AI agents

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

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arXiv AI

Aug 11

OmniV2X: A Generative Foundation Planner for Efficient End-to-End Cooperative Driving

arXiv:2606. 21165v2 Announce Type: replace-cross Abstract: We present OmniV2X, a generative foundation model for vehicle-to-everything (V2X) cooperative driving.

By Juntong Peng, Juanwu Lu, Yupeng Zhou, Can Cui, Yaobin Chen, Ziran Wang

agents fine-tuning multimodal benchmarks

arXiv AI

Aug 11

UI-MOPD: Multi-Platform On-Policy Distillation for Unified GUI Agents

arXiv:2607. 04425v2 Announce Type: replace-cross Abstract: Recent advances in multimodal foundation models and agent systems have driven GUI agents from single-platform task execution toward cross-platform interaction.

By Niu Lian, Tongbo Chen, Zhehao Yu, Chengzhen Duan, Fazhan Liu, Hui Liu, Pei Fu, Jian Luan, Heng Qu, Shu-Tao Xia, Jinpeng Wang

agents efficiency multimodal

arXiv Machine Learning

Aug 11

Learning from Environmental Feedback: Credit Assignment across Multiple Timescales for Agentic Reinforcement Learning

arXiv:2608. 08255v1 Announce Type: new Abstract: Agentic reinforcement learning (RL) often suffers from delayed and sparse rewards in real-world environments.

By Yifu Huo, Shunjie Xing, Chenglong Wang, Peinan Feng, Qiaozhi He, Yan Ding, Anxiang Ma, Yuxin Gao, Tongran Liu, Tong Xiao, Jingbo Zhu

agents reinforcement-learning

arXiv Machine Learning

Aug 11

Activation Probes Surface Code-Security Signals that the Model's Output Misses

arXiv:2608. 09643v1 Announce Type: cross Abstract: AI coding agents now write a growing share of production code, and human security review does not scale at the rate code is generated.

By Ivan Wiryadi

llms agents

arXiv Machine Learning

Aug 11

Benchmarking the Robustness of Agentic Systems to Adversarially-Induced Harms

arXiv:2508. 16481v3 Announce Type: replace Abstract: Ensuring the safe use of agentic systems requires a thorough understanding of the range of malicious behaviors these systems may exhibit.

By Jonathan N\"other, Adish Singla, Goran Radanovic

llms agents benchmarks safety

arXiv Machine Learning

Aug 11

Benchmarking In-context Experiential Learning Through Repeated Product Recommendations

arXiv:2511. 22130v2 Announce Type: replace Abstract: To navigate ever-shifting real-world environments, agents must grapple with incomplete knowledge and adapt their strategies through experience.

By Gilbert Yang, Yaqin Chen, Thomson Yen, Hongseok Namkoong

llms agents benchmarks

arXiv AI

Aug 11

Entropy-based Code Adversarial Translation for Real-world Repository Migration

arXiv:2608. 09273v1 Announce Type: new Abstract: LLMs have demonstrated strong capabilities in code generation and automated program repair, but migrating an entire repository rarely produces a runnable application because long-horizon translation challenges LLM-based agents' ability to maintain repository-level migration objectives.

By Yushun Tang, Yisen Cao, Zhicheng Chen, Lin Peng, Junkang Mao, Fengyi Song, Yantao Jia

llms agents benchmarks safety

arXiv AI

Aug 11

MasDrift: Benchmarking Authorization Preservation Across Multi-Agent Architectures

arXiv:2608. 07556v1 Announce Type: cross Abstract: Multi-agent systems (MAS) decompose long-horizon tasks across supervisors and subagents, but delegated goals do not necessarily carry their original authorization boundaries.

By Zhuoning Xu, Xiucheng Zhang, Hanjun Luo, Yingbin Jin, Yinpeng Dong, Hanan Salam

agents benchmarks safety

arXiv AI

Aug 11

TCS-BENCH: Benchmarking State-of-the-Art Generative AI Theoretical Computer Science Research Ability

arXiv:2608. 09538v1 Announce Type: cross Abstract: We introduce TCS-Bench, a benchmark for evaluating Large Language Models (LLMs) on research-level Theoretical Computer Science (TCS) proof generation.

By Vincent Cohen-Addad, Dimitris Paparas, Ernest van Wijland, Max Springer, Julien Canitrot-Paradis, Honghao Lin, David Woodruff, Adarsh Kumarappan, Rajesh Jayaram, Rudrajit Das, Lalit Jain, Ola Svensson, Silvio Lattanzi, Mislav Balunovic, Theophane Weber, Vahab Mirrokni

llms agents benchmarks

arXiv AI

Aug 11

Learning Preference Adaptation for Large Language Model Personalization via Verbal Reinforcement Learning

arXiv:2608. 09507v1 Announce Type: cross Abstract: Natural language user preferences provide an interpretable interface for LLM personalization.

By Yuting Liu, Wei Wu, Jianzhe Zhao, Guibing Guo

llms rag agents reinforcement-learning

arXiv AI

Aug 11

Towards Expert-level Medical AI for Real-time Video Consultations

arXiv:2608. 09861v1 Announce Type: new Abstract: Audio-visual interaction is the standard for patient-physician consultations, enabling natural communication and effective assessment of illness through non-verbal cues.

