Wentao Zhang, Xiaoyu Zhang, Chengwei Liu, Yutao Hu, Tao Zhang, Junsheng Wu, Hongping Gan, Yang Liu
原文摘要
When people face a hard problem, they often restate it in a clearer way before solving it. We study whether the same habit helps large language models by rewriting the statement of a problem. A legal rewrite must keep the meaning, the constraints, and the correct answer unchanged. We test three kinds of rewrites. Decomposition makes hidden subgoals explicit. Typed Reformulation casts the task into a more suitable solving frame. Concept Reduction removes surface detail and exposes the task structure. We evaluate them on eight math, logic, and code benchmarks with eight models, all under the same chain-of-thought solving prompt. The effect is large but depends on the problem type. An oracle that picks the best rewrite after the fact raises accuracy by +27.6% on average. Yet no single rewrite helps every problem, and a mismatched rewrite can hurt. Still, the effect follows clear patterns. Each kind of rewrite fits certain problem types. A simple fixed rule learned from one set of problems transfers part of the gain to new problems. It gains +11.0% on the problems it chooses to rewrite, while blind rewriting loses 5.6% on a similar share of problems. Rewriting is therefore not a universal way to solve problems. The question is when and how to rewrite.
A cooperative detector can lose a correct vehicle detection when surrounding LiDAR measurements change, even if the returns on the vehicle remain unchanged. We study this failure as Weather-Induced Context Interference. On our CoInfra development set, controlled removal outside annotated object boxes turns 22.1% of V2X-ViT's previously correct detections into misses. Restoring network features around the objects to their values before removal recovers 98.6% of these misses. The same restoration in each of three equally sized regions excluding the objects recovers none. Candidate analysis finds accurate boxes that are not selected, while other targets lack well-localized candidates. With paired reference features unavailable at inference, Adverse-Weather Cooperative Repair (AWCR) repairs candidates from current observations. A lightweight Local Correction head generates bounded alternatives, and Quality Selection ranks them alongside original boxes with a correction weight. In V2X-R simulation, AWCR also filters peer returns before detection using Point Classifier trained on generated-point labels. Candidate repair improves mean car BEV AP@0.7 by 4.22 points across three fits on natural adverse-weather CoInfra development inputs and vehicle AP@0.7 by 5.00 points on a rain-affected public recording. It also improves released HEAL, FadeLead, and V2X-DGW detectors with their pretrained weights fixed. On the full V2X-Radar-C validation benchmark, HEAL with AWCR achieves state-of-the-art LiDAR-only cooperative detection performance of 68.22 vehicle BEV AP@0.7.