arXiv AI
MM-BrowseComp: A Comprehensive Benchmark for Multimodal Browsing Agents
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Visual-Seeker: Towards Visual-Native Multimodal Agentic Search via Active Visual Reasoning
arXiv:2606. 15231v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have demonstrated impressive capabilities in many visual tasks, but they often struggle with factual grounding when confronted with complex, open-world scenarios.
Hugging Face Trending Papers
Jun 30
SimpleSearch-VL: A Simple Recipe for Multimodal Agentic Deep Search
We present SimpleSearch-VL, an efficient, reliable, and practical framework for multimodal agentic search. Its core idea is to improve the agent's own search-and-verification process rather than scaling data, tools, or auxiliary model components.
arXiv Computer Vision
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ARK: A Dual-Axis Multimodal Retrieval Benchmark along Reasoning and Knowledge
arXiv:2602.09839v2 Announce Type: replace Abstract: Existing multimodal retrieval benchmarks largely emphasize semantic matching on daily-life images and offer limited diagnostics of professional kno...
arXiv AI
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MELLON - Multimodal Enhanced LLM for Online Navigation
arXiv:2608. 09121v1 Announce Type: new Abstract: Web navigation agents are capable of addressing various types of tasks on different websites.
arXiv Computation and Language
Sep 23
ICDAR2026 Competition on Multimodal Reasoning over Documents in Multiple Domains
The ICDAR2026 Competition on Multimodal Reasoning over Documents in Multiple Domains introduced a new Visual Question Answering benchmark that tests reasoning over documents from eight distinct domains such as business reports, scientific papers, and engineering drawings. Twenty valid submissions from eight teams were evaluated, featuring approaches ranging from zero‑shot vision‑language models to multi‑agent ensembles and fine‑tuned multimodal systems. Results indicate that the most effective systems employ structured evidence extraction, retrieval, verification, and orchestration across multiple components rather than single‑pass prompting.
arXiv AI
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ProMSA:Progressive Multimodal Search Agents for Knowledge-Based Visual Question Answering
arXiv:2606. 27974v1 Announce Type: cross Abstract: Knowledge-based Visual Question Answering (KB-VQA) requires models to combine image understanding with external knowledge.