AI agents

Tool use, function calling, orchestration and the protocols that let models act rather than only answer.

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arXiv AI

Jun 2

"Skill issues'': data-centric optimization of lakehouse agents

arXiv:2606. 01185v1 Announce Type: new Abstract: Coding agents are becoming users of data infrastructure, but their success depends not only on model quality: it also depends on the skills and environment files that teach agents how to use a system.

By Nicole Rose Schneider, Davide Ghilardi, Giacomo Piccinini, Jacopo Tagliabue

agents

arXiv AI

Jun 2

HomeFlow: A Data Flywheel for Smart Home Agent Training with Verifiable Simulation

arXiv:2606. 01230v1 Announce Type: new Abstract: Large language model agents are moving beyond text-only interaction toward physical-world control, with smart homes as a representative domain.

By Yi Gu, Huacan Wang, Shuo Zhang, Yuqing Hou, Lei Xue, Weipeng Ming, Chen Liu, Fangzhou Yu, Kuan Li, Ronghao Chen, Sen Hu, Xiaofeng Mou, Yi Xu

llms agents reinforcement-learning fine-tuning benchmarks

arXiv AI

Jun 2

Visual Persuasion: What Influences Decisions of Vision-Language Models?

arXiv:2602. 15278v2 Announce Type: replace-cross Abstract: The web is littered with images, once created for human consumption and now increasingly interpreted by agents using vision-language models (VLMs).

By Manuel Cherep, Pranav M R, Pattie Maes, Nikhil Singh

llms agents diffusion multimodal safety

arXiv AI

Jun 2

Are LLMs Ready for Neural-integrated Mechanistic Modeling? A Benchmark and Agentic Framework

arXiv:2602. 18008v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have shown promise in constructing mechanistic models from data.

By Zihan Guan, Rituparna Datta, Mengxuan Hu, Shunshun Liu, Aiying Zhang, Prasanna Balachandran, Sheng Li, Anil Vullikanti

llms agents benchmarks

arXiv AI

Jun 2

Large Language Model Guided Incentive Aware Reward Design for Cooperative Multi-Agent Reinforcement Learning

arXiv:2603. 24324v4 Announce Type: replace-cross Abstract: Designing effective auxiliary rewards for cooperative multi-agent systems remains challenging, as misaligned incentives can induce suboptimal coordination, particularly when sparse task rewards provide insufficient grounding for coordinated behavior.

By Dogan Urgun, Gokhan Gungor

llms agents reinforcement-learning safety

arXiv AI

Jun 2

Claudini: Autoresearch Discovers State-of-the-Art Adversarial Attack Algorithms for LLMs

arXiv:2603. 24511v2 Announce Type: replace-cross Abstract: We show that AI agents are capable of discovering novel algorithms for adversarial attacks against LLMs, advancing the state of the art on white-box jailbreaking and prompt injection evaluations.

By Alexander Panfilov, Peter Romov, Igor Shilov, Yves-Alexandre de Montjoye, Jonas Geiping, Maksym Andriushchenko

llms agents multimodal benchmarks safety

arXiv AI

Jun 2

Where Do Deep-Research Agents Go Wrong? Span-Level Error Localization in Agent Trajectories

arXiv:2606. 02060v1 Announce Type: new Abstract: Deep-research agents solve tasks through long trajectories of search, tool use, evidence inspection, and answer synthesis.

By Jiaming Wang, Ziteng Feng, Jiangtao Wu, Ruihao Li, Qianqian Xie, Yuxiang Ren, He Zhu, Xueming Han, Fanyu Meng, Junlan Feng, Jiaheng Liu

llms agents benchmarks

arXiv AI

Jun 2

Towards a General Intelligence and Interface for Wearable Health Data

arXiv:2605. 22759v2 Announce Type: replace Abstract: While ubiquitous wearable sensors capture a wealth of behavioral and physiological information, effectively transforming these signals into personalized health insights is challenging.

By Girish Narayanswamy, Maxwell A. Xu, A. Ali Heydari, Samy Abdel-Ghaffar, Marius Guerard, Kara Vaillancourt, Zhihan Zhang, Jake Garrison, Levi Albuquerque, Dimitris Spathis, Hong Yu, Hamid Palangi, Xuhai "Orson" Xu, David G. T. Barrett, Joseph Breda, Jed McGiffin, Yubin Kim, Yuwei Zhang, Naghmeh Rezaei, Samuel Solomon, Karan Ahuja, Tim Althoff, Jake Sunshine, Ming-Zher Poh, Benjamin Yetton, Ari Winbush, Nicholas B. Allen, James M. Rehg, Isaac Galatzer-Levy, Yun Liu, John Hernandez, Anupam Pathak, Conor Heneghan, Yuzhe Yang, Ahmed A. Metwally, Pushmeet Kohli, Mark Malhotra, Shwetak Patel, Xin Liu, Daniel McDuff

llms rag agents

arXiv AI

Jun 2

Automated Conjecture Resolution with Formal Verification

arXiv:2604. 03789v2 Announce Type: replace-cross Abstract: Recent advances in large language models have significantly improved their ability to perform mathematical reasoning, extending from elementary problem solving to increasingly capable performance on research-level problems.

