Large language models (LLMs) are increasingly deployed as automated reviewers whose numeric scores directly determine accept/reject outcomes, yet whether those scores faithfully reflect the models’ own critiques remains underexplored. We identify threshold-alignment failure: an LLM reviewer may clearly articulate serious weaknesses while still assigning scores above the decision boundary, rendering its critique inconsequential. We introduce DSAR (Dimension-Specialist Audit and Revision), a training-free protocol that decomposes evidence elicitation across dimension specialists, applies severity-coded adversarial audit, and enforces deterministic score corrections for critical findings before thresholding. On PropAlign-240 under a shared Qwen2.5-72B-Instruct backbone, six standard reviewer protocols keep most human-rejected proposals above the decision threshold, yielding false-positive rates of 89.2–100.0%. DSAR raises reject recall to 45.9% and achieves the lowest MAE (0.334), with all decision-level gains statistically significant (McNemar p < 0.008). Ablations are consistent with severity-based audit correction, rather than longer reasoning or additional agents, as the main source of the improvement. These findings argue that LLM reviewers should be evaluated not only by critique quality, but by whether critiques translate into threshold-consistent decisions.