Omni Language Models Overview

• Omni Language Models are large-scale AI systems that integrate text, images, audio, and video inputs to enable unified multi-modal reasoning and content generation.
• They employ advanced architectures with modality-specific encoders and unified tokenization to align diverse data in a shared latent space.
• Recent benchmarks, such as a 4-point improvement on OmniBench and reduced latency in OpenOmni, demonstrate significant progress in cross-modal performance.

Omni LLMs (OLMs) are large-scale models explicitly designed to integrate, process, and reason across multiple modalities—including text, images, audio, and video—within a unified architecture. The central objective of OLMs is to move beyond the limitations of traditional language-only or bi-modal systems by enabling cross-modal reasoning, rich omni-understanding, and multi-sensory generation. OLMs are evaluated on their ability to concurrently interpret and synthesize information spanning vision, audition, and linguistic content, with state-of-the-art benchmarks and design principles now focusing on tri-modal and even quadri-modal integration capabilities.

1. Definitions, Historical Context, and Scope

Omni LLMs are a conceptual and technical progression from unimodal LLMs and multimodal LLMs (MLLMs), where the latter predominantly target text-image or text-audio pairs. An OLM is defined by its ability to:

• Accept and align text, visual (image and video), and auditory (speech, environmental, musical) signals as input.
• Reason over and integrate these modalities for downstream tasks such as recognition, detailed captioning, instruction following, and content generation.

Early benchmarks such as OmniBench (Li et al., 23 Sep 2024) and OmnixR (Chen et al., 16 Oct 2024) explicitly formalized the “omni” paradigm by requiring models to attend to and fuse at least three forms of input (text, image, audio), exposing fundamental differences in model reasoning versus prior approaches limited to two modalities. Subsequent models and benchmarks (e.g., Baichuan-Omni (Li et al., 11 Oct 2024), MGM-Omni (Wang et al., 29 Sep 2025), OpenOmni (Luo et al., 8 Jan 2025), Ola (Liu et al., 6 Feb 2025), Capybara-OMNI (Ji et al., 10 Apr 2025), and Omni-Captioner (Ma et al., 14 Oct 2025)) have operationalized these capabilities in open-source and proprietary frameworks.

2. Model Architectures and Training Paradigms

Current OLMs employ a modular or plug-in architecture in which modality-specific encoders (e.g., vision transformer for images, Whisper derivatives for audio, LLM backbone for text) are aligned into a shared latent space. Integration strategies generally fall into:

• Unified tokenization: Projection of all modalities into a common token space, followed by concatenation before processing with the core transformer (e.g., Qwen2.5-7B in Ola (Liu et al., 6 Feb 2025), Capybara-OMNI (Ji et al., 10 Apr 2025)).
• Dual-track or “brain-mouth” separation: A reasoning/comprehension track (“brain”) and a generation/audio-synthesis track (“mouth”) as exemplified by MGM-Omni (Wang et al., 29 Sep 2025), where text-based reasoning is decoupled from real-time speech synthesis.

Technical innovations include progressive modality alignment—where models are first trained on text–image pairs, then video, then audio (with video serving as a bridge), and finally with cross-modal instruction tuning (Liu et al., 6 Feb 2025, Ji et al., 10 Apr 2025). Attention pooling, feature fusion (e.g., local-global attention), and plug-and-play adapters for modality-specific features are common in recent OLMs.

A general block-level input flow for an OLM can be represented as:

[Text, Image, Video, Audio] → [Encoders/Adapters] → [Unified Embedding] → [LLM Backbone] → [Decoder(s): Text/Speech/Other]

3. Data Curation, Benchmarking, and Evaluation Protocols

Omni-context pretraining requires vast, diverse, and high-quality data. Datasets for OLMs must not only be large but must also support fine-grained cross-modal alignment, e.g., synchronized video+audio+text or image+audio+text. Data curation employs filtering (e.g., for quality and diversity), deduplication, and synthetic data generation pipelines (such as Omni-Detective in (Ma et al., 14 Oct 2025)).

Major benchmarks for OLMs include:

• OmniBench: Evaluates tri-modal reasoning by requiring high-level reasoning across fused image, audio, and text inputs (Li et al., 23 Sep 2024).
• Omni-Cloze: A cloze-style evaluation that inserts modality-specific blanks into generated captions for fine-grained assessment of detail and hallucination (Ma et al., 14 Oct 2025).
• OmnixR: Assesses generalization and multi-modal reasoning in both synthetic and naturally occurring (realistic) contexts (Chen et al., 16 Oct 2024).
• Additional benchmarks target ASR, VQA, OCR, video QA, audio understanding, and emotional synthesis.

