ASA: Backbone-Training-Free Representation Engineering for Tool-Calling Agents

The paper introduces the Activation Steering Adapter (ASA), a training-free method designed to enhance tool-calling capabilities in LLM agents without requiring backbone training. ASA utilizes a router-conditioned mixture of steering vectors and a probe-guided signed gate to improve tool-use accuracy, achieving a strict tool-use F1 score increase from 0.18 to 0.50 on the MTU-Bench benchmark with the Qwen2.5-1.5B model, while significantly reducing false positives. This approach is critical for practitioners as it offers a lightweight and efficient solution to address the representation-behavior gap in LLMs, enabling more reliable domain-specific tool integration.