3 citations · 4 across the 9 of their papers we have counts for
6 papers · 1 filter
Does a Language Server Save Tokens for Coding Agents? A Measurement Methodology and Preliminary Study
Pengcheng Xu
Coding agents spend most of their context budget on retrieval. Lexical retrieval (grep) is universal, instant, and zero-setup, but noisy: it cannot tell a definition from a call fr…
Thought-Retriever: Don't Just Retrieve Raw Data, Retrieve Thoughts for Memory-Augmented Agentic Systems
Tao Feng, Pengrui Han, Guanyu Lin +2
Large language models (LLMs) have transformed AI research thanks to their powerful internal capabilities and knowledge. However, existing LLMs still fail to effectively incorporate…
Rethinking the Reranker: Boundary-Aware Evidence Selection for Robust Retrieval-Augmented Generation
Jiashuo Sun, Pengcheng Jiang, Saizhuo Wang +13
Retrieval-Augmented Generation (RAG) systems remain brittle under realistic retrieval noise, even when the required evidence appears in the top-K results. A key reason is that retr…
AcademicEval: Live Long-Context LLM Benchmark
Haozhen Zhang, Tao Feng, Pengrui Han +1
Large Language Models (LLMs) have recently achieved remarkable performance in long-context understanding. However, current long-context LLM benchmarks are limited by rigid context…
In-Context Learning May Not Elicit Trustworthy Reasoning: A-Not-B Errors in Pretrained Language Models
Pengrui Han, Peiyang Song, Haofei Yu +1
Recent advancements in artificial intelligence have led to the creation of highly capable large language models (LLMs) that can perform tasks in a human-like manner. However, LLMs…
Paper Copilot: A Self-Evolving and Efficient LLM System for Personalized Academic Assistance
Guanyu Lin, Tao Feng, Pengrui Han +2
As scientific research proliferates, researchers face the daunting task of navigating and reading vast amounts of literature. Existing solutions, such as document QA, fail to provi…