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20222026
most citedAdaptive Perception Transformer for Temporal Action Localization

5 citations · 6 across the 9 of their papers we have counts for

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6 papers · 1 filter

cs.LG2026

Draft in Parallel, Condition Through Depth: Adjacent Causal Injection for Speculative Decoding

Haohui Zhang, Keyu Chen, Haocheng Sun +4

Parallel speculative drafting generates multiple candidates in one backbone pass, but independent token selection can produce inconsistent continuations that shorten the accepted p…

cs.CV2026

Video-IFBench: Evaluating Instruction Following of Multimodal LLMs in Video Understanding Scenarios

Hongbo Liu, Peixian Chen, Sihan Liu +12

Multimodal Large Language Models (MLLMs) have shown strong performance in video understanding. However, their ability to follow instructions in this domain remains under-explored.…

cs.CV2026

STBridge: Shared-Target Alignment for Bridging Understanding and Generation in UMMs

Ye Wang, Hongjun Wang, Hao Fang +7

Unified multimodal models (UMMs) aim to integrate visual understanding and generation within a single architecture, but architectural unification alone does not ensure semantic con…

cs.AI2026

Bridging Interleaved Multi-Modal Reasoning as a Unified Decision Process

Zican Hu, Xuyang Hu, Yiming Liu +10

Unified multi-modal models (UMMs) have shown promising interleaved text-image reasoning capabilities, yet effectively optimizing such multi-turn generation via reinforcement learni…

cs.LG2026

MHPO: Modulated Hazard-aware Policy Optimization for Stable Reinforcement Learning

Hongjun Wang, Wei Liu, Weibo Gu +2

Regulating the importance ratio is critical for the training stability of Group Relative Policy Optimization (GRPO) based frameworks. However, prevailing ratio control methods, suc…

cs.CV2026

Youtu-VL: Unleashing Visual Potential via Unified Vision-Language Supervision

Zhixiang Wei, Yi Li, Zhehan Kan +38

Despite the significant advancements represented by Vision-Language Models (VLMs), current architectures often exhibit limitations in retaining fine-grained visual information, lea…