Weizhe Lin

Weizhe Lin

Machine Learning Researcher / Ph.D. in Engineering (University of Cambridge)

Posts by Collection

publications

Detecting personal attributes through analyzing online forums

Presented at Cambridge Language Sciences Early Careers Researchers Symposium, 2019

Zhilin Wang, Xiaodong Wu, Weizhe Lin, Elena Rastorgueva.

Recommended citation: Zhilin Wang, Xiaodong Wu, Weizhe Lin, Elena Rastorgueva. (2019). Detecting personal attributes through analyzing online forums. Cambridge Language Sciences Early Careers Researchers Symposium. https://github.com/Zhilin123/Publications/blob/master/Cambridge%20Language%20Sciences%20ECR.pdf

No, you’re not alone: A better way to find people with similar experiences on Reddit

Published in EMNLP Workshop W-NUT, 2019

Zhilin Wang, Elena Rastorgueva, Weizhe Lin, Xiaodong Wu.

Recommended citation: Zhilin Wang, Elena Rastorgueva, Weizhe Lin, Xiaodong Wu. (2019). No, you’re not alone: A better way to find people with similar experiences on Reddit. Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019), 307–315. https://doi.org/10.18653/v1/D19-5540 https://aclanthology.org/D19-5540/

Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans

Published in Nature Machine Intelligence, 2021

Weizhe Lin: collaborative author. Machine learning methods offer great promise for fast and accurate detection and prognostication of coronavirus disease 2019 (COVID-19) from standard-of-care chest radiographs (CXR) and chest computed tomography (CT) images. Many articles have been published in 2020 describing new machine learning-based models for both of these tasks, but it is unclear which are of potential clinical utility. In this systematic review, we consider all published papers and preprints, for the period from 1 January 2020 to 3 October 2020, which describe new machine learning models for the diagnosis or prognosis of COVID-19 from CXR or CT images. All manuscripts uploaded to bioRxiv, medRxiv and arXiv along with all entries in EMBASE and MEDLINE in this timeframe are considered. Our search identified 2,212 studies, of which 415 were included after initial screening and, after quality screening, 62 studies were included in this systematic review. Our review finds that none of the models identified are of potential clinical use due to methodological flaws and/or underlying biases. This is a major weakness, given the urgency with which validated COVID-19 models are needed. To address this, we give many recommendations which, if followed, will solve these issues and lead to higher-quality model development and well-documented manuscripts.

Recommended citation: Michael Roberts, Derek Driggs, Matthew Thorpe, Julian Gilbey, Michael Yeung, Stephan Ursprung, Angelica I. Aviles-Rivero, Christian Etmann, Cathal McCague, Lucian Beer, Jonathan R. Weir-McCall, Zhongzhao Teng, Effrossyni Gkrania-Klotsas, James H. F. Rudd, Evis Sala, Carola-Bibiane Schönlieb. (2021). Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans. Nature Machine Intelligence, 3(3), 199–217. https://doi.org/10.1038/s42256-021-00307-0 https://www.nature.com/articles/s42256-021-00307-0

Message-Aware Graph Attention Networks for Large-Scale Multi-Robot Path Planning

Published in IEEE Robotics and Automation Letters, 2021

Qingbiao Li (equal contribution), Weizhe Lin (equal contribution), Zhe Liu, Amanda Prorok.

Recommended citation: Qingbiao Li, Weizhe Lin, Zhe Liu, Amanda Prorok. (2021). Message-Aware Graph Attention Networks for Large-Scale Multi-Robot Path Planning. IEEE Robotics and Automation Letters, 6(3), 5533–5540. https://doi.org/10.1109/LRA.2021.3077863 https://ieeexplore.ieee.org/abstract/document/9424371/

Learning Similarity between Movie Characters and Its Potential Implications on Understanding Human Experiences

Published in NAACL Workshop WNU, 2021

Zhilin Wang, Weizhe Lin, Xiaodong Wu.

