AI agents

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

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arXiv Machine Learning

Oct 15

MolHop: Scaffold-Hopping Molecular Design with Agentic Reinforcement Learning

arXiv:2610.19920v1 Announce Type: new Abstract: Molecular design is often formulated as an optimization problem over existing molecular spaces. However, molecular optimization and the discovery of st...

By Yanyi Su, Chenyan Wang, Yulei Gong, Yeyun Chen, Xiaohui Yang, Jun Cheng

llms agents reinforcement-learning

arXiv AI

Oct 15

Auditing LLM Program Search for Bermudan Stopping with Primal--Dual Certificates

arXiv:2610.14063v1 Announce Type: cross Abstract: LLM program-search agents are usually judged by the score that drives their search. We study LLM-driven search over Python exercise policies for Berm...

By Yuhua Qian

llms agents benchmarks

arXiv AI

Oct 15

A Reference Architecture for Integrating Agentic AI into Enterprise Software: Responsibilities, Interactions, and Variability

arXiv:2610.14818v1 Announce Type: new Abstract: Agentic AI moves generative AI applications toward systems that coordinate reasoning, enterprise context, model inference, and external actions. Integr...

By Mingqin Yu, Fethi Rabhi, Liming Zhu, Qinghua Lu

agents

arXiv AI

Oct 15

Two-Stage Multi-modal Learning for Dynamic Audio-Visual Navigation: From Physical Alignment to Scene Aggregation

arXiv:2610.14077v1 Announce Type: cross Abstract: Embodied Audio-Visual Navigation (AVN) requires an agent to locate sound targets by jointly leveraging visual and auditory cues. While prior work has...

By Jia Li, Yinfeng Yu

llms agents robotics multimodal benchmarks safety

arXiv AI

Oct 15

Cortex: Measurement-Gated Evolution and Pruning of Coding-Agent Skills and Rules

arXiv:2610.14216v1 Announce Type: cross Abstract: Coding agents such as Claude Code also follow text files that developers write for them: a project file, skills the agent may choose to open, and rul...

By Samuele Giampieri

llms agents efficiency

arXiv AI

Oct 15

RealityBench: Simulated Environments to Evaluate a Vision Language Model's Ability to Document Human Physical Work

arXiv:2610.14397v2 Announce Type: cross Abstract: Augmenting worker capabilities in the physical world through guidance has been a decades-long goal for AI agents. One key supporting capability is to...

By Andrew Perrault (Relantic, Inc., Columbus, OH, USA), Anmol Takiar (Relantic, Inc., Columbus, OH, USA), Dominik Winecki (Relantic, Inc., Columbus, OH, USA), Arnab Nandi (Relantic, Inc., Columbus, OH, USA)

llms agents multimodal benchmarks

arXiv AI

Oct 15

Jevolution: A Framework for Studying Communication and Collective Dynamics in AI Agents

arXiv:2610.14449v1 Announce Type: cross Abstract: Experiments with remote decision-making agents depend on more than the policy: the world may change during inference, the simulator determines which...

By Xirui Huang, Nikhil Hooda, Utkarsh Gupta

agents

arXiv AI

Oct 15

Co-adaptive Rule Routing and Constraining for Structured Knowledge Retrieval

arXiv:2610.14465v1 Announce Type: cross Abstract: Structured knowledge retrieval acquires task-relevant evidence from structured knowledge sources for downstream tasks. Existing methods rely on uncon...

By Zhisheng Qi, Haoyu Han, Jiaxi Li, Bing He, Huiyuan Chen, Franck Dernoncourt, Ryan A. Rossi, Mahantesh M. Halappanavar, Jin Lu, Yu Zhang, Tianming Liu, Yu Wang

rag agents reinforcement-learning fine-tuning

arXiv Computer Vision

Oct 15

PhySGA: A Physics-Space Grounded Agentic Framework for Generalizable and Interactive 3D Composition

arXiv:2610.20560v1 Announce Type: cross Abstract: 3D object composition is pivotal to digital content creation and robot simulation, while also serving as a critical testbed for physical intelligence...

By Weichen Lyu, Chong Xia, Mingyu Dong, Long Xu, Shengjun Zhang, Yueqi Duan

llms agents diffusion robotics safety

arXiv AI

Oct 15

MMTA: Benchmarking Multimodal Temporal Analysis with Time Series, Text, and Vision

arXiv:2610.17856v2 Announce Type: cross Abstract: Time-series analysis is increasingly studied in multimodal settings, yet most benchmarks pair temporal signals with text or with visualizations deriv...

By Ziyang Zhang, Shenyi Li, Yilin Wang, Ziyun Cui, Bowen Zhou, Wen Wu, Chao Zhang

llms agents robotics multimodal benchmarks safety

arXiv Computation and Language

Oct 15

Dual-Evolving Reinforcement Learning for Automated Reward Design

arXiv:2512.13399v3 Announce Type: replace-cross Abstract: Crafting effective reward signals remains a central challenge in reinforcement learning (RL) for complex reasoning. Existing methods for auto...

