@inproceedings{zhang-etal-2026-prunetir,
title = "{P}rune{TIR}: Inference-Time Tool Call Pruning for Effective yet Efficient Tool-Integrated Reasoning",
author = "Zhang, Luan and
Song, Dandan and
Wu, Zhijing and
Chen, Zhengyu and
Zhang, Chen and
Tian, Yuhang and
Ma, Huipeng and
Li, Chenhao and
Zhou, Changzhi and
Li, Xudong and
Zhou, Ruochen and
Zhang, Shuhao",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.989/",
pages = "20889--20907",
ISBN = "979-8-89176-462-0",
abstract = "Tool-integrated reasoning (TIR) enables large language models (LLMs) to enhance their capabilities by interacting with external tools, such as code interpreters (CI). Most recent studies focus on exploring various methods to equip LLMs with the ability to use tools. However, how to further boost the reasoning ability of already tool-capable LLMs at inference time remains underexplored. Improving reasoning at inference time requires no additional training and can help LLMs better leverage tools to solve problems. We observe that, during tool-capable LLM inference, both the number and the proportion of erroneous tool calls are negatively correlated with answer correctness. Moreover, erroneous tool calls are typically resolved successfully within a few subsequent turns. If not, LLMs often struggle to resolve such errors even with many additional turns. Building on the above observations, we propose **PruneTIR**, a rather effective yet efficient framework that enhances the tool-integrated reasoning at inference time. During LLM inference, PruneTIR prunes trajectories, resamples tool calls, and suspends tool usage through three components: *Success-Triggered Pruning*, *Stuck-Triggered Pruning and Resampling*, and *Retry-Triggered Tool Suspension*. These three components enable PruneTIR to mitigate the negative impact of erroneous tool calls and prevent LLMs from getting stuck in repeated failed resolution attempts, thereby improving overall LLM performance. Extensive experimental results demonstrate the effectiveness of PruneTIR, which significantly improves Pass@1 and efficiency while reducing the working context length for tool-capable LLMs."
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<abstract>Tool-integrated reasoning (TIR) enables large language models (LLMs) to enhance their capabilities by interacting with external tools, such as code interpreters (CI). Most recent studies focus on exploring various methods to equip LLMs with the ability to use tools. However, how to further boost the reasoning ability of already tool-capable LLMs at inference time remains underexplored. Improving reasoning at inference time requires no additional training and can help LLMs better leverage tools to solve problems. We observe that, during tool-capable LLM inference, both the number and the proportion of erroneous tool calls are negatively correlated with answer correctness. Moreover, erroneous tool calls are typically resolved successfully within a few subsequent turns. If not, LLMs often struggle to resolve such errors even with many additional turns. Building on the above observations, we propose **PruneTIR**, a rather effective yet efficient framework that enhances the tool-integrated reasoning at inference time. During LLM inference, PruneTIR prunes trajectories, resamples tool calls, and suspends tool usage through three components: *Success-Triggered Pruning*, *Stuck-Triggered Pruning and Resampling*, and *Retry-Triggered Tool Suspension*. These three components enable PruneTIR to mitigate the negative impact of erroneous tool calls and prevent LLMs from getting stuck in repeated failed resolution attempts, thereby improving overall LLM performance. Extensive experimental results demonstrate the effectiveness of PruneTIR, which significantly improves Pass@1 and efficiency while reducing the working context length for tool-capable LLMs.</abstract>
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%0 Conference Proceedings
%T PruneTIR: Inference-Time Tool Call Pruning for Effective yet Efficient Tool-Integrated Reasoning
%A Zhang, Luan
%A Song, Dandan
%A Wu, Zhijing
%A Chen, Zhengyu
%A Zhang, Chen
%A Tian, Yuhang
%A Ma, Huipeng
%A Li, Chenhao
%A Zhou, Changzhi
%A Li, Xudong
%A Zhou, Ruochen
%A Zhang, Shuhao
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F zhang-etal-2026-prunetir
%X Tool-integrated reasoning (TIR) enables large language models (LLMs) to enhance their capabilities by interacting with external tools, such as code interpreters (CI). Most recent studies focus on exploring various methods to equip LLMs with the ability to use tools. However, how to further boost the reasoning ability of already tool-capable LLMs at inference time remains underexplored. Improving reasoning at inference time requires no additional training and can help LLMs better leverage tools to solve problems. We observe that, during tool-capable LLM inference, both the number and the proportion of erroneous tool calls are negatively correlated with answer correctness. Moreover, erroneous tool calls are typically resolved successfully within a few subsequent turns. If not, LLMs often struggle to resolve such errors even with many additional turns. Building on the above observations, we propose **PruneTIR**, a rather effective yet efficient framework that enhances the tool-integrated reasoning at inference time. During LLM inference, PruneTIR prunes trajectories, resamples tool calls, and suspends tool usage through three components: *Success-Triggered Pruning*, *Stuck-Triggered Pruning and Resampling*, and *Retry-Triggered Tool Suspension*. These three components enable PruneTIR to mitigate the negative impact of erroneous tool calls and prevent LLMs from getting stuck in repeated failed resolution attempts, thereby improving overall LLM performance. Extensive experimental results demonstrate the effectiveness of PruneTIR, which significantly improves Pass@1 and efficiency while reducing the working context length for tool-capable LLMs.
%U https://aclanthology.org/2026.findings-emnlp.989/
%P 20889-20907
Markdown (Informal)
[PruneTIR: Inference-Time Tool Call Pruning for Effective yet Efficient Tool-Integrated Reasoning](https://aclanthology.org/2026.findings-emnlp.989/) (Zhang et al., Findings 2026)
ACL
- Luan Zhang, Dandan Song, Zhijing Wu, Zhengyu Chen, Chen Zhang, Yuhang Tian, Huipeng Ma, Chenhao Li, Changzhi Zhou, Xudong Li, Ruochen Zhou, and Shuhao Zhang. 2026. PruneTIR: Inference-Time Tool Call Pruning for Effective yet Efficient Tool-Integrated Reasoning. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 20889–20907, Budapest, Hungary. Association for Computational Linguistics.