arxiv: 2604.10695 · v2 · submitted 2026-04-12 · 💻 cs.CV

Recognition: unknown

Retrieving to Recover: Towards Incomplete Audio-Visual Question Answering via Semantic-consistent Purification

Jiayu Zhang , Shuo Ye , Qilang Ye , Zihan Song , Jiajian Huang , Zitong Yu

Authors on Pith no claims yet

Pith reviewed 2026-05-10 15:58 UTC · model grok-4.3

classification 💻 cs.CV

keywords Audio-Visual Question AnsweringMissing ModalitiesRetrieval-Based RecoverySemantic EmbeddingsAdaptive PurificationMultimodal Robustness

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The pith

R²ScP recovers missing audio or visual data in question answering by retrieving similar real examples and purifying their semantics rather than inventing new features.

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

The paper argues that audio-visual question answering systems degrade sharply when sound or sight is missing because generative methods that invent the absent data capture only shared patterns and produce hallucinations. It proposes shifting to retrieval of actual examples that match the available modality through shared semantic spaces, followed by adaptive cleaning to strip out irrelevant noise while keeping domain-specific details. A two-stage training process then aligns the semantics across retrieved and original sources. This approach is presented as more reliable for real-world interruptions than synthesis-based fixes.

Core claim

R²ScP shifts missing-modality handling from generative imputation to retrieval-based recovery: cross-modal retrieval via unified semantic embeddings acquires missing domain-specific knowledge, a context-aware adaptive purification mechanism removes latent semantic noise from the retrieved data, and a two-stage training strategy explicitly models semantic relationships between knowledge from different sources, yielding improved AVQA accuracy and robustness under modal incompleteness.

What carries the argument

The R²ScP framework, which performs cross-modal retrieval through unified semantic embeddings to fetch missing knowledge and applies context-aware adaptive purification to eliminate noise before two-stage training aligns the sources.

If this is right

AVQA systems maintain higher accuracy when one modality is absent or corrupted.
Robustness increases against real-world data interruptions such as sensor failures.
Hallucinations decrease because unique modality-specific knowledge comes from retrieved real data rather than synthesis.
Semantic consistency improves through the two-stage training that links retrieved and original knowledge sources.

Where Pith is reading between the lines

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

The retrieval-plus-purification pattern could extend to other multimodal tasks that suffer from partial inputs, such as video description or speech-to-text under noise.
Adopting retrieval over generation may reduce the need for large generative models in resource-constrained settings.
Real-time deployment would require efficient indexing of large semantic embedding databases to keep retrieval latency low.

Load-bearing premise

Cross-modal retrieval through unified semantic embeddings can reliably supply the exact missing modality-specific details, and the purification step removes only noise without discarding essential information.

What would settle it

A controlled test in which retrieved samples contain semantic mismatches that the adaptive purification fails to filter, resulting in lower accuracy than generative baselines on the same incomplete inputs.

Figures

Figures reproduced from arXiv: 2604.10695 by Jiajian Huang, Jiayu Zhang, Qilang Ye, Shuo Ye, Zihan Song, Zitong Yu.

**Figure 2.** Figure 2: Overview of the proposed R2ScP framework (when the audio modality is missing). (a) The CMR module retrieves candidate features from a unified semantic space, while the CAP mechanism acts as a semantic filter that refines the coarse retrieved features using the common knowledge between the visual and audio modalities. (b) The overall architecture processes available and purified representations for the answ… view at source ↗

**Figure 3.** Figure 3: Two-stage training strategy sequentially performs expert pre-training and expert mixing optimization. [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗

**Figure 4.** Figure 4: Impact of purification budget k and number of retrieved samples n. Method Avg. R 2ScP (ours) 70.72 w/o modality-specific expert pretraining 68.98 w/o expert mixing training 64.21 w/o ranking loss 69.62 [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗

**Figure 5.** Figure 5: Generalization analysis on the Music-AVQA dataset across various missing rates [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗

**Figure 6.** Figure 6: Trends in expert loads during experts mixing [PITH_FULL_IMAGE:figures/full_fig_p008_6.png] view at source ↗

**Figure 7.** Figure 7: t-SNE visualization of our model and other methods on the Music-AVQA dataset. [PITH_FULL_IMAGE:figures/full_fig_p009_7.png] view at source ↗

read the original abstract

Recent Audio-Visual Question Answering (AVQA) methods have advanced significantly. However, most AVQA methods lack effective mechanisms for handling missing modalities, suffering from severe performance degradation in real-world scenarios with data interruptions. Furthermore, prevailing methods for handling missing modalities predominantly rely on generative imputation to synthesize missing features. While partially effective, these methods tend to capture inter-modal commonalities but struggle to acquire unique, modality-specific knowledge within the missing data, leading to hallucinations and compromised reasoning accuracy. To tackle these challenges, we propose R$^{2}$ScP, a novel framework that shifts the paradigm of missing modality handling from traditional generative imputation to retrieval-based recovery. Specifically, we leverage cross-modal retrieval via unified semantic embeddings to acquire missing domain-specific knowledge. To maximize semantic restoration, we introduce a context-aware adaptive purification mechanism that eliminates latent semantic noise within the retrieved data. Additionally, we employ a two-stage training strategy to explicitly model the semantic relationships between knowledge from different sources. Extensive experiments demonstrate that R$^{2}$ScP significantly improves AVQA and enhances robustness in modal-incomplete scenarios.

