Paper page - TVIR: Building Deep Research Agents Towards Text–Visual Interleaved Report Generation

Source: https://huggingface.co/papers/2606.02320 Authors:

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Abstract

A multimodal deep research benchmark and agent framework are introduced to evaluate and improve the factual reliability and visual alignment of automated report generation systems.

Deep Research Agents have shown strong capability in multi-step information retrieval, reasoning, and long-form report generation, but existing benchmarks and systems remain predominantly text-centric, with limited evaluation of whethervisual elementsare factually reliable and well aligned with the surrounding analysis. To address this gap, we introduce TVIR (Text--Visual Interleaved Report Generation), which includes TVIR-Bench, a benchmark of 100 expert-curatedmultimodal deep researchtasks that requirevisual elementsto serve specific analytical sub-goals, and TVIR-Agent, ahierarchical multi-agent frameworkthat serves as a strong baseline for constructing outlines, retrieving images, generating charts with traceable sources, and composing reports through context-aware sequential writing. We further develop a dual-path evaluation framework that combinesTextual AssessmentandVisual Assessment. Experiments across nine deep research systems show that TVIR-Agent achieves strong overall performance, underscoring the importance of explicit multimodal design and evaluation forevidence-driven report generation.

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