Recommended citation: Zhilin Wang, Weizhe Lin, Xiaodong Wu. (2021). Learning Similarity between Movie Characters and Its Potential Implications on Understanding Human Experiences. Proceedings of the Third Workshop on Narrative Understanding, 24–35. https://doi.org/10.18653/v1/2021.nuse-1.3 https://aclanthology.org/2021.nuse-1.3/

Looking at the Body: Automatic Analysis of Body Gestures and Self-Adaptors in Psychological Distress

Published in IEEE Transactions on Affective Computing, 2023

Weizhe Lin, Indigo Orton, Qingbiao Li, Gabriela Pavarini, Marwa Mahmoud.

Recommended citation: Weizhe Lin, Indigo Orton, Qingbiao Li, Gabriela Pavarini, Marwa Mahmoud. (2023). Looking at the Body: Automatic Analysis of Body Gestures and Self-Adaptors in Psychological Distress. IEEE Transactions on Affective Computing, 14(2), 1175–1187. https://doi.org/10.1109/TAFFC.2021.3101698 https://ieeexplore.ieee.org/abstract/document/9506822/

Combining Unstructured Content and Knowledge Graphs into Recommendation Datasets

Published in KaRS, 2022

Weizhe Lin, Linjun Shou, Ming Gong, Jian Pei, Zhilin Wang, Bill Byrne, Daxin Jiang.

Recommended citation: Weizhe Lin, Linjun Shou, Ming Gong, Jian Pei, Zhilin Wang, Bill Byrne, Daxin Jiang. (2022). Combining Unstructured Content and Knowledge Graphs into Recommendation Datasets. Fourth Knowledge-aware and Conversational Recommender Systems Workshop (KaRS@RecSys 2022), 45–52. https://ceur-ws.org/Vol-3294/short5.pdf

An Inner Table Retriever for Robust Table Question Answering

Published in ACL, 2023

Weizhe Lin, Rexhina Blloshmi, Bill Byrne, Adrià de Gispert, Gonzalo Iglesias.

Recommended citation: Weizhe Lin, Rexhina Blloshmi, Bill Byrne, Adrià de Gispert, Gonzalo Iglesias. (2023). An Inner Table Retriever for Robust Table Question Answering. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 9909–9926. https://doi.org/10.18653/v1/2023.acl-long.551 https://aclanthology.org/2023.acl-long.551/

LI-RAGE: Late Interaction Retrieval Augmented Generation with Explicit Signals for Open-Domain Table Question Answering

Published in ACL, 2023

Weizhe Lin, Rexhina Blloshmi, Bill Byrne, Adrià de Gispert, Gonzalo Iglesias.

Recommended citation: Weizhe Lin, Rexhina Blloshmi, Bill Byrne, Adrià de Gispert, Gonzalo Iglesias. (2023). LI-RAGE: Late Interaction Retrieval Augmented Generation with Explicit Signals for Open-Domain Table Question Answering. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), 1557–1566. https://doi.org/10.18653/v1/2023.acl-short.133 https://aclanthology.org/2023.acl-short.133/

More Robust Schema-Guided Dialogue State Tracking via Tree-Based Paraphrase Ranking

Published in EACL, 2023

Alexandru Coca, Bo-Hsiang Tseng, Weizhe Lin, Bill Byrne.

Recommended citation: Alexandru Coca, Bo-Hsiang Tseng, Weizhe Lin, Bill Byrne. (2023). More Robust Schema-Guided Dialogue State Tracking via Tree-Based Paraphrase Ranking. Findings of the Association for Computational Linguistics: EACL 2023, 1443–1454. https://doi.org/10.18653/v1/2023.findings-eacl.106 https://aclanthology.org/2023.findings-eacl.106/

Grounding Description-Driven Dialogue State Trackers with Knowledge-Seeking Turns

Published in SIGDIAL, 2023

Alexandru Coca, Bo-Hsiang Tseng, Jinghong Chen, Weizhe Lin, Weixuan Zhang, Tisha Anders, Bill Byrne.

