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 30

Correct codes for the wrong reasons? validating LLMs as measurement instruments for theoretical constructs

arXiv:2606. 28574v1 Announce Type: cross Abstract: When a large language model (LLM) codes a construct in text as a human annotator would, that agreement makes the LLM a reliable coder.

By Manuel Pita

llms

arXiv AI

Jun 30

On the Faithfulness of Post-Hoc Concept Bottleneck Models

arXiv:2606. 30498v1 Announce Type: cross Abstract: Human decision-making interprets the world through high-level concepts, such as recognizing a bird by its belly color.

By Laines Schmalwasser, Jan Blunk, Niklas Penzel, Julia Niebling, Joachim Denzler

llms multimodal benchmarks

arXiv Machine Learning

Jun 30

Adaptive Block Diffusion: Resolving Training-Inference Mismatch in Diffusion Language Models

arXiv:2606. 29275v1 Announce Type: new Abstract: Diffusion Language Models (DLMs) are typically trained under fixed context structures, restricting denoising to predetermined token subsets.

By Gagan Jain

llms diffusion

arXiv AI

Jun 30

Beyond Scaling Law: A Data-Efficient Distillation Framework for Reasoning

arXiv:2508. 09883v2 Announce Type: replace-cross Abstract: Large language models (LLMs) demonstrate remarkable reasoning capabilities in tasks such as algorithmic coding and mathematical problem-solving.

By Xiaojun Wu, Xiaoguang Jiang, Huiyang Li, Jucai Zhai, Dengfeng Liu, Qiaobo Hao, Huang Liu, Zhiguo Yang, Ji Xie, Ninglun Gu, Jin Yang, Kailai Zhang, Yelun Bao, Jun Wang

llms reinforcement-learning fine-tuning efficiency benchmarks

arXiv Machine Learning

Jun 30

Multi-Block Diffusion Language Models

arXiv:2606. 29215v1 Announce Type: new Abstract: Block Diffusion Language Models (BD-LMs) improve diffusion-based text generation with KV caching and flexible-length generation.

By Yijie Jin, Jiajun Xu, Yuxuan Liu, Chenkai Xu, Yi Tu, Jiajun Li, Dandan Tu, Xiaohui Yan, Kai Yu, Pengfei Liu, Zhijie Deng

llms diffusion benchmarks

arXiv AI

Jun 30

LLM Agents Grounded in Self-Reports Enable General-Purpose Simulation of Individuals

arXiv:2411. 10109v3 Announce Type: replace Abstract: Machine learning can predict human behavior well when substantial structured data are available for well-defined outcomes.

By Joon Sung Park, Carolyn Q. Zou, Jonne Kamphorst, Niles Egan, Aaron Shaw, Benjamin Mako Hill, Carrie Cai, Meredith Ringel Morris, Percy Liang, Robb Willer, Michael S. Bernstein

llms agents benchmarks

arXiv AI

Jun 30

CostBench: Evaluating Multi-Turn Cost-Optimal Planning and Adaptation in Dynamic Environments for LLM Tool-Use Agents

arXiv:2511. 02734v3 Announce Type: replace Abstract: Current evaluations of Large Language Model (LLM) agents primarily emphasize task completion, often overlooking resource efficiency and adaptability.

By Jiayu Liu, Cheng Qian, Zhaochen Su, Qing Zong, Shijue Huang, Bingxiang He, Yi R. Fung

llms agents benchmarks

arXiv AI

Jun 30

Fuzzing Large Language Models to Elicit Hidden Behaviours

arXiv:2606. 29646v1 Announce Type: cross Abstract: Sleeper agents are the canonical model organism of deception: models trained to behave normally but to emit an unsafe behaviour on a specific trigger.

By Mohammed Abu Baker, Lakshmi Babu-Saheer

llms agents

arXiv AI

Jun 30

TraceLab: Characterizing Coding Agent Workloads for LLM Serving

arXiv:2606. 30560v1 Announce Type: cross Abstract: Coding agents are rapidly becoming a major application of agentic LLMs, but serving them efficiently remains challenging.

By Kan Zhu, Mathew Jacob, Chenxi Ma, Yi Pan, Stephanie Wang, Arvind Krishnamurthy, Baris Kasikci

llms agents benchmarks

arXiv AI

Jun 30

When Web Agents Finish but Still Fail: Reproducible Triggers and Trace Diagnostics for Parallel Web Exploration

arXiv:2606. 20724v2 Announce Type: replace Abstract: Long-horizon web agents often fail in ways hidden by final-answer evaluation: they may visit useful pages, produce a well-formed answer, and terminate confidently while still missing fields, over-including unsupported items, or relying on stale evidence.

By Aagam Sogani, Botao Rui, Swetha Vaidyanathan, Rishi Agarwal, Minghao Yan, Shivaram Venkataraman

llms agents benchmarks

arXiv AI

Jun 30

GPC: Large-Scale Generative Pretraining for Transferable Motor Control

arXiv:2606. 29148v1 Announce Type: cross Abstract: Developing controllers capable of completing a wide range of tasks in a natural and life-like manner is a key challenge in enabling practical applications of physics-based character animation.

