5 papers
Beyond Scaling Law: A Data-Efficient Distillation Framework for Reasoning
Xiaojun Wu, Xiaoguang Jiang, Huiyang Li +11
Large language models (LLMs) demonstrate remarkable reasoning capabilities in tasks such as algorithmic coding and mathematical problem-solving. Recent methods have improved reason…
TextBFGS: A Case-Based Reasoning Approach to Code Optimization via Error-Operator Retrieval
Zizheng Zhang, Yuyang Liao, Chen Chen +8
Iterative code generation with Large Language Models (LLMs) can be viewed as an optimization process guided by textual feedback. However, existing LLM self-correction methods predo…
Learning When Not to Attend Globally
Xuan Luo, Kailai Zhang, Xifeng Yan
When reading books, humans focus primarily on the current page, flipping back to recap prior context only when necessary. Similarly, we demonstrate that Large Language Models (LLMs…
Beyond Benchmarks: LLM Evaluation with an Anthropomorphic and Lifecycle-oriented Roadmap
Jun Wang, Ninglun Gu, Kailai Zhang +9
Despite their rapid advancement, large language models (LLMs) suffer from a critical disconnect between benchmark scores and real-world utility. Current evaluation remains fragment…
TN-AutoRCA: Benchmark Construction and Agentic Framework for Self-Improving Alarm-Based Root Cause Analysis in Telecommunication Networks
Keyu Wu, Qianjin Yu, Manlin Mei +4
Root Cause Analysis (RCA) in telecommunication networks is a critical task, yet it presents a formidable challenge for Artificial Intelligence (AI) due to its complex, graph-based…