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

Jun 30

Cybersecurity is the True Frontier for Generative AI Success or Failure

arXiv:2606. 28929v1 Announce Type: cross Abstract: Cybersecurity is a real-life test-bed for many machine learning problems at once, especially when considering modern strides in using Large Language Models (LLMs) to automate processes as ``agents.

By Edward Raff, Maor Ashkenazi, Sagar Samtani, David J. Elkind, Sven Krasser

llms agents computer-vision

arXiv Machine Learning

Jun 30

Memory-Managed Long-Context Attention: A Preliminary Study of Editable Request-Local Memory

arXiv:2606. 28876v1 Announce Type: cross Abstract: Long-context language models often conflate two different goals: compressing history into an efficient state, and maintaining reliable long-term memory.

By Junyi Zou, Avrova Donz

llms benchmarks

arXiv Machine Learning

Jun 30

MOSAIC: Orchestrating Collaborative Knowledge Tracing with Hierarchical Semantic Alignment

arXiv:2606. 29049v1 Announce Type: new Abstract: Knowledge Tracing (KT) is important for personalized education but traditionally suffers from two key limitations: a reliance on shallow ID-based representations that neglect semantic depth and a restriction to single-granularity mastery estimation that overlooks hierarchical knowledge dependencies.

By Xinjin Li, Mengyue Wang, Yuzhen Lin, Pengbin Feng, Ziqi Sha, Yeyang Zhou, Yu Ma

llms rag benchmarks safety

arXiv AI

Jun 30

Primary ICD Category Prediction using LLM-based Probing

arXiv:2606. 28798v1 Announce Type: new Abstract: Objective: ICD codes are central to reimbursement, research, and population health surveillance, yet automated coding systems often struggle to integrate diagnostic signals from both clinical narratives and structured electronic health record (EHR) variables.

By Chengyuan Liu, Xinyue Zhang, Yao Li, Guanting Chen

llms rag multimodal

arXiv AI

Jun 30

LLM-based Multimodal Personality Recognition via Facial Action Unit-Text Semantic Fusion

arXiv:2606. 29900v1 Announce Type: cross Abstract: Personality recognition in asynchronous video interviews (AVIs) has become increasingly important due to their widespread adoption in modern recruitment.

By Tianyi Zhang, Wei Shan, Yuan Zong, Tianhua Qi, Wenming Zheng

llms rag multimodal benchmarks safety

arXiv AI

Jun 30

Aristotelian Virtue Profiling of LLMs through Ethical Dilemmas

arXiv:2606. 28683v1 Announce Type: new Abstract: Large Language Models (LLMs) often face ethical tradeoffs in which several responses may be defensible but express different priorities, such as fairness, honesty, courage, or restraint.

By Ioannis Tzachristas, John Pavlopoulos

llms safety

arXiv Machine Learning

Jun 30

The Contagion Tensor: A Framework for Measuring Output-Distribution Coupling in Multi-Agent LLM Systems -- and Auditing the Claims It Enables

arXiv:2606. 28839v1 Announce Type: new Abstract: We introduce the Contagion Tensor, a measurement framework for quantifying how large language model (LLM) output distributions couple across modalities, agents, and time steps.

By Zewen Liu

llms agents benchmarks

arXiv AI

Jun 30

Scaling Textual Gradients via Sampling-Based Momentum

arXiv:2506. 00400v4 Announce Type: replace-cross Abstract: LLM-based prompt optimization, which uses LLM-provided ``textual gradients'' (feedback) to refine prompts, has emerged as an effective method for automatic prompt engineering.

By Zixin Ding, Junyuan Hong, Zhan Shi, Jiachen T. Wang, Zinan Lin, Li Yin, Meng Liu, Zhangyang Wang, Yuxin Chen

llms benchmarks

arXiv AI

Jun 30

Hierarchical Experimentalist Agents

arXiv:2606. 29315v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used to take actions in the real world and support human decision-making, yet most agents rely on parametric knowledge, fixed post-training data, retrieval, or search.

By Abhranil Chandra, Sankaran Vaidyanathan, Utsav Dhanuka, Varun Gandhi, Scott Niekum

llms agents benchmarks

arXiv AI

Jun 30

SpreadsheetBench 2: Evaluating Agents on End-to-End Business Spreadsheet Workflows

arXiv:2606. 29955v1 Announce Type: cross Abstract: Spreadsheets are widely used for business analysis, financial modeling, reporting, and decision-making.

