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arxiv: 2605.08762 · v1 · submitted 2026-05-09 · 💻 cs.SD · cs.LG

Recognition: no theorem link

Omni-DeepSearch: A Benchmark for Audio-Driven Omni-Modal Deep Search

Tao Yu , Yiming Ding , Shenghua Chai , Minghui Zhang , Zhongtian Luo , Xinming Wang , Xinlong Chen , Zhaolu Kang
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Junhao Gong Yuxuan Zhou Haopeng Jin Zhiqing Cui Jiabing Yang Yifan Zhang Hongzhu Yi Zheqi He Xi Yang Yan Huang Liang Wang
Authors on Pith no claims yet

Pith reviewed 2026-05-12 03:17 UTC · model grok-4.3

classification 💻 cs.SD cs.LG
keywords audio-driven searchomni-modal benchmarkcross-modal retrievalmultimodal reasoningmodel evaluationdeep searchGemini-3-Pro
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The pith

Omni-DeepSearch shows current omni-modal models reach at most 43.44% accuracy when they must start from audio and actively search text, images and video for answers.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces Omni-DeepSearch, a benchmark of 640 samples where models receive one or more audio clips plus a question and must extract clues from the sound, issue searches across other modalities, and reason step by step to a short verifiable answer. It tests whether models can operate in an audio-first setting that demands retrieval rather than receiving all evidence at once. A reader would care because everyday tasks such as identifying an event from background noise or following up on spoken clues routinely require exactly this sequence of inference and cross-modal lookup. The authors apply a multi-stage filter to guarantee each question truly needs the audio and external evidence. Their tests on recent models reveal persistent failures in audio interpretation, query construction, tool reliability, multi-hop retrieval, and cross-modal checking.

Core claim

Given one or more audio clips and a related question, models must infer useful clues from audio, invoke text, image, and video search tools, and perform multi-hop reasoning to produce a short, objective, and verifiable answer. Omni-DeepSearch contains 640 samples across 15 fine-grained categories, covering four retrieval target modalities and four audio content types. Experiments on recent closed-source and open-source omni-modal models show that this task remains highly challenging: the strongest evaluated model, Gemini-3-Pro, achieves only 43.44% average accuracy.

What carries the argument

The Omni-DeepSearch benchmark together with its multi-stage filtering pipeline that enforces audio dependence, retrieval necessity, visual modality necessity, and answer uniqueness.

If this is right

  • Current models have clear weaknesses in inferring entities and relations directly from audio.
  • Reliable tool calling and query formulation are required before multi-hop retrieval can succeed.
  • Cross-modal verification remains a separate failure point even after evidence is retrieved.
  • Progress on audio-driven search would directly improve the usefulness of multimodal agents in open-world settings.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same audio-first protocol could be applied to other single-modality starting points such as a single image or text snippet to test symmetric capabilities.
  • Low scores suggest that tighter integration between audio encoders and external search APIs might raise performance without new model scale.
  • Real-time versions of the benchmark could expose whether latency in tool use compounds the reasoning errors already observed.

Load-bearing premise

The multi-stage filtering pipeline successfully creates questions that depend on the audio input and cannot be answered without cross-modal retrieval.

What would settle it

An experiment showing that a model can answer the questions at high accuracy using only the audio clips with no searches, or that the questions can be solved from text alone, would demonstrate that the filtering did not achieve its intended guarantees.

Figures

Figures reproduced from arXiv: 2605.08762 by Haopeng Jin, Hongzhu Yi, Jiabing Yang, Junhao Gong, Liang Wang, Minghui Zhang, Shenghua Chai, Tao Yu, Xinlong Chen, Xinming Wang, Xi Yang, Yan Huang, Yifan Zhang, Yiming Ding, Yuxuan Zhou, Zhaolu Kang, Zheqi He, Zhiqing Cui, Zhongtian Luo.

