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Gao-Yuan Du

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#artificial intelligence Preprint Oct 2026

RAGStress: A controlled benchmark for evaluating retrieval-augmented generation under knowledge-base degradation

Retrieval-Augmented Generation (RAG) is typically evaluated under the implicit assumption that the underlying knowledge base (KB) is clean, leaving the behaviour of RAG systems under realistic KB degradation poorly characterised. We introduce RAGStress, a controlled evaluation benchmark for stress-testing RAG systems u...

Shi-Qi Yang, Jie Ma, Gao-Yuan Du · 0 citations
#artificial intelligence Preprint Sep 2026

Greedy Decoding Is Not Precision-Invariant: Cross-Precision Output Divergence in LLM Inference

An empirical error-propagation analysis is developed and finds that 22 layers of accumulated body error do not distinguish flipping from non-flipping steps; the outcome depends primarily on the top-two logit margin at the LM head relative to the directional perturbation between the top-two candidates.

Gao-Yuan Du, A. Khan, Rex Zhou et al. · 0 citations
Open access Jul 2026

Deciphering spatial heterogeneity by multimodal spatial transcriptomics modelling with SpatialModal

Abstract Motivation Advances in spatial transcriptomics (ST) technologies have made it possible to jointly acquire gene expression and histological image information while preserving spatial coordinates. This breakthrough presents unprecedented opportunities for the precise dissection of spatial heterogeneity in comple...

Xingyi Li, Dongmin Zhao, Xiangting Jia et al. · 0 citations
Open access Jul 2026

Geometric-aware deep learning for deciphering tissue structure from spatially resolved transcriptomics.

SpatialGEO, a geometric-aware deep learning framework that integrates gene expression profiles with spatial coordinates to generate biologically meaningful low-dimensional embeddings, enabling the dissection of complex tissue architectures, achieves superior performance in tissue structure dissection and data denoising...

Xing-Yi Li, Xiang-Ting Jia, Dong-Min Zhao et al. · 1 citation
#small language model Open access Aug 2026

Evaluating teacher-supervised small medical language models for institution-constrained clinical QA.

For institution-constrained clinical QA, in-domain teacher-supervised fine-tuning was the most reliable evaluated adaptation path, and public-benchmark transfer was not a dependable proxy for local utility in the primary internal comparison.

Gao-Yuan Du, D. Dimitriadis, Xue-Ping Li · 0 citations

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