Unified AI across text, vision, audio, video, 3D, sensors and actions. Collaboration: agenten@magenta.de
One intelligence. Many modalities. One shared world.
Omnimodal AI explores systems that can understand, connect, reason across, and potentially generate multiple forms of information within a unified architecture.
The goal is not simply to support many input types.
The deeper challenge is to build AI systems that can connect text, images, audio, video, documents, 3D information, sensor streams, tools, and actions into a coherent representation of a task or environment.
That makes omnimodal AI relevant to multimodal models, agents, robotics, world models, embodied AI, physical AI, human-computer interaction, and increasingly general-purpose AI systems.
Working definition: Omnimodal AI is the development of AI systems that can process, connect, reason across, and potentially generate information across multiple modalities within a shared computational and semantic framework.
The Omnimodal organization is being developed as an open technical reference and tooling project for unified multimodal and any-to-any AI systems.
Explore the modality capabilities of an AI system and identify where understanding, generation, grounding, reasoning, or action support may still be limited.
Explore how an omnimodal benchmark can be structured across multiple modalities, tasks, reasoning requirements, and evaluation dimensions.
A visual map of the omnimodal stack across text, vision, audio, video, 3D, sensors, tools, actions, memory, reasoning, and world models.
A practical self-assessment for teams evaluating whether an AI system is truly ready for cross-modal and any-to-any use cases.
The term multimodal AI usually describes systems that work with more than one modality.
Examples include:
That is already a major shift from single-modality AI.
But omnimodal systems aim at a broader question:
Can an AI system move coherently across many modalities while preserving meaning, context, state, and intent?
An omnimodal system should not treat every modality as an isolated channel.
Instead, it should be able to connect them.
For example:
The system becomes valuable when these signals reinforce one another.
A useful way to think about omnimodal AI is as a stack.
Text remains one of the most important interfaces to AI systems.
It can represent:
Text is also frequently used as the coordination layer between other modalities.
Vision models can interpret:
Vision becomes more powerful when connected to language, audio, video, 3D information, and action.
Audio can contain information that is not available in text alone.
Examples include:
Real-time speech systems also introduce latency and streaming requirements that differ from text-only inference.
Video adds time.
A video system may need to reason about:
Video understanding is therefore not simply image understanding repeated across frames.
It introduces temporal structure.
Documents combine multiple information types.
A single document may include:
Document understanding is therefore naturally multimodal.
Spatial intelligence requires understanding more than a flat image.
Relevant representations may include:
3D understanding becomes increasingly important for robotics, augmented reality, simulation, and physical AI.
Sensors connect AI systems to the physical world.
Examples include:
Sensor data can be noisy, incomplete, delayed, or inconsistent.
That makes fusion and validation essential.
Modern AI systems increasingly use external tools.
Tools can provide:
Tool use can be viewed as another form of interaction beyond passive perception.
The most advanced systems may not only interpret the world.
They may act within it.
Actions can include:
This creates a transition from multimodal perception toward omnimodal intelligence with action.
An important direction in omnimodal research is any-to-any modeling.
The idea is simple in principle:
A system should be able to accept one or more modalities and produce one or more modalities.
Examples might include:
text → image
image → text
speech → text
text → speech
video → text
text + image → speech
audio + video → text
image + text → action
sensor stream → prediction
world state → action
Any-to-any systems introduce difficult technical problems.
They must preserve:
Adding more modalities does not automatically create an intelligent system.
The quality of the connections matters.
Cross-modal reasoning is one of the defining challenges.
An AI system may receive evidence from several modalities that must be combined.
Examples:
A useful system should not simply process each modality independently.
It should be able to reconcile them.
This includes:
Omnimodal generation introduces another challenge:
Does information stay consistent when transformed between modalities?
Examples:
Cross-modal consistency should be treated as an evaluation dimension of its own.
Grounding connects an AI output to evidence.
Evidence may come from:
An omnimodal system should ideally make it possible to distinguish between:
Grounding becomes especially important when multiple modalities contribute to one decision.
Video, audio, sensors, and actions are inherently temporal.
An omnimodal system may need to reason about:
Temporal reasoning is essential for:
A system that understands individual frames but not sequence is limited.
Spatial reasoning connects:
It matters for:
Spatial reasoning is one of the areas where vision, 3D information, sensors, world models, and action naturally converge.
Agents add goals, memory, tools, and actions to multimodal perception.
An omnimodal agent may need to:
This is qualitatively different from a model that only answers a single multimodal question.
The agent must maintain coherence across time and modalities.
World models attempt to represent how an environment behaves.
Omnimodal systems can contribute to world models by combining:
A world model should ideally preserve the properties necessary for prediction and planning.
For example:
Omnimodal perception can provide richer evidence for these representations.
Physical AI connects models to real environments.
Relevant systems include:
These systems may combine:
vision + audio + sensors + language + world models + actions
Physical AI therefore provides one of the strongest long-term use cases for omnimodal systems.
The challenge is not only perception.
It is turning diverse observations into reliable decisions and actions.
Sensor fusion combines information from multiple sensors.