By Sitao Cheng, Tianle Li, Xuhan Huang, Xunjian Yin, Difan Zou

agents reinforcement-learning robotics benchmarks

arXiv AI

Oct 15

Evolving in Thought Space: Training a Small Model at Test Time Unlocks Better Discoveries

arXiv:2610.06269v2 Announce Type: replace Abstract: Open-ended scientific discovery often requires repeatedly proposing and evaluating candidate solutions. LLM-based systems can support this process...

By Chonghe Jiang, Ao Qu, Siyuan Liu, Ruoyun Ma, Zijian Zhou, Dingyi Zhuang, Bo Liu, Han Zheng, Hanfei Yu, Baichuan Mo, Jinhua Zhao, Paul Pu Liang

llms agents reinforcement-learning benchmarks

arXiv AI

Oct 15

ASIWorld: Benchmarking Autonomous Agents in a Persistent Survival Sandbox

arXiv:2610.14807v1 Announce Type: new Abstract: Evaluating autonomous agents requires examining whether they can translate goals into coherent action across extended interactions. In persistent envir...

By Jianfeng Lin, Runze Wang, Haorong Hong, Yuxuan Xie, Zeno Zeng, Zhongyuan Liu

agents benchmarks

arXiv AI

Oct 15

ProCARE: Real-World Study Automation via Profile-Grounded Evidence Contracts

arXiv:2610.16175v1 Announce Type: new Abstract: Real-world studies (RWS) analyze routinely collected healthcare data, such as electronic health records and insurance claims, to estimate treatment eff...

By Zhaoyu Fan, Bowen Han, Jincheng Ou, Yanwei Ren, Zehua He, Kaiyu Huang, Pengcheng He, Shanshan Ni, Chengchen Gong, Shuai Xue, Qingjiang Shi

llms agents benchmarks safety

arXiv AI

Oct 15

Phantom Merge: When Your Large Language Model Agents Pick One but Tell You About Another

arXiv:2610.16141v1 Announce Type: new Abstract: LLM agents increasingly operate over multiple entities, where reliable response generation requires not only retrieving relevant evidence but also pres...

By Wangshu Zhu, Qianwen Guo, Yushun Dong

llms agents

arXiv AI

Oct 15

Neos: Learning to Interleave Search and Visual Reasoning

arXiv:2610.17043v1 Announce Type: new Abstract: Recent multimodal deep search agents have advanced tool use and visual content retrieval, yet they still struggle to convert intermediate visual eviden...

By Ekko He, Jack Hong, Shilin Yan, Xintong Zhang, Yujie Zhang, Wenjie Li, Haoran Sun, Kaixun Jiang, Jiyuan Fu, Guannan Zhang, Hongwei Xue

agents reinforcement-learning fine-tuning multimodal benchmarks

arXiv AI

Oct 15

AutoMARS: Automatic and Scalable Multi-Agent Large Language Model Framework for Supply Chain Risk Assessment

arXiv:2610.17206v1 Announce Type: new Abstract: We propose AutoMARS (Automatic Multi-Agent Risk Assessment), an agentic framework for automatic and scalable comparative risk assessment in supply chai...

By Yinzhu Quan, Zefang Liu, Frederick Benaben, Benoit Montreuil

llms agents

arXiv Machine Learning

Oct 15

What Happens to the Goal? Distinct Failures of Policy Alignment in Goal Misgeneralization

arXiv:2610.19731v1 Announce Type: new Abstract: Goal misgeneralization is a common failure mode in which an agent behaves competently out of distribution but does not pursue the intended goal. Howeve...

By Julia Barth, Armin Sommer, Kunvar Thaman, Jannik Schilling

agents robotics safety

arXiv Machine Learning

Oct 15

Beyond Equation Search: Conjecture-Guided Discovery with SCOPE and ClosureBench

The paper introduces SCOPE, a framework that enables multiple scientist agents to pursue conjecture-driven research trajectories by modeling equations, formulating questions, and conducting active experiments, then sharing evidence to refine their conjectures. It also presents ClosureBench, an interactive benchmark of 60 closure-discovery tasks across five scientific domains, designed to evaluate equation discovery beyond accuracy by measuring evidence faithfulness, mechanism insight, and experimental effectiveness. Experiments show that SCOPE outperforms diverse baselines on both ClosureBench and a real-world scientific discovery case, yielding more accurate, reliable, and effective scientific discoveries.

By Yuxiao Hu, Qian Li, Ding Wang, Siheng Chen, Linfeng Zhang, Jinyue Yan, Yuntian Chen

agents benchmarks

arXiv Machine Learning

Oct 15

TANGO: RNA Topology and Geometry Co-Design

arXiv:2610.20277v1 Announce Type: new Abstract: Designing RNA sequences that satisfy target tertiary-structure objectives is an important problem in RNA engineering, with broad applications in synthe...

By Tianmeng Hu, Biao Luo, Ke Li

agents reinforcement-learning fine-tuning

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