Recommended citation: Alexandru Coca, Bo-Hsiang Tseng, Jinghong Chen, Weizhe Lin, Weixuan Zhang, Tisha Anders, Bill Byrne. (2023). Grounding Description-Driven Dialogue State Trackers with Knowledge-Seeking Turns. Proceedings of the 24th Annual Meeting of the Special Interest Group on Discourse and Dialogue, 444–456. https://doi.org/10.18653/v1/2023.sigdial-1.42 https://aclanthology.org/2023.sigdial-1.42/

Fine-grained Late-interaction Multi-modal Retrieval for Retrieval Augmented Visual Question Answering

Published in NeurIPS, 2023

Weizhe Lin, Jinghong Chen, Jingbiao Mei, Alexandru Coca, Bill Byrne.

Recommended citation: Weizhe Lin, Jinghong Chen, Jingbiao Mei, Alexandru Coca, Bill Byrne. (2023). Fine-grained Late-interaction Multi-modal Retrieval for Retrieval Augmented Visual Question Answering. Advances in Neural Information Processing Systems, 36, 22820–22840. https://doi.org/10.52202/075280-0990 https://proceedings.neurips.cc/paper_files/paper/2023/hash/47393e8594c82ce8fd83adc672cf9872-Abstract-Conference.html

Self-supervised deep learning for tracking degradation of perovskite light-emitting diodes with multispectral imaging

Published in Nature Machine Intelligence, 2023

Kangyu Ji, Weizhe Lin, Yuqi Sun, Lin-Song Cui, Javad Shamsi, Yu-Hsien Chiang, Jiawei Chen, Elizabeth M. Tennyson, Linjie Dai, Qingbiao Li, Kyle Frohna, Miguel Anaya, Neil C. Greenham, Samuel D. Stranks.

Recommended citation: Kangyu Ji, Weizhe Lin, Yuqi Sun, Lin-Song Cui, Javad Shamsi, Yu-Hsien Chiang, Jiawei Chen, Elizabeth M. Tennyson, Linjie Dai, Qingbiao Li, Kyle Frohna, Miguel Anaya, Neil C. Greenham, Samuel D. Stranks. (2023). Self-supervised deep learning for tracking degradation of perovskite light-emitting diodes with multispectral imaging. Nature Machine Intelligence, 5(11), 1225–1235. https://doi.org/10.1038/s42256-023-00736-z https://www.nature.com/articles/s42256-023-00736-z

PreFLMR: Scaling Up Fine-Grained Late-Interaction Multi-modal Retrievers

Published in ACL, 2024

Weizhe Lin, Jingbiao Mei, Jinghong Chen, Bill Byrne.

Recommended citation: Weizhe Lin, Jingbiao Mei, Jinghong Chen, Bill Byrne. (2024). PreFLMR: Scaling Up Fine-Grained Late-Interaction Multi-modal Retrievers. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 5294–5316. https://doi.org/10.18653/v1/2024.acl-long.289 https://aclanthology.org/2024.acl-long.289/

Improving Hateful Meme Detection through Retrieval-Guided Contrastive Learning

Published in ACL, 2024

Jingbiao Mei, Jinghong Chen, Weizhe Lin, Bill Byrne, Marcus Tomalin.

Recommended citation: Jingbiao Mei, Jinghong Chen, Weizhe Lin, Bill Byrne, Marcus Tomalin. (2024). Improving Hateful Meme Detection through Retrieval-Guided Contrastive Learning. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 5333–5347. https://doi.org/10.18653/v1/2024.acl-long.291 https://aclanthology.org/2024.acl-long.291/

Control-DAG: Constrained Decoding for Non-Autoregressive Directed Acyclic T5 using Weighted Finite State Automata

Published in NAACL, 2024

Jinghong Chen, Weizhe Lin, Jingbiao Mei, Bill Byrne.

