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

Sep 22

Critical-State RL: Diagnosing Trainable States for Multi-Turn Tool Use

arXiv:2609.24985v1 Announce Type: new Abstract: Multi-turn tool-use failures can hinge on a single model call, yet reward variation alone does not reveal which call would benefit from training. When...

By Zixiang Chen, Wenting Zhao, Zhepeng Cen, Akshara Prabhakar, Jielin Qiu, Jianguo Zhang, Zhiwei Liu, Tulika Manoj Awalgaonkar, Liangwei Yang, Shelby Heinecke, Silvio Savarese, Huan Wang

agents reinforcement-learning benchmarks

arXiv Machine Learning

Sep 22

EAVer: Long-Form Factuality Verification as an End-to-End Agentic Policy

arXiv:2609.22223v1 Announce Type: cross Abstract: Long-form factuality verification is commonly implemented as a static decompose-search-verify pipeline, with separately prompted modules processing c...

By Kening Zheng, Aoying Zheng, Zhigang Chang, Yazhi Guo, Miaotian Guo, Qingwei Zong, Xianhai Xie, Weiqiang Jin, Chengze Li, Hanrong Zhang, Jie Yang, Wei-Chieh Huang, Lingzhe Zhang, Liancheng Fang, Xin Zou, Hanqian Li, Jiahao Huo, Yibo Yan, Zizhuang Deng, Lei Miao, Wei Guo, Haihong Tang, Bo Zheng, Philip S. Yu

llms agents reinforcement-learning benchmarks safety

arXiv Machine Learning

Sep 22

Semantics Delivery Network: Rethinking Web Retrieval Infrastructure for LLM Agents

arXiv:2609.22486v1 Announce Type: cross Abstract: Large language models (LLMs) increasingly rely on external sources when answering questions that require proprietary information or up-to-date live w...

By Peichun Hua, Yunming Xiao

llms rag agents

arXiv Machine Learning

Sep 22

Defusing Explosive Prompts: Understanding and Preventing Trigger-Based Prompt Injections in LLM Agents

arXiv:2609.22510v1 Announce Type: cross Abstract: As LLM applications integrate with external tools, they are increasingly exposed to indirect prompt injection (IPI), where adversarial instructions a...

By Justin Szczepaniak, Elad Feldman, Naum Viner, Ben Nassi

llms agents benchmarks safety

arXiv Machine Learning

Sep 22

One to More, More to One: Category-Aware Iterative Expert Training for Software Engineering Agents

arXiv:2609.23377v1 Announce Type: cross Abstract: Repository-level software engineering (SWE) comprises heterogeneous task categories, whose progress under pooled agentic reinforcement learning can b...

By Jie Zhao, Ziyu Jiang, Suhang Zheng, Minghui Shan, Xiaoxiao Xu, Lin Qu

agents reinforcement-learning fine-tuning efficiency

arXiv Machine Learning

Sep 22

Jev-Mem: System-One-Controlled Agentic Memory for Efficient AI Agents

arXiv:2609.23986v1 Announce Type: cross Abstract: Agentic memory is becoming essential for long-horizon AI agents, yet many existing systems rely on autoregressive LLMs to control how memories are or...

By Dongming Jiang, Yi Li, Bingzhe Li

llms agents

arXiv Machine Learning

Sep 22

MCP-GRANITE Benchmark: GRANularity Interface TEsting for MCP-Based LLM Agents

arXiv:2609.24161v1 Announce Type: cross Abstract: As LLM agents increasingly interact with external tools through standardized protocols such as MCP, tool-interface design becomes a critical yet unde...

By Demetris Paschalides, Moysis Symeonides, George Pallis, Marios D. Dikaiakos

llms agents benchmarks

arXiv Machine Learning

Sep 22

D-JEPA: A Decision-Aligned Latent World Model

arXiv:2609.24749v1 Announce Type: cross Abstract: Latent world models predict the consequences of actions, but accurate prediction does not guarantee that latent distance reflects which candidate wil...

By Shuaijun Liu, Chengyu Wu, Qifu Wen, Feiyang You, Chenglong Zhang, Shuyang Hao, Xi Lin, Ningxin Su

agents robotics safety

arXiv Machine Learning

Sep 22

onPanda: Efficient Annotation of On-Policy Alignment Data for LLMs and Agents via Token-Level Correction

arXiv:2609.24983v1 Announce Type: cross Abstract: We present onPanda, an interactive tool for efficiently annotating LLM alignment data and agent trajectories. onPanda adopts token-level correction a...

By Lei Yang, Mengyin Liu, Jia Wang, Hangyu Guo, Liang Zhao, Zheng Ge, Kang An, Binxing Jiao, Qi Han, Daxin Jiang, Siqi Shen, Xiangyu Zhang

llms agents fine-tuning benchmarks safety

arXiv Machine Learning

Sep 22

Memento No More: Coaching AI Agents to Master Multiple Tasks via Hints Internalization

arXiv:2502.01562v3 Announce Type: replace Abstract: As the general capabilities of artificial intelligence (AI) agents continue to evolve, their ability to learn to master multiple complex tasks thro...

By Minttu Alakuijala, Ya Gao, Georgy Ananov, Samuel Kaski, Pekka Marttinen, Alexander Ilin, Harri Valpola

llms agents nlp efficiency

arXiv Machine Learning

Sep 22

PADiff: Predictive and Adaptive Diffusion Policies for Ad Hoc Teamwork

arXiv:2511.07260v3 Announce Type: replace-cross Abstract: Ad hoc teamwork (AHT) requires agents to collaborate with previously unseen teammates, which is crucial for many real-world applications. The...

By Hohei Chan, Xinzhi Zhang, Antao Xiang, Weinan Zhang, Mengchen Zhao

agents diffusion reinforcement-learning multimodal

arXiv Machine Learning

Sep 22

TRACES: Proactive Safety Auditing for Multi-Turn LLM Agents via Trajectory-State Modeling

arXiv:2605.27690v2 Announce Type: replace-cross Abstract: LLM agents increasingly operate through multi-turn tool use and environment interaction, where safety risks often emerge from intermediate st...

By Jiaqian Li, Yanshu Li, Boxuan Zhang, Ruixiang Tang, Kuan-Hao Huang

llms agents benchmarks safety