Motivated by recursive self-improvement (RSI), we study how LLM agents can learn through external memory without updating model parameters. Extracting useful experience from a single attempt is difficult: flawed reasoning can produce success, while poor execution can obscure a sound strategy. Preserving its value over...
Yu-Tong Song, Jiang Wu, Yu-Xin Ren et al.· 0 citations
DuoLM is introduced, a framework for fast and effective personalized recommendation that integrates a compact recommendation model with an efficient serving runtime that accelerates inference through virtual token IDs for embedding access, shared history computation, and CUDA Graph replay.
Jiang Wu, Yu-Tong Song, Hong-Bo Wang et al.· 0 citations
This work introduces an approach to apply Constitutional AI training with domain-specific mental health principles for safe, domain-adapted CAI systems in computational mental health applications.
Chenhan Lyu, Yu-Tong Song, Pengfei Zhang et al.· International Conference on...· 7 citations
PsPLUG is proposed, a lightweight plug-in that learns a user-specific residual after accounting for the requested style, and better preserves user preferences while providing precise control over the balance between personalization and style adherence.
Yutong Song, Jiang Wu, Shaofan Yuan et al.· 1 citation
This work presents CARD, a hierarchical framework that achieves effective personalization through progressive refinement that first clusters users according to shared stylistic patterns and learns group-specific LoRA adapters, enabling robust generalization and strong low-resource performance.
Large language models (LLMs) have exhibited exceptional capabilities in natural language understanding and generation, image recognition, and multimodal tasks, charting a course toward artificial general intelligence and emerging as a central issue in the global technological race. This article conducts a comprehensive...
Guo-Qiang Liang, Jingqian Gong, Mengxuan Li et al.· Journal of information scien...· 0 citations
This work introduces audio multi-perspective clustering, where a model directly partitions speech recordings according to a natural-language perspective while inferring both the number of clusters and their assignments.
Wen-Jun Huang, Q. Chu, Tiger Shao et al.· 0 citations
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