Recommended citation: Jinghong Chen, Weizhe Lin, Jingbiao Mei, Bill Byrne. (2024). Control-DAG: Constrained Decoding for Non-Autoregressive Directed Acyclic T5 using Weighted Finite State Automata. Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers), 508–518. https://doi.org/10.18653/v1/2024.naacl-short.42 https://aclanthology.org/2024.naacl-short.42/

Direct Preference Optimization for Neural Machine Translation with Minimum Bayes Risk Decoding

Published in NAACL, 2024

Guangyu Yang, Jinghong Chen, Weizhe Lin, Bill Byrne.

Recommended citation: Guangyu Yang, Jinghong Chen, Weizhe Lin, Bill Byrne. (2024). Direct Preference Optimization for Neural Machine Translation with Minimum Bayes Risk Decoding. Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers), 391–398. https://doi.org/10.18653/v1/2024.naacl-short.34 https://aclanthology.org/2024.naacl-short.34/

Towards Efficient Agents: A Co-Design of Inference Architecture and System

Preprint on arXiv, 2025

Weizhe Lin, Hui-Ling Zhen, Shuai Yang, Xian Wang, Renxi Liu, Hanting Chen, Wangze Zhang, Chuansai Zhou, Yiming Li, Chen Chen, Xing Li, Zhiyuan Yang, Xiaosong Li, Xianzhi Yu, Zhenhua Dong, Mingxuan Yuan, Yunhe Wang.

Recommended citation: Weizhe Lin, Hui-Ling Zhen, Shuai Yang, Xian Wang, Renxi Liu, Hanting Chen, Wangze Zhang, Chuansai Zhou, Yiming Li, Chen Chen, Xing Li, Zhiyuan Yang, Xiaosong Li, Xianzhi Yu, Zhenhua Dong, Mingxuan Yuan, Yunhe Wang. (2025). Towards Efficient Agents: A Co-Design of Inference Architecture and System. arXiv preprint 2512.18337. https://arxiv.org/abs/2512.18337

Pangu Pro MoE: Mixture of Grouped Experts for Efficient Sparsity

Preprint on arXiv, 2025

Huawei Pangu Team.

Recommended citation: Yehui Tang, Xiaosong Li, Fangcheng Liu, Wei Guo, Hang Zhou, Yaoyuan Wang, Kai Han, Xianzhi Yu, Jinpeng Li, Hui Zang, Fei Mi, Xiaojun Meng, Zhicheng Liu, Hanting Chen, Binfan Zheng, Can Chen, Youliang Yan, Ruiming Tang, Peifeng Qin, Xinghao Chen, Dacheng Tao, Yunhe Wang. (2025). Pangu Pro MoE: Mixture of Grouped Experts for Efficient Sparsity. arXiv preprint 2505.21411. https://arxiv.org/abs/2505.21411

Robust Adaptation of Large Multimodal Models for Retrieval Augmented Hateful Meme Detection

Published in EMNLP, 2025

Jingbiao Mei, Jinghong Chen, Guangyu Yang, Weizhe Lin, Bill Byrne.

Recommended citation: Jingbiao Mei, Jinghong Chen, Guangyu Yang, Weizhe Lin, Bill Byrne. (2025). Robust Adaptation of Large Multimodal Models for Retrieval Augmented Hateful Meme Detection. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, 23806–23828. https://doi.org/10.18653/v1/2025.emnlp-main.1215 https://aclanthology.org/2025.emnlp-main.1215/

X-LeBench: A Benchmark for Extremely Long Egocentric Video Understanding

Published in EMNLP, 2025

Wenqi Zhou, Kai Cao, Hao Zheng, Yunze Liu, Xinyi Zheng, Miao Liu, Per Ola Kristensson, Walterio W. Mayol-Cuevas, Fan Zhang, Weizhe Lin (Corresponding author), Junxiao Shen.