Figure 1
Figure 1. Figure 1: Overview of Omni-DeepSearch. In data construction, tasks are built across four audio categories and four retrieval settings. Text and image-text tasks are constructed over Wikipedia knowledge-graph paths, while video tasks are collected from filtered candidate videos. In data filtering, multi-stage LLM-based checks ensure audio dependence, retrieval necessity, visual modality necessity, and answer uniquene… view at source ↗
Figure 2
Figure 2. Figure 2: Data statistics of the Omni-DeepSearch bench. 43 [PITH_FULL_IMAGE:figures/full_fig_p043_2.png] view at source ↗
read the original abstract

Current omni-modal benchmarks mainly evaluate models under settings where multiple modalities are provided simultaneously, while the ability to start from audio alone and actively search for cross-modal evidence remains underexplored. In this paper, we introduce \textbf{Omni-DeepSearch}, a benchmark for audio-driven omni-modal deep search. Given one or more audio clips and a related question, models must infer useful clues from audio, invoke text, image, and video search tools, and perform multi-hop reasoning to produce a short, objective, and verifiable answer. Omni-DeepSearch contains 640 samples across 15 fine-grained categories, covering four retrieval target modalities and four audio content types. A multi-stage filtering pipeline ensures audio dependence, retrieval necessity, visual modality necessity, and answer uniqueness. Experiments on recent closed-source and open-source omni-modal models show that this task remains highly challenging: the strongest evaluated model, Gemini-3-Pro, achieves only 43.44\% average accuracy. Further analyses illustrate key bottlenecks in audio entity inference, query formulation, tool-use reliability, multi-hop retrieval, and cross-modal verification. These results highlight audio-driven omni-modal deep search as an important and underexplored direction for future multimodal agents.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 3 minor

Summary. The paper introduces Omni-DeepSearch, a benchmark of 640 samples for audio-driven omni-modal deep search. Given one or more audio clips and a related question, models must infer clues from audio, invoke text/image/video search tools, and perform multi-hop reasoning to produce short, objective answers. The benchmark covers 15 fine-grained categories, four retrieval target modalities, and four audio content types. A multi-stage filtering pipeline is used to enforce audio dependence, retrieval necessity, visual modality necessity, and answer uniqueness. Experiments on closed- and open-source omni-modal models show the task is challenging, with Gemini-3-Pro achieving the highest average accuracy of 43.44%; additional analyses identify bottlenecks in audio entity inference, query formulation, tool-use reliability, multi-hop retrieval, and cross-modal verification.

Significance. If the benchmark's construction and filtering pipeline are shown to be valid, the work would be significant for defining an underexplored audio-initiated cross-modal search task and for providing empirical evidence of current model limitations along with concrete bottleneck analyses. This could usefully guide development of omni-modal agents. The empirical evaluation against recent models and the scale (640 samples) are strengths, but the overall impact depends on demonstrating that the reported accuracies reflect the intended capabilities rather than artifacts of the benchmark design.

major comments (1)
  1. [Abstract (and benchmark construction section describing the multi-stage filtering pipeline)] The central claim that the task remains highly challenging (with Gemini-3-Pro at 43.44% accuracy) rests on the assumption that the 640 samples genuinely require audio dependence, retrieval necessity, visual modality necessity, and answer uniqueness. The abstract asserts that the multi-stage filtering pipeline ensures these properties, but provides no validation details such as human verification rates, inter-annotator agreement, or ablation results (e.g., performance when audio is removed or when search is disallowed). This is load-bearing for interpreting the low accuracies as evidence of model limitations rather than benchmark leakage.
minor comments (3)
  1. [Experiments] No error bars, confidence intervals, or statistical tests are reported for the accuracy figures across models or categories.
  2. [Experiments] No human baseline performance is provided to contextualize the 43.44% figure and the claimed difficulty of the task.
  3. [Benchmark description] Additional details on the selection and balance of the 15 fine-grained categories, as well as the distribution across audio content types and retrieval modalities, would improve reproducibility and interpretation.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. The point raised regarding the validation of the benchmark construction is well-taken and critical for the interpretability of our results. We address it in detail below and will incorporate the necessary additions in the revised version.

read point-by-point responses

  1. Referee: The central claim that the task remains highly challenging (with Gemini-3-Pro at 43.44% accuracy) rests on the assumption that the 640 samples genuinely require audio dependence, retrieval necessity, visual modality necessity, and answer uniqueness. The abstract asserts that the multi-stage filtering pipeline ensures these properties, but provides no validation details such as human verification rates, inter-annotator agreement, or ablation results (e.g., performance when audio is removed or when search is disallowed). This is load-bearing for interpreting the low accuracies as evidence of model limitations rather than benchmark leakage.

