---
title: "Who cites ToolACE: Winning the Points of LLM Function Calling"
description: "98 papers of the Elamur Research library cite «ToolACE: Winning the Points of LLM Function Calling», found in their bibliographies; the most…"
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---
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# Who cites «ToolACE: Winning the Points of LLM Function Calling»

98 papers of our library cite it (of the 149,439 whose bibliographies we read) · 33 with a page here, the most cited first · [← back to «ToolACE: Winning the Points of LLM Function Calling»](https://md.research.elamur.ai/free/en/papers/liu-2024-toolace-winning-points-llm-function/)

1. [**Olmo 3 arXiv:2512.13961**Team Olmo: Allyson Ettinger, Amanda Bertsch, Bailey Kuehl, et al.*2025 · 130 citations*](https://md.research.elamur.ai/free/en/papers/ettinger-2025-olmo-3/)
2. [**ToolRL: Reward is All Tool Learning Needs arXiv:2504.13958**Cheng Qian, Emre Can Acikgoz, Qi He, et al.*2025 · 94 citations*](https://md.research.elamur.ai/free/en/papers/qian-2025-toolrl-reward-all-tool-learning/)
3. [**NVIDIA Nemotron Nano 2: An Accurate and Efficient Hybrid Mamba-Transformer Reasoning Model arXiv:2508.14444**NVIDIA: Aarti Basant, Abhijit Khairnar, Abhijit Paithankar, et al.*2025 · 39 citations*](https://md.research.elamur.ai/free/en/papers/basant-2025-nvidia-nemotron-nano-2-accurate/)
4. **[APIGen-MT: Agentic Pipeline for Multi-Turn Data Generation via Simulated Agent-Human Interplay](https://md.research.elamur.ai/free/en/papers/prabhakar-2025-apigen-mt-agentic-pipeline-multi/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Akshara Prabhakar, Zuxin Liu, Ming Zhu, et al.*2025 · 36 citations*
5. **[G-Safeguard: A Topology-Guided Security Lens and Treatment on LLM-based Multi-agent Systems](https://md.research.elamur.ai/free/en/papers/wang-2025-g-safeguard-topology-guided-security/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Shilong Wang, Guibin Zhang, Miao Yu, et al.*2025 · 30 citations*
6. **[Adaptive Tool Use in Large Language Models with Meta-Cognition Trigger](https://md.research.elamur.ai/free/en/papers/li-2025-adaptive-tool-use-large-language/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Wenjun Li, Dexun Li, Kuicai Dong, et al.*2025 · 16 citations*
7. **[NESTFUL: A Benchmark for Evaluating LLMs on Nested Sequences of API Calls](https://md.research.elamur.ai/free/en/papers/basu-2025-nestful-benchmark-evaluating-llms-nested/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Kinjal Basu, Ibrahim Abdelaziz, Kiran Kate, et al.*2025 · 16 citations*
8. [**Qwen3-Coder-Next Technical Report arXiv:2603.00729**Ruisheng Cao, Mouxiang Chen, Jiawei Chen, et al.*2026 · 15 citations*](https://md.research.elamur.ai/free/en/papers/cao-2026-qwen3-coder-next-technical/)
9. **[ToolHop: A Query-Driven Benchmark for Evaluating Large Language Models in Multi-Hop Tool Use](https://md.research.elamur.ai/free/en/papers/ye-2025-toolhop-query-driven-benchmark-evaluating/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Junjie Ye, Zhengyin Du, Xuesong Yao, et al.*2025 · 15 citations*
10. **[Can a Single Model Master Both Multi-turn Conversations and Tool Use? CoALM: A Unified Conversational Agentic Language Model](https://md.research.elamur.ai/free/en/papers/acikgoz-2025-can-single-model-master-both/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Emre Can Acikgoz, Jeremiah Greer, Akul Datta, et al.*2025 · 13 citations*
11. **[CRITICTOOL: Evaluating Self-Critique Capabilities of Large Language Models in Tool-Calling Error Scenarios](https://md.research.elamur.ai/free/en/papers/huang-2025-critictool-evaluating-self-critique-capabilities/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Shiting Huang, Zhen Fang, Zehui Chen, et al.*2025 · 12 citations*
12. **[MCP-Flow: Facilitating LLM Agents to Master Real-World, Diverse and Scaling MCP Tools](https://md.research.elamur.ai/free/en/papers/wang-2026-mcp-flow-facilitating-llm-agents/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**WenHao Wang, Peizhi Niu, Zhao Xu, et al.*2026 · 7 citations*
13. **[LoopTool: Closing the Data–Training Loop for Robust LLM Tool Calls](https://md.research.elamur.ai/free/en/papers/zhang-2026-looptool-closing-data-training-loop/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Kangning Zhang, Weiwen Liu, Wenxiang Jiao, et al.*2026 · 6 citations*
14. [**rStar2-Agent: Agentic Reasoning Technical Report arXiv:2508.20722**Ning Shang, Yifei Liu, Yi Zhu, et al.*2025 · 6 citations*](https://md.research.elamur.ai/free/en/papers/shang-2025-rstar2-agent-agentic-reasoning/)
15. **[EnvScaler: Scaling Tool-Interactive Environments for LLM Agent via Programmatic Synthesis](https://md.research.elamur.ai/free/en/papers/song-2026-envscaler-scaling-tool-interactive-environments/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Xiaoshuai Song, Haofei Chang, Guanting Dong, et al.*2026 · 5 citations*
16. **[ToolScope: Enhancing LLM Agent Tool Use through Tool Merging and Context-Aware Filtering](https://md.research.elamur.ai/free/en/papers/liu-2026-toolscope-enhancing-llm-agent-tool/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Marianne Menglin Liu, Daniel Garcia, Fjona Parllaku, et al.*2026 · 4 citations*
