InterLV-Search: Benchmarking Interleaved Multimodal Agentic Search
Summary
InterLV-Search is a new benchmark introduced in this paper to evaluate interleaved language-vision agentic search, highlighting limitations in current systems regarding visual evidence seeking and multimodal integration.
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Paper page - InterLV-Search: Benchmarking Interleaved Multimodal Agentic Search
Source: https://huggingface.co/papers/2605.07510
Abstract
InterLV-Search benchmark evaluates interleaved language-vision agentic search by repeatedly using textual and visual evidence to condition later search, revealing current systems’ limitations in visual evidence seeking and multimodal evidence integration.
Existing benchmarks formultimodal agentic searchevaluate multimodal search and visual browsing, but visual evidence is either confined to the input or treated as an answer endpoint rather than part of aninterleaved search trajectory. We introduce InterLV-Search, a benchmark for Interleaved Language-Vision Agentic Search, in which textual and visual evidence is repeatedly used to condition later search. It contains 2,061 examples across three levels: activevisual evidence seeking, controlled offline interleaved multimodal search, and open-web interleaved multimodal search. Beyond existing benchmarks, it also includesmultimodal multi-branch samplesthat involve comparison between multiple entities during the evidence search. We construct Level 1 and Level 2 withautomated pipelinesand Level 3 with a machine-led, human-supervised open-web pipeline. We further provide InterLV-Agent for standardized tool use, trajectory logging, and evaluation. Experiments on proprietary and open-sourcemultimodal agentsshow that current systems remain far from solving interleaved multimodal search, with the best model below 50% overall accuracy, highlighting challenges invisual evidence seeking,search control, and multimodal evidence integration. We release the benchmark data and evaluation code at https://github.com/hbhalpha/InterLV-Search-Bench
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