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.
Chiyao Zhou, Zebing Wang, Kexin Deng et al.· SemEval@ACL· 1 citation
While Multimodal Large Language Models (MLLMs) have demonstrated remarkable success in 2D medical image understanding, their extension to 3D volumetric imaging remains hindered by prohibitive annotation costs and dataset opacity. Current data formats, predominantly consisting of rigid Visual Question Answering (VQA) pairs or unstructured final clinical reports, typically fail to capture explicit clinical reasoning. To address this limitation, we introduce a large-scale structured reasoning dataset constructed via a novel slice-wise data synthesis paradigm. Inspired by the genuine diagnostic workflow of radiologists, this paradigm models visual cognition by decomposing the complex 3D reading process, translating global clinical priors into fine-grained, per-slice observations that are subsequently synthesized into an interpretable Chain-of-Thought (CoT). Crucially, this synthesized reasoning framework enforces essential clinical principles: sequential spatial tracking, multi-slice spatial awareness for artifact mitigation, and differential exclusion. To validate this approach, we instruction-tune a standard 2D-pretrained MLLM baseline using the synthesized data to enhance its volumetric comprehension. Comprehensive evaluations across multiple 3D medical benchmarks demonstrate that our method yields significant performance improvements over the 2D baseline. Furthermore, the resulting model exhibits robust spatial reasoning capabilities and rivals resource-intensive native 3D architectures, effectively bridging the performance gap. Ultimately, this data-centric strategy unlocks deep volumetric understanding and highly interpretable clinical logic without requiring computationally expensive 3D-specific pre-training. The complete repository, including datasets and training workflows, is publicly available at https://github.com/2020420145009/hounsfield.
Zhuoyuan Fu, Zeshang Li, Yiqiong Zhang et al.· 0 citations