When Does Correction Become Repair? Mechanistic Auditing of Internal Interventions in Tool-Using LLMs

Before invoking external tools, an agentic LLM must select among a K-way action space: executing a call, seeking clarification, answering directly, or declining.

While internal activation steering can alter these pre-execution decisions, conventional aggregate metrics obscure where altered states land and what collateral damage they inflict.

SAKIKO: An Auditing Framework

We present SAKIKO, an auditing framework that formalizes representation repair via directional error discovery, router-conditioned intervention, destination-resolved verification, and prospectively frozen statistical licensing.

Across seven LLMs on When2Call and MetaTool, channel-keyed interventions induce direction-specific net gains in five models;

across three sealed evaluations, none of 59 budget-matched random directions matches calibrated target gain.

Crucially, destination auditing shows that behavioral movement does not equal repair: an intervention achieving +55 net gain corrupts over half of the baseline-correct decisions it touches, and promising point estimates on Qwen3-4B and Gemma-2-9B are formally declined due to finite-sample uncertainty.

SAKIKO establishes the necessity of outcome-resolved adjudication before claiming internal repair.

Code: https://github.com/ruizheliUOA/mechanistic-tool-use-llm.