HiLight, an Evidence Emphasis framework that decouples evidence selection from reasoning for frozen LLM solvers, is introduced, an Evidence Emphasis framework that decouples evidence selection from reasoning for frozen LLM solvers.
Shao-Ang Li, Yanhang Shi, Yufei Li et al.· arXiv.org· 0 citations
Synthetic data can scale training supervision when real-world data are limited, but noise and distribution mismatch can reduce its value. Existing synthetic data selection methods often emphasize fidelity or diversity rather than the learner's evolving needs. We propose FROST, an online framework that estimates synthet...
Yan-Ran Wu, S. Lakdawala, Renzo Tassara Miller et al.· 0 citations
Student-Aware CoT Optimization for Recommendation Distillation (SCOReD), a CoT optimization framework tailored to recommendation that first parses each teacher trace into typed segments and uses the student LLM's attention to score the importance of each segment.
H. S. Shahgir, Yufei Li, Xiaohan Wei et al.· 0 citations
Large Language Models (LLMs) have emerged as powerful assets for recommender systems. However, deploying them as generative recommenders or zero-shot rankers at web-scale remains bottlenecked by prohibitive computational overhead and grounding challenges. In this paper, we revitalize the classic, highly efficient two-t...
A simple method, Self-Guided TTT (S-TTT), which improves accuracy for both Qwen3-4B-Thinking-2507 and Llama-3.1-8B-Instruct, achieving up to a 15% relative improvement.
Xinyu Zhu, Zhenqin Xu, Xiaohan Wei et al.· 0 citations
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