By Haocheng Ju, Guoxiong Gao, Jiedong Jiang, Bin Wu, Zeming Sun, Shurui Liu, Leheng Chen, Yutong Wang, Yuefeng Wang, Zichen Wang, Wanyi He, Peihao Wu, Liang Xiao, Ruochuan Liu, Bryan Dai, Bin Dong

llms agents

arXiv AI

Jun 2

MedSynapse-V: Bridging Visual Perception and Clinical Intuition via Latent Memory Evolution

arXiv:2604. 26283v3 Announce Type: replace-cross Abstract: High-precision medical diagnosis relies not only on static imaging features but also on the implicit diagnostic memory experts instantly invoke during image interpretation.

By Chunzheng Zhu, Jiaqi Zeng, Junyu Jiang, Jianxin Lin, Yijun Wang

agents nlp reinforcement-learning efficiency benchmarks safety

arXiv AI

Jun 2

Attested Tool-Server Admission: A Security Extension to the Model Context Protocol

arXiv:2605. 24248v2 Announce Type: replace-cross Abstract: The Model Context Protocol (MCP) standardizes how a large-language-model (LLM) agent and an external tool server exchange messages, but not trust: a host reads a server's self-declared tool list and dispatches calls, with no notion of which servers it may use, at what sensitivity, or which of a server's tools are in bounds.

By Alfredo Metere

llms agents safety

arXiv AI

Jun 2

Algorithmic algorithm development with LLMs: A Case Study on LLM-Usage for Contraction Order Optimization in Tensor Networks

arXiv:2606. 01975v1 Announce Type: new Abstract: We consider LLM-based algorithm development through a case study on contractionorder optimisation for tensor networks with OpenEvolve.

By Fabian Hoppe, Melven R\"ohrig-Z\"ollner, Philipp Knechtges

llms agents benchmarks

arXiv AI

Jun 2

Generic Interpretation Approach for Transformer Models Incorporating Heterogenous Attention Structures

arXiv:2605. 27458v2 Announce Type: replace-cross Abstract: Transformer has significantly propelled the development of artificial intelligence, and certainly the development of agents as well.

By Yongjin Cui, Xiaohui Fan, Huajun Chen

llms agents

arXiv Machine Learning

Jun 2

Same Payload, Different Channel: Measuring Trust Asymmetry in Tool-Using Language Models

arXiv:2606. 00566v1 Announce Type: new Abstract: As language models take on agentic roles that span calling external APIs, reading tool outputs, and acting on instructions embedded in third-party content, their attack surface expands well beyond what users type.

By Mohammed Sameer Syed (University of Arizona), Rozhin Yasaei (University of Arizona)

llms agents safety

arXiv Machine Learning

Jun 2

A multimodal dataset of photoplethysmography and continuous behavioral responses to ASMR and nature videos

arXiv:2606. 00752v1 Announce Type: new Abstract: Autonomous Sensory Meridian Response (ASMR) is a somatosensory phenomenon characterized by pleasant tingling sensations and cardiovascular slowing.

By Tushar Das, Daigo Hozaki, Koushlendra Kumar Singh, Hirohito M. Kondo

agents multimodal

arXiv Machine Learning

Jun 2

Distributed GNEP Algorithms without Multiplier Sharing and Applications to Multi-Robot Coordination and Contextual Bandit-Based Active Learning

arXiv:2606. 00759v1 Announce Type: new Abstract: Recent advances in artificial intelligence have expanded the focus from classical optimization to include equilibrium analysis in noncooperative games.

By Shao-An Yin

agents reinforcement-learning robotics safety

arXiv Machine Learning

Jun 2

When Parallelism Pays Off: Cohesion-Aware Task Partitioning for Multi-Agent Coding

arXiv:2606. 00953v1 Announce Type: new Abstract: Multi-agent Large Language Model (LLM) systems offer a way to decompose complex tasks, such as coding, through parallelization and context isolation.

By Xu Yang, Lunyiu Nie, Ethan Chandra, Stanislav Gannutin, Fangru Lin, Swarat Chaudhuri

llms agents

arXiv Machine Learning

Jun 2

Truthful AI Advisors: A Pre-Specified Benchmark for Large Language Model Honesty Under Preference Misalignment

arXiv:2606. 01456v1 Announce Type: new Abstract: Large language models are increasingly deployed as advisors whose objective is not aligned with the user's: recommenders optimize for engagement, sales assistants for purchases, negotiation agents for concessions.

By Hamidreza Hasani Balyani, Seyed Pouyan Mousavi Davoudi, Alireza Amiri-Margavi, Amin Gholami Davodi, Arshia Gharagozlou

llms rag agents benchmarks safety

arXiv Machine Learning

Jun 2

Genotype-Conditioned Molecular Generation via Evidence-Grounded Multi-Objective Latent Perturbation in Diffusion Models

arXiv:2606. 01461v1 Announce Type: new Abstract: Developing effective anticancer therapeutics remains challenging due to tumor heterogeneity and the absence of well-defined molecular targets across cancer subtypes.

By Brenda Nogueira, Gisela A. Gonzalez-Montiel, Nitesh V. Chawla, Nuno Moniz

llms agents diffusion

arXiv Machine Learning

Jun 2

Safety Game: Inference-Time Alignment of Black-Box LLMs via Constrained Optimization

arXiv:2510. 09330v3 Announce Type: replace Abstract: Ensuring that large language models (LLMs) comply with safety requirements is a central challenge in AI deployment.

By Tuan Nguyen, Long Tran-Thanh

llms agents reinforcement-learning fine-tuning safety

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