    Authors: We agree that additional validation is necessary to substantiate the claims. The manuscript describes the multi-stage filtering pipeline in Section 3, which combines automated checks for audio dependence (via clue extraction requiring audio), retrieval necessity (questions not answerable from audio alone), visual modality necessity, and answer uniqueness. However, we did not provide quantitative human validation metrics or ablation studies. In the revision, we will expand this section to include: (1) human verification results on a subset of 200 samples, with inter-annotator agreement measured via Fleiss' kappa among three annotators; (2) ablation experiments where we remove audio input or disable search tools and report performance drops for Gemini-3-Pro and other models. These will demonstrate that the properties hold and that the low accuracies are not due to leakage. We believe this will address the concern effectively. revision: yes

Circularity Check

0 steps flagged

No circularity in benchmark construction or evaluation

full rationale

The paper introduces Omni-DeepSearch as a new benchmark via audio collection, question formulation, and a multi-stage filtering pipeline, followed by direct empirical testing of external closed- and open-source models. No mathematical derivations, equations, parameter fittings, or predictive claims exist that could reduce to the paper's own inputs by construction. The filtering pipeline is presented as a methodological design choice asserted to enforce audio dependence and retrieval necessity, without any self-referential derivation or uniqueness theorem imported from prior author work. Evaluation metrics such as Gemini-3-Pro's 43.44% accuracy are straightforward measurements on the constructed dataset and do not involve fitted inputs renamed as predictions or self-citation chains. The work is therefore self-contained as benchmark creation and external model assessment.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the effectiveness of an unvalidated multi-stage filtering pipeline and the assumption that the described search tools and reasoning steps accurately test the intended capability.

axioms (1)
  • domain assumption Multi-stage filtering pipeline ensures audio dependence, retrieval necessity, visual modality necessity, and answer uniqueness
    Invoked in abstract to justify sample validity but without supporting evidence or details.

pith-pipeline@v0.9.0 · 5587 in / 1381 out tokens · 72953 ms · 2026-05-12T03:17:16.535049+00:00 · methodology

discussion (0)

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    Lack of Self-Correction:The model explicitly listed "Row 1 to Row 6" in its thoughts, indicating that its visual processing unit is unable to provide a high-fidelity "count-and-verify" signal that can override flawed internal hypotheses. 19 Omni-DeepSearch Outcome: Final Answer Error The agent processed the correct visual evidence but reached an incorrect...

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    Visual Skepticism :After retrieving images of the Edison Telegraph (Turn 6), the agent expresses uncertainty in its thought log, perceiving that the images lack the clarity or specific "angle" needed to definitively count the holes

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    wheel with 8 holes

    The Textual Shift:Instead of searching for higher-resolution visual close-ups, the agent makes a strategic pivot. It assumes the count must be explicitly mentioned in historical documentation. It spends the remaining turns scouring patent texts and museum records for a phrase like "wheel with 8 holes."

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    search spiral

    Information Stalemate:Because historical patent descriptions often detail the *function* of a gear rather than its aesthetic cutout count, the model finds zero textual results. This results in a "search spiral" where the model repeatedly micro-adjusts its textual queries, hoping to find a written confirmation that does not exist. Outcome: Final T urn Budg...

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    famous" entities over

    Fame Heuristic Over Precision:The model exhibited a strong preference for "famous" entities over "niche" ones. Upon recognizing "traditional instrumental music" and "village name," it bypassed the specific auditory signatures of Tuvan throat singing (the ensembleAlash) and defaulted to the high-frequency category ofIrish Folk. By choosing the well-knownT ...

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    Popularity Bias

    Recursive Logical Dead-end:This initial "Popularity Bias" led to a structural failure in the "Name Reversal" step. Since the model was anchored to the Irish ensemble, it attempted to force the logic on the venue "**Kennedy Hall**." Its search for the non-existent unincorporated community "**Hall Kennedy**" on a state highway created a recursive loop. The ...

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    decorated instrument strap symbols

    Descriptive Overload:Instead of identifying theSubject(the musician) first, the model treated the search engine like a visual captioning tool. Queries like "decorated instrument strap symbols" are too fine-grained and semantically noisy for broad video search engines, which prioritize entities (names, titles) over visual scene descriptions

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    skull and crossbones

    Visual Hallucination (Final T urn):In its desperation, the model began hallucinating specific symbols like "skull and crossbones" to narrow the search, moving further away from the ground truth (Musical notes). Outcome: Recursive Failure & Timeout • Result:Failure. The model spent its entire reasoning budget micro-adjusting descriptive queries without eve...

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    intent" is lost before it reaches the

    Format without Content:The model successfully maintains the JSON/tool-calling format but fails to populate the specific arguments. This suggests a systemic breakdown in the final stage of response generation where the "intent" is lost before it reaches the "parameter" field

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    It correctly reasons that it needs to search for the genus, yet every time it tries to act, the output pipeline results in a null query

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    logical collapse

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    refined British delivery,

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    historical individual

    Search Direction Misalignment:This misidentification derailed the search for the "historical individual." The model looked for medical students played by Ralph Fiennes, which yielded no results. Fiennes played T.E. Lawrence in a TV movie, but Lawrence does not fit the "medical student" or "women’s college" biographical clues. Conclusion: Identifying a uni...

