---
title: "Adaptive Tool Use in Large Language Models with"
description: "The authors, from Huawei Noah's Ark Lab, propose MeCo (Meta-Cognition-oriented trigger), which… 48 references, cited by 9 papers here."
authors: ["Wenjun Li", "Dexun Li", "Kuicai Dong", "Cong Zhang", "Hao Zhang", "Weiwen Liu", "Yasheng Wang", "Ruiming Tang", "Yong Liu"]
year: 2025
venue: "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)"
source: "https://aclanthology.org/2025.acl-long.655/"
doi: "10.18653/v1/2025.acl-long.655"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/li-2025-adaptive-tool-use-large-language/"
---
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# Adaptive Tool Use in Large Language Models with Meta-Cognition Trigger

**Authors:** [Wenjun Li](https://md.research.elamur.ai/free/en/authors/wenjun-li/), [Dexun Li](https://md.research.elamur.ai/free/en/authors/dexun-li/), [Kuicai Dong](https://md.research.elamur.ai/free/en/authors/kuicai-dong/), [Cong Zhang](https://md.research.elamur.ai/free/en/authors/cong-zhang/), [Hao Zhang](https://md.research.elamur.ai/free/en/authors/hao-zhang/), [Weiwen Liu](https://md.research.elamur.ai/free/en/authors/weiwen-liu/), [Yasheng Wang](https://md.research.elamur.ai/free/en/authors/yasheng-wang/), [Ruiming Tang](https://md.research.elamur.ai/free/en/authors/ruiming-tang/) and [Yong Liu](https://md.research.elamur.ai/free/en/authors/yong-liu/) · **Year:** 2025 · **Venue:** [Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)](https://md.research.elamur.ai/free/en/venues/acl/) ([ACL 2025](https://md.research.elamur.ai/free/en/venues/acl/2025/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2025.acl-long.655/) · [PDF](https://aclanthology.org/2025.acl-long.655.pdf) · [DOI 10.18653/v1/2025.acl-long.655](https://doi.org/10.18653/v1/2025.acl-long.655) · [OpenAlex](https://openalex.org/W4412945277)

**Citations:** 7 citations (OpenAlex, 2026-10-14) · [9](https://md.research.elamur.ai/free/en/papers/li-2025-adaptive-tool-use-large-language/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
LLMs equipped with tools often call them even when they could answer from internal knowledge. This adds latency and exposes the model to failures from external APIs. The authors, from Huawei Noah's Ark Lab, propose MeCo (Meta-Cognition-oriented trigger), which decides whether a tool or retrieval call is actually needed. Building on Representation Engineering (RepE), they generate tool-related queries and Yes/No answers with proprietary LLMs. From contrastive prompt pairs they derive a linear "meta-cognition" probe, the first PCA component of the representation differences. The probe scores the hidden state of the model's first response token. Two thresholds tuned on validation data then decide whether to keep or override the model's own Yes/No choice. MeCo requires no fine-tuning and adds little cost. The authors also release a new benchmark, MeCa, covering tool-use and RAG decisions. On MetaTool and MeCa, MeCo improves decision accuracy over naive prompting and a P(Yes) baseline, both before and after SFT, and it transfers across benchmarks. This makes it a cheap, practical add-on for agent pipelines.

## Key points
- The meta-cognition probe separates positive and negative examples far better than existing honesty and confidence probes. A single probe is chosen from layers −5 to −2, rather than averaging the per-layer probes (Llama-3-8B has 32).
- The strongest decision signal comes from the first token under the "Yes/No + explanation" prompting format.
- The MeCa benchmark has two parts: MeCa-Tool (six tasks, evaluated with and without context) and MeCa-RAG, which frames adaptive RAG as one particular kind of tool-calling decision.
- On MeCa-RAG, decision accuracy improved as follows:

  | Model | Naive | P(Yes) | MeCo |
  |---|---|---|---|
  | LM3-8B | 63.0% | 75.0% | 76.0% |
  | Mist-7B | 84.0% | 84.0% | 86.0% |

  GPT-4-Turbo with naive prompting reached 84.0%.
- Fine-tuned models lost accuracy on out-of-distribution MeCa-Tool tasks, while MeCo's gains held up. The evaluation covers only the decision to call a tool, not end-to-end task completion.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

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## Cite

Cite as: Wenjun Li, Dexun Li, Kuicai Dong, Cong Zhang, Hao Zhang, Weiwen Liu, Yasheng Wang, Ruiming Tang and Yong Liu. 2025. Adaptive Tool Use in Large Language Models with Meta-Cognition Trigger. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{li2025adaptive,
  title = {{Adaptive Tool Use in Large Language Models with Meta-Cognition Trigger}},
  author = {Wenjun Li, Dexun Li, Kuicai Dong, Cong Zhang, Hao Zhang, Weiwen Liu, Yasheng Wang, Ruiming Tang and Yong Liu},
  booktitle = {Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2025},
  doi = {10.18653/v1/2025.acl-long.655},
  url = {https://doi.org/10.18653/v1/2025.acl-long.655}
}
```

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*Updated 2 November 2026*