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A Divide-And-Conquer Approach for Multi-label Multi-hop Relation Detection in Knowledge Base QA

Published in Findings of EMNLP 2021

Divide-and-conquer strategy for multi-label, multi-hop relation detection in knowledge-base question answering.

Recommended citation: Deyu Zhou, Yanzheng Xiang, Linhai Zhang, Chenchen Ye, Qian-Wen Zhang, Yunbo Cao. 2021. "A Divide-And-Conquer Approach for Multi-label Multi-hop Relation Detection in Knowledge Base QA." In Findings of EMNLP 2021.
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G3R: A Graph-Guided Generate-and-Rerank Framework for Cross-domain Text-to-SQL Generation

Published in Findings of ACL 2023

A graph-guided generate-and-rerank framework that advances cross-domain text-to-SQL generation.

Recommended citation: Yanzheng Xiang, Qian-Wen Zhang, Xu Zhang, Zejie Liu, Yunbo Cao, Deyu Zhou. 2023. "G3R: A Graph-Guided Generate-and-Rerank Framework for Cross-domain Text-to-SQL Generation." In Findings of ACL 2023.
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Addressing Order Sensitivity of In-Context Demonstration Examples in Causal Language Models

Published in Findings of ACL 2024

Mitigating sensitivity to demonstration order in in-context learning with causal language models.

Recommended citation: Yanzheng Xiang, Hanqi Yan, Lin Gui, Yulan He. 2024. "Addressing Order Sensitivity of In-Context Demonstration Examples in Causal Language Models." In Findings of ACL 2024.
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Encourage or Inhibit Monosemanticity? Revisit Monosemanticity from a Feature Decorrelation Perspective

Published in EMNLP 2024

Analyzing how feature decorrelation influences monosemanticity in language models.

Recommended citation: Hanqi Yan, Yanzheng Xiang, Guangyi Chen, Yifei Wang, Lin Gui, Yulan He. 2024. "Encourage or Inhibit Monosemanticity? Revisit Monosemanticity from a Feature Decorrelation Perspective." In EMNLP 2024.
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The Mystery of In-Context Learning: A Comprehensive Survey on Interpretation and Analysis

Published in EMNLP 2024

Surveying interpretability and analysis techniques for understanding in-context learning.

Recommended citation: Yuxiang Zhou, Jiazheng Li, Yanzheng Xiang, Hanqi Yan, Lin Gui, Yulan He. 2024. "The Mystery of In-Context Learning: A Comprehensive Survey on Interpretation and Analysis." In EMNLP 2024.
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SciReplicate-Bench: Benchmarking LLMs in Agent-driven Algorithmic Reproduction from Research Papers

Published in COLM 2025

Benchmarking agentic LLM systems on reproducing algorithms described in research papers.

Recommended citation: Yanzheng Xiang, Hanqi Yan, Shuyin Ouyang, Lin Gui, Yulan He. 2025. "SciReplicate-Bench: Benchmarking LLMs in Agent-driven Algorithmic Reproduction from Research Papers." In COLM 2025.
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PECAN: LLM-Guided Dynamic Progress Control with Attention-Guided Hierarchical Weighted Graph for Long-Document QA

Published in Findings of ACL 2025

Dynamic progress control with hierarchical graph reasoning for long-document question answering.

Recommended citation: Xinyu Wang, Yanzheng Xiang, Lin Gui, Yulan He. 2025. "PECAN: LLM-Guided Dynamic Progress Control with Attention-Guided Hierarchical Weighted Graph for Long-Document QA." In Findings of ACL 2025.
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Stop the Flip-Flop: Context-Preserving Verification for Fast Revocable Diffusion Decoding

Published in ICML 2026

COVER accelerates revocable diffusion decoding while preserving context and generation quality.

Recommended citation: Yanzheng Xiang, Lan Wei, Yizhen Yao, Qinglin Zhu, Hanqi Yan, Chen Jin, Philip Alexander Teare, Dandan Zhang, Lin Gui, Amrutha Saseendran, Yulan He. 2026. "Stop the Flip-Flop: Context-Preserving Verification for Fast Revocable Diffusion Decoding." In ICML 2026.
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Muse Glimmer 30B: An On-Device Agentic Model

Published in Meta Superintelligence Labs Technical Report, 2026

A 30B multimodal model for local autonomous agents, combining multi-step reasoning, tool use, and failure recovery. I am listed as a core contributor in Section 11.1.

Recommended citation: Muse Glimmer team. 2026. "Muse Glimmer 30B: An On-Device Agentic Model." Meta Superintelligence Labs. Technical report.
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Follow the Latent Roadmap: Navigating Revocable Decoding for Diffusion LLMs with Anchor Tokens

Published in EMNLP 2026 (Oral)

Anchor-supervised revocable decoding uses reliable tokens to guide generation and verify uncertain tokens in diffusion LLMs.

Recommended citation: Yizhen Yao, Qinglin Zhu, Runcong Zhao, Xiangxiang Dai, Yanzheng Xiang, Yulan He, Lin Gui. 2026. "Follow the Latent Roadmap: Navigating Revocable Decoding for Diffusion LLMs with Anchor Tokens." In EMNLP 2026 (Oral).
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Latent Refinement Decoding: Enhancing Diffusion-Based Language Models by Refining Belief States

Published in NeurIPS 2026

A decoding framework that refines token beliefs and feeds predictions back to improve diffusion language model generation.

Recommended citation: Qinglin Zhu, Yizhen Yao, Runcong Zhao, Yanzheng Xiang, Amrutha Saseendran, Chen Jin, Philip Teare, Bin Liang, Yulan He, Lin Gui. 2026. "Latent Refinement Decoding: Enhancing Diffusion-Based Language Models by Refining Belief States." In NeurIPS 2026.
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Towards Automatic Code Reproduction for Scientific Papers: Benchmarks and Methodologies

Invited Talk, Meta, LLaMA Community Meet-up, London, United Kingdom, June 04, 2025

I presented our latest work on SciReplicate-Bench and shared methodologies for building agentic LLM systems that can reliably reproduce code from scientific publications. The talk covered benchmarking strategies, memory management, and tooling considerations for research automation.

Discrete Mathematics

Course teaching, Southeast University, 2021

Course teaching at Southeast University in 2021.

Computer Programming for Data Scientists

Course teaching, King's College London, 2024

Course teaching at King’s College London in 2024.