uir-cis at SemEval-2026 Task 12: Mitigating Prior-Induced Hallucinations in Retrieval-Augmented Reasoning via Precision-Oriented Decoding
This paper describes a system for the SemEval-2026 Task 12 on Abductive Event Reasoning (AER). We systematically address the pervasive “over-selection” hallucination pathology in Instruction-tuned Large Language Models (LLMs), where generative models erroneously align distractor options with semantic priors rather than retrieved evidence. The proposed architecture utilizes a 32-billion-parameter foundational model within a Retrieval-Augmented Generation (RAG) pipeline. To combat the hallucination bottleneck and adapt to the strict penalty for incorrect predictions, we propose a Precision-Oriented Decoding (POD) strategy, which tightly couples low-temperature Zero-shot Chain-of-Thought (CoT) sampling with scaled marginalization (majority voting). De-ployed efficiently on consumer-grade hardware via Low-Rank Adaptation (LoRA) fine-tuning, our system achieved a highly competitive average score of 0.802 on the official test set. Through a rigorous three-stage empirical evolution and analysis of the diagnostic failure of an asymmetric logical ensemble, the experimental results demonstrate that epistemic noise suppression is strictly superior to heuristic recall compensation in abductive reasoning tasks.