Why Thinking Hurts? Diagnosing and Rectifying the Reasoning Shift in Foundation Recommender Models

• URL: http://arxiv.org/abs/2602.16587v1
• Date: Wed, 18 Feb 2026 16:38:21 GMT
• Title: Why Thinking Hurts? Diagnosing and Rectifying the Reasoning Shift in Foundation Recommender Models
• Authors: Luankang Zhang, Yonghao Huang, Hang Lv, Mingjia Yin, Liangyue Li, Zulong Chen, Hao Wang, Enhong Chen,
• Abstract summary: We propose a training-free Inference-Time Subspace Alignment framework.<n>By compressing reasoning chains and applying bias-subtracted contrastive decoding, our approach mitigates ungrounded textual drift.<n>Experiments show this effectively calibrates inference, allowing foundation models to leverage reasoning without sacrificing ID-grounded accuracy.
• Score: 44.74420486421283
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Integrating Chain-of-Thought (CoT) reasoning into Semantic ID-based recommendation foundation models (such as OpenOneRec) often paradoxically degrades recommendation performance. We identify the root cause as textual inertia from the General Subspace, where verbose reasoning dominates inference and causes the model to neglect critical Semantic ID. To address this, we propose a training-free Inference-Time Subspace Alignment framework. By compressing reasoning chains and applying bias-subtracted contrastive decoding, our approach mitigates ungrounded textual drift. Experiments show this effectively calibrates inference, allowing foundation models to leverage reasoning without sacrificing ID-grounded accuracy.

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