Tool-Augmented Language Model Agents

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17 papers in the last four weeks, up 42% on the four weeks before. 0.2% of all new papers.

Jul 13Week of Sep 28

Latest papers 132

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TADDLE: A Tool-Augmented Agent for Detecting Deficient LLM-Generated Peer Reviews
May 26, 2026Hanqi Duan, Xiang LiTool-Augmented Language Model Agents LLM Agents
East China Normal University
Stateful Inference for Low-Latency Multi-Agent Tool Calling
May 25, 2026Victor NorgrenLLM Inference Efficiency Multi-Agent LLM Systems
LayerScale, Inc.
SPEAR: Code-Augmented Agentic Prompt Optimization
May 25, 2026Mengyin Lu, Cong Feng, Huimin Han +6Tool-Augmented Language Model Agents Automatic Prompt Optimization
LinkedIn Corporation
Retrieval as Reasoning: Self-Evolving Agent-Native Retrieval via LLM-Wiki
May 25, 2026Haoliang Ming, Feifei Li, Xiaoqing Wu +1Multi-Hop QA Retrieval-Augmented Generation
WeChat, Tencent Inc., Beijing, China
Tool-Schema Compression Enables Agentic RAG Under Constrained Context Budgets
May 24, 2026Furkan SakizliTool-Augmented Language Model Agents Agentic RAG
Independent Researcher
How Many Tools Should an LLM Agent See? A Chance-Corrected Answer
May 23, 2026Vyzantinos Repantis, Ameya Gawde, Harshvardhan Singh +1Tool-Use Evaluation Tool-Augmented Language Model Agents
Meta Platforms
ContextEcho: A Benchmark for Persona Drift in Long Agentic-Coding Sessions
May 22, 2026Xianzhong Ding, Yangyang Yu, Changwei Liu +1Tool-Augmented Language Model Agents AI Coding Agents
Center for Advanced AI, Accenture
Boiling the Frog: A Multi-Turn Benchmark for Agentic Safety
May 21, 2026Piercosma Bisconti, Matteo Prandi, Federico Pierucci +11LLM Safety Benchmarks Computer-Use Agent Benchmarks
Icaro Foundation · Sapienza University of Rome · Sant’Anna School of Advanced Studies +6
Search-E1: Self-Distillation Drives Self-Evolution in Search-Augmented Reasoning
May 21, 2026Zihan Liang, Yufei Ma, Ben Chen +4Tool-Augmented Language Model Agents On-Policy Self-Distillation
SpecHop: Continuous Speculation for Accelerating Multi-Hop Retrieval Agents
May 21, 2026Mehrdad Saberi, Keivan Rezaei, Soheil FeiziMulti-Hop QA Tool-Augmented Language Model Agents
University of Maryland, College Park
Terminal-World: Scaling Terminal-Agent Environments via Agent Skills
May 20, 2026Zihao Cheng, Hongru Wang, Zeming Liu +6Tool-Augmented Language Model Agents Terminal Agents
School of Computer Science and Engineering, Beihang University, Beijing, China · Independent Researcher · 3Beijing Institute of Technology +2
EnvFactory: Scaling Tool-Use Agents via Executable Environments Synthesis and Robust RL
May 18, 2026Minrui Xu, Zilin Wang, Mengyi DENG +12Tool-Augmented Language Model Agents Agentic RL
LARK, HKUST (GZ) · University of Cambridge · UCL +2
Firefly: Illuminating Large-Scale Verified Tool-Call Data Generation from Real APIs
May 17, 2026Yuxuan Lu, Ziyi Wang, Yingzhou Lu +12Tool-Augmented Language Model Agents Synthetic Data Generation
Northeastern University · Independent Researcher
To Call or Not to Call: Diagnosing Intrinsic Over-Calling Bias in LLM Agents
May 16, 2026Wei Shi, Ziheng Peng, Sihang Li +4Tool-Augmented Language Model Agents Causal Interventions in Language Models
Shanghai Jiao Tong University · Shanghai Artificial Intelligence Laboratory · Renmin University of China +2
R2V Agent: Teaching SLMs When to Ask for Help
May 15, 2026Raghu Vamshi Hemadri, Humaira Firdowse Mohammed, Rishabh Maheshwary +5AI Agent Reliability Tool-Augmented Language Model Agents
New York University Tandon School of Engineering · University of California, San Diego · ServiceNow Research +3
Argus: Evidence Assembly for Scalable Deep Research Agents
May 15, 2026Zhen Zhang, Liangcai Su, Zhuo Chen +7Multi-Agent LLM Systems Tool-Augmented Language Model Agents
MiroMind AI
From Text to Voice: A Reproducible and Verifiable Framework for Evaluating Tool Calling LLM Agents
May 14, 2026Md Tahmid Rahman Laskar, Xue-Yong Fu, Seyyed Saeed Sarfjoo +3Audio-Language Model Evaluation Voice Agent Evaluation
Dialpad Inc.
Latency-Quality Routing for Functionally Equivalent Tools in LLM Agents
May 14, 2026Kexin Chu, Dawei Xiang, Wei ZhangTool-Augmented Language Model Agents LLM Tool Use
University of Connecticut
WildClawBench: A Benchmark for Real-World, Long-Horizon Agent Evaluation
May 11, 2026Shuangrui Ding, Xuanlang Dai, Long Xing +14Computer-Use Agent Benchmarks Tool-Augmented Language Model Agents
1Shanghai AI Laboratory, 2The Chinese University of Hong Kong · 1Shanghai AI Laboratory, 3Fudan University · 1Shanghai AI Laboratory, 4University of Science and Technology of China +8
Rethinking Agentic Search with Pi-Serini: Is Lexical Retrieval Sufficient?
May 11, 2026Tz-Huan Hsu, Jheng-Hong Yang, Jimmy LinTool-Augmented Language Model Agents Agentic Search
University of Waterloo · Stencilzeit
LLM Agents Already Know When to Call Tools -- Even Without Reasoning
May 10, 2026Chung-En Sun, Linbo Liu, Ge Yan +2Tool-Augmented Language Model Agents AI Agent Benchmarks
University of California, San Diego
SearchSkill: Teaching LLMs to Use Search Tools with Evolving Skill Banks
May 9, 2026Jinchao Hu, Meizhi Zhong, Kehai Chen +1Tool-Augmented Language Model Agents Agentic Search
School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen · Independent Researcher Shenzhen / Beijing, China
Tool Calling is Linearly Readable and Steerable in Language Models
May 8, 2026Zekun Wu, Ze Wang, Seonglae Cho +4AI Agent Reliability Tool-Augmented Language Model Agents
University College London · Holistic AI · Imperial College London
Switchcraft: AI Model Router for Agentic Tool Calling
May 8, 2026Sharad Agarwal, Pooria Namyar, Alec Wolman +3Tool-Augmented Language Model Agents Cost-Aware Inference
Microsoft Research · Microsoft · Stanford
MIST: Multimodal Interactive Speech-based Tool-calling Conversational Assistants for Smart Homes
May 7, 2026Maximillian Chen, Xuanming Zhang, Michael Peng +3Tool-Augmented Language Model Agents Multimodal Large Language Models
Columbia University · Seoul National University
AgenticRAG: Agentic Retrieval for Enterprise Knowledge Bases
May 7, 2026Susheel Suresh, Hazel Mak, Shangpo Chou +2Tool-Augmented Language Model Agents Agentic Search
Microsoft Corporation
OpenSeeker-v2: Pushing the Limits of Search Agents with Informative and High-Difficulty Trajectories
May 5, 2026Yuwen Du, Rui Ye, Shuo Tang +4Supervised Fine-Tuning Tool-Augmented Language Model Agents
1Shanghai Jiao Tong University
TSCG: Deterministic Tool-Schema Compilation for Agentic LLM Deployments
May 4, 2026Furkan SakizliTool-Augmented Language Model Agents LLM Tool Use
Independent Researcher
FitText: Evolving Agent Tool Ecologies via Memetic Retrieval
May 4, 2026Kyle Zheng, Han Zhang, Renliang Sun +2Tool-Use Evaluation Tool-Augmented Language Model Agents
UCLA
Are Tools All We Need? Unveiling the Tool-Use Tax in LLM Agents
Apr 30, 2026Kaituo Zhang, Zhen Xiong, Mingyu Zhong +4Tool-Augmented Language Model Agents Agentic Reasoning
University of Houston · University of Southern California · 3New York University +2

