Omni-Modal Language Models Overview

• Omni-modal language models are unified neural systems that process text, images, audio, and video through dedicated encoders and transformer-based fusion.
• They employ modality-specific encoders (e.g., ViT for images, Whisper for audio) with cross-modal adapters to create shared latent spaces for joint reasoning.
• These models enable real-time, temporal, and interactive applications in dialogue, retrieval, and translation, despite challenges like modality gaps and data scaling.

Omni-modal LLMs (OLMs) are large-scale neural architectures capable of ingesting, aligning, and jointly reasoning over multiple input modalities—including text, vision (images, video), audio (speech, sound, music), and in some frameworks, further implicit modalities. In contrast to classical multimodal systems that process paired inputs in isolated or pipeline architectures, OLMs fuse diverse sensory streams into a unified representational and generative space, enabling higher-level, modality-invariant reasoning, temporal understanding, and real-time interactive capabilities.

1. Scope and Formal Definition

OLMs process multiple heterogeneous input streams by mapping each modality through dedicated encoders into a shared latent space, followed by cross-modal fusion in a transformer-based backbone. Let $$\mathcal{M} = \{\mathrm{text},\,\mathrm{image},\,\mathrm{audio},\,\mathrm{video},\,\dots\}$$, each input $x_m$ is embedded via an encoder $E_m$ (possibly pretrained; e.g., ViT for images, Whisper for audio), linearly projected if required, then concatenated in a single input sequence: $$H = [h^\mathrm{text};\,h^\mathrm{image};\,h^\mathrm{audio};\,h^\mathrm{video};\,\dots], \quad h^m = E_m(x_m) \in \mathbb{R}^{n_m \times d}$$ These tokens are fed into a unified transformer where cross-attention, sometimes augmented with explicit positional or temporal embeddings, integrates across modalities for prediction, sequence generation, or retrieval tasks (Li et al., 11 Oct 2024, Li et al., 23 Sep 2024, Li et al., 16 Nov 2025).

A key property is "modality invariance": the ability to arrive at convergent, coherent reasoning or generation regardless of which subset(s) of modalities are provided as input (Wang et al., 16 Oct 2025, Chen et al., 16 Oct 2024).

2. Model Architectures and Fusion Mechanisms

Modern OLM architectures implement several key design choices:

• Encoders: Specialized for each modality (e.g., ViT for images/video, Whisper or BEATs for audio/speech, BPEs for text), producing variable-length token streams (Chen et al., 10 Dec 2025, Li et al., 16 Nov 2025, Li et al., 11 Oct 2024, Liu et al., 6 Feb 2025).
• Adapters/Projectors: These modules map encoder outputs to the dimension of the LLM’s embedding space, allowing concatenation.
• Cross-modal fusion: Achieved via interleaved token input (Chen et al., 10 Dec 2025), sequence concatenation (Li et al., 11 Oct 2024, Ji et al., 10 Apr 2025, Guo et al., 26 Feb 2025), early/late fusion blocks, or layer-wise alignment (e.g., 3D RoPE positional encoding (Li et al., 16 Nov 2025); CTC-based mapping for speech (Zhang et al., 16 Jun 2025)).
• Unified sequence modeling: OLMs treat any modality’s token stream as a language-like sequence, enabling generation and understanding through standard autoregressive or masked modeling (Guo et al., 26 Feb 2025).
• Expert routing: In Mixture-of-Experts frameworks (e.g., Uni-MoE-2.0-Omni), per-token gating dynamically activates modality-specialized computation, scaling efficiently across up to ten modalities (Li et al., 16 Nov 2025).

An illustrative architecture is ChronusOmni, which temporally interleaves explicit timestamp tokens with visual and audio features for fine-grained, unified time-dependent reasoning (Chen et al., 10 Dec 2025). Stream-Omni employs both sequence concatenation (vision-text) and CTC-supervised layer mapping (speech-text), assigning alignment mechanics by modality semantics (Zhang et al., 16 Jun 2025).

