Who Missed the Jump? Evidence-Grounded Credit Assignment for Multi-Agent Figure Skating Reasoning

Reliable video reasoning requires both a correct answer and the evidence that supports it. In figure skating, a brief takeoff or landing interval can determine a judgment, yet answer accuracy and evidence localization need not improve together. Dividing the task among cooperating agents introduces a second ambiguity: an outcome reward does not identify where evidence delivery failed or which failures need correction. Our approach makes explicit evidence requirements a common reference for evaluation, diagnosis, and training. We introduce **SkateEG**, with 7,200 video-grounded questions over 1,194 programs and 2,400 text-only controls. Its annotated records specify where to find the evidence and whether records are jointly required or sufficient alternatives. Our **EDGE** framework first diagnoses each record's earliest confirmed failure in request, acquisition, description, or use, then selects sufficient-path targets for negative task credit and localized hindsight. This selection avoids correcting unused alternatives when another path already supplies complete support. Positive task credit reinforces supported delivery, while unresolved targets receive zero task weight. On 600 independent failures, the first-break nominee matches repair-replay point rankings in 86.0% of cases, versus 62.5% for a same-information judge, without additional trajectory executions for diagnosis. Across three training seeds, the 7B system reaches 63.2% accuracy and 53.2% GroundedAcc, the proportion of test questions answered correctly with complete evidence support, exceeding MATPO by 2.9 and 8.3 percentage points, respectively. Matched negative-credit and alternative-path controls support the value of the target-selection rules. Together, these results connect explicit evidence requirements to learning from failed handoffs in a three-agent pipeline with annotated training evidence. Code and data will be released.