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OMNIFLOW: A Physics-Grounded Multimodal Agent for Generalized Scientific Reasoning

arXiv:2603.15797v1 Announce Type: new Abstract: Large Language Models (LLMs) have demonstrated exceptional logical reasoning capabilities but frequently struggle with the continuous spatiotemporal dynamics governed by Partial Differential Equations (PDEs), often resulting in non-physical hallucinations. Existing approaches typically resort to costly, domain-specific fine-tuning, which severely limits cross-domain generalization and interpretability. To bridge this gap, we propose OMNIFLOW, a neuro-symbolic architecture designed to ground frozen multimodal LLMs in fundamental physical laws without requiring domain-specific parameter updates. OMNIFLOW introduces a novel \textit{Semantic-Symbolic Alignment} mechanism that projects high-dimensional flow tensors into topological linguistic descriptors, enabling the model to perceive physical structures rather than raw pixel values. Furthermore, we construct a Physics-Guided Chain-of-Thought (PG-CoT) workflow that orchestrates reasoning thr

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Hao Wu, Yongheng Zhang, Yuan Gao, Fan Xu, Fan Zhang, Ruobing Xie, Ruijian Gou, Yuxuan Liang, Xiaomeng Huang, Xian Wu · · 1 min read · 7 views
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arXiv:2603.15797v1 Announce Type: new Abstract: Large Language Models (LLMs) have demonstrated exceptional logical reasoning capabilities but frequently struggle with the continuous spatiotemporal dynamics governed by Partial Differential Equations (PDEs), often resulting in non-physical hallucinations. Existing approaches typically resort to costly, domain-specific fine-tuning, which severely limits cross-domain generalization and interpretability. To bridge this gap, we propose OMNIFLOW, a neuro-symbolic architecture designed to ground frozen multimodal LLMs in fundamental physical laws without requiring domain-specific parameter updates. OMNIFLOW introduces a novel \textit{Semantic-Symbolic Alignment} mechanism that projects high-dimensional flow tensors into topological linguistic descriptors, enabling the model to perceive physical structures rather than raw pixel values. Furthermore, we construct a Physics-Guided Chain-of-Thought (PG-CoT) workflow that orchestrates reasoning through dynamic constraint injection (e.g., mass conservation) and iterative reflexive verification. We evaluate OMNIFLOW on a comprehensive benchmark spanning microscopic turbulence, theoretical Navier-Stokes equations, and macroscopic global weather forecasting. Empirical results demonstrate that OMNIFLOW significantly outperforms traditional deep learning baselines in zero-shot generalization and few-shot adaptation tasks. Crucially, it offers transparent, physically consistent reasoning reports, marking a paradigm shift from black-box fitting to interpretable scientific reasoning.

Executive Summary

This article introduces OMNIFLOW, a novel physics-grounded multimodal agent designed to facilitate generalized scientific reasoning. The model leverages a neuro-symbolic architecture to project high-dimensional flow tensors into topological linguistic descriptors, enabling it to perceive physical structures rather than raw pixel values. The OMNIFLOW architecture is further augmented by a Physics-Guided Chain-of-Thought workflow, which injects dynamic constraint injection and iterative reflexive verification to facilitate transparent, physically consistent reasoning reports. Empirical results demonstrate OMNIFLOW's superior performance in zero-shot generalization and few-shot adaptation tasks compared to traditional deep learning baselines. Notably, OMNIFLOW offers interpretable scientific reasoning capabilities, marking a significant shift from black-box fitting to transparent reasoning.

Key Points

  • ▸ OMNIFLOW introduces a novel semantic-symbolic alignment mechanism for projecting high-dimensional flow tensors into topological linguistic descriptors.
  • ▸ The Physics-Guided Chain-of-Thought workflow enables dynamic constraint injection and iterative reflexive verification to facilitate transparent reasoning.
  • ▸ OMNIFLOW outperforms traditional deep learning baselines in zero-shot generalization and few-shot adaptation tasks.

Merits

Strength

OMNIFLOW's ability to ground frozen multimodal LLMs in fundamental physical laws without requiring domain-specific parameter updates significantly enhances its cross-domain generalization and interpretability.

Demerits

Limitation

The current implementation of OMNIFLOW is limited to a specific set of physical laws and may require significant modifications to accommodate more complex physical systems.

Expert Commentary

The introduction of OMNIFLOW marks a significant advancement in the field of artificial intelligence, particularly in the realm of scientific reasoning. The model's ability to ground frozen multimodal LLMs in fundamental physical laws without requiring domain-specific parameter updates offers a promising solution to the long-standing challenge of cross-domain generalization and interpretability. Furthermore, OMNIFLOW's emphasis on transparent, physically consistent reasoning reports aligns with the growing interest in interpretable machine learning. However, the current implementation of OMNIFLOW is limited to a specific set of physical laws and may require significant modifications to accommodate more complex physical systems.

Recommendations

  • ✓ Future research should focus on expanding OMNIFLOW's capabilities to accommodate more complex physical systems and exploring its applications in real-world domains.
  • ✓ Developers should prioritize the implementation of OMNIFLOW in a way that ensures transparency and interpretability, particularly in high-stakes domains.

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