AI Agent

AgentForge: A Flexible Low-Code Platform for Reinforcement Learning Agent Design

Francisco Erivaldo Fernandes Junior, Antti Oulasvirta

专题命中 软件智能体 ：agent(title,abstract)；分类 cs.LG、cs.SE

Comments This paper has been accepted at the 17th International Conference on Agents and Artificial Intelligence (ICAART 2025)

DARS: Dynamic Action Re-Sampling to Enhance Coding Agent Performance by Adaptive Tree Traversal

Vaibhav Aggarwal, Ojasv Kamal, Abhinav Japesh, Zhijing Jin, Bernhard Schölkopf

专题命中 软件智能体 ：agent(title,abstract)；分类 cs.CL、cs.SE

Agentic Bug Reproduction for Effective Automated Program Repair at Google

Runxiang Cheng, Michele Tufano, Jürgen Cito, José Cambronero, Pat Rondon, Renyao Wei, Aaron Sun, Satish Chandra

专题命中 软件智能体 ：agentic(title)；agent(abstract)；分类 cs.AI、cs.SE

Every Software as an Agent: Blueprint and Case Study

Mengwei Xu

专题命中 软件智能体 ：agent(title,abstract)；分类 cs.AI、cs.SE

MRJ-Agent: An Effective Jailbreak Agent for Multi-Round Dialogue

Fengxiang Wang, Ranjie Duan, Peng Xiao, Xiaojun Jia, Shiji Zhao, Cheng Wei, YueFeng Chen, Chongwen Wang, Jialing Tao, Hang Su, Jun Zhu, Hui Xue

专题命中 软件智能体 ：agent(title,abstract)；分类 cs.AI、cs.CL

A Behavior Tree-inspired programming language for autonomous agents

Oliver Biggar, Iman Shames

专题命中 软件智能体 ：autonomous agent(title,abstract)；分类 cs.AI、cs.SE

AEGIS: An Agent-based Framework for General Bug Reproduction from Issue Descriptions

Xinchen Wang, Pengfei Gao, Xiangxin Meng, Chao Peng, Ruida Hu, Yun Lin, Cuiyun Gao

专题命中 软件智能体 ：agent(title,abstract)；分类 cs.AI、cs.SE

A Multi-Task Role-Playing Agent Capable of Imitating Character Linguistic Styles

Siyuan Chen, Qingyi Si, Chenxu Yang, Yunzhi Liang, Zheng Lin, Huan Liu, Weiping Wang

专题命中 软件智能体 ：agent(title,abstract)；分类 cs.AI、cs.CL

An Autonomous GIS Agent Framework for Geospatial Data Retrieval

Huan Ning, Zhenlong Li, Temitope Akinboyewa, M. Naser Lessani

专题命中 软件智能体 ：agent(title,abstract)；分类 cs.AI、cs.CL

PyBench: Evaluating LLM Agent on various real-world coding tasks

Yaolun Zhang, Yinxu Pan, Yudong Wang, Jie Cai

专题命中 软件智能体 ：agent(title,abstract)；分类 cs.AI、cs.SE

Comments 16 pages

AutoGRAMS: Autonomous Graphical Agent Modeling Software

Ben Krause, Lucia Chen, Emmanuel Kahembwe

专题命中 软件智能体 ：agent(title)；AI agent(abstract)；分类 cs.AI、cs.CL

MASAI: Modular Architecture for Software-engineering AI Agents

Daman Arora, Atharv Sonwane, Nalin Wadhwa, Abhav Mehrotra, Saiteja Utpala, Ramakrishna Bairi, Aditya Kanade, Nagarajan Natarajan

专题命中 软件智能体 ：AI agent(title)；agentic(abstract)；分类 cs.AI、cs.SE

An Autonomous Non-monolithic Agent with Multi-mode Exploration based on Options Framework

JaeYoon Kim, Junyu Xuan, Christy Liang, Farookh Hussain

专题命中 软件智能体 ：agent(title,abstract)；分类 cs.AI、cs.LG

Comments IEEE IJCNN 2023

Dynamic Neural Control Flow Execution: An Agent-Based Deep Equilibrium Approach for Binary Vulnerability Detection

Litao Li, Steven H. H. Ding, Andrew Walenstein, Philippe Charland, Benjamin C. M. Fung

专题命中 软件智能体 ：agent(title,abstract)；分类 cs.AI、cs.LG

Voyager: An Open-Ended Embodied Agent with Large Language Models

Guanzhi Wang, Yuqi Xie, Yunfan Jiang, Ajay Mandlekar, Chaowei Xiao, Yuke Zhu, Linxi Fan, Anima Anandkumar

