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ARXIV:2605.03361 · MULTIMODAL RETRIEVAL · SUBMITTED 06 MAY · 20:25 UTC · FRESHNESS STALE
ARXIV:2605.03361MULTIMODAL RETRIEVALSUBMITTED 06 MAY · 20:25 UTCFRESHNESS STALEHonglei Zhang · Yuting Chen · Chenpeng Hu · Siyue Zhang · Yilei Shi · arXiv
A new benchmark for text-audio retrieval that evaluates advanced reasoning capabilities beyond simple semantic matching.
Opportunity summary
Pain A new benchmark for text-audio retrieval that evaluates advanced reasoning capabilities beyond simple semantic matching.
Evidence 0 refs | 3 sources | 50% coverage
Blocker Evidence unverified
A new benchmark for text-audio retrieval that evaluates advanced reasoning capabilities beyond simple semantic matching. However, most existing benchmarks concentrate on semantic matching and fail to capture the fact that real-world queries often demand…
As multimodal content continues to expand at a rapid pace, audio retrieval has emerged as a key enabling technology for media search, content organization, and intelligent assistants. However, most existing benchmarks concentrate on semantic…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Our evaluation of ten state-of-the-art models reveals the following findings: All models struggle with reasoning-intensive audio retrieval, performing particularly poorly on Negation and Duration…
Multimodal Retrieval moved forward this cycle; last verified May 2026. Public score 7.0/10. Production flags indicate code availability.
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A new benchmark for text-audio retrieval that evaluates advanced reasoning capabilities beyond simple semantic matching.
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10.48550/arXiv.2605.03361A new benchmark for text-audio retrieval that evaluates advanced reasoning capabilities beyond simple semantic matching.
Abstract
As multimodal content continues to expand at a rapid pace, audio retrieval has emerged as a key enabling technology for media search, content organization, and intelligent assistants. However, most existing benchmarks concentrate on semantic matching and fail to capture the fact that real-world queries often demand advanced reasoning abilities, including negation understanding, temporal ordering, concurrent event recognition, and duration discrimination. To address this gap, we introduce ReasonAudio, the first reasoning-intensive benchmark for Text-Audio Retrieval, comprising 1,000 queries and 10,000 composite audio clips across five fundamental reasoning tasks: Negation, Order, Overlap, Duration, and Mix. Despite their intuitive nature for humans and straightforward construction, these tasks pose significant challenges to current models. Our evaluation of ten state-of-the-art models reveals the following findings: All models struggle with reasoning-intensive audio retrieval, performing particularly poorly on Negation and Duration while showing relatively better results on Overlap and Order. Moreover, Multimodal Large Language Model-based embedding models fail to inherit the reasoning capabilities of their backbones through contrastive fine-tuning, suggesting that current training paradigms are insufficient to preserve reasoning capacity in retrieval settings
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Proof status
unverified0 refs; 3 sources; 50% coverage.
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Dimensions overall score 7.0
PROBLEM
A new benchmark for text-audio retrieval that evaluates advanced reasoning capabilities beyond simple semantic matching. However, most existing benchmarks concentrate on semantic matching and fail to capture the fact that real-world queries often demand advanced reasoning abilit...
METHOD
As multimodal content continues to expand at a rapid pace, audio retrieval has emerged as a key enabling technology for media search, content organization, and intelligent assistants. However, most existing benchmarks concentrate on semantic matching and fail to capture the fact...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Our evaluation of ten state-of-the-art models reveals the following findings: All models struggle with reasoning-intensive audio retrieval, performing particularly poorly on Negation and Duration while sh...
WHY NOW
Multimodal Retrieval moved forward this cycle; last verified May 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed public claims while anchored extraction refreshes.
A new benchmark for text-audio retrieval that evaluates advanced reasoning capabilities beyond simple semantic matching. However, most existing benchmarks concentrate on semantic matching and fail to capture the fact that real-world queries often demand advanced reasoning abilities, including negation understanding, temporal ordering, concurrent event recognition, and duration discrimination.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
As multimodal content continues to expand at a rapid pace, audio retrieval has emerged as a key enabling technology for media search, content organization, and intelligent assistants. However, most existing benchmarks concentrate on semantic matching and fail to capture the fact that real-world queries often demand advanced reasoning abilities, including negation understanding, temporal ordering, concurrent event recognition, and duration discrimination.
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. Our evaluation of ten state-of-the-art models reveals the following findings: All models struggle with reasoning-intensive audio retrieval, performing particularly poorly on Negation and Duration while showing relatively better results on Overlap and Order. Code availability is flagged in the production record; the public repository link still needs proof alignment.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Multimodal Retrieval moved forward this cycle; last verified May 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A new benchmark for text-audio retrieval that evaluates advanced reasoning capabilities beyond simple semantic matching.
Segment
Multimodal Retrieval
Adoption evidence
No public code link in the paper record yet
Commercial read
7.0/10 public viability
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reason
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proof status
unverified
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confidence low
next verification path
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Source missing: Build Passport payload.
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Evidence coverage
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stale
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Build readiness
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passport absent
stale
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Artifact maturity
GitHub and Hugging Face maturity payloads
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stale
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Technical feasibility
partial
Current read
Runnable path is not fully verified.
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Gaps
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Run minimal reproduction from the Build Passport prototype path.
Market urgency
missing
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0 references, 3 sources, 50% evidence coverage.
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Buyer clarity
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Integration burden
missing
Current read
No public implementation surface observed.
Evidence
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Gaps
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Write integration checklist from prototype path and target workflow.
Capital intensity
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Run cost passport or mark the cost field not applicable.
Regulatory load
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Current read
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Build Passport ledger does not include regulatory flags.
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Classify regulatory flags before commercialization planning.
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Paper authors are not treated as operators without consent.
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Regulatory need unclassified.
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ARTIFACTS
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DEFENSIBILITY
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OPPORTUNITYKERNEL CHANGES SINCE LAST VIEW
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