Papers with Tool-Integrated Reasoning
ET-Agent: Incentivizing Effective Tool-Integrated Reasoning Agent via Behavior Calibration (2026.acl-long)
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| Challenge: | Existing training frameworks for Large Language Models (LLMs) focus on answers’ accuracy, overlooking specific alignment for behavior patterns. |
| Approach: | They propose a training framework for calibrating agent’s tool-use behavior through two synergistic perspectives: self-evolving data flywheel and behavior calibration training. |
| Outcome: | The proposed framework improves the accuracy, efficiency, reasoning conciseness, and tool execution accuracy of large language models. |
Beyond Accuracy: Unveiling Inefficiency Patterns in Tool-Integrated Reasoning (2026.acl-long)
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| Challenge: | Tool-Integrated Reasoning (TIR) is a tool that can be used to solve complex tasks. |
| Approach: | They propose a hardware-aware TIR-efficiency metric that unifies internal reasoning and external tool-use costs while explicitly accounting for non-reusable KV-Cache and long-tool-response scenarios. |
| Outcome: | The proposed metric explains wall-clock latency significantly better than token-count metric in a simulated high-concurrency industrial setting. |
MatchTIR: Fine-Grained Supervision for Tool-Integrated Reasoning via Bipartite Matching (2026.acl-long)
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| Challenge: | Existing reinforcement learning methods rely on outcome- or trajectory-level rewards, assigning uniform advantages to all steps within a trajectory. |
| Approach: | They propose a framework that introduces fine-grained supervision via bipartite matching-based turn-level reward assignment and dual-level advantage estimation. |
| Outcome: | The proposed framework surpasses the majority of 8B competitors on three benchmarks. |
CriticSearch: Fine-Grained Credit Assignment for Search Agents via a Retrospective Critic (2026.findings-acl)
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| Challenge: | Existing search agent pipelines rely on sparse outcome rewards, leading to inefficient exploration and unstable training. |
| Approach: | They propose a tool-integrated reasoning framework that provides turn-level feedback via a retrospective critic mechanism. |
| Outcome: | The proposed framework outperforms baselines in multi-hop reasoning benchmarks and achieves faster convergence and training stability. |
Discovery and Reinforcement of Tool-Integrated Reasoning Chains via Rollout Trees (2026.acl-long)
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| Challenge: | Existing approaches to augment Large Language Models (LLMs) with computational capabilities have focused on short Chain-of-thought (CoT) integrating tool-use into long CoT remains underexplored due to the scarcity of training data and the challenge of integrating it without compromising the model’s intrinsic long-chain reasoning. |
| Approach: | They propose a framework that enables spontaneous tool-use during long CoT reasoning without additional human annotation. |
| Outcome: | Experiments on AIME and GPQA-Diamond show that DART significantly outperforms existing methods, successfully harmonizing tool execution with long CoT reasoning. |
Invocation Refiner: A Plug-and-Play Module for Rectifying LLM Tool Invocations (2026.findings-acl)
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| Challenge: | Large Language Models (LLMs) have shown remarkable capabilities in Tool-Integrated Reasoning (TIR) however, the practical application is often hindered by frequent errors in tool invocations, such as incorrect tool names, invalid parameters, wrong tool-call order, or malformed invocation formats. |
| Approach: | They propose a specialized post-processing module that performs independent reasoning on the input of a frozen upstream LLM and an advanced RL algorithm to improve the tool-use reliability of base LLMs. |
| Outcome: | The proposed module improves task completion rates and invocation accuracy over the raw outputs of various upstream LLMs on a diverse set of tool-use and reasoning benchmarks. |
When to Trust Tools? Adaptive Tool Trust Calibration For Tool-Integrated Math Reasoning (2026.findings-acl)
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| Challenge: | Large reasoning models have performance enhancements but still suffer from shortcomings due to limitations of the underlying language models. |
| Approach: | They propose a framework that allows the model to choose when to trust or ignore the tool results based on the confidence score of generated code blocks. |
| Outcome: | The proposed framework reduces the "Tool Ignored" issue by 4.1% to 7.5%. |
E3-TIR: Enhanced Experience Exploitation for Tool-Integrated Reasoning (2026.findings-acl)
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| Challenge: | Existing training paradigms for Large Language Models (LLMs) suffer from inefficient exploration and mode degradation due to a lack of prior guidance, while SFT-then-RL is limited by high data costs and capability plateaus caused by low-entropy collapse. |
| Approach: | They propose an Enhanced Experience Exploitation paradigm that integrates expert prefixes, expert guided, and self-exploration to improve agent training. |
| Outcome: | The proposed model achieves a 6% performance improvement over traditional paradigms on tool-use tasks while requiring less than 10% of the synthetic data. |
TInR: Exploring Tool-Internalized Reasoning in Large Language Models (2026.acl-long)
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| Challenge: | Existing methods rely on external tool documentation during reasoning, leading to tool mastery difficulty, tool size constraints, and inference inefficiency. |
| Approach: | They propose a tool-internalized reasoning framework for unified reasoning and tool usage that integrates external tools into Large Language Models (LLMs) to address these issues, they propose 'tool-internet-based' reasoning. |
| Outcome: | The proposed method achieves superior performance across in-domain and out-of-domain settings, highlighting its effectiveness and efficiency. |