AGENTIC-SYSTEMSApril 3, 20261 min read14 views

Dynin-Omni: Omnimodal Unified Large Diffusion Language Model

Dynin-Omni is the first masked-diffusion-based omnimodal foundation model that unifies text, image, speech, and video processing within a single architecture. Outperforming existing open-source models, it sets a new benchmark for real-time omnimodal systems and embodied AI agents.

Computer Science > Computation and Language

Title: Dynin-Omni: Omnimodal Unified Large Diffusion Language Model

Abstract: We present Dynin-Omni, the first masked-diffusion-based omnimodal foundation model that unifies text, image, and speech understanding and generation, together with video understanding, within a single architecture. Unlike autoregressive unified models that serialize heterogeneous modalities, or compositional unified models that require orchestration with external modality-specific decoders, Dynin-Omni natively formulates omnimodal modeling as masked diffusion over a shared discrete token space, enabling iterative refinement under bidirectional context. Dynin-Omni adopts a multi-stage training strategy with model-merging-based modality expansion and omnimodal alignment. We evaluate Dynin-Omni across 19 multimodal benchmarks spanning language reasoning, image generation and editing, video understanding, and speech recognition and synthesis. Dynin-Omni achieves 87.6 on GSM8K, 1733.6 on MME-P, 61.4 on VideoMME, 0.87 on GenEval, and 2.1 WER on LibriSpeech test-clean, consistently outperforming existing open-source unified models while remaining competitive with strong modality-specific expert systems. These results demonstrate the potential of masked diffusion as a unified paradigm for any-to-any modeling, providing a flexible foundation for real-time omnimodal systems, unified cross-modal retrieval and generation, and embodied multimodal agents.

Source: arXiv cs.AI Recent

More in this category

agentic-systems

VeriSimpl: Robust Optimization Modeling from Natural Language using Simplification-based Verification

Researchers have introduced VeriSimpl, a novel framework that leverages LLMs and simplification-based verification to accurately translate natural language descriptions into executable optimization formulations.

agentic-systems

SonicSampler: Unified Tile-Aware Kernels for LLM Sampling and Speculative Verification

Researchers introduced SonicSampler, a unified suite of tile-aware Triton kernels designed to accelerate LLM sampling and speculative verification. By fusing the entire pipeline into a single CUDA Graph-compatible kernel, SonicSampler achieves up to 16x speedups over state-of-the-art baselines.

agentic-systems

PersonaTrail: Benchmarking Personalized Web Agents through Browsing Trails

Researchers have introduced PersonaTrail, a novel benchmark evaluating personalized web agents through realistic browsing histories. Alongside it, the PACMem memory framework structures raw browsing data to significantly boost agent performance in inferring user preferences.

agentic-systems

Benchmarking the Personalization Capabilities of Large Language Models

A new study introduces SDR-Bench and SDR-Arena to benchmark the personalization capabilities of LLMs in two-party persuasion scenarios, revealing a personalization plateau among frontier models.

agentic-systems

Incomplete Prompt Jailbreaks in Large Language Models

Researchers have conceptualized Incomplete Prompt Jailbreaks (IPJ), a vulnerability where incomplete harmful prompts bypass LLM safeguards due to delayed refusal behaviors. The discovery of functional neurons offers a novel path for precise neuron-level defense interventions.

agentic-systems

Benchmarking Large Language Models on Multi-Sensor Physical Hazard Assessment

A new benchmark evaluates five leading large language models on multi-sensor physical hazard assessments, revealing a critical vulnerability when evaluating combined sensor risks below individual thresholds. Despite near-perfect accuracy on single-sensor breaches, all models failed to issue precautionary signals for multi-sensor hazards.