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ARXIV:2603.05697 · MULTIMODAL RETRIEVAL · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.05697MULTIMODAL RETRIEVALSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
Benchmark dataset and evaluation suite for multimodal retrieval and reasoning, highlighting the bottleneck in current MLLMs and providing a testbed for retrieval-centric advances.
Opportunity summary
Pain Benchmark dataset and evaluation suite for multimodal retrieval and reasoning, highlighting the bottleneck in current MLLMs and providing a testbed for retrieval-centric advances.
Evidence 0 refs | 0 sources | 17% coverage
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Benchmark dataset and evaluation suite for multimodal retrieval and reasoning, highlighting the bottleneck in current MLLMs and providing a testbed for retrieval-centric advances. However, these settings do not assess a critical real-world requirement, which…
Multimodal large language models (MLLMs) achieve strong performance on benchmarks that evaluate text, image, or video understanding separately. However, these settings do not assess a critical real-world requirement, which involves retrieving relevant evidence from…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Multimodal large language models (MLLMs) achieve strong performance on benchmarks that evaluate text, image, or video understanding separately.
Multimodal Retrieval moved forward this cycle; last verified April 2026. Public score 7.0/10.
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Benchmark dataset and evaluation suite for multimodal retrieval and reasoning, highlighting the bottleneck in current MLLMs and providing a testbed for retrieval-centric advances.
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10.48550/arXiv.2603.05697Benchmark dataset and evaluation suite for multimodal retrieval and reasoning, highlighting the bottleneck in current MLLMs and providing a testbed for retrieval-centric advances.
Abstract
Multimodal large language models (MLLMs) achieve strong performance on benchmarks that evaluate text, image, or video understanding separately. However, these settings do not assess a critical real-world requirement, which involves retrieving relevant evidence from large, heterogeneous multimodal corpora prior to reasoning. Most existing benchmarks restrict retrieval to small, single-modality candidate sets, substantially simplifying the search space and overstating end-to-end reliability. To address this gap, we introduce MultiHaystack, the first benchmark designed to evaluate both retrieval and reasoning under large-scale, cross-modal conditions. MultiHaystack comprises over 46,000 multimodal retrieval candidates across documents, images, and videos, along with 747 open yet verifiable questions. Each question is grounded in a unique validated evidence item within the retrieval pool, requiring evidence localization across modalities and fine-grained reasoning. In our study, we find that models perform competitively when provided with the corresponding evidence, but their performance drops sharply when required to retrieve that evidence from the full corpus. Additionally, even the strongest retriever, E5-V, achieves only 40.8% Recall@1, while state-of-the-art MLLMs such as GPT-5 experience a significant drop in reasoning accuracy from 80.86% when provided with the corresponding evidence to 51.4% under top-5 retrieval. These results indicate that multimodal retrieval over heterogeneous pools remains a primary bottleneck for MLLMs, positioning MultiHaystack as a valuable testbed that highlights underlying limitations obscured by small-scale evaluations and promotes retrieval-centric advances in multimodal systems.
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PROBLEM
Benchmark dataset and evaluation suite for multimodal retrieval and reasoning, highlighting the bottleneck in current MLLMs and providing a testbed for retrieval-centric advances. However, these settings do not assess a critical real-world requirement, which involves retrieving...
METHOD
Multimodal large language models (MLLMs) achieve strong performance on benchmarks that evaluate text, image, or video understanding separately. However, these settings do not assess a critical real-world requirement, which involves retrieving relevant evidence from large, hetero...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Multimodal large language models (MLLMs) achieve strong performance on benchmarks that evaluate text, image, or video understanding separately.
WHY NOW
Multimodal Retrieval moved forward this cycle; last verified April 2026. Public score 7.0/10.
Abstract-backed public claims while anchored extraction refreshes.
Benchmark dataset and evaluation suite for multimodal retrieval and reasoning, highlighting the bottleneck in current MLLMs and providing a testbed for retrieval-centric advances. However, these settings do not assess a critical real-world requirement, which involves retrieving relevant evidence from large, heterogeneous multimodal corpora prior to reasoning.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Multimodal large language models (MLLMs) achieve strong performance on benchmarks that evaluate text, image, or video understanding separately. However, these settings do not assess a critical real-world requirement, which involves retrieving relevant evidence from large, heterogeneous multimodal corpora prior to reasoning.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Multimodal large language models (MLLMs) achieve strong performance on benchmarks that evaluate text, image, or video understanding separately.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Multimodal Retrieval moved forward this cycle; last verified April 2026. Public score 7.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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Benchmark dataset and evaluation suite for multimodal retrieval and reasoning, highlighting the bottleneck in current MLLMs and providing a testbed for retrieval-centric advances.
Segment
Multimodal Retrieval
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