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PrismAudio: Decomposed Chain-of-Thought and Multi-dimensional Rewards for Video-to-Audio Generation

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
Chain-of-ThoughtReinforcement LearningVideo-to-Audio Generation
Abstract:

Video-to-Audio (V2A) generation requires balancing four critical perceptual dimensions: semantic consistency, audio-visual temporal synchrony, aesthetic quality, and spatial accuracy; yet existing methods suffer from objective entanglement that conflates competing goals in single loss functions and lack human preference alignment. We introduce PrismAudio, the first framework to integrate Reinforcement Learning into V2A generation with specialized Chain-of-Thought (CoT) planning. Our approach decomposes monolithic reasoning into four specialized CoT modules (Semantic, Temporal, Aesthetic, and Spatial CoT), each paired with targeted reward functions. This CoT-reward correspondence enables multidimensional RL optimization that guides the model to jointly generate better reasoning across all perspectives, solving the objective entanglement problem while preserving interpretability. To make this optimization computationally practical, we propose Fast-GRPO, which employs hybrid ODE-SDE sampling that dramatically reduces the training overhead compared to existing GRPO implementations. We also introduce AudioCanvas, a rigorous benchmark that is more distributionally balanced and covers more realistically diverse and challenging scenarios than existing datasets, with 300 single-event classes and 501 multi-event samples. Experimental results demonstrate that PrismAudio achieves state-of-the-art performance across all four perceptual dimensions on both the in-domain VGGSound test set and out-of-domain AudioCanvas benchmark. The project page is available at~\url{https://PrismAudio.github.io}.

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This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
If you have any questions, please contact: mingzhang23@m.fudan.edu.cn

Overview

Overall Novelty Assessment

PrismAudio introduces a reinforcement learning framework for video-to-audio generation that decomposes perceptual alignment into four specialized Chain-of-Thought modules (Semantic, Temporal, Aesthetic, Spatial), each paired with targeted reward functions. The paper resides in the 'Chain-of-Thought Reinforcement Learning' leaf under 'Preference Optimization and Multi-Dimensional Reward Learning', which contains only two papers total. This represents a sparse, emerging research direction within the broader taxonomy of 25 papers across the field, suggesting the work explores relatively uncharted territory in applying decomposed reasoning and RL to video-to-audio synthesis.

The taxonomy reveals that most prior work in video-to-audio generation clusters around diffusion-based architectures, multimodal alignment mechanisms, and temporal synchronization techniques. Neighboring leaves include 'Multimodal Diffusion Transformers' (e.g., DiffAVA) and 'Frame-Level Synchronization' methods, which address perceptual quality through architectural design or deterministic conditioning rather than preference optimization. PrismAudio diverges by introducing explicit human preference alignment through RL, positioning it at the intersection of generative modeling and reward-based learning—a boundary that remains underexplored in the taxonomy structure.

Among 14 candidates examined, the core PrismAudio framework (decomposed CoT with multi-dimensional RL) shows no clear refutation across 2 candidates, suggesting novelty in this specific integration. However, the Fast-GRPO algorithm examined 8 candidates with 1 refutable match, and the AudioCanvas benchmark examined 4 candidates with 1 refutable match, indicating these components face more substantial prior work. The limited search scope (14 total candidates, not hundreds) means these statistics reflect top-K semantic matches rather than exhaustive coverage, so undetected overlaps remain possible.

Based on the available signals from 14 examined candidates, the work appears to occupy a genuinely sparse research direction in preference-aligned video-to-audio generation, though individual algorithmic and benchmarking contributions show varying degrees of prior overlap. The taxonomy context confirms that RL-based perceptual optimization remains underrepresented compared to diffusion and alignment-focused approaches, lending credence to the framework's positioning as exploratory work in an emerging subfield.