The goal may be to improve:
A robotics system might combine:
An omnimodal AI architecture could potentially connect these physical signals with language, memory, and reasoning.
But sensor fusion introduces important challenges:
These systems require careful validation.
Long-running AI systems need more than context windows.
They may require memory.
Memory can include:
Omnimodal memory is particularly challenging because different modalities represent information differently.
A useful memory architecture may need to preserve relationships across:
The question becomes:
What should the system remember, in what form, and for how long?
Context engineering becomes more complex as modalities increase.
A system may need to decide:
Context is therefore not only about token count.
It is about selecting and structuring relevant information across modalities.
Different modalities create different inference workloads.
Text generation may emphasize:
Vision may require:
Audio may require:
Video may require:
3D and sensor systems may require:
A unified system therefore needs an inference architecture that can handle heterogeneous workloads.
Evaluating omnimodal systems is difficult because capability is multidimensional.
A useful evaluation framework may need to measure:
A single aggregate score can hide important weaknesses.
For example, a system may be excellent at image understanding but poor at:
Capability profiles can therefore be more informative than one leaderboard number.
The Capability Profiler project is based on this idea.
Instead of asking:
Is this model omnimodal?
a better question is:
Which omnimodal capabilities does the system actually demonstrate?
A useful profile can distinguish:
This avoids reducing a complex architecture to a marketing label.
Omnimodal benchmarks should be explicit about what they measure.
Important questions include:
Which modalities are included?
What input-to-output relationship is being tested?
Does the task require genuine cross-modal reasoning?
Can the answer be tied to observable evidence?
Does temporal sequence matter?
Does spatial structure matter?
Does the system need to choose or execute an action?
What happens if one modality is degraded or missing?
Can the system detect disagreement between modalities?
The Benchmark Studio project explores this type of benchmark structure.
Omnimodal systems can fail in unique ways.
Examples include:
A robust system should degrade gracefully.
It should also be able to identify when information is insufficient.
Real systems do not always receive complete data.
Examples:
An omnimodal architecture should ideally understand which modalities are missing and how that affects confidence.
This is particularly important for physical AI.
Different modalities can disagree.
For example:
or:
The system should not silently merge conflicting evidence.
It should detect uncertainty or contradiction.
This is an important research area for trustworthy omnimodal systems.
More modalities can increase capability.
They can also increase the action surface of a system.
Relevant questions include:
Omnimodal capability should therefore develop alongside validation, observability, and control.
Omnimodal systems may need to connect models from different providers and frameworks.
Interoperability can matter at:
A unified system does not require every component to come from one model.
It may instead require strong interoperability between specialized components.
A complex omnimodal system may contain several specialized models.
For example:
Orchestration determines:
Omnimodal intelligence may therefore emerge from both unified models and orchestrated systems.
Adding modalities adds validation requirements.
Validation may include:
A system should not be called reliable merely because each individual model performs well independently.
The full system needs to be validated.
Omnimodal systems produce complex execution traces.
Useful observability may include:
Observability makes it possible to understand why the system behaved as it did.
These terms overlap but emphasize different things.
Usually describes systems working with multiple modalities.
Emphasizes flexible conversion or generation between modalities.
Can be used as a broader architectural concept emphasizing unified intelligence across many modalities, tools, sensors, and actions.
There is no single universal taxonomy.
For this project, omnimodal is used as an umbrella term for systems attempting to unify perception, reasoning, generation, context, and action across many information types.
The Omnimodal project is particularly interested in questions such as:
The Omnimodal organization is intentionally focused on a small number of practical resources.
Live Space: https://huggingface.co/spaces/omnimodal/capability-profiler
Profiles an AI system across modality support, cross-modal reasoning, grounding, generation, and related capabilities.
Live Space: https://huggingface.co/spaces/omnimodal/benchmark-studio
Explores how benchmarks can evaluate multi-modality capability without reducing everything to a single score.
Live Space: https://huggingface.co/spaces/omnimodal/omnimodal-map
A visual architecture map connecting modalities, memory, reasoning, tools, world models, and actions.
Live Space: https://huggingface.co/spaces/omnimodal/omnimodal-readiness
A self-assessment for evaluating whether a model or system is ready for genuine cross-modal and any-to-any workflows.
Longer-term
A machine-readable capability dataset could document models and systems by supported modalities and evaluation dimensions.
The aim is not to create a large number of shallow projects.
The aim is to build a small set of useful, connected resources.
Any-to-any model
A system designed to accept and generate multiple modalities in flexible combinations.
Cross-modal reasoning
Reasoning that requires information from more than one modality.
Embodied AI
AI that perceives and acts within a physical or simulated environment.
Grounding
Connecting an AI output to observable or retrieved evidence.
Modality
A type of information such as text, image, audio, video, 3D, or sensor data.
Multimodal AI
AI systems capable of processing more than one modality.
Omnimodal AI
AI systems designed to unify understanding, reasoning, generation, and potentially action across many modalities.
Physical AI
AI systems that interact with the physical world through sensors, machines, robots, or other embodied systems.