专题命中 软件智能体 ：agent(title,abstract)；分类 cs.AI、cs.LG

Comments Project website and open-source codebase: https://voyager.minedojo.org/

Configurable Agent With Reward As Input: A Play-Style Continuum Generation

Pierre Le Pelletier de Woillemont, Rémi Labory, Vincent Corruble

专题命中 软件智能体 ：agent(title,abstract)；分类 cs.AI、cs.LG

Comments 2021 IEEE Conference on Games (CoG), 2021

Journal ref 2021 IEEE Conference on Games (CoG), 2021, pp. 1-8

Adversary agent reinforcement learning for pursuit-evasion

X. Huang

专题命中 软件智能体 ：agent(title,abstract)；分类 cs.AI、cs.LG

Comments 17 pages, 14 figures

How to Train Your Agent to Read and Write

Li Liu, Mengge He, Guanghui Xu, Mingkui Tan, Qi Wu

专题命中 软件智能体 ：agent(title,abstract)；分类 cs.AI、cs.CL

What Did You Think Would Happen? Explaining Agent Behaviour Through Intended Outcomes

Herman Yau, Chris Russell, Simon Hadfield

专题命中 软件智能体 ：agent(title,abstract)；分类 cs.AI、cs.LG

Creating A Neural Pedagogical Agent by Jointly Learning to Review and Assess

Youngnam Lee, Youngduck Choi, Junghyun Cho, Alexander R. Fabbri, Hyunbin Loh, Chanyou Hwang, Yongku Lee, Sang-Wook Kim, Dragomir Radev

专题命中 软件智能体 ：agent(title,abstract)；分类 cs.CL、cs.LG

Comments 9 pages, 9 figures, 7 tables

Discovering space - Grounding spatial topology and metric regularity in a naive agent's sensorimotor experience

Alban Laflaquière, J. Kevin O'Regan, Bruno Gas, Alexander Terekhov

专题命中 软件智能体 ：agent(title,abstract)；分类 cs.AI、cs.LG

Comments 59 pages, 16 figures, submitted to Neural Networks

OmniPath: A Multi-Modal Agentic Framework for Auditing Wheelchair Accessibility

OmniPath: 用于审计轮椅可达性的多模态智能体框架

ASM Mobarak Hossain, Nadim Mahmud, Vaskar Raychoudhury, Md Osman Gani

机构 * Causal AI Lab, Department of Information Systems, University of Maryland Baltimore County（因果AI实验室，信息系统系，马里兰大学巴尔的摩分校） ； Department of Computer Science & Software Engineering, Miami University（计算机科学与软件工程系，迈阿密大学） 纠错

专题命中 软件智能体 ：agentic(title,comments)；agent(abstract)；分类 cs.AI

AI总结 提出OmniPath框架，融合OSM网络拓扑与机载LiDAR数据，通过虚拟遍历量化坡度、横坡等物理摩擦点，按ADA标准分级评估轮椅可达性，经实地验证有效识别标准地图遗漏的障碍。

Comments 10 pages, 13 figures. Submitted to IEEE COMPSAC 2026. OmniPath: A Multi-Modal Agentic Framework for Auditing Wheelchair Accessibility

AutoMegaKernel: A Statically-Checked Agent Harness for Self-Retargeting Megakernel Synthesis

AutoMegaKernel：用于自我重定目标超内核合成的静态检查代理框架

Jaber Jaber, Osama Jaber

机构 * RightNow AI 纠错

专题命中 软件智能体 ：agent(title,abstract)；分类 cs.LG

AI总结 提出AutoMegaKernel系统，将Llama模型编译为单个持久CUDA内核，通过静态调度验证器确保无死锁和无竞争，自动生成10种模型正确超内核，并在NVIDIA推理卡上以W8A16精度超越cuBLAS bf16。

Comments 18 pages, 5 figures. Open-source code, data, and agent harness: https://github.com/RightNow-AI/AutoMegaKernel

First head-to-head comparison of agentic AI applied to the analysis of simulated data of the Einstein Telescope

应用于爱因斯坦望远镜模拟数据分析的智能体AI首次头对头比较

Gianluca Inguglia

机构 * Anthropic ； OpenAI 纠错

专题命中 软件智能体 ：agentic(title,abstract)；分类 cs.AI

AI总结 本文首次直接比较了Claude Code和Codex两种智能体AI系统在无人干预下自主执行引力波数据分析管线的行为、科学结果和计算成本，揭示了速度与可审计性、指令解释差异等关键问题。

Comments Version 2; includes the report autonomoulsy written in PRD style by agentic AI systems as supplemental material

Foundations of a Time-Consistent Counterfactual Actuarial Runtime for Autonomous AI Agents

