Omni-modal Language Models (OLMs)

Last updated: June 10, 2025

Certainly! Below is a meticulous, fact-faithful, and fully referenced synthesis of current research on Omni-modal ° LLMs ° (OLMs °) utilizing only the source material provided, with an emphasis on practical implementation details, empirical findings, and field-facing implications.

1. Definition & Scope of Omni-modal LLMs

Omni-modal LLMs (OLMs) are neural architectures designed to accept, integrate, and reason over an open set of input and output modalities—including but not limited to text, image, audio, video, tabular/graph data, and conceptual entities—within a unified model interface. OLMs aim to achieve comprehensive, human-level understanding and generation across all modalities relevant to sensory perception and knowledge representation, inspired by human cognitive flexibility ° and universality (Zhang et al., 13 Jun 2024 ° , Jiang et al., 16 Dec 2024 ° ). Their design merges advancements from traditional language, vision, and audio models, seeking seamless cross-modal alignment °, unified token spaces, and robust real-world multimodal interaction °.

2. Architectural Patterns and Training Paradigms

a) Modular Encoders and Unified Tokenization

Most state-of-the-art OLMs use a transformer-based LLM ° backbone, with independent encoders per modality (e.g., CLIP/SigLIP for vision, Whisper/SAN-M for audio, and text embedding layers for language). Each encoder projects modality-specific input to a shared latent space, often via projectors ° (MLPs or attention °-based adapters) ensuring all features align with the LLM °’s embedding space (Jiang et al., 16 Dec 2024 ° , Li et al., 11 Oct 2024 ° , Guo et al., 26 Feb 2025 ° ). For instance:

image_features = image_encoder(img)         # e.g., CLIP(SigLIP)
image_tokens = mlp_image_projector(image_features)

audio_features = audio_encoder(waveform)    # e.g., Whisper, SAN-M
audio_tokens = mlp_audio_projector(audio_features)

text_tokens = tokenizer(text)               # standard text tokens

LLM_input = concatenate([image_tokens, audio_tokens, text_tokens])
output = LLM(LLM_input)

To support both understanding and generation, high-fidelity modalities (such as audio and speech output) are often discretized into tokens (e.g., via RVQ or SNAC codecs), enabling the LLM to autoregressively generate ° multimodal sequences (Li et al., 26 Jan 2025 ° , Xie et al., 29 Aug 2024 ° ).

b) Progressive Modality Alignment & Training

A recurrent best practice in OLM pre-training is progressive modality alignment °:

• Begin with text-image paired data ° to solidify vision-language understanding °.
• Incorporate video (as a sequence of images + temporality), then expand to audio/speech, ensuring audio-text ASR ° and video-audio alignment (Liu et al., 6 Feb 2025 ° ).
• Utilize cross-modal video-audio QA and instruction data ° to tightly couple all modalities.
• Supervised fine-tuning is staged and often uses cross-modal, mixed-modality tasks to maximize mutual alignment and prevent catastrophic forgetting.

Local-global pooling and dynamic attention ° mechanisms are sometimes employed during alignment to effectively fuse spatial/temporal context (see Tab. 2 in (Liu et al., 6 Feb 2025 ° )).

c) Balanced Data and Dynamic Training

Given the significant disparity in available data per modality, OLMs now rely on step balance and dynamically adaptive loss weighting ° during pre-training and instruction tuning (Guo et al., 26 Feb 2025 ° ). Concretely:

• Each batch accumulates gradients from all modalities, with normalization by convergence rates or validation loss ° slopes.
• Adaptive weighting ° ensures that modalities which learn more slowly receive increased focus, while faster modalities are attenuated to avoid overfitting.

Formally, for modalities $i$ with validation loss slopes $a_{i}$,

$w_{i} \propto \text{softmax}(-a_{i}/\sum_j |a_{j}|) \quad\text{[see Section 4, 2502.18778]}$

This strategy promotes balanced convergence and avoids training collapse on minority modalities.

d) End-to-End Generation and Real-Time Streaming

To enable fluent, real-time cross-modal interaction ° (e.g., audio-in/audio-out, video live-chat), OLMs such as Baichuan-Omni-1.5 and Mini-Omni ° adopt parallel or sentence-wise decoding: the LLM predicts both text and audio tokens ° in a coordinated sequence, doubling as a voice assistant ° and multimodal answer engine (Li et al., 26 Jan 2025 ° , Xie et al., 29 Aug 2024 ° , Liu et al., 6 Feb 2025 ° ).

For instance, Mini-Omni generates text and seven corresponding audio tokens per LLM decode step, with parallel heads and interleaved outputs (Xie et al., 29 Aug 2024 ° ). Sentence-wise streaming (Ola) further minimizes latency for speech output by emitting spoken responses immediately upon punctuation or utterance boundaries (Liu et al., 6 Feb 2025 ° ).

