Omni-Modal Large Language Models

• Omni-modal Large Language Models are neural architectures that encode diverse data types into a unified latent space for integrated reasoning.
• They leverage transformer-based attention mechanisms to interleave modality-specific token embeddings, enabling robust cross-modal alignment.
• Scalable pretraining, adaptive fusion, and rigorous safety benchmarks are essential to address challenges like modality binding and hallucination.

Omni-modal LLMs (OLLMs) are neural systems that can jointly process, represent, and reason over an unbounded variety of data types—textual, visual, auditory, and potentially even more abstract, conceptual, or sensor-derived modalities—by encoding all such information into a unified latent space. The OLLM paradigm redefines the landscape of artificial intelligence by seeking to move beyond traditional multimodal models (which typically only pair modalities such as vision and language) toward systems that offer general-purpose, plug-and-play capability for any form of structured input. Recent research delineates both the architectural foundations and the persistent challenges of realizing truly universal OLLMs, including modality binding, scalable training, safety, alignment, reasoning, and robust cross-modal grounding.

1. Architectural Principles and Multimodal Unification

Central to the OLLM design philosophy is the construction of a unified representation space in which all modalities—text, images, audio, video, sensor data, and conceptual entities—are “tokenized” and embedded for joint processing by large-scale attention-based models, typically transformer architectures (Unlu et al., 2023, Zhang et al., 13 Jun 2024). The generic workflow involves passing raw data $x_m$ for modality $m$ through a dedicated encoder $f_m$ and then mapping it via a projection $P_m$ into the LLM’s embedding space to yield a set of modality-specific token embeddings: $t_m = P_m(f_m(x_m))$ These tokens are interleaved with textual tokens to form a composite input stream, enabling the transformer’s attention mechanisms to compute interactions both within and across modalities. The result is a system capable of contextually grounded reasoning—mixing, for example, natural language, visual scenes, and spoken cues in a single forward pass.

Recent extensions treat “conceptual entities” (e.g., named entities, geolocations, numeric intervals, corporate records) as implicit modalities. Here, entity spans in text are replaced with compact, modality-specific tokens that encode high-content subspaces within the shared latent space, mitigating transformer context-length bottlenecks and enabling specialized downstream reasoning (e.g., coordinate-based distance estimation, algebraic manipulation over numbers) (Unlu et al., 2023).

2. Pretraining Paradigms and Modal Scalability

State-of-the-art omni-modal systems employ pretraining strategies designed to harmonize modalities, rapidly scale capacity, and promote emergent generalization (Zhang et al., 13 Jun 2024, Luo et al., 8 Jan 2025). The “Multimodal Context” (MiCo) paradigm, for example, eschews naive concatenation in favor of building a richly annotated joint context through shared positional, modality, and contextual embeddings: $$z = [z_I + E_{M^I}, z_A + E_{M^A}, z_V + E_{M^V}, \ldots] + E_{Pos}$$ where each $z_M$ is a modality token sequence. Optimization is conducted over complementary objectives: omni-modal contrastive loss for global alignment, feature matching via MLP heads, and generative captioning losses with masked token recovery.

Scaling studies demonstrate that synchronously increasing model parameters, data volume, and modality diversity triggers the emergence of advanced cross-modal capabilities. For example, MiCo models have established 37 new SOTA results across 10 modalities and 25 cross-modality tasks, including retrieval, captioning, and QA benchmarks (Zhang et al., 13 Jun 2024).

Alternative approaches, such as model-space binding (OmniBind), leverage numerous pre-trained modality experts, binding their embedding spaces together with learnable projectors and routers. These lightweight MLP-based routers dynamically assign weights to specialist encoders, allowing for highly efficient training—even at the 30B parameter scale using unpaired unimodal data—and supporting any-to-any retrieval, localization, and compositional operations in a shared embedding space (Wang et al., 16 Jul 2024).

3. Advanced Reasoning, Alignment, and Integration

Despite significant functional expansion, deep challenges persist in aligning modalities, preventing hallucination, and achieving robust, holistic reasoning. OLLMs often inherit dominant textual priors, demonstrating a tendency to hallucinate responses by relying on language cues at the expense of visual or audio information (Chen et al., 31 Aug 2025). To address these, frameworks such as OmniDPO introduce conditional direct preference optimization. Here, preference pairs are constructed from outputs conditioned on full multimodal evidence (e.g., both audio and video) and degraded inputs (with a key modality masked or injected with noise). The DPO-based objective encourages the model to amplify the probability of correct, fully grounded outputs while suppressing spurious confidence in hallucinated answers: $$L_{vis}(\theta) = -\mathbb{E}[\log \sigma (\beta [\log P_\theta(Y^+|V,A,T) - \log P_\theta(Y^+|V^-,A,T)])]$$ with analogous terms for the audio channel. This direct approach improves multimodal grounding and reasoning, as shown by improved F1 and control over hallucination rates (Chen et al., 31 Aug 2025).

Further, instruction-driven adaptive fusion mechanisms (as in HumanOmni) allow models to dynamically reweight the representation of specialized modality branches (e.g., facial, body, contextual streams for human-centric video) based on the specific inquiry: $F = w_1 \cdot F_1 + w_2 \cdot F_2 + w_3 \cdot F_3$ where the weights are conditioned on user instructions to match relevant features to the cognitive task (Zhao et al., 25 Jan 2025).

