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

Jul 22

Circuit Claims Depend on What Is Extracted and How It Is Compared

arXiv:2607. 18921v1 Announce Type: cross Abstract: Circuit extraction identifies a small set of model components whose presence preserves a target behavior under ablation, and the resulting circuit is often read as the mechanism behind that behavior.

By Yang Sheng, Jie Fu

llms reinforcement-learning efficiency benchmarks

arXiv AI

Jul 22

CodeRescue: Budget-Calibrated Recovery Routing for Coding Agents

arXiv:2607. 19338v1 Announce Type: new Abstract: Coding agents increasingly operate in executable environments where a failed attempt produces actionable feedback rather than merely an incorrect answer.

By Qijia He, Jiayi Cheng, Chenqian Le, Rui Wang, Xunmei Liu, Yixian Chen, Jie Mei, Zhihao Wang, Xupeng Chen, Yuhuan Chen, Tao Wang

llms agents benchmarks

arXiv AI

Jul 22

Cross-Agent Campaign Attribution: Linking Asynchronous Attacks Across LLM Agents

arXiv:2607. 18826v1 Announce Type: cross Abstract: LLM-agent defenses are typically evaluated one session at a time.

By SangJin Park, Myungsub Choi, Jineok Kim, Minseung Kang

llms agents benchmarks safety

arXiv AI

Jul 22

Estimating Rare Events in Language Models with Proper Evaluation

arXiv:2607. 18454v1 Announce Type: cross Abstract: Quantifying the risk of rare failures in language models, such as those triggered by adversarial distribution shifts or very large-scale deployments, requires estimating probabilities far too small for random sampling.

By Nikita Y. Parulekar, Anqi Liu

llms safety

arXiv Machine Learning

Jul 22

More Edits, More Stable: Understanding the Lifelong Normalization in Sequential Model Editing

arXiv:2605. 11836v2 Announce Type: replace Abstract: Lifelong Model Editing aims to continuously update evolving facts in Large Language Models while preserving unrelated knowledge and general capabilities, yet it remains plagued by catastrophic forgetting and model collapse.

By Xin Ma, Wei Chen, Qi Liu, Derong Xu, Zhi Zheng, Tong Xu, Enhong Chen

llms

arXiv AI

Jul 22

SFGA: A Statistics-First Gating Architecture with Adjudicative Escalation for Trustworthy SFT Data Procurement

arXiv:2607. 18960v1 Announce Type: cross Abstract: Procuring supervised fine-tuning (SFT) data forces a buyer to decide, before any downstream training, whether a candidate corpus is worth acquiring.

By Arther Tian, Alex Ding, Simon Wu, Aaron Chan

llms fine-tuning benchmarks

arXiv AI

Jul 22

Find Before You Fine-Tune: A Diagnostic Study of Small LLMs for Cybersecurity QA

arXiv:2607. 18725v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly fine-tuned for critical-domain Question-Answering (QA), yet choosing which small model to adapt, before paying the cost of adaptation, remains difficult.

By Shaswata Mitra, Subash Neupane, Trisha Chakraborty, Himanshu Tripathi, Sudip Mittal, Aritran Piplai, Shahram Rahimi

llms fine-tuning safety

arXiv AI

Jul 22

Data Leakage Prevention in Agentic Applications via Preemptive Hardening

arXiv:2607. 18847v1 Announce Type: cross Abstract: Agentic systems integrate LLM driven planning with interfaces to external tools, making data leakage and tool misuse feasible via instruction/data boundary failures and prompt injection attacks.

By Akansha Shukla, Emily Bellov, Parth Atulbhai Gandhi, Yuval Elovici, Asaf Shabtai

llms agents robotics benchmarks safety

arXiv AI

Jul 22

Decode-Time Grammars: Constrained LLM Generation over a Refinement Order of Grammar Fragments

arXiv:2607. 18357v1 Announce Type: cross Abstract: Large language models now write a growing share of the world's code, increasingly inside agents and serving systems that compile, execute, or dispatch generated code without line-by-line review.

By Shuoming Zhang, Ruiyuan Xu, Haofeng Li, Qiuchu Yu, Yangyu Zhang, Chunwei Xia, Xiaobing Feng, Chenxi Wang, Huimin Cui, Jiacheng Zhao

llms agents data-engineering

arXiv AI

Jul 22

Distribution-First Population Simulation: Collapse, Calibration, and Recall in Non-WEIRD LLM Persona Modeling

arXiv:2607. 18310v1 Announce Type: cross Abstract: Synthetic-population tools increasingly run every individual as an independent large language model (LLM) agent.

By Gurkan Ozkan

llms agents

arXiv Machine Learning

Jul 22

One Model, Many Graphs: Learning over Attributed Graphs across Heterogeneous Modalities with Vision-Language Models

arXiv:2607. 19128v1 Announce Type: new Abstract: Vision-language models (VLMs) provide a unified representation space for textual and visual information, yet their potential as general-purpose backbones for graph-structured data remains largely unexplored.

