ThinkSound: Chain-of-Thought Reasoning in Multimodal Large Language Models for Audio Generation and Editing

Abstract: While end-to-end video-to-audio generation has greatly improved, producing high-fidelity audio that authentically captures the nuances of visual content remains challenging. Like professionals in the creative industries, this generation requires sophisticated reasoning about items such as visual dynamics, acoustic environments, and temporal relationships. We present ThinkSound, a novel framework that leverages Chain-of-Thought (CoT) reasoning to enable stepwise, interactive audio generation and editing for videos. Our approach decomposes the process into three complementary stages: foundational foley generation that creates semantically coherent soundscapes, interactive object-centric refinement through precise user interactions, and targeted editing guided by natural language instructions. At each stage, a multimodal LLM generates contextually aligned CoT reasoning that guides a unified audio foundation model. Furthermore, we introduce AudioCoT, a comprehensive dataset with structured reasoning annotations that establishes connections between visual content, textual descriptions, and sound synthesis. Experiments demonstrate that ThinkSound achieves state-of-the-art performance in video-to-audio generation across both audio metrics and CoT metrics, and excels in the out-of-distribution Movie Gen Audio benchmark. The project page is available at https://ThinkSound-Project.github.io.

• The paper introduces a novel three-stage chain-of-thought framework for video-to-audio synthesis that enhances semantic coherence and temporal precision.
• The work presents AudioCoT, the first large-scale multimodal dataset with detailed CoT annotations to enable controllable and context-aware sound editing.
• ThinkSound’s architecture combines a fine-tuned VideoLLaMA2 with adaptive fusion modules, outperforming previous methods in both objective metrics and interactive editing.

Chain-of-Thought Reasoning for Multimodal Audio Generation: An Expert Analysis of ThinkSound

Introduction

ThinkSound introduces a novel paradigm for video-to-audio (V2A) synthesis, integrating Chain-of-Thought (CoT) reasoning with multimodal LLMs (MLLMs) to address the fine-grained compositional and temporal complexities inherent in realistic sound generation. Unlike prior end-to-end V2A systems, which largely rely on black-box generative models with limited reasoning capacity or multi-stage pipelines with insufficient integration, ThinkSound operationalizes a three-stage interactive framework. This framework facilitates semantically coherent, temporally precise, and user-controllable audio generation and editing, targeting the central pain points of existing approaches: contextual fidelity and user intent alignment.

AudioCoT Dataset: Foundation for Structured Audio Reasoning

The AudioCoT dataset is a pivotal contribution, representing the first large-scale multimodal resource with detailed CoT reasoning annotations that interlink video, text, and audio modalities for the purposes of controllable sound synthesis.

Figure 1: The AudioCoT dataset pipeline extracts multimodal pairs and generates structured CoT annotations as the supervision signal for model training.

The construction pipeline involves three automated annotation stages: foundational Foley CoT, object-centric CoT with ROI-based grounding, and instruction-driven editing CoT. The resulting dataset supports not only dense conditioning with explicit stepwise rationales but also robust data curation through multimodal filtering, region-of-interest tracking, and expert-in-the-loop validation. This resource is critical for endowing MLLMs with fine-grained temporal, semantic, and causal reasoning capabilities for downstream generative tasks.

ThinkSound Architecture

Multimodal CoT Reasoning Engine

The core reasoning engine in ThinkSound is a fine-tuned instantiation of VideoLLaMA2, adapted for the audio-visual domain via pretraining on AudioCoT. This model generates context-aware CoT chains that explicitly decompose acoustic scenes into temporally aligned sound events and actionable attributes. The resulting structured output is then used to condition both initial audio generation and subsequent editing steps.

Figure 2: The ThinkSound pipeline—VideoLLaMA2 generates CoT reasoning, which, through an enhanced MM-DiT multimodal transformer, conditions high-fidelity, temporally accurate audio synthesis driven by both user interaction and text instructions.

Critically, this approach couples global and local reasoning: CoT chains capture the macro-level event chronology while ROI-aware processing enables micro-level, object-centric sound synthesis. This duality enables compositionality and supports interactive refinement loops.

