Agentic Multimodal Models

• Agentic multimodal models are AI systems that proactively plan and integrate specialized agents across various modalities, enabling dynamic, context-aware task execution.
• They coordinate modules for audio analysis, symbolic composition, audio synthesis, and score visualization through iterative validation and constrained synthesis.
• Their design emphasizes robust constraint management, adaptable resource optimization, and reproducibility, making them versatile for complex tasks such as music information retrieval.

Agentic multimodal models are AI systems that couple foundation models with explicit agentic workflows, enabling them to proactively reason over, and interact with, diverse modalities and external tools. Unlike passive pipelines, agentic multimodal models dynamically plan, invoke, and integrate specialized models and APIs throughout a multi-stage loop, adapting their strategies based on the context, constraints, and downstream validation. This agentic design enables controllable, robust, and extensible coordination of subcomponents across modalities—such as audio, symbolic notation, rendered sheet music, and high-fidelity audio—in tasks that require interaction, feedback, and iterative refinement. WeaveMuse (Karystinaios, 14 Sep 2025) serves as an archetypal example of this paradigm in the domain of music understanding and generation.

1. Multi-Agent System Architecture

WeaveMuse organizes its system as a collection of specialist agents, each responsible for a core modality or function in the music information retrieval (MIR) pipeline. These include:

• Music Understanding Agent (Audio Analysis): Ingests raw waveforms or audio stems, segments audio, transcribes symbolic content, and extracts features such as tempo, key, and segment boundaries. It employs open audio-LLMs (e.g. Qwen-audio, Audio Flamingo) to output symbolic summaries or metadata.
• Symbolic Composition Agent: Consumes textual prompts or intermediate representations and generates ABC or MIDI notation, employing LLMs fine-tuned on symbolic music data (e.g. NotaGen, ChatMusician). It performs self-validation for syntax correctness and basic harmonic constraints.
• Audio Synthesis Agent: Receives symbolic music (MIDI/ABC) or audio stems, along with control parameters, to produce high-quality 44.1 kHz stereo audio. It supports dynamic quantization (INT8/INT4) and can execute on both CPU and GPU.
• Score Visualization Agent: Renders ABC/MIDI into engraved PDF sheet music via MuseScore and applies GNN-based correction techniques (e.g. "Cluster and Separate") for staff and voice assignment.

Coordination and dialogue are managed by a Manager Agent ("Core Agent"), which handles user input, maintains persistent state, sequences tool invocations into a directed acyclic “tool graph,” and performs output validation by selectively looping back to analysis or enforcing constraints. All agent interactions are unified through a single API (smolagents library), supporting both local and hosted deployment.

This architecture enables tight, flexible integration of perception (audio analysis), structured symbolic manipulation, synthesis, and visualization, with resource-aware routing and validation at each stage.

2. Workflow, Constraint Management, and Validation

The typical end-to-end workflow follows an analysis–synthesis–render loop that integrates user instruction and iterative agentic validation:

1. User Prompt: Textual input specifying requirements (e.g., "Create a four-bar piano motif in C minor").
2. Symbolic Composition: Manager invokes the composition agent, producing ABC notation.
3. Initial Validation: Manager self-checks output (syntax, constraints) and visualizes the score via the visualization agent (PDF preview).
4. Audio Synthesis: The audio synthesis agent generates a wav file from the symbolic input.
5. Output Verification: Manager calls the audio analysis agent to confirm attributes such as key, tempo, and instrumentation.
6. Feedback Loop: If analysis detects a mismatch (e.g., key error), the manager adapts constraints and re-invokes relevant agents.

The explicit management of constraints (e.g., duration, key signature) and the manager's ability to loop over specialist agents introduce robust self-validation and error correction into the system.

