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20232026
most citedMMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI

16 citations · 28 across the 25 of their papers we have counts for

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Showing 2026Show all

10 papers · 1 filter

cs.CV2026

InfoAgent: Traceable Generation and Repair of Evidence-Grounded Infographics

Yifan Li, Tong Li, Qi Zeng +6

Reliable infographic generation requires facts, symbols, and visual relations to remain consistent through rendering and revision. Correcting one element also requires tracking its…

cs.CV2026

PixelUMM: Encoder-Free Unified Image and Video Understanding and Generation

Cong Wei, Xuanchi Ren, Bryan Chu +8

Unified Multimodal Models (UMMs) often rely on separate visual representations for understanding and generation, increasing visual context length and complicating integration with…

cs.CV2026

VGI-Bench: Probing Visual Intelligence in Video Generation Models

Xuan He, Cong Wei, Yuhao Cheng +20

Recent studies suggest that video generation models can exhibit certain forms of zero-shot visual reasoning through generated frames. Yet reliable evaluation remains challenging: b…

cs.CV2026

Search Beyond What Can Be Taught: Evolving the Knowledge Boundary in Agentic Visual Generation

Haozhe Wang, Weijia Feng, Jinpeng Yu +8

Visual generators excel at rendering, but they confidently fabricate what they do not know. User requests are unbounded, evolving, and deeply long-tailed: new characters, trending…

cs.AI2026

Function-Aware Fill-in-the-Middle as Mid-Training for Coding Agent Foundation Models

Yubo Wang, Jiarong Liang, Yuxuan Zhang +5

Coding agents must integrate external tool returns into ongoing reasoning - a capability that standard left-to-right pretraining on code exposes only in its forward direction. We o…

cs.AI2026

RewardHarness: Self-Evolving Agentic Post-Training

Yuxuan Zhang, Penghui Du, Bo Li +11

Aligning generative models with human preferences remains a central challenge. While RLHF is widely adopted, it relies on large-scale preference data. This limitation is particular…

cs.IR2026

Beyond Semantic Similarity: Rethinking Retrieval for Agentic Search via Direct Corpus Interaction

Zhuofeng Li, Haoxiang Zhang, Cong Wei +16

Modern retrieval systems, whether lexical or semantic, expose a corpus through a fixed similarity interface that compresses access into a single top-k retrieval step before reasoni…

cs.CV2026

Starve to Perceive: Taming Lazy Perception in VLMs with Constrained Visual Bandwidth

Yuhuan Wu, Cong Wei, Fangzhen Lin +2

Vision-Language Models (VLMs) deployed as situated agents in high-resolution visual environments require active perception -- the ability to dynamically decide where to look throug…

cs.AI2026

RationalRewards: Reasoning Rewards Scale Visual Generation Both Training and Test Time

Haozhe Wang, Cong Wei, Weiming Ren +3

Most reward models for visual generation reduce rich human judgments to a single unexplained score, discarding the reasoning that underlies preference. We show that teaching reward…

cs.CV2026

Context Forcing: Consistent Autoregressive Video Generation with Long Context

Shuo Chen, Cong Wei, Sun Sun +5

Recent approaches to real-time long video generation typically employ streaming tuning strategies, attempting to train a long-context student using a short-context (memoryless) tea…