HCMUSDroneBoys at SemEval-2026 Task 11: Asymmetric Counterfactual Debiasing and Rank-Sensitive Logical Invariance Adaptation for Syllogistic Reasoning

AbstractThis paper describes our system for SemEval-2026 Task 11, Subtask 1: binary classification of syllogistic validity in English. The main challenge is the content effect, where language models confuse formal logical validity with how plausible the argument sounds. We propose three techniques that work together to separate logical form from semantic content: (1) Structure-Disentangled Prompting (SDP), which breaks syllogisms into premise-conclusion triples and uses a logic-first instruction template; (2) Asymmetric Counterfactual Debiasing (ACD), a data augmentation method that only generates valid-to-invalid counterfactual pairs, taking advantage of an asymmetry in validity composition to avoid label noise; and (3) Rank-Sensitive Logical Invariance Adaptation (RLIA), where we find that low-rank QLoRA adapters cannot simultaneously learn classification and suppress content-correlated shortcuts, and solve this by increasing adapter rank. Built on Qwen2.5-14B-Instruct, our system achieved a perfect Combined Score of 100.0 on the SemEval-2026 Task 11 Subtask 1 benchmark.