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.

Desk Editor's Note private letter to a colleague

The paper's shift from generating missing modalities to retrieving them via semantic embeddings is a clean idea worth testing, but the abstract gives no numbers or datasets so the gains remain unproven.

read the letter

The main thing here is a move away from generative imputation toward retrieval-based recovery for incomplete audio-visual question answering. They build unified semantic embeddings to pull in missing features from a database, add a context-aware purification step to strip noise, and use two-stage training to tie the sources together. That framing is new relative to the generative baselines they cite, and it directly targets the hallucination problem that comes with synthesizing modality-specific details from scratch.

Referee Report

2 major / 2 minor

Summary. The paper presents R²ScP, a novel framework for incomplete Audio-Visual Question Answering (AVQA) that replaces generative imputation with retrieval-based recovery. It employs cross-modal retrieval using unified semantic embeddings to obtain missing modality-specific knowledge, introduces a context-aware adaptive purification mechanism to eliminate semantic noise from retrieved data, and uses a two-stage training strategy to model semantic relationships between knowledge sources. The abstract asserts that extensive experiments show significant improvements in AVQA performance and robustness under modal-incomplete conditions.

Significance. If the empirical claims hold, this work could meaningfully advance the field by introducing a retrieval paradigm for missing modalities, potentially reducing hallucinations common in generative approaches and improving real-world applicability of AVQA systems. The two-stage training and adaptive purification are positive design choices that explicitly address semantic consistency. However, the significance hinges on whether the method truly recovers unique knowledge rather than shared semantics.

major comments (2)

The central claim of 'significant improvement' and 'enhanced robustness' is asserted without any mention of specific datasets, baselines, evaluation metrics, or quantitative results in the abstract, which undermines the ability to evaluate the effectiveness of the proposed paradigm shift.
The description of acquiring 'unique, modality-specific knowledge' through 'unified semantic embeddings' requires clarification, as such embeddings are typically optimized for cross-modal alignment and may primarily encode shared rather than unique information; without an explicit mechanism (e.g., auxiliary loss or disentanglement) in the architecture to preserve domain-specific signals, the retrieval step risks functioning as soft imputation, weakening the claimed distinction from generative methods.

minor comments (2)

The acronym R²ScP is introduced without immediate expansion in the abstract, which could be clarified for readers.
The phrase 'semantic-consistent purification' in the title could benefit from a brief definition or reference to the adaptive mechanism described.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the thoughtful and constructive comments on our manuscript. We have addressed each major comment point by point below, providing clarifications and indicating revisions where the manuscript can be strengthened without misrepresenting our contributions.

read point-by-point responses

Referee: The central claim of 'significant improvement' and 'enhanced robustness' is asserted without any mention of specific datasets, baselines, evaluation metrics, or quantitative results in the abstract, which undermines the ability to evaluate the effectiveness of the proposed paradigm shift.

Authors: We agree that the abstract would benefit from greater specificity to allow readers to immediately assess the empirical support for our claims. In the revised manuscript, we have updated the abstract to reference the primary evaluation datasets, the key generative imputation baselines, and concrete performance metrics demonstrating the improvements under incomplete modality conditions. revision: yes
Referee: The description of acquiring 'unique, modality-specific knowledge' through 'unified semantic embeddings' requires clarification, as such embeddings are typically optimized for cross-modal alignment and may primarily encode shared rather than unique information; without an explicit mechanism (e.g., auxiliary loss or disentanglement) in the architecture to preserve domain-specific signals, the retrieval step risks functioning as soft imputation, weakening the claimed distinction from generative methods.

Authors: We appreciate this observation on the potential overlap between shared and unique semantics in unified embeddings. Our framework addresses this through the context-aware adaptive purification mechanism, which uses question-specific context to filter semantic noise and retain modality-distinct information from retrieved examples, combined with the two-stage training strategy that explicitly models relationships across knowledge sources to emphasize domain-specific signals. We have expanded the methodology section with additional explanation and supporting ablation analysis to clarify this distinction and show why the approach differs from soft imputation. revision: partial

Circularity Check

0 steps flagged

No circularity: descriptive framework with no derivations or self-referential reductions

full rationale

The paper introduces the R²ScP framework at a conceptual level, describing cross-modal retrieval via unified semantic embeddings, context-aware adaptive purification, and a two-stage training strategy. No equations, mathematical derivations, fitted parameters, or load-bearing self-citations appear in the abstract or framework description. Claims of improved robustness rest on experimental validation rather than any reduction of outputs to inputs by construction. The method is presented as a paradigm shift supported by external benchmarks, with no self-definitional loops or imported uniqueness theorems.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The approach rests on domain assumptions about semantic embeddings and purification rather than new mathematical axioms or invented physical entities.

axioms (2)

domain assumption Unified semantic embeddings capture both inter-modal commonalities and unique modality-specific knowledge
Invoked to justify acquiring missing domain-specific knowledge via cross-modal retrieval.
domain assumption Context-aware adaptive purification can isolate and remove latent semantic noise while preserving essential information
Required for the claim that retrieval maximizes semantic restoration without hallucinations.

pith-pipeline@v0.9.0 · 5508 in / 1319 out tokens · 48843 ms · 2026-05-10T15:58:58.061391+00:00 · methodology

discussion (0)

Reference graph

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