自主AI智能体时间一致性反事实精算运行时的基础

Hao-Hsuan Chen

机构 * Department of Risk Management and Insurance（风险管理与保险系） 纠错

专题命中 软件智能体 ：AI agent(title,abstract)；分类 cs.AI

AI总结 本文提出一种精算运行时层，通过为每个动作分配时间一致的反事实风险费用，并建立边界内无套利和预算保证，为自主AI智能体提供基础数学框架。

Comments 10 pages. Foundational paper of a multi-paper program on actuarial runtime for autonomous AI agents; previously posted on SSRN (id 6761960). Empirical companion: arXiv:2605.25632. Proof companions included as ancillary files

VoxelPrompt: A Vision Agent for End-to-End Medical Image Analysis

Andrew Hoopes, Neel Dey, Victor Ion Butoi, John V. Guttag, Adrian V. Dalca

机构 * Massachusetts Institute of Technology（麻省理工学院） ； Massachusetts General Hospital（麻省总医院） ； Harvard Medical School（哈佛医学院） 纠错

专题命中 软件智能体 ：agent(title,abstract)；分类 cs.AI

Comments 22 pages, vision-language agent, medical image analysis, neuroimage foundation model

AnyPoC: Universal Proof-of-Concept Test Generation for Scalable LLM-Based Bug Detection

AnyPoC：用于可扩展LLM基于Bug检测的通用证明-概念测试生成

Zijie Zhao, Chenyuan Yang, Weidong Wang, Yihan Yang, Ziqi Zhang, Lingming Zhang

机构 * University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校） 纠错

专题命中 软件智能体 ：agent(abstract)；agentic(abstract)；multi-agent(abstract)；分类 cs.AI、cs.CL、cs.SE

AI总结 AnyPoC通过多代理框架生成可执行的证明-概念测试，以验证候选Bug报告，提升自动化Bug检测的实用性。

3D-CoS: A New 3D Reconstruction Paradigm Based on VLM Code Synthesis

3D-CoS：基于VLM代码合成的新型3D重建范式

Yuhao Wang, Puyi Wang, Linjie Li, Zhengyuan Yang, Kevin Qinghong Lin, Yu Cheng

机构 * Shanghai Jiao Tong University（上海交通大学） ； The Chinese University of Hong Kong（香港中文大学） ； Microsoft（微软） ； University of Oxford（牛津大学） 纠错

专题命中 软件智能体 ：agent(abstract,abstract_cn)；planning(abstract)；workflow(abstract)

AI总结 提出3D代码合成（3D-CoS）范式，将3D资产表示为可执行的Blender代码，利用VLM进行程序化重建，实现高可控性和局部编辑能力。

Comments Preprint. 24 pages, 11 figures

AgentKernelArena: Generalization-Aware Benchmarking of GPU Kernel Optimization Agents

AgentKernelArena: GPU核优化代理的通用化意识基准测试

Sharareh Younesian, Wenwen Ouyang, Sina Rafati, Mehdi Rezagholizadeh, Sharon Zhou, Ji Liu, Yue Liu, Yuchen Yang, Hao Li, Ziqiong Liu, Dong Li, Vikram Appia, Zhenyu Gu, Emad Barsoum

机构 * AMD 纠错

专题命中 软件智能体 ：agent(abstract,abstract_cn)；agentic(abstract)；分类 cs.AI、cs.CL、cs.LG

AI总结 本文提出AgentKernelArena，一个用于评估GPU核优化代理的开源基准，通过隔离工作区和统一评分机制，测试代理在不同任务和硬件目标上的性能和通用化能力，发现大多数任务在正确性和编译效率上表现优异，但在PyTorch到HIP的转换任务中存在显著的正确性下降。

Can Coding Agents Reproduce Findings in Computational Materials Science?

编码代理能否在计算材料科学中复现研究成果？

Ziyang Huang, Yi Cao, Ali K. Shargh, Jing Luo, Ruidong Mei, Mohd Zaki, Zhan Liu, Wyatt Bunstine, William Jurayj, Somdatta Goswami, Tyrel McQueen, Michael Shields, Jaafar El-Awady, Paulette Clancy, Benjamin Van Durme, Nicholas Andrews, William Walden, Daniel Khashabi

机构 * Johns Hopkins University（约翰霍普金斯大学） 纠错

专题命中 软件智能体 ：agent(abstract)；workflow(abstract)；agentic(abstract)；分类 cs.AI、cs.CL、cs.SE

AI总结 研究探讨编码代理在计算材料科学领域复现研究结果的能力，提出AutoMat基准测试，发现当前LLM代理在复现复杂科学流程时表现有限，存在流程不完整、方法偏差等问题。