3 papers
cs.CL2026
Quantifying Error Tolerance in Synthetic Data: An Atomic-level Operand vs. Operator Perturbation Study
Jiaxiang Liu, Chenhao Yuan, Shuwen Xu +9
Synthetic data generation has become a cornerstone for advancing large language models. However, the lack of the quantitative analysis for error tolerance became a critical bottlen…
cs.CL2026
Beyond Factual Knowledge: Benchmarking and Learning Step-Level Procedural Rule Reasoning in Large Language Models
Bohan Yu, Pengfei Cao, Chen Han +7
Large language models (LLMs) excel at text understanding and generation, yet still struggle to reliably understand and apply externally provided procedural rules at scale. To evalu…
cs.AI2026
Learning How to Remember: A Meta-Cognitive Management Method for Structured and Transferable Agent Memory
Sirui Liang, Pengfei Cao, Jian Zhao +4
Large language model (LLM) agents increasingly rely on accumulated memory to solve long-horizon decision-making tasks. However, most existing approaches store memory in fixed repre…