Note: While the paper alludes to constraint schemas, structured decoding, and policy-based inference, it does not provide full mathematical formalism. Illustrative, not data-sourced, forms are:

• Constraint schema:

$$C = \{c_1, c_2, ..., c_K\}, \text{ where } c_k = (\text{modality}, \text{format}, \text{requirement})$$

• Structured decoding (constrained generation):

$$\hat{Y} = \arg\max_{Y} \log p(Y|X) \; \text{s.t.} \; Y \models C$$

• Policy-based agent routing:

$\pi(a_t|s_t) = \text{softmax}(f_\theta(s_t, a_t))$

However, these remain illustrative and are not instantiated in code or algorithmic pipelines in the source.

3. Adaptation, Controllability, and Extension

WeaveMuse supports adaptation and controllability at multiple levels:

• Parameter-efficient adapters and distillation: Specialist models can be extended or tailored to music information retrieval tasks using adapters or distilled variants, facilitating targeted transfer without retraining entire models. A typical (not paper-provided) loss would combine MIR-specific objectives and an $L_2$ regularization penalty for adapter parameters:

$$\mathcal{L} = \mathcal{L}_\text{MIR}(f_{\theta + \Delta\theta}(X), Y_\text{gold}) + \lambda \|\Delta\theta\|_2^2$$

• Constraint checks and policy thresholds: Outputs are scored for validity; if $v(o)\in[0,1]$ is a validity metric, the system enforces $v(o)\geq\tau_\text{valid}$ for acceptance.

This provides fine-grained control over model adaptation, output filtering, and continuous domain alignment, especially important for highly structured tasks like MIR.

4. Deployment, Resource Optimization, and Reproducibility

Deployment strategies are heavily resource- and reproducibility-aware:

• Quantization and Inference Optimization: Each tool supports dynamic quantization (INT4/INT8 fallback) and device placement for CPU/GPU, with lazy loading and on-disk caching of large weights. Batching in memory-limited scenarios is adjusted to the available hardware profile. This allows the system to be ported from high-end GPUs to commodity hardware without architectural change.
• Stateful and Reproducible Workflow Management: The manager agent checkpoints intermediate results (to local cache or Hugging Face Space artifacts). Identical planner prompts are used in both local and hosted modes, ensuring full reproducibility. Hardware probes automatically determine quantization and batching policies.

This architecture supports wide accessibility and democratization of MIR tooling.

5. Use Cases, Evaluation, and Empirical Behavior

While the system's design emphasizes qualitative robustness and flexibility, the paper does not provide aggregate numeric benchmarks, user-study data, or explicit evaluation metrics such as F1, BLEU, or Mean Opinion Score. Instead, evaluation is illustrated by example dialogues:

Example Workflow (abridged):

• User: "Compose a four-bar jazz motif in G major and render both score and audio."
• Manager:
  1. Symbolic agent: receives constraints (key=G major, length=4 bars), produces ABC.
  2. Syntax check: passes.
  3. Score visualization: PDF preview displayed.
  4. Audio synthesis: wav played.
  5. Audio analysis: confirms key and tempo.

The qualitative evidence shows how agentic coordination yields intermodal and cross-format consistency, with validation, correction, and flexible routing.

6. Implications and Generalization

WeaveMuse exemplifies an agentic multimodal paradigm for real-world MIR tasks. Its explicit agent orchestration and manager mediation enable:

• Intermodal interaction and cross-format constraint propagation—critical for tasks crossing audio, symbolic notation, and rendered scores.

• Extensibility and model interchangeability, accommodating open-source models of various sizes and types.
• Reproducible deployment and memory management, supporting both local (resource-constrained) and cloud-based (community-accessible) scenarios.
• Future formalization: While the paper outlines design patterns—constraint schemas, structured decoding, and policy-based inference—these remain schematic, indicating a significant area for subsequent formal development and mathematical clarity.

This architecture provides a technically robust foundation for further research and system development in agentic multimodal MIR, as well as a transferable blueprint for agentic multimodal systems in other domains requiring compositional, tool-integrating workflows.