By Jian Zhu, Yuzheng Zhang, Zeyao Ma, Bohan Zhang, Armin Schoepf, Daniel Woloch, Peter Yiliu Wang, Guangyu Robert Yang, Samuel Jacob, Siddharth Nagisetty, Abhiram Chundru, Jean Lin, Spencer Mateega, Jing Zhang

llms agents benchmarks

arXiv AI

Jun 30

Preventing Error Propagation in Multi-Agent AI through Runtime Monitoring

arXiv:2606. 29026v1 Announce Type: new Abstract: Multi-agent AI systems can improve answer selection by allowing different language models to exchange reasoning traces, revise initial predictions, and support a final decision.

By Shahnewaz Karim Sakib, Anindya Bijoy Das

llms agents

arXiv AI

Jun 30

Echoes of Human Malice in Agents: Benchmarking LLMs for Multi-Turn Online Harassment Attacks

arXiv:2510. 14207v3 Announce Type: replace Abstract: Large Language Model (LLM) agents are powering a growing share of interactive web applications, yet remain vulnerable to misuse and harm.

By Trilok Padhi, Pinxian Lu, Abdulkadir Erol, Tanmay Sutar, Gauri Sharma, Mina Sonmez, Munmun De Choudhury, Ugur Kursuncu

llms agents fine-tuning benchmarks safety

arXiv AI

Jun 30

Low-cost concept-based localized explanations: How far can we get with training-free approaches?

arXiv:2606. 29069v1 Announce Type: new Abstract: Concept-based Explainable AI (C-XAI) seeks human-understandable explanations grounded in semantic concepts, yet validation is limited by the scarcity of fine-grained concept annotations.

By Darian Fern\'andez-Guti\'errez, Rafael Bello, Marilyn Bello, Natalia D\'iaz-Rodr\'iguez

llms rag multimodal

arXiv AI

Jun 30

A Large-Scale Dataset for Molecular Structure-Language Description via a Rule-Regularized Method

arXiv:2602. 02320v4 Announce Type: replace-cross Abstract: Molecular function is largely determined by structure.

By Feiyang Cai, Guijuan He, Yi Hu, Jingjing Wang, Joshua Luo, Tianyu Zhu, Srikanth Pilla, Gang Li, Ling Liu, Feng Luo

llms safety

arXiv AI

Jun 30

Carolina Guide: A Multi-Agent RAG System with Institutional Guardrails for Academic Policy Assistance

arXiv:2606. 28360v1 Announce Type: cross Abstract: University students often struggle to navigate complex academic policies, leading to advising bottlenecks and delayed access to critical information.

By Ben Torsion, Jun Zhou

llms rag agents benchmarks safety

arXiv AI

Jun 30

Demonstration-Free Robotic Control via LLM Agents

arXiv:2601. 20334v2 Announce Type: replace-cross Abstract: Robotic manipulation has increasingly adopted vision-language-action (VLA) models, which achieve strong performance but typically require task-specific demonstrations and fine-tuning, and often generalize poorly under domain shift.

By Brian Y. Tsui, Alan Y. Fang, Tiffany J. Hwu

llms agents robotics fine-tuning multimodal benchmarks

arXiv AI

Jun 30

Conversational Query Engine for Mixed-Modality Heterogeneous Enterprise Data Sources

arXiv:2606. 28370v1 Announce Type: cross Abstract: Enterprise business intelligence queries span structured warehouses and unstructured document repositories -- modalities with fundamentally different access methods, cost profiles, and correctness semantics.

By Darshita Rathore, Vineet Kumar, Vaibhav Singal, Ankur Vivek Singh, Anindya Moitra

llms rag agents fine-tuning benchmarks

arXiv AI

Jun 30

When Does Overlap Help? OSU-Mem and a Cell-Conditional Analysis of Trajectory Memory for LLM Agents

arXiv:2606. 28376v1 Announce Type: cross Abstract: Long-horizon large language model (LLM) agents accumulate interaction trajectories that quickly exceed any practical prompt budget, and existing memory methods either truncate aggressively and lose non-local evidence or retain boilerplate that degrades decision quality.

By Mellow Baixuan Chen, Xiangguo Sun

llms agents benchmarks

arXiv AI

Jun 30

How Do LLMs Cite? A Mechanistic Interpretation of Attribution in Retrieval-Augmented Generation

arXiv:2606. 28358v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) aims to enhance the trustworthiness of Large Language Models (LLMs) by grounding their outputs in external documents, often using inline citations for verifiability.

By Ian van Dort (University of Amsterdam), Maria Heuss (University of Amsterdam)

llms rag benchmarks

arXiv AI

Jun 30

SCRIBE: Structured Mid-Level Supervision for Tool-Using Language Models

arXiv:2601. 03555v3 Announce Type: replace Abstract: Training reliable tool-augmented agents remains a significant challenge, largely due to the difficulty of credit assignment in multi-step reasoning.

By Yuxuan Jiang, Francis Ferraro

llms agents reinforcement-learning benchmarks

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