4. Evaluation, Safety Metrics, and Emerging Benchmarks

Evaluating OLLMs requires comprehensive testbeds that go beyond dual-modality accuracy to assess reasoning, safety, grounding, and consistency over complex modality combinations. Benchmarks such as OmniBench and OmnixR provide tri-modal (text + image + audio) and higher-order assessments, exposing systemic reasoning deficits when models must integrate more than two inputs (e.g., accuracy drops of 20–50 percentage points are common in non-text setups) (Li et al., 23 Sep 2024, Chen et al., 16 Oct 2024).

Safety poses unique challenges. Omni-SafetyBench introduces parallelized safety evaluation across 24 modality combinations (including audio-visual harm cases), with conditional metrics that decouple comprehension failure from genuine safety:

• Safety-score: A composite, monotonic function of conditional Attack Success Rate (C-ASR) and conditional Refusal Rate (C-RR):

$$\text{Safety-score} = \frac{(1 - \text{C-ASR})(1 + \lambda \cdot \text{C-RR})}{1 + \lambda}$$

where $\lambda=0.5$ by default.

• Cross-Modal Safety Consistency Score (CMSC-score): Calculated as $\mathrm{exp}(-\alpha \cdot \sigma)$, where $\sigma$ is the standard deviation of Safety-scores, $\alpha=5$. Models generally demonstrate severe weaknesses in audio-visual and joint modalities (scores as low as 0.14) and fail to achieve high safety-consistency (Pan et al., 10 Aug 2025).

5. Training Frameworks and Scaling Systems

The engineering challenges of training OLLMs—especially at scale and across highly heterogeneous architectures—are addressed by specialized frameworks such as VeOmni. VeOmni adopts model-centric distributed recipes that fully decouple parallel strategy (data, sequence, expert sharding) from model code. Its modular architecture (encoders, foundation LLM, decoders) and plug-and-play parallelism enable efficient scaling:

• 3D parallelism (e.g., FSDP + Sequence + Expert Parallelism): Achieves over 2,800 tokens/sec/GPU for 30B parameter MoE models, with scalability to 160K context lengths on 128 GPUs.
• Flexible modality integration: Adding support for a new modality only requires plugging in compatible encoder/decoder modules, rather than extensive engineering changes in the backbone (Ma et al., 4 Aug 2025).

Such frameworks facilitate the rapid development and evaluation of new OLLM architectures, enable system-level benchmarking, and substantially lower the barriers to extensibility.

6. Persistent Challenges and Research Frontiers

Despite rapid progress, current OLLMs display recurring vulnerabilities:

• Trade-offs in capability: Sequential modality extension often compromises core language understanding (reasoning, alignment, instruction following, safety), while single-stage omni-modal fine-tuning is less effective than presumed. Weighted model merging (averaging LLM and modality-specific model parameters with importance weights based on parameter differences) offers partial mitigation but does not fully resolve trade-offs (Zhu et al., 2 Jun 2025).
• Incomplete knowledge sharing and generalization: Experiments reveal that omni-modal extension does not necessarily generalize better than sequential or specialist approaches; specialist models still outperform on their home domain, and merging does not close all gaps.
• Stable grounding and hallucination resistance: Even advanced OLLMs are susceptible to over-relying on text priors, failing to integrate subtle audio-visual cues, or hallucinating when a modality is incomplete or degraded. Conditional preference optimization (OmniDPO), careful architecture modularization, reinforcement learning with context and logic-based rewards, and progressive multi-stage training pipelines with explicit alignment steps are active areas of research (Chen et al., 31 Aug 2025, Yang et al., 26 Jun 2025).

Research frontiers include improved adaptive balance (dynamically synchronized multi-modal sampling and loss scaling), more robust preference optimization and reinforcement learning strategies, deeper benchmarking for safety and tri-modal reasoning, and scaling mechanisms for real-time, low-latency interaction and emotionally rich dialogue synthesis.

7. Open Source Ecosystem and Applied Impact

Major OLLM research efforts are releasing increasingly capable and reproducible open-source baselines—e.g., Baichuan-Omni, Ola, M2-omni, OpenOmni, HumanOmni, Mini-Omni—complemented by large-scale datasets and evaluation toolkits such as OmniEvalKit (Li et al., 11 Oct 2024, Liu et al., 6 Feb 2025, Guo et al., 26 Feb 2025, Luo et al., 8 Jan 2025, Zhao et al., 25 Jan 2025, Xie et al., 29 Aug 2024, Zhang et al., 9 Dec 2024). These resources collectively accelerate the adoption of OLLMs in domains ranging from healthcare (integrating multimodal patient data), robotics, safety monitoring, and universal retrieval, to advanced conversational agents and cognitive computing systems.

In summary, OLLMs mark a transition toward large-scale, universal models that integrate and ground information from arbitrary input types within a single, shared computational substrate. The essential research trajectory—encompassing architectural solutions, scalable training, robust evaluation, and safety/grounding mechanisms—remains dynamic, reflecting both rapid technical advances and enduring scientific challenges.