From Proof to Program: Characterizing Tool-Induced Reasoning Hallucinations in Large Language Models (2026.acl-long)
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| Challenge: | Tool-augmented Language Models can invoke external tools to solve problems beyond their parametric capacity. |
| Approach: | They propose a preference-optimization-based framework that realigns TaLMs to use tool outputs as assistive evidence. |
| Outcome: | The proposed framework improves accuracy and reasoning depth under tool use. |
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| Challenge: | FAIL-TaLMs contains 1,749 examples using 906 tools across 21 categories, including single- and multi-tool usage. |
| Approach: | They introduce a benchmark to examine the shortcomings of tool-augmented language models (TaLMs) that assume 'perfect' information access and tool availability. |
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The Reasoning Trap: How Enhancing LLM Reasoning Amplifies Tool Hallucination (2026.acl-long)
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| Challenge: | Recent studies suggest that strengthening reasoning often coincides with increased hallucination . however, no prior work has examined whether reasoning enhancement itself causes tool hallucinism . |
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MATHSENSEI: A Tool-Augmented Large Language Model for Mathematical Reasoning (2024.naacl-long)
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| Challenge: | TALMs have been successfully employed in question-answering benchmarks, but their efficacy on complex mathematical reasoning benchmarks are open research questions. |
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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. |
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Pushing the Limits of LLM Tool Calling via Experiential Knowledge Integration and Activation (2026.findings-acl)
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| Challenge: | Existing approaches treat tool use as a problem of prompt design, API documents specification, or supervised or unsupervised alignment. |
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| Challenge: | Large language models (LLMs) have an array of reasoning capabilities but face limitations such as error propagation and hallucination. |
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LLMs in the Imaginarium: Tool Learning through Simulated Trial and Error (2024.acl-long)
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| Challenge: | Existing work on tool-augmented LLMs focuses on the broad coverage of tools and the flexibility of adding new tools. |
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Adaptive Tool Use in Large Language Models with Meta-Cognition Trigger (2025.acl-long)
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Wenjun Li, Dexun Li, Kuicai Dong, Cong Zhang, Hao Zhang, Weiwen Liu, Yasheng Wang, Ruiming Tang, Yong Liu
| Challenge: | Existing research expands the tool arrays of large language models (LLMs), but the necessity of using these tools is often overlooked, leading to indiscriminate tool invocation. |
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Knowing When to Quit: Diagnosing and Training LLMs to Abort Futile Reasoning (2026.findings-acl)
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| Challenge: | Large language models generate costly yet semantically void reasoning on beyond-capability tasks . the dominant failure mode is specious reasoning, superficially valid outputs with subtle hallucinations . |
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TART: An Open-Source Tool-Augmented Framework for Explainable Table-based Reasoning (2025.findings-naacl)
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| Challenge: | Current Large Language Models lack ability to understand table structures and apply precise numerical reasoning. |
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