13 papers
SDAM: Structure-Difference-Aware Memory Evolution for Complex Text-to-SQL
Keyan Xu, Dingzirui Wang, Xuanliang Zhang +2
Text-to-SQL aims to convert natural language questions into executable SQL queries. While memory-based agent system improves complex SQL generation, existing memory design neglect…
CurateEvo: Data-Curation Evolving for Agentic Post-Training
Dingzirui Wang, Xuanliang Zhang, Keyan Xu +2
Large language model (LLM) agents require post-training methods that can improve long-horizon decision making from environment feedback. However, existing agentic post-training pip…
Scaling Laws for Agent Harnesses via Effective Feedback Compute
Xuanliang Zhang, Dingzirui Wang, Keyan Xu +2
Agent harnesses shape language-model performance by controlling tool use, feedback, verification, memory, and repair. Yet raw test-time expenditure, such as tokens, tool calls, wal…
AlignEvoSkill: Towards Knowledge-Aware and Task-Aligned Agent Skill Evolution
Dingzirui Wang, Xuanliang Zhang, Keyan Xu +3
Reusable skills play a key role in improving LLM-based agents, but existing skill-evolution methods often fail to ensure that evolved skills both cover the knowledge required by th…
How Do Language Models Understand Tables? A Mechanistic Analysis of Cell Location
Xuanliang Zhang, Dingzirui Wang, Keyan Xu +2
While Large Language Models (LLMs) are increasingly deployed for table-related tasks, the internal mechanisms enabling them to process linearized two-dimensional structured tables…
When Does Context Help? Error Dynamics of Contextual Information in Large Language Models
Dingzirui Wang, Xuanliang Zhang, Keyan Xu +3
Contextual information at inference time, such as demonstrations, retrieved knowledge, or interaction history, can substantially improve large language models (LLMs) without parame…