ASA: Training-Free Representation Engineering for Tool-Calling Agents
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.
698c7b53eb12ea74539168e7
Youjin Wang
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2026-02-12T13:57:24.472Z
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wangyoujin
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wangyoujin
698c7b53eb12ea74539168e8
Run Zhou
698c7b53eb12ea74539168e9
Rong Fu
698c7b53eb12ea74539168ea
Shuaishuai Cao
698c7b53eb12ea74539168eb
Hongwei Zeng
698c7b53eb12ea74539168ec
Jiaxuan Lu
698c7b53eb12ea74539168ed
Sicheng Fan
698c7b53eb12ea74539168ee
Jiaqiao Zhao
698c7b53eb12ea74539168ef
Liangming Pan
ASA: Training-Free Representation Engineering for Tool-Calling Agents
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.
A training-free method called Activation Steering Adapter corrects tool calling behavior in language models by using mid-layer activation interventions guided by a probe and router-conditioned steering vectors.
tool calling activation steering adapter mid-layer activations representation-behavior gap steering vectors probe-guided signed gate continual parameter-efficient fine-tuning distribution shift strict parsers false positive rate Qwen2.5-1.5B