Unified Audio Foundation Model

The generative backbone of ThinkSound is a multimodal flow-matching audio transformer based on conditional flow matching and a Variational Autoencoder (VAE)-derived audio latent space. The architecture integrates video, text (captions, CoT), and audio context representations via modality-dedicated encoder pathways (MetaCLIP for captions, T5 for CoT, and per-modality feature streams), interconnected with shared attention and adaptive gate fusion modules.

Experimentation demonstrates that dual-pathway encoding (CLIP for captions, T5 for CoT) and gated video-audio fusion outperform naive concatenation or single-path integration, particularly with respect to semantic alignment (CLAP) and temporal synchronization (DeSync).

Figure 3: ThinkSound’s multi-stream transformer blocks enable parallel and shared processing of audio, video, and text, supporting flexible input configurations and robust cross-modal reasoning.

CoT-Guided Generation and Editing Pipeline

The generation interface decomposes V2A into three stages:

1. CoT-Guided Foley Generation: Stepwise breakdown of the visual input yields explicit event chronologies, acoustic properties, and spatial/environmental context, directly informing the soundscape.
2. Object-Centric Refinement: User interactions (e.g., clicks) select target regions leading to ROI-based event extraction and localized sound design.
3. Instruction-Based Editing: Natural language modifiers are mapped, via CoT, to specific edit actions (addition, inpainting, extension, removal), allowing high-level user control without sacrificing alignment.

The pipeline is context-preserving: at each stage, audio context is maintained and augmented, not replaced, and all editing is guided by semantically structured CoT control rather than opaque latent modification.

Experimental Results

On the VGGSound test set and the challenging MovieGen Audio Bench, ThinkSound yields state-of-the-art results across all key objective metrics (FD, KL divergence, DeSync) and subjective metrics (MOS-Q for quality, MOS-A for alignment). Most notably, ThinkSound achieves a $\text{CLAP}_\text{CoT}$ score of 0.46 (VGGSound) and 0.51 (MovieGen), reflecting superior semantic and temporal grounding. Ablation studies indicate that removal of CoT reasoning, degradation of structured encoding, or verbosity in CoT chains all produce substantial performance declines, confirming the necessity of focused, explicit reasoning for fidelity and alignment.

Figure 4: Case study—spectrograms from ThinkSound display precise temporal alignment and event detection, avoiding the spurious or delayed sounds present in baseline generative systems.

Object-centric and text-conditioned editing tasks further illustrate that CoT-driven generation not only enhances mainline synthesis but also enables fine-grained, high-level user manipulation, outperforming baselines such as MMAudio, AudioLDM-2, and DDPM-edit across MOS and objective metrics.

Theoretical and Practical Implications

The ThinkSound approach substantiates that explicit, stepwise multimodal reasoning—implemented via large-scale, structured CoT supervision and MLLM fine-tuning—is essential for overcoming the compositional bottlenecks of standard V2A synthesis. Architecturally, modality-dedicated processing with adaptive fusion and global conditioning enhances both controllability and generalization. Practically, this enables a new class of user-interactive, editable sound generation tools for creative industries, film, and gaming, with demonstrated out-of-distribution robustness.

The orthogonality between reasoning quality and generative capacity is experimentally confirmed: advances in either domain (richer CoT structure, increased backbone size) yield independently measurable improvement. In addition, the work lays a theoretical foundation for explicit reasoning control in future multimodal generative models—suggesting that incorporation of physical acoustic modeling, more elaborate event-entity relationships, and finer spatial-temporal manipulations are natural future directions.

Conclusion

ThinkSound marks a significant advancement in controllable, high-fidelity video-to-audio generation by integrating chain-of-thought reasoning with a unified foundation model architecture. Explicit decomposition of sound design into structured, semantically rich CoT steps, tightly coupled to multimodal inputs and augmented by interactive editing capabilities, sets a new standard for synthesis quality, contextual fidelity, and user control. With AudioCoT, ThinkSound also positions itself as a cornerstone for future progress in reasoning-driven, multimodal generative AI for audio applications.