Recommended citation: Wenqi Zhou, Kai Cao, Hao Zheng, Yunze Liu, Xinyi Zheng, Miao Liu, Per Ola Kristensson, Walterio W. Mayol-Cuevas, Fan Zhang, Weizhe Lin, Junxiao Shen. (2025). X-LeBench: A Benchmark for Extremely Long Egocentric Video Understanding. Findings of the Association for Computational Linguistics: EMNLP 2025, 15206–15222. https://doi.org/10.18653/v1/2025.findings-emnlp.822 https://aclanthology.org/2025.findings-emnlp.822/

CULTURE3D: A Large-Scale and Diverse Dataset of Cultural Landmarks and Terrains for Gaussian-Based Scene Rendering

Published in ICCV, 2025

Xinyi Zheng, Steve Zhang, Weizhe Lin (Corresponding author), Aaron Zhang, Walterio W Mayol-Cuevas, Yunze Liu, Junxiao Shen.

Recommended citation: Xinyi Zheng, Steve Zhang, Weizhe Lin, Aaron Zhang, Walterio W Mayol-Cuevas, Yunze Liu, Junxiao Shen. (2025). CULTURE3D: A Large-Scale and Diverse Dataset of Cultural Landmarks and Terrains for Gaussian-Based Scene Rendering. 2025 IEEE/CVF International Conference on Computer Vision (ICCV), 29064–29074. https://doi.org/10.1109/ICCV51701.2025.02699 https://ieeexplore.ieee.org/abstract/document/11443386/

On Extending Direct Preference Optimization to Accommodate Ties

Published in NeurIPS, 2025

Jinghong Chen, Guangyu Yang, Weizhe Lin, Jingbiao Mei, Chenxu Lyu, Bill Byrne.

Recommended citation: Jinghong Chen, Guangyu Yang, Weizhe Lin, Jingbiao Mei, Chenxu Lyu, Bill Byrne. (2025). On Extending Direct Preference Optimization to Accommodate Ties. Advances in Neural Information Processing Systems, 38, 53932–53977. https://doi.org/10.52202/085713-1801 https://proceedings.neurips.cc/paper_files/paper/2025/hash/4dbf3488a8da908a8177d525ede1a43a-Abstract-Conference.html

Unleashing Low-Bit Inference on Ascend NPUs: A Comprehensive Evaluation of HiFloat Formats

Published in ACL, 2026

Pengxiang Zhao, Hui-Ling Zhen, Xing Li, Han Bao, Weizhe Lin, Zhiyuan Yang, Yu Zi Wei, Xin Wang, Mingxuan Yuan, Xianzhi Yu, Zhenhua Dong.

Recommended citation: Pengxiang Zhao, Hui-Ling Zhen, Xing Li, Han Bao, Weizhe Lin, Zhiyuan Yang, Yu Zi Wei, Xin Wang, Mingxuan Yuan, Xianzhi Yu, Zhenhua Dong. (2026). Unleashing Low-Bit Inference on Ascend NPUs: A Comprehensive Evaluation of HiFloat Formats. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track), 844–864. https://doi.org/10.18653/v1/2026.acl-industry.57 https://aclanthology.org/2026.acl-industry.57/

Retrieval-Augmented Defense: Adaptive and Controllable Jailbreak Prevention for Large Language Models

Published in ACL, 2026

Guangyu Yang, Jinghong Chen, Jingbiao Mei, Weizhe Lin, Bill Byrne.

Recommended citation: Guangyu Yang, Jinghong Chen, Jingbiao Mei, Weizhe Lin, Bill Byrne. (2026). Retrieval-Augmented Defense: Adaptive and Controllable Jailbreak Prevention for Large Language Models. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 40849–40868. https://doi.org/10.18653/v1/2026.acl-long.1895 https://aclanthology.org/2026.acl-long.1895/

AgentCollab: A Self-Evaluation-Driven Collaboration Paradigm for Efficient LLM Agents

Preprint on arXiv, 2026

Wenbo Gao, Renxi Liu, Xian Wang, Fang Guo, Shuai Yang, Xi Chen, Hui-Ling Zhen, Hanting Chen, Weizhe Lin, Xiaosong Li, Yaoyuan Wang.

