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    <abstract>This paper presents a system that applies training and inference approaches for SemEval2026 Task 11 Subtask 1, which focuses on binary classification for content-independent validity reasoning in syllogistic inference. Building on fine-tuning of relatively standard language models, additional approaches were explored, including layer-wise deep supervision and in-context learning. Furthermore, models that had been previously trained on datasets related to logical reasoning were adapted to thetask through additional fine-tuning. Finally, refinement was performed at the inference stage by adjusting the softmax-based decision threshold of the selected model. The experimental results illustrate how model selection, training strategies, and threshold adjustment affect not only validity accuracy but also robustness against plausibility-driven bias, thereby contributing to improved logical integrity.</abstract>
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%0 Conference Proceedings
%T Hidetsune at SemEval-2026 Task 11: Adapting Pretrained Reasoning Models with Deep Supervision and Inference Refinement for Content-Independent Validity Classification
%A Takahashi, Hidetsune
%Y Kochmar, Ekaterina
%Y Ghosh, Debanjan
%Y North, Kai
%Y Komachi, Mamoru
%S Proceedings of the 20th International Workshop on Semantic Evaluation (2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-414-9
%F takahashi-2026-hidetsune-semeval
%X This paper presents a system that applies training and inference approaches for SemEval2026 Task 11 Subtask 1, which focuses on binary classification for content-independent validity reasoning in syllogistic inference. Building on fine-tuning of relatively standard language models, additional approaches were explored, including layer-wise deep supervision and in-context learning. Furthermore, models that had been previously trained on datasets related to logical reasoning were adapted to thetask through additional fine-tuning. Finally, refinement was performed at the inference stage by adjusting the softmax-based decision threshold of the selected model. The experimental results illustrate how model selection, training strategies, and threshold adjustment affect not only validity accuracy but also robustness against plausibility-driven bias, thereby contributing to improved logical integrity.
%U https://aclanthology.org/2026.semeval-1.28/
%P 193-199

Markdown (Informal)

[Hidetsune at SemEval-2026 Task 11: Adapting Pretrained Reasoning Models with Deep Supervision and Inference Refinement for Content-Independent Validity Classification](https://aclanthology.org/2026.semeval-1.28/) (Takahashi, SemEval 2026)

• Hidetsune at SemEval-2026 Task 11: Adapting Pretrained Reasoning Models with Deep Supervision and Inference Refinement for Content-Independent Validity Classification (Takahashi, SemEval 2026)

ACL

• Hidetsune Takahashi. 2026. Hidetsune at SemEval-2026 Task 11: Adapting Pretrained Reasoning Models with Deep Supervision and Inference Refinement for Content-Independent Validity Classification. In Proceedings of the 20th International Workshop on Semantic Evaluation (2026), pages 193–199, San Diego, California, USA. Association for Computational Linguistics.