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    Forced Logic Alignment:Because the model is convinced the speaker is Jackman, it attempts to force the "unverified biographical claim" (immigrating to live with an aunt and uncle) onto him. When search results (Turn 2, 4) confirm Jackman was born in Australia and stayed with his father, the model begins to hallucinate or search for "fake" biographies of J...

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    Jackman-cancer

    Topic Over Voice:The model prioritized thetopic of the speech(cancer) over theacoustic profile(the actual voice). Tommy Wiseau’s accent is famously enigmatic and non-native, while Hugh Jackman’s is a clear Australian-English. The model allowed the high-frequency "Jackman-cancer" association to override its auditory sensors

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    Popularity Loop,

    Complexity Collapse:The actual path (Wiseau → Chalmette, LA → Duke of Kent House → Louise Bourgeois) was never explored. The model remained trapped in a "Popularity Loop," eventually ex- hausting its budget trying to link Jackman to the community of Fairmount (conflating him with James Dean). Outcome: Final T urn Stalemate • Result:Failure. The model ran ...

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    These semantically plausible distractors weakened the model’s confidence in the original correct reasoning path

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    Buurdhaab,

    Belief Instability:As the retrieval trajectory grew longer, the model continuously revised its own intermediate conclusions. Instead of consolidating evidence around “Buurdhaab,” it repeatedly reopened previously solved sub-questions and entered a recursive search loop. Unlike typical hallucination failures, the model had already retrieved the correct ans...

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    REMEMBER:The solver has ONLY the audio segment and your question

    You are given the REAL IDENTITIES of the speakers to help you disambiguate, but you must NEVER use these names in the final question. REMEMBER:The solver has ONLY the audio segment and your question. They do NOT see the Video Title, Description. 2.NODE SEQUENCE:A logical chain of entities. Use the provided Node Article Snippets to discover how each entity...

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    Start Node

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    SEMANTIC LEAKAGE

    Identify one specific attribute (a name, a precise location, a specific date, or a technical term) from the final node in the path. The question must be constructed so that this attribute value is the only possible and exact answer. THE CLOAKING PROTOCOL (Anti-Semantic Leakage): • NO SEARCHABLE FINGERPRINTS:Strictly forbid any specific quantities, unique ...

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    fork in the road

    MINIMAL SUFFICIENT SPECIFICITY (ZERO AMBIGUITY):The generated question MUST be concise, BUT uniqueness is paramount. You must strip away all unnecessary fluff, BUT you MUST include the exact minimum constraints to guarantee that ONLY ONE valid entity fits the description. Never create a "fork in the road" by being too vague. 2.DYNAMIC CLOAKING (FIRST HOP ...

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    Listen to ALL provided Audio Clips to identify their respective identities (X 1,X 2, . . . ,Xn)

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    Bridge Entity

    Deduce the "Bridge Entity" based on the generic relationship you describe between the audio subjects

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    Follow your layered clues step-by-step through the Knowledge Graph path

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    Identify one specific attribute from the final node. THE CLOAKING PROTOCOL (Anti-Semantic Leakage): • NO SEARCHABLE FINGERPRINTS:Strictly forbid any specific quantities, unique descriptors, or highly specific biographical/historical anomalies. • BEWARE OF "SEMANTIC LEAKAGE":Simply replacing proper nouns with complicated synonyms is a FAILURE if the underl...

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    30 Omni-DeepSearch

    YOU MUST NOT describe what each audio clip does within the intersection. 30 Omni-DeepSearch

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    the speaker directed it,

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    The speaker in the first track directed this film, the artist in the second track composed its score, and the animal in the third track is featured within it

    JUST STATE THE INTERSECTION: Your first sentence must simply declare that the provided audio tracks intersect at a specific entity type. • BAD (Role/Plot Leakage):"The speaker in the first track directed this film, the artist in the second track composed its score, and the animal in the third track is featured within it." (Instantly guessable without audi...

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    fork in the road

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    You must deduce a ’hidden context’

    If the relationship or the bridge entity is EXPLICITLY mentioned in the spoken words of any audio clip, you CANNOT use it as the subject. You must deduce a ’hidden context’

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Showing first 80 references.