17 papers
InsightEmb: Learning Action-Intent Embeddings for Agentic Insight Retrieval
Tsz Ting Chung, Jiangnan Li, Jie Zhou +1
Self-improving agents accumulate reusable insights from prior trajectories, making retrieval increasingly important for turning accumulated experience into actionable guidance. At…
A New Role for Relevance: Guiding Corpus Interaction in Agentic Search
Jiangnan Li, Yuqing Li, Mo Yu +2
Relevance is a query-dependent estimate of whether a document or excerpt contains useful evidence. Existing retrieval agents use relevance to select top- content, but document r…
ReFreeKV: Towards Threshold-Free KV Cache Compression
Xuanfan Ni, Liyan Xu, Chenyang Lyu +6
To reduce memory consumption during LLM inference, a handful of methods have been proposed for KV cache pruning. While these techniques can accomplish lossless memory reduction on…
Query-focused and Memory-aware Reranker for Long Context Processing
Yuqing Li, Jiangnan Li, Mo Yu +5
Built upon the existing analysis of retrieval heads in large language models, we propose an alternative reranking framework that trains models to estimate passage-query relevance u…
How Far Ahead Do LLMs Plan? Uncovering the Latent Horizon in Chain-of-Thought Reasoning
Liyan Xu, Mo Yu, Fandong Meng +1
Chain-of-thought (CoT) reasoning has become a central mechanism for eliciting multi-step reasoning in Large Language Models (LLMs). Yet recent evidence presents a tension: hidden s…
Mindscape-Aware Retrieval Augmented Generation for Improved Long Context Understanding
Yuqing Li, Jiangnan Li, Zheng Lin +5
Humans understand long and complex texts by relying on a holistic semantic representation of the content. This global view helps organize prior knowledge, interpret new information…