Model Merging Framework Shortens LLM Recommender Reasoning Traces by 24%
_reachsumit · x · 2026-08-12
The paper proposes a model-merging framework for LLM-based recommender systems. By performing fine-grained merging at the attention head level between slow-thinking and fast-thinking models, it reduces verbose reasoning traces by up to 24% while preserving accuracy, effectively lowering inference costs.
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