This 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.

This paper presents our system developed for SemEval-2026 Task 12: Abductive Event Reasoning (AER). The shared task aims at identifying the most plausible cause of a real-world event from multiple-choice options, given retrieved documents as evidence. In this work, we propose using hybrid retrieval that combines BM25 keyword matching with dense semantic search to capture explicit causal keywords. Moreover, we apply extended LoRA fine-tuning that trains both attention and MLP layers of a 32-billion parameter language model with only 0.81% trainable parameters. For final refinement, we perform development set fine-tuning to leverage validation data before inference. We achieve a tie for fifth place in the shared task: our system achieves a score of 0.90 on the official test set evaluation, ranking tied for fifth among participating teams and representing a +0.27 improvement over our baseline.

This paper describes JHARNA-MT, our system for the MMLoSo 2025 Shared Task on translation between high-resource languages (Hindi, English) and four low-resource Indic tribal languages: Bhili, Gondi, Mundari, and Santali. The task poses significant challenges, including data sparsity, morphological richness, and structural divergence across language pairs. To address these, we propose a hybrid translation pipeline that integrates non-parametric retrieval, lexical statistical machine translation (SMT), and LoRA-tuned NLLB-200 neural machine translation under a unified Minimum Bayes Risk (MBR) decoding framework. Exact and fuzzy retrieval exploit redundancy in government and administrative texts, SMT with diagonal alignment priors and back-translation provides lexically faithful hypotheses, and the NLLB-LoRA component contributes fluent neural candidates. MBR decoding selects consensus translations using a metric-matched utility based on a weighted combination of BLEU and chrF, mitigating the complementary error modes of SMT and NMT. Our final system, further enhanced with script-aware digit normalization and entity-preserving post-processing, achieves a private leaderboard score of 186.37 and ranks 2nd overall in the shared task, with ablation studies confirming the contribution of each component.