ASA: Training-Free Representation Engineering for Tool-Calling Agents
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866917256555921408 |
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| author | Wang, Youjin Zhou, Run Fu, Rong Cao, Shuaishuai Zeng, Hongwei Lu, Jiaxuan Fan, Sicheng Zhao, Jiaqiao Pan, Liangming |
| author_facet | Wang, Youjin Zhou, Run Fu, Rong Cao, Shuaishuai Zeng, Hongwei Lu, Jiaxuan Fan, Sicheng Zhao, Jiaqiao Pan, Liangming |
| contents | Adapting LLM agents to domain-specific tool calling remains notably brittle under evolving interfaces. Prompt and schema engineering is easy to deploy but often fragile under distribution shift and strict parsers, while continual parameter-efficient fine-tuning improves reliability at the cost of training, maintenance, and potential forgetting. We identify a critical Lazy Agent failure mode where tool necessity is nearly perfectly decodable from mid-layer activations, yet the model remains conservative in entering tool mode, revealing a representation-behavior gap. We propose Activation Steering Adapter (ASA), a training-free, inference-time controller that performs a single-shot mid-layer intervention and targets tool domains via a router-conditioned mixture of steering vectors with a probe-guided signed gate to amplify true intent while suppressing spurious triggers. On MTU-Bench with Qwen2.5-1.5B, ASA improves strict tool-use F1 from 0.18 to 0.50 while reducing the false positive rate from 0.15 to 0.05, using only about 20KB of portable assets and no weight updates. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_04935 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | ASA: Training-Free Representation Engineering for Tool-Calling Agents Wang, Youjin Zhou, Run Fu, Rong Cao, Shuaishuai Zeng, Hongwei Lu, Jiaxuan Fan, Sicheng Zhao, Jiaqiao Pan, Liangming Software Engineering Artificial Intelligence Adapting LLM agents to domain-specific tool calling remains notably brittle under evolving interfaces. Prompt and schema engineering is easy to deploy but often fragile under distribution shift and strict parsers, while continual parameter-efficient fine-tuning improves reliability at the cost of training, maintenance, and potential forgetting. We identify a critical Lazy Agent failure mode where tool necessity is nearly perfectly decodable from mid-layer activations, yet the model remains conservative in entering tool mode, revealing a representation-behavior gap. We propose Activation Steering Adapter (ASA), a training-free, inference-time controller that performs a single-shot mid-layer intervention and targets tool domains via a router-conditioned mixture of steering vectors with a probe-guided signed gate to amplify true intent while suppressing spurious triggers. On MTU-Bench with Qwen2.5-1.5B, ASA improves strict tool-use F1 from 0.18 to 0.50 while reducing the false positive rate from 0.15 to 0.05, using only about 20KB of portable assets and no weight updates. |
| title | ASA: Training-Free Representation Engineering for Tool-Calling Agents |
| topic | Software Engineering Artificial Intelligence |
| url | https://arxiv.org/abs/2602.04935 |