Yongshi Ye — accepted AI-conference papers

2026

UMEM: Unified Memory Extraction and Management Framework for Generalizable Memory

Yongshi Ye, Hui Jiang, Feihu Jiang, Tian Lan, Yichao Du, Biao Fu +4

ICML 2026poster

Self-evolving memory serves as the trainable parameters for Large Language Models (LLMs)-based agents, where extraction (distilling insights from experience) and management (updating the memory bank) must be tightly coordinated. Existing methods predominately optimize memory management while treatin…

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2025

From Neurons to Semantics: Evaluating Cross-Linguistic Alignment Capabilities of Large Language Models via Neurons Alignment

Chongxuan Huang, Yongshi Ye, Biao Fu, Qifeng Su, Xiaodong Shi

ACL 2025long

Large language models (LLMs) have demonstrated remarkable multilingual capabilities, however, how to evaluate cross-lingual alignment remains underexplored. Existing alignment benchmarks primarily focus on sentence embeddings, but prior research has shown that neural models tend to induce a non-smoo…

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