Publications

2026

1. EMNLP 2026

   Main

   COSE: Confidence-Orchestrated Self-Evolution for Effective LLM Reasoning

   Bowen Wei, Nan Wang, Yuqing Zhou, and 2 more authors

   In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, 2026

   Main Conference acceptance rate: 15.4% (2,719 / 17,669).

   Paper
2. AAAI 2026

   Oral

   Making Sense of LLM Decisions: A Prototype-based Framework for Explainable Classification

   Bowen Wei, Mehrdad Fazli, and Ziwei Zhu

   In Proceedings of the AAAI Conference on Artificial Intelligence, 2026

   Overall acceptance rate: 17.6% (4,167 / 23,680); oral presentation rate: 4.5% of submissions (1,058 / 23,680).

   Paper DOI
3. arXiv

   Preprint

   A Logical-Rule Autoencoder for Interpretable Recommendations

   Bowen Wei^*, Jinhao Pan^*, Yuqing Zhou, and 1 more author

   2026

   * Equal contribution.

   Paper
4. arXiv

   Preprint

   ClawSafety: "Safe" LLMs, Unsafe Agents

   Bowen Wei, Yunbei Zhang, Jinhao Pan, and 5 more authors

   2026

   Paper
5. ICML 2026

   Main

   Knowing Bias, Doing Better: Mitigating Social Bias in LLMs via Know-Bias Neuron Enhancement

   Jinhao Pan, Chahat Raj, Anjishnu Mukherjee, and 4 more authors

   2026

   Paper
6. ACL 2026

   Oral

   VIGNETTE: Socially Grounded Bias Evaluation for Vision-Language Models

   Chahat Raj, Bowen Wei, Aylin Caliskan, and 2 more authors

   In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics, 2026

   Selected as an SAC Highlight at ACL 2026.

   Paper
7. ACL 2026

   Findings

   Context-Aware Decoding for Faithful Vision-Language Generation

   Mehrdad Fazli, Bowen Wei, and Ziwei Zhu

   In Findings of the Association for Computational Linguistics: ACL 2026, 2026

   Paper

2025

1. NeurIPS LAW 2025

   CORTEX: Collaborative LLM Agents for High-Stakes Alert Triage

   Bowen Wei, Yuan Shen Tay, Howard Liu, and 4 more authors

   2025

   Paper
2. ACL 2025

   Main

   ProtoLens: Advancing Prototype Learning for Fine-Grained Interpretability in Text Classification

   Bowen Wei and Ziwei Zhu

   In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Jul 2025

   Main Conference acceptance rate: 20.3% (1,699 / 8,360).

   Paper Abs DOI

   In this work, we propose ProtoLens, a novel prototype-based model that provides fine-grained, sub-sentence level interpretability for text classification. ProtoLens uses a Prototype-aware Span Extraction module to identify relevant text spans associated with learned prototypes and a Prototype Alignment mechanism to ensure prototypes are semantically meaningful throughout training. By aligning the prototype embeddings with human-understandable examples, ProtoLens provides interpretable predictions while maintaining competitive accuracy. Extensive experiments demonstrate that ProtoLens outperforms both prototype-based and non-interpretable baselines on multiple text classification benchmarks. Code and data are available at \urlhttps://github.com/weibowen555/ProtoLens.

3. WACV 2026

   Main

   Mitigating Hallucination in Large Vision-Language Models via Adaptive Attention Calibration

   Mehrdad Fazli, Bowen Wei, Ahmet Sari, and 1 more author

   Jul 2025

   Paper

2024

1. arXiv

   Preprint

   Neural Symbolic Logical Rule Learner for Interpretable Learning

   Bowen Wei and Ziwei Zhu

   Jul 2024

   Paper

2023

1. M.Sc.

   Thesis

   An Empirical Study of the Neural Contextual Bandit Algorithms

   Bowen Wei, Yiling Jia, and Hongning Wang

   Jul 2023

   Paper