Recommended citation: Wenbo Gao, Renxi Liu, Xian Wang, Fang Guo, Shuai Yang, Xi Chen, Hui-Ling Zhen, Hanting Chen, Weizhe Lin, Xiaosong Li, Yaoyuan Wang. (2026). AgentCollab: A Self-Evaluation-Driven Collaboration Paradigm for Efficient LLM Agents. arXiv preprint 2603.26034. https://arxiv.org/abs/2603.26034

DLLM Agent: See Farther, Run Faster

Preprint on arXiv, 2026

Huiling Zhen, Weizhe Lin (equal contribution), Renxi Liu, Kai Han, Yiming Li, Yuchuan Tian, Hanting Chen, Xiaoguang Li, Xiaosong Li, Chen Chen, Xianzhi Yu, Mingxuan Yuan, Youliang Yan, Peifeng Qin, Jun Wang, Yu Wang, Dacheng Tao, Yunhe Wang.

Recommended citation: Huiling Zhen, Weizhe Lin, Renxi Liu, Kai Han, Yiming Li, Yuchuan Tian, Hanting Chen, Xiaoguang Li, Xiaosong Li, Chen Chen, Xianzhi Yu, Mingxuan Yuan, Youliang Yan, Peifeng Qin, Jun Wang, Yu Wang, Dacheng Tao, Yunhe Wang. (2026). DLLM Agent: See Farther, Run Faster. arXiv preprint 2602.07451. https://arxiv.org/abs/2602.07451

EPD-Serve: A Flexible Multimodal EPD Disaggregation Inference Serving System On Ascend

Preprint on arXiv, 2026

Fan Bai, Pai Peng, Zhengzhi Tang, Zhe Wang, Gong Chen, Xiang Lu, Yinuo Li, Huan Lin, Weizhe Lin, Yaoyuan Wang, Xiaosong Li.

Recommended citation: Fan Bai, Pai Peng, Zhengzhi Tang, Zhe Wang, Gong Chen, Xiang Lu, Yinuo Li, Huan Lin, Weizhe Lin, Yaoyuan Wang, Xiaosong Li. (2026). EPD-Serve: A Flexible Multimodal EPD Disaggregation Inference Serving System On Ascend. arXiv preprint 2601.11590. https://arxiv.org/abs/2601.11590

Experience Funnel: A State-Policy Alternating Loop for Self-Evolving Agents

Preprint on arXiv, 2026

Wenbo Gao, Zhaomou Song, Zhiyuan Ji, Renxi Liu, Xing Li, Xianzhi Yu, Xiaoguang Li, James Chung-wai Cheung, Weizhe Lin, Yaoyuan Wang.

Recommended citation: Wenbo Gao, Zhaomou Song, Zhiyuan Ji, Renxi Liu, Xing Li, Xianzhi Yu, Xiaoguang Li, James Chung-wai Cheung, Weizhe Lin, Yaoyuan Wang. (2026). Experience Funnel: A State-Policy Alternating Loop for Self-Evolving Agents. arXiv preprint 2609.08919. https://arxiv.org/abs/2609.08919

JuDi: Revisiting Judge Decoding from First Principles via Training-Free Distributional Divergence

Accepted at EMNLP, 2026

Shengyin Sun, Yiming Li, Renxi Liu, Weizhe Lin, Hui-Ling Zhen, Xianzhi Yu, Mingxuan Yuan, Chen Ma.

Recommended citation: Shengyin Sun, Yiming Li, Renxi Liu, Weizhe Lin, Hui-Ling Zhen, Xianzhi Yu, Mingxuan Yuan, Chen Ma. (2026). JuDi: Revisiting Judge Decoding from First Principles via Training-Free Distributional Divergence. Accepted at EMNLP 2026. https://arxiv.org/abs/2601.04766

Scaling Up, Speeding Up: A Benchmark of Speculative Decoding for Efficient LLM Test-Time Scaling

Published in ICLR, 2026

Shengyin Sun, Yiming Li, Xing Li, Yingzhao Lian, Weizhe Lin, Huiling Zhen, Zhiyuan Yang, Xianzhi Yu, Chen Chen, Mingxuan Yuan, Chen Ma.

