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20242026
most citedVisual Haystacks: A Vision-Centric Needle-In-A-Haystack Benchmark

1 citations · 3 across the 22 of their papers we have counts for

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cs.CL2026

CoVA-SFT: A Large-Scale Dataset for Chain of Visual Abstractions

Tsung-Han Wu, Heekyung Lee, Anya Ji +4

Chain-of-thought (CoT) reasoning has dramatically improved large language models (LLMs) by allowing them to decompose problems into intermediate steps. While CoT is widely effectiv…

cs.CV2026

Animation2Code: Evaluating Temporal Visual Reasoning in Video-to-Code Generation

Anya Ji, Abhijith Varma Mudunuri, David M. Chan +1

While recent vision-language models (VLMs) have achieved significant improvements on static visual-to-code tasks such as generating code for webpages, charts, or SVGs, it remains u…

cs.RO2026

T-Rex: Tactile-Reactive Dexterous Manipulation

Dantong Niu, Zhuoyang Liu, Zekai Wang +31

The ability to react dynamically to tactile signals has long been considered crucial to agile human-level dexterity. Yet contemporary learning-based Vision-Language-Action (VLA) mo…

cs.RO2026

Playful Agentic Robot Learning

Junyi Zhang, Jiaxin Ge, Hanjun Yoo +17

Current agentic robot systems can write executable Code-as-Policy programs, observe feedback, and revise behavior across multiple attempts, but they remain largely task-driven: reu…

cs.CL2026

Unintended Effects of Geographic Conditioning in Large Language Models

Naz Col, David M. Chan

Modern conversational AI systems frequently rely on user metadata to localize responses, yet the unintended regional biases introduced by this hidden context remain poorly understo…

cs.CV2026

Stateful Visual Encoders for Vision-Language Models

Zirui Wang, Junwei Yu, Adam Yala +3

Vision-language models (VLMs) are increasingly used in multi-image, multi-turn agentic settings where decisions depend on visual changes. However, in existing open-weight VLMs, vis…

cs.CL2026

DigitalCoach: Communication and Grounding Gaps in Human and Agentic Computer Use Coaching

Meng Chen, Anya Ji, Tsung-Han Wu +4

Agents are increasingly capable of automating software tasks, but can they teach humans how to use software themselves? We introduce DigitalCoach, a multimodal dataset of 72 human…

cs.CV2026

ScribbleEdit: Synthetic Data for Image Editing with Scribbles and Text

Anya Ji, George Ma, Téa Wright +4

Recent progress in generative models has significantly advanced image editing capabilities, yet precise and intuitive user control remains difficult. Specifically, users often stru…

cs.CV2026

Attend Before Attention: Efficient and Scalable Video Understanding via Autoregressive Gazing

Baifeng Shi, Stephanie Fu, Long Lian +10

Multi-modal large language models (MLLMs) have advanced general-purpose video understanding but struggle with long, high-resolution videos -- they process every pixel equally in th…

cs.CV2026

VisGym: Diverse, Customizable, Scalable Environments for Multimodal Agents

Zirui Wang, Junyi Zhang, Jiaxin Ge +9

Modern Vision-Language Models (VLMs) remain poorly characterized in multi-step visual interactions, particularly in how they integrate perception, memory, and action over long hori…