IBM Research (Chia-Hsuan Lee et al., posted to arXiv:2607.00482 on July 1) proposes DASH, answering the "over-thinking" question of reasoning LLMs at the mechanism level: which segments of self-reflection are effective, and which are idle wheel-spinning. The paper first runs an ablation: even controlling for length, the proportion of ineffective self-reflection in incorrect trajectories is still systematically higher — length is not the original sin, direction is. Step-level annotation is too expensive; DASH's key insight is that each "let me try answer X" in the reasoning chain is a natural checkpoint — comparing it to ground truth can unsupervisedly determine whether subsequent reflection is converging or drifting, and the whole trajectory is sliced into individually scorable segments, with advantages shaped by segment in the advantage function. On AIME25, DASH lifts GRPO from 45.4% to 50.8%, with behavior that takes fewer detours and self-corrects more effectively. The significance isn't just +5.4, but not introducing additional supervision — all RLVR pipelines can be integrated at low cost. Post-training credit assignment has always been stuck at the trajectory level or token level. IBM cuts credit into segment-level, providing Qwen, Claude, Gemini, DeepSeek and other long-CoT reasoning models a universal "reduce tokens + raise accuracy" engineering path.