By Jiayi Yang, Yifang Chen, Yuanfu Sun, Jiajin Liu, Qiaoyu Tan

llms rag multimodal benchmarks

arXiv AI

Jul 22

Hazard or Anomaly? Evaluating VLMs for Understanding Dangers and Discrepancies

arXiv:2607. 18325v1 Announce Type: cross Abstract: Modern safety-critical systems increasingly rely on human-robot interaction to reduce disaster risk and support decision-making during emergencies.

By Murali Indukuri, Mohammad Eskandari, Sree Nitya Kollu, Stephanie Lukin, Cynthia Matuszek

llms robotics multimodal benchmarks safety

arXiv AI

Jul 22

CWind: A Cross-site Router for Large Language Model Inference Serving at Renewable Energy Farms

arXiv:2605. 23348v2 Announce Type: replace-cross Abstract: AI power demand is growing at an unprecedented rate while power grids are often ailing and struggle to keep up.

By Tella Rajashekhar Reddy, Atharva Deshmukh, Liangcheng Yu, Chaojie Zhang, Mike Shepperd, Rohan Gandhi, Anjaly Parayil, Srinivasan Iyengar, Ajay Manchepalli, Debopam Bhattacherjee

llms efficiency

arXiv AI

Jul 22

Computational Humor with Multimodal LLMs: Methods, Datasets, Evaluation, and Challenges

arXiv:2607. 19011v1 Announce Type: cross Abstract: Multimodal humor in memes, cartoons, and comics remains difficult for AI systems because intended meaning depends on non-literal mechanisms, shared cultural knowledge, and communicative intent rather than literal scene description.

By Tuo Liang, Zhe Hu, Disheng Liu, Jing Li, Yu Yin

llms multimodal benchmarks safety

arXiv AI

Jul 22

FindStatBench: Evaluating Large Language Models on Combinatorial Code Synthesis

arXiv:2607. 18260v1 Announce Type: new Abstract: We introduce FindStatBench, an execution benchmark for evaluating large language models on combinatorial code synthesis.

By Soham Dan

llms rag agents benchmarks

arXiv Machine Learning

Jul 22

Vectorizing the Trie: Efficient Constrained Decoding for LLM-based Generative Retrieval on Accelerators

arXiv:2602. 22647v2 Announce Type: replace-cross Abstract: Generative retrieval has emerged as a powerful paradigm for LLM-based recommendation.

By Zhengyang Su, Isay Katsman, Yueqi Wang, Ruining He, Lukasz Heldt, Raghunandan Keshavan, Shao-Chuan Wang, Xinyang Yi, Mingyan Gao, Onkar Dalal, Lichan Hong, Ed Chi, Ningren Han

llms efficiency benchmarks

arXiv Machine Learning

Jul 22

Visual Semantic Decoding of Electrocorticography from Video Stimuli using End-to-End Deep Learning

arXiv:2607. 18923v1 Announce Type: new Abstract: ECoG-based visual semantic decoding enables inference of semantic interpretation of visual perception from complex, noisy brain activity.

By Stella Ho, Joel Villalobos, Joseph West, Jingyang Liu, Weijie Qi, Haruhiko Kishima, Ryohei Fukuma, Takufumi Yanagisawa, Sam E. John, David B. Grayden

llms

arXiv Machine Learning

Jul 22

AdaFlash: Adaptive Speculative Decoding via On-Policy Distilled Diffusion Drafters

arXiv:2607. 19223v1 Announce Type: new Abstract: Speculative decoding, in which a lightweight draft model first generates a draft sequence that is then verified in parallel by the target model, has become a prevalent paradigm for accelerating large language model inference.

By Yu-Yang Qian, Hao-Cong Wu, Chen Chen, Jiacheng Sun, Zhenhua Dong, Peng Zhao, Zhi-Hua Zhou

llms diffusion efficiency benchmarks

arXiv Machine Learning

Jul 22

Node-as-Agent: Graph Agentic Network

arXiv:2508. 00429v5 Announce Type: replace-cross Abstract: Graph Neural Networks (GNNs) have achieved remarkable success in graph-based learning by propagating information among neighbor nodes via predefined aggregation mechanisms.

By Minghao Guo, Xi Zhu, Qingyue Jiao, Xiujin Liu, Haochen Xue, Chong Zhang, Shuhang Lin, Jingyuan Huang, Ziyi Ye, Yongfeng Zhang

llms rag agents fine-tuning

arXiv AI

Jul 22

SR-Agent: An Experience-Driven Agentic Framework for Post-Ranking Strategy Refinement in E-Commerce Recommendation

arXiv:2607. 17719v2 Announce Type: replace Abstract: User experience is a first-class objective in industrial e-commerce recommender systems (RS).

By Hanchen Yang, Kaiwen Yang, Junpeng Zhuang, Yang He, Keting Cen, Bochao Liu, Zhongbo Sun, An Liu, Zhongteng Han, Chenyi Lei

llms agents

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