Recommended citation: Shengyin Sun, Yiming Li, Xing Li, Yingzhao Lian, Weizhe Lin, Huiling Zhen, Zhiyuan Yang, Xianzhi Yu, Chen Chen, Mingxuan Yuan, Chen Ma. (2026). Scaling Up, Speeding Up: A Benchmark of Speculative Decoding for Efficient LLM Test-Time Scaling. International Conference on Learning Representations, 2026, 103030–103060. https://proceedings.iclr.cc/paper_files/paper/2026/hash/a8808b75b299d64a23255bc8d30fb786-Abstract-Conference.html

TrimR: Verifier-based Training-Free Thinking Trimming for Efficient Test-Time Scaling

Published in ICLR, 2026

Weizhe Lin, Xing Li, Zhiyuan Yang, Xiaojin Fu, Huiling Zhen, Yaoyuan Wang, Xianzhi Yu, Wulong Liu, Xiaosong Li, Mingxuan Yuan.

Recommended citation: Weizhe Lin, Xing Li, Zhiyuan Yang, Xiaojin Fu, Huiling Zhen, Yaoyuan Wang, Xianzhi Yu, Wulong Liu, Xiaosong Li, Mingxuan Yuan. (2026). TrimR: Verifier-based Training-Free Thinking Trimming for Efficient Test-Time Scaling. International Conference on Learning Representations, 2026, 18235–18258. https://proceedings.iclr.cc/paper_files/paper/2026/hash/1eeacdf8770e6dd5164cdeec8bcfa8cc-Abstract-Conference.html

Human-Inspired Perspectives: A Survey on AI Long-Term Memory

Published in Proceedings of the IEEE, 2026

Zihong He, Weizhe Lin (Corresponding author), Hao Zheng, Fan Zhang, Matt W. Jones, Laurence Aitchison, Xuhai Xu, Miao Liu, Hai-Ning Liang, Per Ola Kristensson, Junxiao Shen.

Recommended citation: Zihong He, Weizhe Lin, Hao Zheng, Fan Zhang, Matt W. Jones, Laurence Aitchison, Xuhai Xu, Miao Liu, Hai-Ning Liang, Per Ola Kristensson, Junxiao Shen. (2026). Human-Inspired Perspectives: A Survey on AI Long-Term Memory. Proceedings of the IEEE, 114(3), 484–515. https://doi.org/10.1109/JPROC.2026.3713201 https://ieeexplore.ieee.org/abstract/document/11640865/

MemDLM: Memory-Enhanced DLM Training

Accepted at NeurIPS, 2026

Zehua Pei, Hui-Ling Zhen, Weizhe Lin, Sinno Jialin Pan, Yunhe Wang, Mingxuan Yuan, Bei Yu.

Recommended citation: Zehua Pei, Hui-Ling Zhen, Weizhe Lin, Sinno Jialin Pan, Yunhe Wang, Mingxuan Yuan, Bei Yu. (2026). MemDLM: Memory-Enhanced DLM Training. Accepted at NeurIPS 2026. https://arxiv.org/abs/2603.22241

PSD: Pushing the Pareto Frontier of Diffusion LLMs via Parallel Speculative Decoding

Accepted at NeurIPS, 2026

Shengyin Sun, Yiming Li, Renxi Liu, Xinqi Li, Hui-Ling Zhen, Weizhe Lin, Chen Chen, Xianzhi Yu, Mingxuan Yuan, Chen Ma.

Recommended citation: Shengyin Sun, Yiming Li, Renxi Liu, Xinqi Li, Hui-Ling Zhen, Weizhe Lin, Chen Chen, Xianzhi Yu, Mingxuan Yuan, Chen Ma. (2026). PSD: Pushing the Pareto Frontier of Diffusion LLMs via Parallel Speculative Decoding. Accepted at NeurIPS 2026. https://arxiv.org/abs/2605.15609