Author

Meng-Chen Fan

h-index 4 View on Semantic Scholar

We have 4 of 11 papers

We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.

Not the right person? Other researchers publish under this name.

Preprint Oct 2026

MedSTP: Preserving Subspace and Transition Evidence for Medical VQA Token Pruning

Medical vision-language models (VLMs) for visual question answering (VQA) often pass long visual-token sequences to the language model, making visual-token pruning attractive but risky for medical images, where answers may depend on localized boundary and texture evidence. We study training free pre-decoding pruning an...

Meng-Chen Fan, Qi-Zhen Lan, Xi Xiao et al. · 0 citations

#artificial intelligence Preprint Oct 2026

BrainTRACE: Tracing Longitudinal, Multimodal, and Volumetric Evidence in Brain MRI Clinical Reasoning

Brain MRI interpretation is a longitudinal clinical reasoning problem: radiologists compare serial studies, integrate information across MRI sequences, localize findings within volumetric anatomy, and translate this evidence into report-grounded assessments. Existing medical VQA and 3D imaging benchmarks capture import...

Qi-Zhen Lan, Meng-Chen Fan, Hang Zhang et al. · 2 citations

Preprint Aug 2026

Trust Is Not Enough: Influence Calibration for On-Policy Self-Distillation in Agentic RL

Influence Calibration for Self-Distillation (ICSD) improves all matched aggregate metrics over trust-only allocation under Group Relative Policy Optimization (GRPO) and Group-in-Group Policy Optimization (GiGPO) and raises cosine compatibility with the RL gradient.

Qi-Zhen Lan, Xi Xiao, Xiang-Chen Guan et al. · 2 citations

#machine learning Preprint Aug 2026

CRAD: Class-wise Reliability-Aware Distillation for Decentralized Heterogeneous Federated Learning

This work proposes Class-wise Reliability-Aware Distillation (CRAD), which, per class, first discards teachers that disagree with the peer consensus and then takes a weighted average of the rest, weighting each teacher by its per-class reliability (precision, or inverse variance).

Baraa Bilbeisi, Meng-Chen Fan, Bao-Cheng Geng et al. · 1 citation