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Xiao-Ming Wu

ACL 2026×2ICLR 2026×2KDD 2026SIGIR 2026可解释性检索与知识评测与基准对齐与偏好安全
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0 一作 · 6 篇 · ACL 2026 / ICLR 2026 / KDD 2026 / SIGIR 2026

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2026 顶会论文

6
作者位次 #10/10ACL 2026Poster· Natural Language Processing

MSE arth: A Multimodal Benchmark for Earth Science Phenomenon Discovery with MLLM s

Xiangyu Zhao · Wanghan Xu · Bo LIU · Yuhao Zhou · Fenghua Ling · Ben Fei · Xiaoyu Yue · LEI BAI · Wenlong Zhang · Xiao-Ming Wu

摘要

The rapid advancement of multimodal large language models (MLLMs) offers new opportunities for complex scientific challenges, yet their application in earth science—especially at the graduate level—remains underexplored due to a lack of benchmarks reflecting the depth and complexity of geoscienti...

多模态评测与基准数据与预训练OpenReview 项目页
作者位次 #9/9ACL 2026Poster· Natural Language Processing

FOREVER : Forgetting Curve-Inspired Memory Replay for Language Model Continual Learning

Yujie Feng · Hao Wang · Jian Li · Xu Chu · Zhaolu Kang · Yiran Liu · Yasha Wang · Philip S. Yu · Xiao-Ming Wu

摘要

Continual learning (CL) for large language models (LLMs) aims to enable sequential knowledge acquisition without catastrophic forgetting. Memory replay methods are widely used for their practicality and effectiveness, but most rely on fixed, step-based heuristics that often misalign with the mode...

检索与知识对齐与偏好可解释性OpenReview 项目页
作者位次 #7/7SIGIR 2026Poster· Information Retrieval (Industry)

Full Retraining, Incremental Fine-tuning, and Hybrid Serving: Model Updating and Serving for Industrial Generative Recommender Systems

Lu Fan · Qijiong Liu · Zhongzhou Liu · Guoyuan An · Wei Guo · Yong Liu · Xiao-Ming Wu

摘要

Generative recommendation casts recommendation as conditional sequence generation over text- or token-based representations and has shown strong promise in industrial systems. However, keeping such models up to date in dynamic environments is difficult: full retraining on sliding windows is expen...

评测与基准可解释性强化学习与后训练OpenReview

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