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20242026
most citedCoEvolve: Training LLM Agents via Agent-Data Mutual Evolution

1 citations · 1 across the 30 of their papers we have counts for

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cs.CV2026

LongHorizon-Harness: Advancing Long-Horizon Agents for Real-World Tasks

Ziyu Ma, Hailang Huang, Shun Zou +5

Large language model (LLM) agents increasingly undertake long-horizon tasks that require sustained reasoning, tool use, and revision across many interdependent steps. However, exis…

cs.CV2026

Towards High-Resolution Visual Perception via Hierarchical Entity Exploration

Ziyu Ma, Shidong Yang, Yuxiang Ji +5

High-resolution (HR) image perception remains a key challenge in multimodal large language models (MLLMs), as fine-grained details are often lost when the image is processed as a w…

cs.CV2026

Towards Memory-Efficient Autoregressive Video Generation via Instance-Specific Parametric Absorption

Xiaomeng Fu, Jia Li, Yiming Hu +5

Autoregressive (AR) streaming models have emerged as a powerful paradigm for long video generation. However, the linearly growing Key-Value (KV) cache poses a significant bottlenec…

cs.CV2026

Generation Enhances Understanding in Unified Multimodal Models via Multi-Representation Generation

Zihan Su, Hongyang Wei, Kangrui Cen +4

Unified Multimodal Models (UMMs) integrate both visual understanding and generation within a single framework. Their ultimate aspiration is to create a cycle where understanding an…

cs.CV2026

Visually-Guided Policy Optimization for Multimodal Reasoning

Zengbin Wang, Feng Xiong, Liang Lin +5

Reinforcement learning with verifiable rewards (RLVR) has significantly advanced the reasoning ability of vision-language models (VLMs). However, the inherent text-dominated nature…

cs.CV2026

Visual Enhanced Depth Scaling for Multimodal Latent Reasoning

Yudong Han, Yong Wang, Zaiquan Yang +3

Multimodal latent reasoning has emerged as a promising paradigm that replaces explicit Chain-of-Thought (CoT) decoding with implicit feature propagation, simultaneously enhancing r…