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The paper introduces LatentRec, a framework that replaces inference-time chain-of-thought reasoning in large language model (LLM) recommender systems with training-time per-step supervision. It employs a 2.5 M-parameter Continuous Thought Module, Soft Token Selection, and a Per-step Reasoning Loss to guide intermediate hidden states toward the target semantic ID. After training, the module can be removed, yielding a 4–5 % relative improvement in HR@10 on Amazon benchmarks while reducing inference latency by 18.3×.
arXiv:2607.26621v3 Announce Type: replace-cross Abstract: Large language models (LLMs) have demonstrated strong reasoning capabilities, motivating their use as the backbone of foundation recommendati...
arXiv:2607. 26621v2 Announce Type: cross Abstract: Large language models (LLMs) have demonstrated strong reasoning capabilities, motivating their adoption as backbones for foundation recommendation models (FRMs).
arXiv:2607. 16972v1 Announce Type: new Abstract: Continuous Chain-of-Thought methods replace verbose reasoning traces with a short sequence of dense latent representations.
arXiv:2606. 14142v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly adopted as backbones for Generative Recommendation (GR), promising access to pretrained world knowledge.
arXiv:2608. 10447v1 Announce Type: cross Abstract: Large language model-based recommender systems are increasingly adopting slow-thinking models that generate step-by-step reasoning before making predictions, often achieving higher accuracy than fast-thinking models that predict directly.