mlsanityData to 14 Oct 2026

Megrez-Omni Technical Report

arXiv 2502.15803 ↗First posted 19 Feb 2025cs.LG cs.CLcited by 7

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Abstract (arXiv)

In this work, we present the Megrez models, comprising a language model (Megrez-3B-Instruct) and a multimodal model (Megrez-3B-Omni). These models are designed to deliver fast inference, compactness, and robust edge-side intelligence through a software-hardware co-design approach. Megrez-3B-Instruct offers several advantages, including high accuracy, high speed, ease of use, and a wide range of applications. Building on Megrez-3B-Instruct, Megrez-3B-Omni is an on-device multimodal understanding LLM that supports image, text, and audio analysis. It achieves state-of-the-art accuracy across all three modalities and demonstrates strong versatility and robustness, setting a new benchmark for multimodal AI models.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 93 of its 112 references that are in the map. First posted on arXiv 19 Feb 2025.

Builds on

93 of 112 references.

References · 93

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38 more
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50 most cited of 93 All 93 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 4; 2026: 3.

7 papers.

  1. 2025Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systemscited by 126
  2. 2025Perception, Reason, Think, and Plan: A Survey on Large Multimodal Reasoning Modelscited by 90
  3. 2025DIFFA: Large Language Diffusion Models Can Listen and Understandcited by 8
  4. 2026Watch, Remember, Reason: Human-View Video Understanding with MLLMscited by 4
  5. 2025LLMEval-Fair: A Large-Scale Longitudinal Study on Robust and Fair Evaluation of Large Language Modelscited by 2
  6. 2026ALARM: Audio-Language Alignment for Reasoning Modelscited by 1
  7. 2026Valley3: Scaling Omni Foundation Models for E-commercecited by 1