3. Training Paradigms and Data Strategies

OLMs require highly heterogeneous data and sophisticated multi-stage training recipes:

• Progressive curriculum: Most OLMs use staged modality introduction—starting with text-vision, then expanding to video, then audio, and finally tri-modal or more complex alignment (e.g., Ola’s curriculum (Liu et al., 6 Feb 2025); Capybara-OMNI’s three-stage alignment (Ji et al., 10 Apr 2025)).
• Supervised cross-entropy: Targeted generation (captioning, QA, ASR) via cross-entropy, sometimes with auxiliary contrastive objectives for paired data alignment (Li et al., 11 Oct 2024, Unlu et al., 2023).
• Reinforcement learning (RL): Models like ChronusOmni and HumanOmniV2 incorporate task-aligned RL, using rewards based on metric-grounded evaluation (e.g., IoU for temporal retrieval, METEOR/CIDEr for captioning, LLM-referee–judged rewards for context fidelity) (Chen et al., 10 Dec 2025, Yang et al., 26 Jun 2025).
• Loss weighting and balancing: Gradient accumulation, adaptive weight rebalancing, and step-balance strategies address the vastly different dataset sizes and loss scale per modality (Guo et al., 26 Feb 2025).
• Instruction tuning: Large instruction-tuned corpora (e.g., OmniInstruct (Li et al., 23 Sep 2024)), curated to ensure that tasks require true cross-modal reasoning, are crucial for conversational and real-world deployment (Ji et al., 10 Apr 2025, Tong et al., 15 Oct 2025).

Optimal performance requires strict data curation to avoid shortcut learning (e.g., ensuring that no one modality suffices), careful freezing schedules to prevent catastrophic forgetting of language skills, and balancing text-only data to maintain core LLM capabilities in open-setting OLMs (Zhu et al., 2 Jun 2025, Ji et al., 10 Apr 2025).

4. Temporal and Streaming Reasoning

Time-aware capabilities distinguish omni-modal LLMs from traditional MLLMs. ChronusOmni and streaming frameworks such as OmniMMI and M4 enable real-time, contextually-grounded reasoning over continuous multi-modal streams (Chen et al., 10 Dec 2025, Wang et al., 29 Mar 2025):

• Temporal grounding: Explicit timestamp tokens or temporally-aligned token interleaving establish fine-grained metric time, replacing positional embeddings for better synchronization.
• Proactive interaction: M4’s multiplexing allows for live highlight-spot detection and real-time response, including alerting and turn-taking in continuous video (Wang et al., 29 Mar 2025).
• Multi-turn memory: InteractiveOmni and similar models demonstrate explicit long-horizon memory and dialogue retention via multi-modal, multi-turn data and memory-centric training (Tong et al., 15 Oct 2025).

The main challenge is maintaining temporal coherence, streaming inference efficiency, and context integration for long/video/audio sequences, where context length and modality-specific context compression become bottlenecks.

5. Evaluation: Benchmarks, Metrics, and Failure Modes

Evaluation of OLMs spans closed, open-source, and hybrid systems using a diverse suite of benchmarks:

Benchmark	Modalities	Metric(s)	Targeted Ability
XModBench (Wang et al., 16 Oct 2025)	Text/Image/Audio	Consistency, Modality Gap, Directional Imbalance	Modality-invariant reasoning, consistency diagnosis
OmniBench (Li et al., 23 Sep 2024)	Text/Image/Audio	Accuracy on tri-modal MCQA	Integrated cross-modal reasoning
OmniMMI (Wang et al., 29 Mar 2025)	Video+Audio+Text	SG, AP, MD, PA, SI, PT	Streaming, proactive, multi-turn tasks
OmnixR (Chen et al., 16 Oct 2024)	Text/Image/Audio/Video	Cross-modal accuracy, Δ-gap	Synthetic & real cross-modal integration
IntentBench (Yang et al., 26 Jun 2025)	Video+Audio	MC/F1, LLM-judged chain-of-thought	Contextual, emotional, and intent reasoning