Sensor fusion
Combining information from multiple sensors.
Spatial reasoning
Understanding relationships involving position, depth, geometry, orientation, or movement.
Temporal reasoning
Understanding sequences, duration, changes, and events over time.
World model
An internal representation or predictive model of an environment and how it may change.
Omnimodal AI describes systems designed to understand, connect, reason across, and potentially generate many different modalities within a unified architecture.
Not exactly. Multimodal usually means working with multiple modalities. Omnimodal can be used more broadly for systems attempting to unify many modalities, tools, sensors, and actions.
Any-to-any AI refers to models that can accept and generate different modalities in flexible combinations.
Potential modalities include text, images, audio, speech, video, documents, 3D data, sensor streams, and structured data. Tool interactions and actions may also be treated as part of the broader system.
Sensors connect AI systems to real-world state. This is especially important for robotics, physical AI, industrial systems, vehicles, and wearables.
World models need information about state, time, space, and action. Omnimodal inputs can provide richer evidence for building those representations.
Cross-modal consistency means preserving compatible information when data is interpreted or generated across different modalities.
Modality grounding connects an AI conclusion to the actual image, audio, video, document, sensor signal, or other evidence that supports it.
An omnimodal agent can combine multiple input modalities with memory, tools, reasoning, and actions while maintaining task context across time.
Evaluation should consider multiple dimensions such as modality support, cross-modal reasoning, grounding, temporal reasoning, spatial reasoning, consistency, robustness, tool use, and action quality.
No. Supporting many modalities is only useful if the system can use them reliably and connect them meaningfully.
No. Omnimodal capability may come from one unified model or from an orchestrated system of specialized models and tools.
Complex multimodal systems can fail at many layers. Observability helps reconstruct which modalities, models, tools, and actions contributed to a result.
Individual components can work correctly while the full multimodal system fails because of timing, grounding, alignment, or integration problems.
This project prioritizes primary technical documentation, model cards, dataset cards, benchmark methodology, and reproducible research.
Relevant areas include:
https://huggingface.co/models?pipeline_tag=image-text-to-text
https://huggingface.co/docs/transformers/index
https://huggingface.co/docs/hub/spaces
https://huggingface.co/docs/evaluate/index
https://huggingface.co/docs/hub/model-cards
https://huggingface.co/docs/hub/datasets-cards
The project may add curated research collections as the field develops.
The public Omnimodal AI — Multimodal Systems, Any-to-Any & Physical AI collection combines this project's practical Spaces with selected research on native omni-modal agents, any-to-any foundation models, unified multimodal architectures, and cross-modal generation.
Explore the Omnimodal AI Collection
Selected papers currently include:
OmniGAIA: Towards Native Omni-Modal AI Agents
https://huggingface.co/papers/2602.22897
Dynin-Omni: Omnimodal Unified Large Diffusion Language Model
https://huggingface.co/papers/2604.00007
NExT-OMNI: Towards Any-to-Any Omnimodal Foundation Models with Discrete Flow Matching
https://huggingface.co/papers/2510.13721
MODUS: Decoder-Only Any-to-Any Modeling of Diverse Modalities
https://huggingface.co/papers/2607.25948
The collection is maintained as a curated companion to the Omnimodal reference and project Spaces. New resources should be added when they contribute useful evidence on cross-modal reasoning, any-to-any generation, native multimodal agents, world models, sensor integration, physical AI, or reproducible multimodal evaluation.
We are open to research collaborations, technical partnerships, benchmark contributions, dataset contributions, infrastructure support, and industry cooperation around omnimodal and multimodal AI.
We especially welcome collaboration with:
Potential collaboration areas include:
We are especially interested in collaborations that create open, reproducible, and useful resources for the wider AI ecosystem.
Contact: agenten@magenta.de
Capability before labels.
A system should be described by what it can actually do rather than by a single marketing term.
Connections matter.
Many modalities are useful only when the system can combine them coherently.
Grounding matters.
Outputs should remain connected to the evidence that supports them.
Time and space matter.
Video, audio, 3D, sensors, robotics, and physical AI require temporal and spatial reasoning.
Action changes the problem.
Once AI can affect external systems, validation and control become essential.
Evaluation should be multidimensional.
One aggregate score cannot describe every omnimodal capability.
Open where possible.
Methods, benchmarks, datasets, and evidence become more useful when they can be inspected and reproduced.
Omnimodal is an independent Hugging Face community project focused on unified AI across text, vision, audio, video, documents, 3D, sensors, tools, world models, and actions.
Last updated: September 2026
Map and compare multimodal model capabilities.
Design clear any-to-any multimodal benchmark suites.
Explore the architecture of omnimodal AI systems.
Assess readiness for unified multimodal AI systems.
Map and compare multimodal model capabilities.
Design clear any-to-any multimodal benchmark suites.
Explore the architecture of omnimodal AI systems.
Assess readiness for unified multimodal AI systems.
Assess readiness for unified multimodal AI systems.
Explore the architecture of omnimodal AI systems.
Design clear any-to-any multimodal benchmark suites.
Map and compare multimodal model capabilities.
