This paper describes the YNU-HPCC system for SemEval-2026 Task 12, Abductive EventReasoning (AER). Given multi-document retrieved evidence with distractors, the task requires selecting all direct-cause options for a target event and outputting an answer set. The main challenges are sparse and dispersed evidence in long documents and a boundary-sensitive set-level evaluation. This paper proposes a two-stage framework. Stage 1 trains a DeBERTa-v3-base student with retrieval-guided evidence modeling: documents are split into overlapping windows, BM25 ranks and filters candidate windows, and Top-K pooling aggregates window-level scores into option probabilities. Stage 2 distills soft targets from a Qwen-14B teacher with temperature scaling and high-confidence filtering to reduce pseudo-label noise and improve generalization. The system achieves an official dev score of 0.9712(micro-F1 0.9746, macro-F1 0.9745) and improves the test score from 0.46 to 0.73, ranking 84th out of 221 submissions.