Taxonomy

Core-task Taxonomy Papers
25
3
Claimed Contributions
14
Contribution Candidate Papers Compared
2
Refutable Paper

Research Landscape Overview

Core task: video-to-audio generation with multi-dimensional perceptual alignment. The field has evolved into a rich landscape organized around several complementary branches. Multimodal Representation Alignment Mechanisms explore how to bridge visual and auditory modalities, often using cross-modal encoders and attention schemes (e.g., Q-Former Alignment[11], Visually Aligned Sound[12]). Temporal Synchronization and Rhythmic Coordination address the challenge of matching audio events to visual dynamics, with works like Rhythmic Foley[5] and Text-to-Audio Sync[3] emphasizing precise timing. Diffusion-Based Architectures and Conditioning Strategies leverage generative models conditioned on video features (e.g., DiffAVA[16], Foley-Flow[2]), while Unified and Joint Audio-Visual Generation Frameworks pursue end-to-end synthesis (e.g., Integrated Audio-Visual[21]). Multi-Instruction and Controllable Synthesis focuses on user-guided generation (Draw an Audio[14], MultiSoundGen[15]), and Benchmarking and Evaluation Frameworks provide standardized metrics. Cross-Domain and Auxiliary Audio-Visual Learning incorporates tasks like spatial audio (Spatial Audio-Visual[13]) and music generation (Video Echoed Music[20]), with Theoretical and Interdisciplinary Foundations grounding the work in broader cognitive and signal-processing principles. A particularly active line of research centers on Preference Optimization and Multi-Dimensional Reward Learning, where the goal is to align generated audio not only with visual content but also with human perceptual preferences across multiple dimensions. PrismAudio[0] exemplifies this direction by employing chain-of-thought reinforcement learning to iteratively refine audio outputs based on multi-faceted reward signals, closely related to PrismAudio CoT[17]. This contrasts with purely diffusion-driven approaches like Foley-Flow[2] or STA-V2A[1], which rely on deterministic conditioning without explicit preference modeling. Meanwhile, works such as FoleyMaster[18] and Kling-Foley[8] emphasize large-scale pretraining and temporal coherence, highlighting a trade-off between model capacity and fine-grained perceptual tuning. The open question remains how to balance computational efficiency with the nuanced, multi-dimensional alignment that human listeners expect.

Claimed Contributions

PrismAudio framework with decomposed Chain-of-Thought and multi-dimensional RL

The authors propose PrismAudio, which decomposes monolithic reasoning into four specialized CoT modules (Semantic, Temporal, Aesthetic, and Spatial), each paired with targeted reward functions. This enables multi-dimensional RL optimization that addresses objective entanglement and lack of human preference alignment in video-to-audio generation.

2 retrieved papers
Fast-GRPO algorithm for efficient RL training

The authors introduce Fast-GRPO, a novel algorithm that uses hybrid ODE-SDE sampling strategy to enable efficient multi-dimensional RL training of diffusion models. It applies SDE sampling only to a subset of steps while using deterministic ODE sampling elsewhere, reducing computational overhead.

8 retrieved papers
Can Refute
AudioCanvas benchmark dataset

The authors construct AudioCanvas, a new benchmark featuring high modality alignment through rigorous filtering, advanced scene complexity with diverse single-event and multi-event samples, and precise audio captions with rich structured CoT reasoning for comprehensive evaluation.

4 retrieved papers
Can Refute

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

PrismAudio framework with decomposed Chain-of-Thought and multi-dimensional RL

The authors propose PrismAudio, which decomposes monolithic reasoning into four specialized CoT modules (Semantic, Temporal, Aesthetic, and Spatial), each paired with targeted reward functions. This enables multi-dimensional RL optimization that addresses objective entanglement and lack of human preference alignment in video-to-audio generation.

Contribution

Fast-GRPO algorithm for efficient RL training

The authors introduce Fast-GRPO, a novel algorithm that uses hybrid ODE-SDE sampling strategy to enable efficient multi-dimensional RL training of diffusion models. It applies SDE sampling only to a subset of steps while using deterministic ODE sampling elsewhere, reducing computational overhead.

Contribution

AudioCanvas benchmark dataset

The authors construct AudioCanvas, a new benchmark featuring high modality alignment through rigorous filtering, advanced scene complexity with diverse single-event and multi-event samples, and precise audio captions with rich structured CoT reasoning for comprehensive evaluation.

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