By Yi Shi, Yifeng Jiang, Chen Tessler, Xue Bin Peng

llms nlp reinforcement-learning fine-tuning efficiency

arXiv AI

Jun 30

Linguistic Firewall: Geometry as Defense in Multi-Agent Systems Routing

arXiv:2606. 30555v1 Announce Type: new Abstract: The rapid integration of Large Language Models (LLMs) has driven the evolution of Multi-Agent Systems (MAS), where specialized agents collaborate to execute complex workflows.

By Dvir Alsheich, Adar Peleg, Ben Hagag, Rom Himelstein, Amit Levi, Avi Mendelson

llms rag agents robotics multimodal

arXiv AI

Jun 30

Customized Generative AI Agent for Transportation Engineering Practice: A Development and Continued Pre-training Guideline

arXiv:2606. 29014v1 Announce Type: new Abstract: Recent advancements in generative artificial intelligence (AI) and large language models (LLMs) have shown significant promise in automating complex reasoning, summarization, and question-answering tasks.

By Dianwei Chen (Terry), Yuan-Zheng Lei (Terry), Zifan Zhang (Terry), Yuchen Liu (Terry), Xianfeng (Terry), Yang

llms agents nlp fine-tuning benchmarks safety

arXiv AI

Jun 30

Modeling Earth-Scale Human-Like Societies with One Billion Agents

arXiv:2506. 12078v2 Announce Type: replace-cross Abstract: Understanding the dynamic evolution of complex social phenomena requires both high-fidelity modeling of human behavior and large-scale simulations.

By Haoxiang Guan, Jiyan He, Liyang Fan, Zhenzhen Ren, Shaobin He, Xin Yu, Yuan Chen, Xueyin Xu, Shuxin Zheng, Yan Gao, Enhong Chen, Tie-Yan Liu, Zhen Liu

llms agents diffusion

arXiv AI

Jun 30

Dynamic Parsing and Updating Natural Language Specification using VLMs for Robust Vision-Language Tracking

arXiv:2606. 29357v1 Announce Type: cross Abstract: Vision-language tracking guided by natural language specifications leverages high-level semantic cues of target objects to substantially boost tracking accuracy and robustness.

By Xiao Wang, Liye Jin, Dan Xu, Yuehang Li, Lan Chen, Yaowei Wang, Yonghong Tian, Jin Tang

llms multimodal benchmarks safety

arXiv AI

Jun 30

LC-ICL: Label-Guided Contrastive In-Context Learning for Robust Information Extraction

arXiv:2606. 29407v1 Announce Type: cross Abstract: There has been increasing interest in exploring the capabilities of advanced large language models (LLMs) in the field of information extraction (IE), specifically focusing on tasks related to named entity recognition (NER) and relation extraction (RE).

By Xiao You, Tianwei Yan, Shan Zhao

llms nlp

arXiv AI

Jun 30

Bridging VideoQA and Video-Guided Agentic Tasks via Generalized Keyframe Extraction

arXiv:2606. 29445v1 Announce Type: cross Abstract: Video understanding is a fundamental capability for multimodal intelligence, and recent Multimodal Large Language Models (MLLMs) have achieved remarkable performance on Video Question Answering (VideoQA) benchmarks.

By Sunqi Fan, Qingle Liu, Runqi Yin, Meng-Hao Guo, Shuojin Yang

llms agents nlp multimodal benchmarks

arXiv AI

Jun 30

A Hybrid Framework for Song Lyric Annotation Based on Human-LLM Alignment

arXiv:2606. 29273v1 Announce Type: cross Abstract: Emotion recognition of song lyrics is a challenging task since lyrics may not necessarily align with the overall emotion of a song.

By Rashini Liyanarachchi, Frank Tran, Md Mahmudul Hasan, Aditya Joshi, Erik Meijering

llms safety

arXiv AI

Jun 30

MESA: Prioritizing Vulnerable Communication Channels for Securing Multi-Agent Systems

arXiv:2606. 30602v1 Announce Type: cross Abstract: Multi-agent systems (MAS) are increasingly used to automate complex, distributed workflows.

By Kunyang Li, Kyle Domico, Jonathan Gregory, Patrick McDaniel

llms agents benchmarks

arXiv AI

Jun 30

C$^{2}$R: Cross-sample Consistency Regularization Mitigates Feature Splitting and Absorption in Sparse Autoencoders

arXiv:2606. 30609v1 Announce Type: cross Abstract: Sparse Autoencoders (SAEs) are widely used to interpret large language models by decomposing activations into sparse, human-understandable features, but scaling to large dictionaries exposes fundamental challenges.

By Haoran Jin, Xiting Wang, Shijie Ren, Hong Xie, Defu Lian

llms safety

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