3. Multi-modal Model Merging and Catastrophic Forgetting

Some OLM research investigates model merging ° as a strategy to combine independently fine-tuned, modality-specific models (e.g., text-image, text-audio) into a more comprehensive OLM, in lieu of costly joint retraining (Wei et al., 26 May 2025 ° , Zhu et al., 2 Jun 2025 ° ). Key insights:

• Weighted averaging ° of model weights using parameter shift ° deltas ($\Delta_{\text{avg}}$) better preserves each model's domain strengths compared to naive averaging or full re-finetuning.
• SVD ° and noise-reduction techniques can optimize the merged parameter vectors, focusing on high-value subspaces and suppressing cross-task interference.
• Merged models regain some degraded core abilities, but performance on complex reasoning and instruction tasks still lags behind either expert specialists or monolithically trained OLMs.

Model merging is thus a promising path for decentralized, scalable OLM deployment, but alone does not realize the full vision of seamless omni-modality (Zhu et al., 2 Jun 2025 ° ).

4. Benchmarks and Limitations

Leading benchmarks for OLMs now include rigorous, tri- and quad-modal datasets and evaluation protocols:

• OmniBench ° (Li et al., 23 Sep 2024 ° ): Requires reasoning over text, audio, and image simultaneously; accuracy for open models remains below 50% on complex reasoning tasks.
• OmnixR ° (Chen et al., 16 Oct 2024 ° ): Systematically compares OLMs' consistency and chain-of-thought across text, audio, image, and video inputs; demonstrates substantial accuracy and reasoning drops outside pure-text settings.
• Streaming and Agentic Evaluation: OmniMMI (Wang et al., 29 Mar 2025 ° ) targets real-time, streaming multi-modal interaction, with proactive subtasks (alerting, silence, multi-turn) that reflect practical deployments. Performance drops dramatically as context length and turn count increase even for SOTA ° OLMs.

Across all major benchmarks, open OLMs still struggle with:

• Deep tri-modal or quad-modal reasoning,
• Consistent instruction-following and chain-of-thought across all modalities,
• Modal bias (over-reliance on text or visual cues),
• Robustness under context window expansion and multi-turn dialog scenarios.

5. Key Open Challenges and Future Directions

• Trade-off Management: Extending a text LLM to multimodality ° often degrades core language skills ° (reasoning, safety, instruction-following) except in very large models (Zhu et al., 2 Jun 2025 ° ). Methods to maintain language robustness, perhaps by parameter isolation ° or explicit regularization, are essential.
• Efficient, Balanced Curriculum: Data and loss balancing strategies are now necessary for meaningful, scalable omni-modal pretraining (Guo et al., 26 Feb 2025 ° , Chen et al., 26 Sep 2024 ° ).
• Advanced Modality Fusion: Despite progress, more sophisticated tri- and quad-modal fusion strategies ° (beyond early/late fusion) and dynamic modality weighting are needed (Li et al., 23 Sep 2024 ° , Jiang et al., 16 Dec 2024 ° ).
• Benchmarks Reflecting Real Agent Scenarios: Streaming, context-managing, and proactive evaluation are rapidly advancing as the new gold standard (Wang et al., 29 Mar 2025 ° ).
• Model Merging/Decentralized Growth: More work is needed to make merging a turn-key tool for open OLM expansion without catastrophic forgetting or modal interference (Wei et al., 26 May 2025 ° ).
• Instruction-driven Data and Reasoning: Large-scale, instruction-rich, human-verified datasets (e.g., OmniInstruct) are shown to be essential for robust multi-modal generalization (Li et al., 23 Sep 2024 ° ).
• Open-source Advancement: Leading OLMs (Ola, Lyra, Baichuan-Omni-1.5, River-Omni, M2-omni) are open-sourcing full pipelines, weights, and curation tools, enhancing community-driven benchmarking and rapid iteration (Li et al., 11 Oct 2024 ° , Liu et al., 6 Feb 2025 ° , Zhong et al., 12 Dec 2024 ° ).

6. Summary Table: Key Components and Best Practices

7. Conclusions

Omni-modal LLMs represent a paradigm shift towards truly generalist AI, but there remain fundamental trade-offs and engineering challenges. Best practice currently involves modular encoders, progressive alignment, balanced curriculum and instruction tuning, advanced streaming outputs, and increasing use of model merging for scalable, decentralized development. Human-level multimodal reasoning, streaming, and proactive interaction are not yet solved, but open-source platforms and rigorous benchmarks are accelerating progress.

For practitioners, deploying an OLM today entails careful trade-off analysis between modality support and language robustness, progressive modal alignment using staged data and loss balancing, and thorough evaluation on complex, instruction-rich, and agentic multi-modal benchmarks. The field is rapidly evolving, and the best results are achieved by integrating practices from multiple concurrent research fronts, all of which now emphasize real-world applicability, efficiency, and extensibility.