17. **[Unlocking Implicit Experience: Synthesizing Tool-Use Trajectories from Text](https://md.research.elamur.ai/free/en/papers/xu-2026-unlocking-implicit-experience-synthesizing-tool/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zhihao Xu, Rumei Li, Jiahuan Li, et al.*2026 · 4 citations*
18. **[MT-OSC: Path for LLMs that Get Lost in Multi-Turn Conversation](https://md.research.elamur.ai/free/en/papers/singh-2026-mt-osc-path-llms-that/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Jyotika Singh, Fang Tu, Miguel Ballesteros, et al.*2026 · 3 citations*
19. **[Failure makes the agent stronger: Enhancing Accuracy through Structured Reflection for Reliable Tool Interactions](https://md.research.elamur.ai/free/en/papers/su-2026-failure-makes-agent-stronger-enhancing/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Junhao Su, Yuanliang Wan, Junwei Yang, et al.*2026 · 2 citations*
20. **[Tool Calling for Arabic LLMs: Data Strategies and Instruction Tuning](https://md.research.elamur.ai/free/en/papers/ersoy-2025-tool-calling-arabic-llms-data/) [Arabic Natural Language Processing Conference (ArabicNLP)](https://md.research.elamur.ai/free/en/venues/arabicnlp/)**Asım Ersoy, Enes Altinisik, Kareem Mohamed Darwish, et al.*2025 · 2 citations*
21. **[Mem2ActBench: A Benchmark for Evaluating Long-Term Memory Utilization in Task-Oriented Autonomous Agents](https://md.research.elamur.ai/free/en/papers/shen-2026-mem2actbench-benchmark-evaluating-long-term/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yiting Shen, Kun Li, Wei Zhou, et al.*2026 · 1 citation*
22. **[Robust Tool Use via Fission-GRPO: Learning to Recover from Execution Errors](https://md.research.elamur.ai/free/en/papers/zhang-2026-robust-tool-use-fission-grpo/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zhiwei Zhang, Fei Zhao, Rui Wang, et al.*2026 · 1 citation*
23. **[Agent-Dice: Disentangling Knowledge Updates via Geometric Consensus for Agent Continual Learning](https://md.research.elamur.ai/free/en/papers/wu-2026-agent-dice-disentangling-knowledge-updates/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zheng Wu, Xingyu Lou, Xinbei Ma, et al.*2026*
24. **[ARCHITECT: Uncertainty-Aware Dynamic Tool Learning via Causal Intervention for Open-World Agents](https://md.research.elamur.ai/free/en/papers/wang-2026-architect-uncertainty-aware-dynamic-tool/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zhangyi Wang, Jiexiang Xu, Bingnan Yu, et al.*2026*
25. **[Assertion-Conditioned Compliance: A Provenance-Aware Vulnerability in Multi-Turn Tool-Calling Agents](https://md.research.elamur.ai/free/en/papers/waqas-2026-assertion-conditioned-compliance-provenance-aware/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Daud Waqas, Aaryamaan Golthi, Erika Hayashida, et al.*2026*
26. **[C-World: A Computer Use Agent Environment Creator](https://md.research.elamur.ai/free/en/papers/xi-2026-c-world-computer-use-agent/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Ziqiao Xi, Shuang Liang, Qi Liu, et al.*2026*
27. **[EfficientTool: A Cost-Effective Aligning Framework for Tool-Conditioned Agents in SME Scenarios](https://md.research.elamur.ai/free/en/papers/mu-2026-efficienttool-cost-effective-aligning-framework/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yuanqi Mu, Bingfeng Pi, Defei Xia, et al.*2026*
28. **[GenesisFunc: Multi-Agent Data Generation for Accurate and Generalizable Function-Calling](https://md.research.elamur.ai/free/en/papers/xu-2026-genesisfunc-multi-agent-data-generation/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Hao-Xiang Xu, Chong Deng, Jiaqing Liu, et al.*2026*
29. **[SGA-MCTS: Decoupling Planning from Execution via Training-Free Atomic Experience Retrieval](https://md.research.elamur.ai/free/en/papers/xie-2026-sga-mcts-decoupling-planning-execution/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Xin Xie, Dongyun Xue, Wuguannan Yao, et al.*2026*
30. **[The Bitter Lesson of Diffusion Language Models for Agentic Workflows: A Comprehensive Reality Check](https://md.research.elamur.ai/free/en/papers/lu-2026-bitter-lesson-diffusion-language-models/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Qingyu Lu, Liang Ding, Kanjian Zhang, et al.*2026*
31. **[ToolCPT: Improving Tool Utilization in LLM Agents via Continuous Pre-training](https://md.research.elamur.ai/free/en/papers/yang-2026-toolcpt-improving-tool-utilization-llm/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yifan Yang, Jinghui Lu, Evadeng, et al.*2026*
32. **[Training Verifier to Assessing Complex Real-World Tool-Use Trajectories](https://md.research.elamur.ai/free/en/papers/sun-2026-training-verifier-assessing-complex-real/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Linzhuang Sun, Mingyang Chen, Hao Liang, et al.*2026*
33. [**Survey of Tool Use in Large Language Models (大语言模型的工具使用综述) Acta Automatica Sinica (自动化学报)**Yining Zheng (郑逸宁), Zhen Yu (余镇), Bufan Li (李不凡), et al.*2025*](https://md.research.elamur.ai/free/en/papers/zheng-2025-tool-use-large/)

65 more papers of our library cite it; they have no page here yet.

- [← back to](https://md.research.elamur.ai/free/en/papers/liu-2024-toolace-winning-points-llm-function/) — ToolACE: Winning the Points of LLM Function Calling