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P$^3$Rec is proposed, a framework that jointly extracts and internalizes complementary prior and posterior preference reasoning knowledge and achieves more complete preference reasoning while preserving efficient recommendation.
Experiments show that \textsc{AtomRec} consistently outperforms state-of-the-art agentic and memory-augmented baselines, with around 8.5\% average relative improvement across metrics.
To further optimize the reasoning trajectory, HiLaR combines final recommendation feedback with layer-aware process rewards derived from the marginal target-likelihood gain of each state, and generally outperforms strong sequential, generative, and LLM-based recommendation baselines.
I Reasoning via Multi-Teacher Distillation is proposed, a novel framework that 'compiles' the reasoning abilities of large teacher LLMs into a lightweight student Small Language Model (SLM), which significantly outperforms state-of-the-art baselines in both recommendation accuracy and inference efficiency.
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