pith:OHNVEV57
Omni-Embed-Audio: Leveraging Multimodal LLMs for Robust Audio-Text Retrieval
Multimodal LLM audio encoders match state-of-the-art retrieval while excelling at complex user queries and hard negatives.
arxiv:2604.18360 v2 · 2026-04-20 · cs.SD · cs.CL
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\pithnumber{OHNVEV57C5R7ZRKJT5IFR7ZLGC}
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Record completeness
Claims
OEA achieves comparable text-to-audio retrieval performance to state-of-the-art M2D-CLAP, while demonstrating clear advantages in two critical areas: (1) dominant text-to-text retrieval (+22% relative improvement), and (2) substantially superior hard negative discrimination (+4.3%p HNSR@10, +34.7% relative TFR@10), revealing that LLM backbones provide superior semantic understanding of complex queries.
That the five User-Intent Query formulations accurately capture real-world search behavior and that observed gains are caused by the multimodal LLM backbone rather than differences in training data, scale, or other unstated factors.
Omni-Embed-Audio uses multimodal LLMs to match CLAP on standard audio retrieval while improving text-to-text retrieval by 22% relative and hard negative discrimination by 4.3 points HNSR@10 on user-intent queries.
Receipt and verification
| First computed | 2026-06-02T02:04:17.864783Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
71db5257bf1763fcc5499f5058ff2b309f79469835fc035e7a297e751df47bb3
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/OHNVEV57C5R7ZRKJT5IFR7ZLGC \
| jq -c '.canonical_record' \
| python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 71db5257bf1763fcc5499f5058ff2b309f79469835fc035e7a297e751df47bb3
Canonical record JSON
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