arXiv AI

Speculate While You Reason: Teaching Agents to Predict Their Next Tool Call via Joint Agent-Speculator RL

arXiv:2607. 25816v1 Announce Type: new Abstract: Large language model agents often spend substantial wall-clock time waiting for tool call results.

arXiv AI

Jun 2

On Effectiveness and Efficiency of Agentic Tool-calling and RL Training

arXiv:2606. 00135v1 Announce Type: cross Abstract: Tool-calling is a central component of modern large language model (LLM) agents, equipping them with skills beyond their parametric knowledge.

By Tong Liu, Cheng Qian, Matej Cief, Yuan He, Daniele Dan, Nikolaos Aletras, Gabriella Kazai

llms agents reinforcement-learning benchmarks

arXiv AI

1d ago

Learning Agent Execution for KV-Cache Management in Agentic Serving

arXiv:2608. 14624v1 Announce Type: new Abstract: Multi-agent LLM systems have emerged as an important deployment paradigm for AI services, where each user request is decomposed into a sequence of specialized agents.

By Rui Zhang, Chaeeun Kim, Shaoting Feng, Kuntai Du, Yuhan Liu, Yi Zhong, Cheng-Wei Ching, Junchen Jiang, Liting Hu

llms agents efficiency

arXiv AI

Jul 7

SPORK: Self-Speculative Forking to Accelerate Agentic LLM Inference

arXiv:2607. 03333v1 Announce Type: cross Abstract: LLM agents are becoming a common interface for research, coding, and question answering, yet their Thought-Action-Observation loop is often serial: the model reasons, emits a tool call, then idles the GPU until the result returns.

By Huajun Bai, Weiwei Lv, Huichuan Zheng, Youyou Lu, Jiwu Shu

llms agents nlp benchmarks

arXiv Machine Learning

Jul 15

Speculate with Memory: Lossless Acceleration for LLM Agents

arXiv:2607. 12236v1 Announce Type: new Abstract: Speculative execution accelerates LLM agents by using a smaller, cheaper model to predict and pre-launch the next step while the environment is idle.

By Yu Li, Qinyuan Ye, Prafulla Kumar Choubey, Jiaxin Zhang, Chien-Sheng Wu

llms agents benchmarks

arXiv AI

Jul 7

AgentGym2: Benchmarking Large Language Model Agents in De-Idealized Real-World Environments

arXiv:2607. 05174v1 Announce Type: new Abstract: Language agents, i.

By Zhiheng Xi, Dingwen Yang, Jiaqi Liu, Jixuan Huang, Honglin Guo, Baodai Huang, Tinggang Chen, Qi Zhang, Zhonghang Lu, Chenyu Liu, Jiajun Sun, Jiazheng Zhang, Dingwei Zhu, Xin Guo, Junzhe Wang, Zhihao Zhang, Yuming Yang, Junjie Ye, Minghe Gao, Dongrui Liu, Jiaming Ji, Guohao Li, Tao Gui, Qi Zhang, Xuanjing Huang

llms agents benchmarks

arXiv AI

Jul 14

AgentAbstain: Do LLM Agents Know When Not to Act?

arXiv:2607. 10059v1 Announce Type: new Abstract: Agent systems based on large language models (LLMs) are increasingly deployed for autonomous tasks, yet existing evaluations mostly focus on task success rather than whether agents know when to abstain.

By Xun Liu, Yi Evie Zhang, Vira Kasprova, Parisa Rabbani, Pardis Sadat Zahraei, Tianyu Zhang, Ali Ebrahimpour-Boroojeny, Varun Chandrasekaran

llms agents benchmarks

arXiv AI

Jun 15

Closing the Reflection Gap: A Free Calibration Bonus for Agentic RL

arXiv:2606. 14211v1 Announce Type: new Abstract: LLMs are increasingly deployed as agents that interact with external environments and observe feedback such as execution results, error messages, and tool outputs.

By Yinglun Zhu

llms agents reinforcement-learning benchmarks data-engineering

Hugging Face Trending Papers

Jun 19

Training the Orchestrator: A Supervised Approach to End-to-End PDDL Planning with LLM Agents

Translating natural-language planning intent into verified plans is a longstanding challenge: people communicate goals in language, while classical planners require formal PDDL specifications. Recent agentic frameworks bridge this gap by orchestrating a pool of specialized repair agents inside a verifier-checked refinement loop, but the orchestrator at the centre is itself a prompted frontier LLM, paying a frontier-LLM API call at every refinement step.

llms agents fine-tuning benchmarks

arXiv Machine Learning

Jun 25

ASAP: Agent-System Co-Design for Wall-Clock-Centered Auto HPO Research for ML Experiments

arXiv:2606. 25207v1 Announce Type: new Abstract: Hyperparameter Optimization (HPO) is essential for maximizing machine learning model performance, and its core challenge is sample efficiency: finding strong configurations within a limited budget.

By Taicheng Guo, Haomin Zhuang, Kehan Guo, Yujun Zhou, Nitesh V. Chawla, Olaf Wiest, Xiangliang Zhang

llms agents safety

arXiv AI

Jun 16

EvoMemBench: Benchmarking Agent Memory from a Self-Evolving Perspective

arXiv:2605. 18421v2 Announce Type: replace-cross Abstract: Recent benchmarks for Large Language Model (LLM) agents mainly evaluate reasoning, planning, and execution.

By Yuyao Wang, Zhongjian Zhang, Mo Chi, Kaichi Yu, Yuhan Li, Miao Peng, Bing Tong, Chen Zhang, Yan Zhou, Jia Li

llms agents benchmarks

arXiv AI

Jun 16

ACCORD: Action-Conditioned Contextual Grounding for Language Agents

arXiv:2606. 16432v1 Announce Type: cross Abstract: User instructions are often underspecified because humans rely on implicit assumptions about the surrounding environment.

By Lai Jiang, Cheng Qian, Zhenhailong Wang, Pan Lu, Heng Ji, Hao Peng

llms agents robotics benchmarks

arXiv AI

Jul 7

Agent Reinforcement Learning via Pivotal-Aware Self-Feedback Retry

arXiv:2607. 03702v1 Announce Type: new Abstract: Large language model (LLM) agents have shown strong decision-making capabilities in long-horizon interactive tasks, yet they still struggle to effectively leverage failed trajectories: full retries incur high interaction costs, while experience retrieval tends to dilute critical experience signals.

By Weiyang Guo, Zesheng Shi, Longhui Zhang, Zeen Zhu, Min Zhang, Jing Li

llms agents reinforcement-learning benchmarks

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