Key metrics include accuracy under ablation (removal of modalities), hallucination versus detail rates, word error rates (WER), and alignment-specific measures (e.g., for fine-grained captioning and instruction-following).

4. Key Advances and Unique Features

Fundamental methodological advancements include:

• Agentic tool-calling and iterative investigation (as in the “Omni-Detective” pipeline (Ma et al., 14 Oct 2025)), where LLMs engage with and validate observations across modalities before producing detailed captions.
• Progressive curriculum learning, aligning modalities sequentially to mitigate catastrophic forgetting and efficiently leverage limited cross-modal data (Liu et al., 6 Feb 2025, Ji et al., 10 Apr 2025).
• Decoupled decoding for real-time, high-fidelity speech generation, including chunk-based parallel decoding and streaming text-to-speech synthesis (Wang et al., 29 Sep 2025).
• Direct preference optimization (DPO) and reinforcement learning with verifiable rewards (RLVR) for tuning both alignment and expressiveness, especially in speech and emotion synthesis (Luo et al., 8 Jan 2025).

Recent open-source OLMs (e.g., OpenOmni (Luo et al., 8 Jan 2025), Capybara-OMNI (Ji et al., 10 Apr 2025)) demonstrate competitive performance with smaller model and data scales by leveraging pivot-based alignment and efficient data utilization strategies.

5. Challenges, Trade-offs, and Future Directions

Despite progress, OLM development faces persistent challenges:

• Modality extension (fine-tuning LLMs to integrate new modalities) frequently degrades core language abilities such as instruction following and reasoning, as demonstrated by direct measurement on IFEval, HumanEval+, and safety benchmarks (Zhu et al., 2 Jun 2025).
• “Co-growth” of detail and hallucination: Increasing output detail often correlates with a rise in ungrounded or fabricated content. Controlled, tool-calling data generation (e.g., iterative investigator loops using tool APIs) partially mitigates this effect (Ma et al., 14 Oct 2025).
• Merging strategies (e.g., weighted average of model weights) partially reconcile trade-offs between language proficiency and omni-modality, but require careful calibration (e.g., using Δ_avg parameter shifts for weight assignment) (Zhu et al., 2 Jun 2025).
• Multi-modal fine-tuning is subject to negative transfer effects; conflicting gradient updates from different modalities may drive weight space in divergent (non-intersecting) directions.

Future research is converging on:

• Improved model merging and fusion paradigms that better balance core LLM abilities and cross-modal extensions.
• Architectures and training regimes that minimize catastrophic interference, such as continual learning, modality-specific adapters, and hybrid fine-tuning strategies.
• Datasets and benchmarks that more robustly evaluate integrated performance (instruction following, hallucination aversion, emotional expressivity) under real-world noise and compositional complexity.

6. Representative Results and Impact

Recent models and pipelines demonstrate significant advances:

• OpenOmni (Luo et al., 8 Jan 2025): Achieves a 4-point absolute improvement on OmniBench over VITA (with 7B parameters versus 56B), reduces inference latency by 5×, and improves emotional classification by 7.7%, all with markedly less data.
• Ola (Liu et al., 6 Feb 2025): Outperforms open-source omni-modal baselines in image, video, and audio understanding, reaching state-of-the-art or near state-of-the-art scores on MMBench-1.1 (84.3%), MMMU (57.0%), and VideoMME (68.4%).
• Omni-Captioner (Ma et al., 14 Oct 2025): Surpasses Gemini 2.5 Flash and matches Gemini 2.5 Pro on MMAU/MMAR, sets a new detailed captioning benchmark on VDC, and establishes the best trade-off between detail and hallucination on video-SALMONN 2.

The proliferation of open-source models with full releases of weights, data, and code (e.g., Baichuan-Omni (Li et al., 11 Oct 2024), MGM-Omni (Wang et al., 29 Sep 2025), Capybara-OMNI (Ji et al., 10 Apr 2025)) is accelerating iterative innovation and supporting robust empirical verification of methodological claims.

7. Conclusion

Omni LLMs represent a central direction for contemporary multimodal AI research, aiming to unify and extend deep learning systems’ perceptual, cognitive, and generative capabilities across diverse input and output channels. Despite persistent limitations—particularly in cross-modal reasoning, instruction following, and hallucination control—recent architectural, data, and evaluation innovations have established a foundation for future advances. The field continues to progress through a combination of open-source collaboration, agentic data pipelines, curriculum-guided training, model-merging strategies, and increasingly sophisticated benchmarking—all aimed at achieving robust, verifiable, and richly interactive omni-understanding.