By Mahvish Nagda, Jihyeon Lee, Matthew Thompson, Chunjong Park, Tim Strother, Valentin Li\'evin, Roma Ruparel, Akshay Goel, Teya Bergamaschi, Suhana Bedi, Meet Shah, Pavel Dubov, Liviu Panait, Toshiyuki Fukuzawa, Sam Schmidgall, Craig Schiff, Joseph Xu, Aliya Rysbek, Yana Lunts, Jan Freyberg, Rebecca Hemengway, Sunny Virmani, David Racz, Carey Radebaugh, Jo\"elle Barral, Kavi Goel, Dale R. Webster, Katherine Chou, Avinatan Hassidim, Yossi Matias, James Manyika, Gregory Wayne, Tao Tu, Yun Liu, Ethan Goh, Christina Chen, Ryutaro Tanno, Po-Hsuan Cameron Chen, Mike Schaekermann, Anil Palepu

llms agents multimodal benchmarks

arXiv AI

Aug 11

M$^3$Prune: Hierarchical Communication Graph Pruning for Efficient Multi-Modal Multi-Agent Retrieval-Augmented Generation

arXiv:2511. 19969v2 Announce Type: replace Abstract: Recent advancements in multi-modal retrieval-augmented generation (mRAG), which enhance multi-modal large language models (MLLMs) with external knowledge, have demonstrated that the collective intelligence of multiple agents can significantly outperform a single model through effective communication.

By Weizi Shao, Taolin Zhang, Zijie Zhou, Chen Chen, Chengyu Wang, Xiaofeng He

llms rag agents efficiency multimodal benchmarks

arXiv AI

Aug 11

When LLM Agents Negotiate: Private Information and Dynamic Bargaining in Supply Chains

arXiv:2608. 07538v1 Announce Type: new Abstract: As LLM agents move from decision support to autonomous procurement, firms need to know whether delegated negotiators create value, divide it predictably, and avoid money-losing contracts.

By Chen Liang, Fasheng Xu

llms agents reinforcement-learning benchmarks safety

arXiv AI

Aug 11

Argus: A General-Purpose Agentic Reasoning Runtime for Long-Horizon Tasks

arXiv:2608. 05144v2 Announce Type: replace Abstract: Long-horizon reasoning requires an agentic runtime that can persist when evidence supports its current approach and pivot when measurements reveal failure, hidden constraints, or a misspecified objective.

By Boxiu Li, Zimo Wen, Yijia Fan, Chuan Wen, Fan Yang, Hangxi Guo, Jiaao Wu, Jiachen Zhang, Junxiang Lei, Mukai Li, Ruize Tang, Runjing Gu, Shibo Hu, Sihan Chen, Sufeng Guo, Wanbo Zhang, Xian Zhang, Xiaoyu Chen, Xuanhe Zhou, Xuyao Huang, Yifei Gao, Yifei Shen, Yilin Chen, Yuheng Wu, Yuzhe Zhang, Zelong Zhao, Zhijie Deng

llms agents reinforcement-learning benchmarks

arXiv AI

Aug 11

TRACE: TRajectory Attribution for Automated Context Engineering

arXiv:2608. 09153v1 Announce Type: new Abstract: Production AI agents fail when their context sources -- system prompts, knowledge bases, tool descriptions, and procedural skills -- contain errors or gaps.

By Yikai Zhao, Pradeep Kumar Misra, Saurabh Pandey

agents fine-tuning benchmarks

arXiv AI

Aug 11

SiriusDeliver: Automating Data Warehouse Delivery at Tencent

arXiv:2608. 09185v1 Announce Type: cross Abstract: Enterprise data warehouses (DWs) support business-critical analytics, but warehouse task delivery remains a complicated production process involving context retrieval, workflow configuration, code generation, platform submission, and failure diagnosis.

By Haining Xie, Xiaokai Zhou, Jiaming Yang, Siqi Shen, Ziwei Wang, Yifeng Zheng, Tengyue Xu, Yipeng Shi, Zefang Zong, Yang Li, Peng Chen, Jie Jiang, Debiao He, Xiao Yan, Jiawei Jiang

llms agents data-engineering

arXiv AI

Aug 11

Agentic Router: An Execution-Grounded Continual Learning Approach With Memory

arXiv:2608. 09184v1 Announce Type: new Abstract: Large language model (LLM) agents provide a promising interface for command-line-based network operations, but a plausible command may still fail or introduce operational risk after execution.

By Yuxuan Chen, Rongpeng Li, Zhifeng Zhao, Yuntao Liu, Xing Xu, Honggang Zhang

llms rag agents fine-tuning

arXiv AI

Aug 11

P$^{3}$: Joint Program-and-Proof Planning for Verified Code Generation

arXiv:2608. 09277v1 Announce Type: new Abstract: Verified code generation asks a large language model (LLM) to generate both an executable program and a machine-checkable proof that the program meets a formal specification, promising software that is correct by construction.

By Zenan Li, Ziran Yang, Peiyang Song, Zhaoyu Li, Kaiyu Yang

llms agents benchmarks

arXiv AI

Aug 11

Open-World Hierarchical Perception: Taxonomic Abstraction over Class-Agnostic Proposals for the Safe Handling of Out-of-Vocabulary Road Objects

arXiv:2608. 07577v1 Announce Type: cross Abstract: A closed-set detector for autonomous driving must assign every object one of a fixed set of labels.

By Felix Schaller

agents computer-vision robotics benchmarks safety

arXiv AI

Aug 11

The Politician, the Liar, and the Obedient Worker: Emerging Behavior of LLM Agents in Hierarchical Games

arXiv:2608. 09574v1 Announce Type: new Abstract: LLMs are rapidly embedding themselves into daily life: drafting our emails, managing our schedules, and making decisions on our behalf.

By Fatemeh Seyedin, Adrian Weller, Jinhyuk Yun, Mahmoudreza Babaei

llms rag agents

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