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Xiao Luo

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Book Open access Aug 2026

POLO: Preference-Guided Multi-Turn Reinforcement Learning for Sample-Efficient Lead Optimization

POLO (Preference-guided Optimization for Lead Optimization), a multi-turn Reinforcement Learning framework for sample-efficient lead optimization, which extracts learning signals from each trajectory at two complementary levels: trajectory-level policy optimization reinforces successful multi-turn strategies, while tur...

Ziqing Wang, Yibo Wen, William Pattie et al. · 0 citations
Jul 2026

Von Mises-Fisher Mixture Model with Dynamic Shrinkage for Realistic Test-Time Transduction

This work systematically revisit transduction from the perspective of penalized likelihood estimation (PLE), showing that PLE with a KL-divergence anchor term naturally yields an adaptive shrinkage behavior between prior anchors and empirical estimates.

Jiazhen Huang, Zhiming Liu, Changhu Wang et al. · 2 citations
Book Open access Aug 2026

POLO: Preference-Guided Multi-Turn Reinforcement Learning for Sample-Efficient Lead Optimization

Lead optimization in drug discovery requires iteratively refining molecular candidates while preserving structural similarity to the original compound. Since each evaluation is costly, sample efficiency, the ability to achieve strong performance with limited oracle calls, becomes critical. Existing methods, from geneti...

Ziqing Wang, Yibo Wen, William Pattie et al. · 0 citations
#machine learning Preprint Aug 2026

GTA-RAG: Graph-Trajectory-Augmented Reinforcement Learning for Multi-Turn Retrieval-Augmented Reasoning

This work presents \textsc{GTA-RAG}, a graph-trajectory-augmented RL framework for multi-turn retrieval-augmented reasoning that consistently outperforms RL-based RAG baselines with both Qwen2.5-3B and Qwen2.5-7B backbones, while substantially improving evidence-chain coverage.

Jun Chen, Yongchao Liu, Pengyu Qiu et al. · 0 citations
Book Open access Jul 2026

EviRAG: Evidence-Guided Retrieval-Augmented Generation for Medical Vision-Language Models

An evidence-guided retrieval-augmented framework EviRAG is proposed that decomposes retrieval into structured and unstructured alignment levels and substantially reduces clinical inconsistencies compared to strong medical vision-language baselines.

Yiyang Gu, Jiayue Fan, Kaili-May Liu et al. · 1 citation
Book Open access Jul 2026

When Context Bites: Detecting RAG Poisoning via Document-Level Attention Collapse

D-SCAN (Document-level Signal Collapse Analysis), a lightweight detection framework that monitors attention dynamics to identify attacked generations and proposes D-SCAN, a lightweight detection framework that can detect attacks even when they fail to alter the final answer.

Yingtao Ren, Ziyi Zhao, Yiwei Fu et al. · 0 citations

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