CORA-Diff is a novel, training-free method for optimizing the inference process of diffusion language models (DLMs). Traditional decoders often use a fixed denoising horizon, yet many predictions stabilize early, but the decoder continues to run until all positions are resolved, causing repeated dense forward passes. Existing accelerators often rely on learned filters, modified scores, dependency models, or cache-specific mechanisms. CORA-Diff leverages native trajectory signals to identify residual positions likely to match the deterministic dense endpoint. This method applies confidence-and-persistence gating only to positions that the rule leaves unresolved, while accepted tokens remain visible as context, and the block terminates once all positions are resolved. This approach requires no backbone change, learned acceptance model, or logit modification. Our theory explains why high-confidence, persistent predictions are more likely to match the fixed-horizon dense endpoint, and paired post-intervention trajectories provide direct empirical support. We select one operating point on a separate GSM8K calibration subset and freeze it for all evaluations. Under a matched Learn2PD-style LLaDA protocol, CORA-Diff has the lowest measured runtime in all eight task-length settings. Task scores match or exceed dense decoding in five settings, and the largest observed drop is 1.22 points. Its incremental speedups over EOS-aware dense decoding are 2.70x and 3.32x on GSM8K and HumanEval. It also reaches 13.14x under the fixed-horizon 1024/1024 mechanism-isolation protocol and transfers to Dream without retuning at 3.18x-3.53x. Blogger's Review: The CORA-Diff algorithm reduces repeated denoising computation while preserving task quality by leveraging native trajectory signals and confidence gating, which is a significant research achievement with important reference value for improving the inference efficiency of diffusion language models.