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

Beyond Independent Manipulation: Individual Fairness-aware Strategic Classification with Peer Imitation

arXiv:2606. 00827v1 Announce Type: cross Abstract: Strategic classification (SC) investigates scenarios where agents manipulate their features to obtain favorable decisions from predictive models.

By Xinpeng Lv, Chunyuan Zheng, Yunxin Mao, Renzhe Xu, Jinxuan Yang, Yuanlong Chen, Wangrong Huang, Shaowu Yang, Wenjing Yang, Xinwang Liu, Peng Cui, Haotian Wang

agents robotics safety

arXiv AI

Jun 2

HALO: Learning Human-Robot Collaboration via Heterogeneous-Agent Lyapunov Policy Optimization

arXiv:2603. 03741v2 Announce Type: replace-cross Abstract: To improve generalization and resilience in human-robot collaboration (HRC), robots must contend with diverse combinations of human behaviors and contexts, motivating multi-agent reinforcement learning (MARL).

By Hao Zhang, Yaru Niu, Yikai Wang, Ding Zhao, H. Eric Tseng

agents reinforcement-learning robotics

arXiv AI

Jun 2

Context Matters: Repository-Aware Security Analysis of the Agent Skill Ecosystem

arXiv:2603. 16572v2 Announce Type: replace-cross Abstract: Agent skills extend local AI agents, such as Claude Code and OpenClaw, with additional functionality.

By Florian Holzbauer, David Schmidt, Gabriel Gegenhuber, Sebastian Schrittwieser, Johanna Ullrich

llms agents

arXiv AI

Jun 2

VESTA: Visual Exploration with Statistical Tool Agents

arXiv:2606. 00384v1 Announce Type: new Abstract: Fitting quantitative models to data is a central step in scientific workflows, yet it remains one of the least automated.

By William Rudman, Abhishek Divekar, Kanishk Jain, Sebastian Joseph, Stella S. R. Offner, Matthew Lease, Kyle Mahowald, Greg Durrett, Junyi Jessy Li

llms agents multimodal benchmarks

arXiv AI

Jun 2

CodeCytos: AI-assisted spatial molecular imaging analysis via code-augmented agent action space

arXiv:2606. 00472v1 Announce Type: cross Abstract: Conventional tissue image analysis software provides foundational capabilities for cellular analysis, including segmentation, basic morphological feature extraction, and spatial organization analysis.

By Hung Q. Vo, Huy Q. Vo, Son T. Ly, Zhihao Wan, Anh-Vu Nguyen, Hong Zhao, Jianting Sheng, Stephen T. C. Wong, Hien V. Nguyen

llms agents computer-vision benchmarks

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

Observation, Not Prediction: Conversation-Level Disaggregated Scheduling for Agentic Serving

arXiv:2606. 01839v1 Announce Type: cross Abstract: LLM-based agents resolve a user task through many turns of dependent inference and tool calls, producing a workload whose total cost is unknown when the task arrives.

By Jianru Ding, Ryien Hosseini, Pouya Mahdi Gholami, Mingyuan Xiang, Henry Hoffmann

llms agents efficiency

arXiv AI

Jun 2

From "Weak" Signals to Strong Models: Preference Delta Aggregation with LoRA Merging

arXiv:2606. 00357v1 Announce Type: new Abstract: Training strong large language models (LLMs) requires high-quality supervision, which is often scarce.

By Qi Sun, Siyue Zhang, Yulin Chen, Yuxiang Xue, Ru Peng, Chen Zhao

llms agents fine-tuning benchmarks safety

arXiv AI

Jun 2

SkillRevise: Improving LLM-Authored Agent Skills via Trace-Conditioned Skill Revision

arXiv:2606. 01139v1 Announce Type: new Abstract: Agent skills are procedural artifacts that enable LLM agents to execute workflows, verify constraints, and recover from failures.

By Yuxuan Liu, Zhaochen Su, Lingyun Xie, Yuhao Zhang, Qing Zong, Jiahe Guo, Zhongwei Xie, Yiyan Ji, Yauwai Yim, Hongyu Luo, Xiyu Ren, Ruan Chenyu, Haoran Li, Yangqiu Song

llms agents benchmarks

arXiv AI

Jun 2

TuneAgent: Agentic Operating System Kernel Tuning with Reinforcement Learning

arXiv:2508. 12551v2 Announce Type: replace-cross Abstract: Linux kernel tuning is essential for optimizing operating system (OS) performance, yet remains challenging due to the complex kernel space, sparse performance feedback, and strong workload sensitivity.

