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20242026
most citedSeeing Sarcasm Through Different Eyes: Analyzing Multimodal Sarcasm Perception in Large Vision-Language Models

3 citations · 3 across the 20 of their papers we have counts for

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cs.CL2026

Look Ahead Before You Distill: Future Trajectory Validation of Teacher Guidance for Agentic On-Policy Distillation

Chishui Chen, Yaoyou Fan, Te Sun +11

On-policy distillation (OPD) provides teacher supervision on states visited by the student, reducing the distribution gap between training and inference. However, in multi-turn age…

cs.CL2026

Where to Place the Query? Unveiling and Mitigating Positional Bias in In-Context Learning for Diffusion LLMs via Decoding Dynamics

Zhengheng Li, Panrui Li, Xuyang Liu +1

While In-Context Learning (ICL) is extensively studied in Autoregressive (AR) LLMs, its mechanism within Diffusion Large Language Models (dLLMs) remains largely unexplored. Unlike…

cs.CL2026

STDec: Spatio-Temporal Stability Guided Decoding for dLLMs

Yuzhe Chen, Jiale Cao, Xuyang Liu +3

Diffusion Large Language Models (dLLMs) have achieved rapid progress, viewed as a promising alternative to the autoregressive paradigm. However, most dLLM decoders still adopt a gl…

cs.CL2025

The Devil behind the mask: An emergent safety vulnerability of Diffusion LLMs

Zichen Wen, Jiashu Qu, Zhaorun Chen +13

Diffusion-based large language models (dLLMs) have recently emerged as a powerful alternative to autoregressive LLMs, offering faster inference and greater interactivity via parall…

cs.CL2025

Shifting AI Efficiency From Model-Centric to Data-Centric Compression

Xuyang Liu, Zichen Wen, Shaobo Wang +14

The advancement of large language models (LLMs) and multi-modal LLMs (MLLMs) has historically relied on scaling model parameters. However, as hardware limits constrain further mode…

cs.CL20253 cited

Seeing Sarcasm Through Different Eyes: Analyzing Multimodal Sarcasm Perception in Large Vision-Language Models

Junjie Chen, Xuyang Liu, Subin Huang +2

With the advent of large vision-language models (LVLMs) demonstrating increasingly human-like abilities, a pivotal question emerges: do different LVLMs interpret multimodal sarcasm…