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Zhihui Fu

ACL 2026×4ICLR 2026KDD 2026安全多模态可解释性评测与基准效率与系统
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0 一作 · 6 篇 · ACL 2026 / ICLR 2026 / KDD 2026

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2026 顶会论文

6
作者位次 #8/10ACL 2026Poster· Natural Language Processing

“ I See What You Did There”: Can Large Vision-Language Models Understand Multimodal Puns?

Naen Xu · Jiayi Sheng · Changjiang Li · Chunyi Zhou · Yuyuan Li · Tianyu Du · Jun Wang · Zhihui Fu · Jinbao Li · Shouling Ji

摘要

Puns are a common form of rhetorical wordplay that exploits polysemy and phonetic similarity to create humor. In multimodal puns, visual and textual elements synergize to ground the literal sense and evoke the figurative meaning simultaneously. Although Vision-Language Models (VLMs) are widely us...

多模态评测与基准推理OpenReview 项目页
作者位次 #8/11ACL 2026Poster· Natural Language Processing

Compiling Activation Steering into Weights via Null-Space Constraints for Stealthy Backdoors

Rui Yin · Tianxu Han · Naen Xu · Changjiang Li · Ping He · Chunyi Zhou · Jun Wang · Zhihui Fu · Tianyu Du · Jinbao Li · Shouling Ji

摘要

Safety-aligned large language models (LLMs) are increasingly deployed in real-world pipelines, yet this deployment also enlarges the supply-chain attack surface: adversaries can distribute backdoored checkpoints that behave normally under standard evaluation but jailbreak when a hidden trigger is...

安全可解释性评测与基准OpenReview 项目页
作者位次 #2/4ACL 2026Findings· Findings of ACL

POP : Prefill-Only Pruning for Efficient Large Model Inference

Junhui He · Zhihui Fu · Jun Wang · Qingan Li

摘要

Large Language Models (LLMs) and Vision-Language Models (VLMs) have demonstrated remarkable capabilities.However, their deployment is hindered by significant computational costs. Existing structured pruning methods, while hardware-efficient, often suffer from significant accuracy degradation. In...

效率与系统多模态可解释性OpenReview 项目页
作者位次 #2/7ACL 2026Findings· Findings of ACL

D iffu S pec: Unlocking Diffusion Language Models for Speculative Decoding

Guanghao Li · Zhihui Fu · Min Fang · Qibin Zhao · Ming Tang · Chun Yuan · Jun Wang

摘要

Autoregressive (AR) decoding in large language models (LLMs) is latency-bounded by strictly sequential token generation.Speculative decoding mitigates this bottleneck by letting a fast drafter propose multi-token candidates that are then verified in parallel by the target model; yet most existing...

效率与系统对齐与偏好可解释性OpenReview 项目页

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