed to improve language model performance on clinical prediction tasks by teaching models not just what to predict but why.

By Buxin Su, Bingxuan Li, Cheng Qian, Yiwei Wang, Jin Jin, Bingxin Zhao

llms fine-tuning

arXiv AI

Jun 10

Can Multi-Agent LLMs Identify Their Peers? Stylometric Fingerprinting in Role-Constrained Political Analysis

arXiv:2606. 09854v1 Announce Type: cross Abstract: Multi-agent large language model (LLM) pipelines for political statement analysis are vulnerable to peer-preservation bias: models tend to protect peer models from deactivation and show identity-dependent scoring distortions.

By Juergen Dietrich

llms agents fine-tuning safety

arXiv AI

Jun 10

PreAct-Bench: Benchmarking Predictive Monitoring in LLMs

arXiv:2606. 09890v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed as autonomous agents capable of executing multi-step action trajectories toward a given objective.

By Hainiu Xu, Italo Luis da Silva, Jiangnan Ye, Yuhao Wang, Wei Liu, Linyi Yang, Jonathan Richard Schwarz, Nicola Paoletti, Yulan He, Hanqi Yan

llms agents benchmarks safety

arXiv AI

Jun 10

The Role of Feedback Alignment in Self-Distillation

arXiv:2606. 11173v1 Announce Type: new Abstract: Conditioning a language model on additional context, such as feedback on a previous attempt, typically improves its response.

By Semih Kara, O\u{g}uzhan Ersoy

llms efficiency safety

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