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TADDLE: A Tool-Augmented Agent for Detecting Deficient LLM-Generated Peer Reviews

Hanqi Duan, Xiang Li

East China Normal University

Tool-Augmented Language Model Agents LLM Agents Automated Peer Review

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LLM-generated peer reviews are increasingly common at major venues, yet their deficiencies are hard to detect because they are uniformly fluent and well-structured. Existing work either classifies authorship without judging quality, or scores quality with features designed for human-written reviews; no prior system detects deficiencies in LLM-generated reviews at the level of individual defect types. To bridge the gap, we introduce TADDLE, a Tool-Augmented Agent for Detecting Deficient LLM-Generated Peer Reviews, together with the first expert-annotated benchmark for this task. Our benchmark comprises 1,800 reviews on 50 ICLR 2025 papers, multi-label-annotated by 18 domain experts against a taxonomy of six defect categories (plus a non-deficient label). TADDLE decomposes detection into four specialized analysis tools -- Verify, Correct, Complete, and Transform -- orchestrated by an agent; an integrator synthesizes their outputs into binary and multi-label classifications via two-stage semi-supervised learning. Extensive experiments show that TADDLE performs strongly on both binary detection and the multi-label classification task. We release the benchmark and code at https://github.com/AquariusAQ/TADDLE.

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Latest papers 132