By Hongyu Lin, Yuchen Li, Haoran Luo, Zhenghong Lin, Libo Zhang, Mingjie Xing, Yanjun Wu

llms agents reinforcement-learning

arXiv AI

Jun 2

Implicit Regularization for Multi-label Feature Selection

arXiv:2411. 11436v2 Announce Type: replace-cross Abstract: In this paper, we address the problem of feature selection in the context of multi-label learning, by using a new estimator based on implicit regularization and label embedding.

By Dou El Kefel Mansouri, Khalid Benabdeslem, Seif-Eddine Benkabou

rag agents benchmarks safety

arXiv AI

Jun 2

SkillPager: Query-Adaptive Intra-Skill Navigation via Semantic Node Retrieval

arXiv:2606. 00822v1 Announce Type: cross Abstract: Skill-based LLM agents increasingly rely on long procedural documents, but full-document prompting wastes tokens and dilutes information critical to execution.

By Zicai Cui, Zihan Guo, Weiwen Liu, Weinan Zhang

llms agents benchmarks

arXiv Machine Learning

Jun 2

Test-Time Compute for Frozen Embedding Models through Agentic Program Search

arXiv:2605. 11374v5 Announce Type: replace Abstract: Test-time compute is widely believed to benefit only large reasoning models, leaving small models with nothing to gain.

By Han Xiao

llms rag agents

arXiv AI

Jun 2

LC-ERD: Mining Latent Logic for Self-Evolving Reasoning via Consistency-Regulated Reward Decomposition

arXiv:2605. 24005v2 Announce Type: replace Abstract: The evolution of Large Language Model (LLM) reasoning is bottlenecked by the scarcity of high-quality process data.

By Yanyu Chen, Jiyue Jiang, Dianzhi Yu, Zheng Wu, Jiahong Liu, Jiaming Han, Xiao Guo, Jinhu Qi, Yu Li, Yifei Zhang, Irwin King

llms agents safety

arXiv AI

Jun 2

Skill-Based Mixture-of-Experts: Adaptive Routing for Heterogeneous Reasoning via Inferred Skills

arXiv:2503. 05641v4 Announce Type: replace-cross Abstract: Combining existing pre-trained LLMs is a promising approach for diverse reasoning tasks.

By Justin Chih-Yao Chen, Sukwon Yun, Elias Stengel-Eskin, Tianlong Chen, Mohit Bansal

llms agents benchmarks

arXiv AI

Jun 2

Honest Lying: Understanding Memory Confabulation in Reflexive Agents

arXiv:2605. 29463v2 Announce Type: replace-cross Abstract: Reflexion-style agents rely on self-generated reflections as memory, implicitly assuming that agents can accurately diagnose their own failures.

By Prakhar Dixit, Sadia Kamal, Tim Oates

agents benchmarks

arXiv AI

Jun 2

FedS2R: One-Shot Federated Domain Generalization for Synthetic-to-Real Semantic Segmentation in Autonomous Driving

arXiv:2507. 19881v2 Announce Type: replace-cross Abstract: Federated domain generalization has shown promising progress in image classification by enabling collaborative training across multiple clients without sharing raw data.

By Tao Lian, Jose L. G\'omez, Antonio M. L\'opez

agents computer-vision robotics efficiency

arXiv AI

Jun 2

GenPT: Beyond Self-Report for Reliable LLM Psychometrics via Generative Projective Testing

arXiv:2606. 00860v1 Announce Type: cross Abstract: Self-report questionnaires remain the prevailing tool for probing the psychological states of persona-conditioned agents (PC-Agents).

By Ming Wang, Shuang Wu, Bixuan Wang, Lu Lin, Yuxin Chen, Xiaocui Yang, Daling Wang, Shi Feng, Yifei Zhang, Yufan Sun

llms agents benchmarks 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 Machine Learning

Jun 2

Reinforcement Learning for Optimal Experiment Design in Parameter Identification of Mechatronic Systems

arXiv:2606. 00059v1 Announce Type: cross Abstract: Informative excitation signals are critical for accurate system identification of mechatronic systems, yet classical system identification (SI) approaches require expert knowledge and hand-crafted signal design to respect hardware safety constraints, limiting their generalizability.

By Julian Langschwert, Georg Schaefer, Jakob Rehrl, Stefan Huber, Simon Hirlaender

agents reinforcement-learning safety

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