# Seeking and Updating with Live Visual Knowledge

arXiv: https://arxiv.org/abs/2504.05288 · HTML: https://mlsanity.com/paper/2504.05288/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Apr 2025.

## Built on by (most cited first, 24 of 24)

- [Vision-DeepResearch Benchmark: Rethinking Visual and Textual Search for Multimodal Large Language Models](https://mlsanity.com/paper/2602.02185/) (2026)
- [SenseNova-MARS: Empowering Multimodal Agentic Reasoning and Search via Reinforcement Learning](https://mlsanity.com/paper/2512.24330/) (2025)
- [Video-Browser: Towards Agentic Open-web Video Browsing](https://mlsanity.com/paper/2512.23044/) (2025)
- [HyperEyes: Dual-Grained Efficiency-Aware Reinforcement Learning for Parallel Multimodal Search Agents](https://mlsanity.com/paper/2605.07177/) (2026)
- [BrowseComp-V3: A Visual, Vertical, and Verifiable Benchmark for Multimodal Browsing Agents](https://mlsanity.com/paper/2602.12876/) (2026)
- [Visual-Seeker: Towards Visual-Native Multimodal Agentic Search via Active Visual Reasoning](https://mlsanity.com/paper/2606.15231/) (2026)
- [MINED: Probing and Updating with Multimodal Time-Sensitive Knowledge for Large Multimodal Models](https://mlsanity.com/paper/2510.19457/) (2025)
- [MTA-Agent: An Open Recipe for Multimodal Deep Search Agents](https://mlsanity.com/paper/2604.06376/) (2026)
- [SUPERGLASSES: Benchmarking Vision Language Models as Intelligent Agents for AI Smart Glasses](https://mlsanity.com/paper/2602.22683/) (2026)
- [MMKU-Bench: A Multimodal Update Benchmark for Diverse Visual Knowledge](https://mlsanity.com/paper/2603.15117/) (2026)
- [S1-DeepResearch: Beyond Search, Toward Real-World Long-Horizon Research Agents](https://mlsanity.com/paper/2606.15367/) (2026)
- [VideoSearcher: Empowering Video Deep Research with Multi-Tool Agentic Reasoning via Reinforcement Learning](https://mlsanity.com/paper/2607.02927/) (2026)
- [Glance-or-Gaze: Incentivizing LMMs to Adaptively Focus Search via Reinforcement Learning](https://mlsanity.com/paper/2601.13942/) (2026)
- [Progressive Multimodal Search and Reasoning for Knowledge-Intensive Visual Question Answering](https://mlsanity.com/paper/2509.00798/) (2025)
- [PIXELRAG: Web Screenshots Beat Text for Retrieval-Augmented Generation](https://mlsanity.com/paper/2606.28344/) (2026)
- [DeepVoyager-VL: Incentivizing Vision-in-the-Loop Search for Long-Horizon Multimodal Agents](https://mlsanity.com/paper/2608.01827/) (2026)
- [POINTS-Seeker: An Open Recipe for Multimodal Search Agents with Visual Memory Management](https://mlsanity.com/paper/2604.14029/) (2026)
- [Eliciting Self-Verification in Multimodal Reasoning Agents with Reinforcement Learning](https://mlsanity.com/paper/2609.08025/) (2026)
- [LiteSearch-VL: Small Multimodal Search Agents via Trajectory Distillation and Synthetic Step-DPO](https://mlsanity.com/paper/2608.29357/) (2026)
- [Dependency-Aware Trajectory Refinement for Efficient Multi-Turn Agent Fine-Tuning](https://mlsanity.com/paper/2609.18417/) (2026)
- [MM-ContextFold: Context Folding for Multimodal Agentic Retrieval](https://mlsanity.com/paper/2609.23121/) (2026)
- [SCAD: Structured Credit Assignment and Distillation for Long-Horizon Agents](https://mlsanity.com/paper/2610.03372/) (2026)
- [Harness Compilation: Which Decisions Should a Small Vision-Language Model Keep?](https://mlsanity.com/paper/2610.11231/) (2026)
- [OneSearch-VL: Unified Multimodal Deep Research Agent for Image and Video](https://mlsanity.com/paper/2610.12419/) (2026)

## Abstract (arXiv)

The visual world around us constantly evolves, from real-time news and social media trends to global infrastructure changes visible through satellite imagery and augmented reality enhancements. However, Multimodal Large Language Models (MLLMs), which automate many tasks, struggle to stay current, limited by the cutoff dates in their fixed training datasets. To quantify this stagnation, we introduce LiveVQA, the first-of-its-kind dataset featuring 107,143 samples and 12 categories data specifically designed to support research in both seeking and updating with live visual knowledge. Drawing from recent news articles, video platforms, and academic publications in April 2024-May 2025, LiveVQA enables evaluation of how models handle latest visual information beyond their knowledge boundaries and how current methods help to update them. Our comprehensive benchmarking of 17 state-of-the-art MLLMs reveals significant performance gaps on content beyond knowledge cutoff, and tool-use or agentic visual seeking framework drastically gain an average of 327% improvement. Furthermore, we explore parameter-efficient fine-tuning (PEFT) methods to update MLLMs with new visual knowledge. We dive deeply to the critical balance between adapter capacity and model capability when updating MLLMs with new visual knowledge. All the experimental dataset and source code are publicly available at: https://livevqa.github.io.