Consistent findings include:

• OLMs achieve 30–50% accuracy on rigorous tri-modal tasks, well below human and even strong bimodal benchmarks (Li et al., 23 Sep 2024, Chen et al., 16 Oct 2024).
• Text→Vision and Vision→Text asymmetry (directional imbalance) is pronounced, with text being the “dominant anchor” (Wang et al., 16 Oct 2025).
• Audio is consistently the most challenging modality for cross-modal mapping (Wang et al., 16 Oct 2025, Chen et al., 16 Oct 2024).
• Temporal and spatial reasoning, and tasks requiring cross-modal memory, are notably difficult, with sharp accuracy drops over multiple context turns (Wang et al., 29 Mar 2025, Chen et al., 10 Dec 2025).

Extract-Then-Answer (ETA) prompting and chain-of-thought reasoning can close the modality gap in synthetic settings but fail in the face of noisy or naturalistic multi-modal data (Chen et al., 16 Oct 2024).

6. Limitations and Future Research Directions

OLMs face substantial open challenges:

• Incomplete modality invariance: State-of-the-art models, including Gemini-2.5-Pro and Qwen2.5-Omni, exhibit large modality disparities and fail to achieve consistency on semantically identical query pairs across input modalities (Wang et al., 16 Oct 2025).
• Data bottlenecks: High-quality, parallel multi-modal and especially tri-modal corpora remain scarce, limiting robust pre-training and testing (Liu et al., 6 Feb 2025, Ji et al., 10 Apr 2025).
• Computational scaling: Efficient large-batch, long-context training for heterogeneous modality streams requires advanced distributed frameworks (e.g., VeOmni’s model-centric recipe zoo for multi-dimensional parallelism (Ma et al., 4 Aug 2025)).
• Entity and implicit modality integration: Beyond classical modalities, integrating “conceptual entities” (numeric, geospatial, temporal, organizational) as latent modalities, as proposed by (Unlu et al., 2023), is largely unexplored at scale.
• Real-time synthesis and interaction: Direct, streaming speech-to-speech translation, emotional control, and transparent intermediate state feedback are in early stages, with systems like Phi-Omni-ST and OpenOmni suggesting promising paths (Hu et al., 4 Jun 2025, Luo et al., 8 Jan 2025).

Research directions include balanced, end-to-end omni-modal pretraining, architecture-level innovations for explicit cross-modal fusion and memory, RL-based alignment to close the modality gap, and expanding the modality set to encompass haptics, 3D, and unstructured sensory streams (Zhu et al., 2 Jun 2025, Unlu et al., 2023).

7. Applications and Impact

Omni-modal LLMs underpin advances in:

• Agentic dialogue systems and real-time conversational AI, capable of seamless perception and response over continuous audio-visual-text streams (Tong et al., 15 Oct 2025, Wang et al., 29 Mar 2025).
• Cross-modal retrieval, instruction following, and entity-centric reasoning in open worlds (Unlu et al., 2023, Yang et al., 26 Jun 2025).
• Proactive embodied agents, with capabilities in alerting, planning, and turn-taking (Wang et al., 29 Mar 2025).
• Accessibility (e.g., speech-to-speech translation (Hu et al., 4 Jun 2025)) and multi-lingual, multi-modal translation interfaces.
• Open OLM benchmarks and reproducible code/data pipelines (e.g., Baichuan-Omni (Li et al., 11 Oct 2024), Capybara-OMNI (Ji et al., 10 Apr 2025), InteractiveOmni (Tong et al., 15 Oct 2025)) accelerating the democratization of omni-modal research.

OLMs are rapidly closing the gap with specialized, proprietary models in vision, audio, and video, but fully modality-invariant reasoning, robust context grounding, and real-world, long-horizon, interactive deployment remain open challenges for the field.