Papers by Lixing Yu

2 papers
Identifying Cellular Niches in Spatial Transcriptomics: An Investigation into the Capabilities of Large Language Models (2025.acl-long)

Copied to clipboard

Challenge: Spatial transcriptomic technologies allow measuring gene expression profile and spatial information of cells in tissues simultaneously.
Approach: They propose a spatial transcriptomic approach to identify spatial niches using a zero-shot large language models by transforming spatial transcriptomics data into spatial context prompts.
Outcome: The proposed model improves performance by leveraging gene expression of neighboring cells/spots, cell type composition, tissue information, and external knowledge.
Dissecting Failure Dynamics in Large Language Model Reasoning (2026.acl-long)

Copied to clipboard

Challenge: Large Language Models achieve strong performance through extended inference-time deliberation, yet how their reasoning failures arise remains poorly understood.
Approach: They propose a framework that probes and redirects critical transitions using uncertainty signals.
Outcome: Empirical evaluations show that GUARD improves reasoning performance . GUard probes critical transitions and redirects them using uncertainty signals .

What is GenGO?

GenGO is an NLP powered publication search system. It currenctly indexes 30k+ papers from ACL Anthology, and implements multi-aspect summarization, semantic search, and more!

Information

About
Limitations