Action-Contract Preservation: Detecting and Repairing Hidden Decision Drift in Quantized LLMs

A quantized checkpoint enters an action-bearing system through a controller whose score boundaries dispatch tools, invoke retrieval, abstain, or stop generation. Standard benchmark recovery does not determine whether those boundaries still implement the FP16 action policy. We formulate this deployment question as Action-Contract Preservation (ACP): a paired, family-wise test that freezes action semantics, arbitration, operating-point selection, report examples, and repair permissions, and separately checks controller preservation and externally labeled action error. A boundary–perturbation decomposition then isolates two causes of drift—reference requests near a frontier and quantization error larger than their available margin—and motivates BoundaryLock, which combines sensitivity-guided 3/4/8-bit allocation with signed-margin training. Applied to four Llama and Qwen checkpoints, ACP yields explicit qualification decisions. At 90% BFCL recall, it rejects GPTQ with threshold search on Llama-3.1-70B and Qwen-2.5-72B: false tool triggers rise from 8.2% to 11.9$\pm$0.5% and from 7.6% to 10.9$\pm$0.4%, respectively, although MMLU stays within 0.8 points of FP16. The paired audit also detects drift in coupled routing and labeled retrieval and termination endpoints. Against fully crossed QAT with threshold search—matched on data, 500 updates, parameter-weighted bit budget, and threshold rule—BoundaryLock lowers frontier crossing by 1.8–2.3 points and false tool triggers by 0.8–1.7 points across all checkpoints; it retains reported benchmark quality and 1.73–1.84$\times$ FP16 throughput. ACP therefore turns a benchmark-close replacement into an explicit action-contract decision, and its geometry supplies an effective target for low-bit repair.