1 citations · 1 across the 8 of their papers we have counts for
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ContinualSkillBench: Can LLM Agents Truly Evolve Their Capabilities?
Tianyi Guan, Yiding Wang, Haotong Yang +5
Modern agent frameworks equip large language models with external skill libraries to solve complex tasks. However, it remains unclear whether these systems can effectively evolve t…
Xetrieval: Mechanistically Explaining Dense Retrieval
Zhixin Cai, Jun Bai, Yang Liu +7
Explaining why dense retrievers assign high relevance scores remains challenging because retrieval decisions are made through opaque high-dimensional embeddings. Existing explanati…
The AI Hippocampus: How Far are We From Human Memory?
Zixia Jia, Jiaqi Li, Yipeng Kang +12
Memory plays a foundational role in augmenting the reasoning, adaptability, and contextual fidelity of modern Large Language Models and Multi-Modal LLMs. As these models transition…
ReflectEvo: Improving Meta Introspection of Small LLMs by Learning Self-Reflection
Jiaqi Li, Xinyi Dong, Yang Liu +6
We present a novel pipeline, ReflectEvo, to demonstrate that small language models (SLMs) can enhance meta introspection through reflection learning. This process iteratively gener…
RAM: Towards an Ever-Improving Memory System by Learning from Communications
Jiaqi Li, Xiaobo Wang, Wentao Ding +4
We introduce an innovative RAG-based framework with an ever-improving memory. Inspired by humans'pedagogical process, RAM utilizes recursively reasoning-based retrieval and experie…