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ARXIV:2605.09931 · TOOL-INTEGRATED REASONING · SUBMITTED 12 MAY · 20:15 UTC · FRESHNESS FRESH
ARXIV:2605.09931TOOL-INTEGRATED REASONINGSUBMITTED 12 MAY · 20:15 UTCFRESHNESS FRESHLuan Zhang · Dandan Song · Zhijing Wu · Zhengyu Chen · Chen Zhang · Yuhang Tian · +5 at arXiv
PruneTIR enhances inference-time reasoning for tool-capable LLMs by optimizing tool calls.
Opportunity summary
Pain PruneTIR enhances inference-time reasoning for tool-capable LLMs by optimizing tool calls.
Evidence 0 refs | 0 sources | 0% coverage
Blocker Evidence unverified
PruneTIR enhances inference-time reasoning for tool-capable LLMs by optimizing tool calls. Most recent studies focus on exploring various methods to equip LLMs with the ability to use tools.
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…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Tool-integrated reasoning (TIR) enables large language models (LLMs) to enhance their capabilities by interacting with external tools, such as code interpreters (CI). Code availability…
Tool-Integrated Reasoning moved forward this cycle; last verified May 2026. Public score 7.0/10. Production flags indicate code availability.
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PruneTIR enhances inference-time reasoning for tool-capable LLMs by optimizing tool calls.
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10.48550/arXiv.2605.09931PruneTIR enhances inference-time reasoning for tool-capable LLMs by optimizing tool calls.
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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Dimensions overall score 7.0
PROBLEM
PruneTIR enhances inference-time reasoning for tool-capable LLMs by optimizing tool calls. Most recent studies focus on exploring various methods to equip LLMs with the ability to use tools.
METHOD
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.
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Tool-integrated reasoning (TIR) enables large language models (LLMs) to enhance their capabilities by interacting with external tools, such as code interpreters (CI). Code availability is flagged in the p...
WHY NOW
Tool-Integrated Reasoning moved forward this cycle; last verified May 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed public claims while anchored extraction refreshes.
PruneTIR enhances inference-time reasoning for tool-capable LLMs by optimizing tool calls. Most recent studies focus on exploring various methods to equip LLMs with the ability to use tools.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Tool-integrated reasoning (TIR) enables large language models (LLMs) to enhance their capabilities by interacting with external tools, such as code interpreters (CI). Code availability is flagged in the production record; the public repository link still needs proof alignment.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Tool-Integrated Reasoning moved forward this cycle; last verified May 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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PruneTIR enhances inference-time reasoning for tool-capable LLMs by optimizing tool calls.
Segment
Tool-Integrated Reasoning
Adoption evidence
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Commercial read
7.0/10 public viability
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