From Phenomenological Fitting to Endogenous Deduction: A Paradigm Leap via Meta-Principle Physics Architecture

• URL: http://arxiv.org/abs/2604.08245v1
• Date: Thu, 09 Apr 2026 13:35:17 GMT
• Title: From Phenomenological Fitting to Endogenous Deduction: A Paradigm Leap via Meta-Principle Physics Architecture
• Abstract summary: We construct the Meta-Principle Physics Architecture (MPPA)<n>MPPA embeds three core meta-principles - Connectivity, Conservation, Periodicity - into its architecture.<n> Experiments show MPPA significant improvements: physical reasoning (from near zero to 0.436, 0.436 vs 0.000), 2.18x mathematical task improvement (0.330 vs 0.151), 52% logical task gain (0.456 vs 0.300), and 3.69% lower validation perplexity (259.45 vs 269.40), with only 11.8% more parameters (242.40M vs 216.91M)
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The essence of current neural network architectures is phenomenological fitting: they learn input-output statistical correlations via massive parameters and data, yet lack intrinsic understanding of the fundamental principles governing physical reality. This paper proposes a paradigm leap from pure phenomenological fitting to the fusion of phenomenological fitting and endogenous deduction. By embedding physical meta-principles into neural network architecture, we construct the Meta-Principle Physics Architecture (MPPA). Specifically, MPPA embeds three core meta-principles - Connectivity, Conservation, Periodicity - into its architecture, implemented via three core components: the Gravitator realizes Connectivity via standard causal attention; the Energy Encoder implements Conservation via log-domain energy tracking and delayed compensation; the Periodicity Encoder fulfills Periodicity via FFT-based spectral analysis and delayed modulation. These components collaborate via a learnable independent gating fusion mechanism, forming a complete physical cognition framework of 'local relational connectivity - global conservation constraint - evolutionary periodic law'. Experiments show MPPA achieves significant improvements: physical reasoning (from near zero to 0.436, 0.436 vs 0.000), 2.18x mathematical task improvement (0.330 vs 0.151), 52% logical task gain (0.456 vs 0.300), and 3.69% lower validation perplexity (259.45 vs 269.40), with only 11.8% more parameters (242.40M vs 216.91M). Notably, MPPA shows strong generalization on out-of-distribution physical scenarios, proving the robustness and interpretability of this principle-embedded design. This work establishes a new theoretical foundation and technical path for next-generation AI with physical common sense, causal reasoning, and mathematical rigor.

Related papers

• UniPixie: Unified and Probabilistic 3D Physics Learning via Flow Matching [29.8272108619491]
  We introduce UNIPIXIE, a framework trained to predict a continuous path of physically plausible material properties from a single visual input.<n>By learning a direct mapping along an object's softest-to-stiffest spectrum on our PIXIEMULTIVERSE dataset, UNIPIXIE allows for controllable generation of diverse, physically valid material fields.
  arXiv  Detail & Related papers  (2026-06-03T20:08:23Z)
• TIDE: Asymmetric Neural Circuits for Stabilized Temporal Inhibitory-Excitatory Dynamics [51.37317469455798]
  Recent Continuous Thought Machine architecture decouples internal computation from external inputs via neural dynamics.<n>We propose to model neural dynamics using asymmetric Excitatory-Inhibitory (E-I) networks.<n>We introduce Temporal Inhibitory-Excitatory Dynamic Engine (TIDE), a neuro-inspired architecture that computes internal representations through neural dynamics stabilized.
  arXiv  Detail & Related papers  (2026-05-19T05:59:13Z)
• Information as Maximum-Caliber Deviation: A bridge between Integrated Information Theory and the Free Energy Principle [51.56484100374058]
  This work proposes that information can be defined as the deviation $$ of realized dynamics from a constrained maximum-caliber (MaxCal) path ensemble over a finite time horizon.<n>Under this definition, each of the cause/effect repertoires central to IIT 3.0 emerge directly from MaxCal variational principles.<n>Information $$ is shown to be equivalent to prediction error under accompanying predictive coding models.
  arXiv  Detail & Related papers  (2026-05-03T07:22:55Z)
• Mesh Field Theory: Port-Hamiltonian Formulation of Mesh-Based Physics [5.4806374384787695]
  MeshFT-Net is a structure-preserving framework for mesh-based continuum physics.<n>It cleanly separates the physics' topological structure from its metric structure.<n>By eliminating non-physical degrees of freedom and learning only metric-dependent structure, MeshFT provides a principled inductive bias for stable, faithful, and data-efficient learning-based physical simulation.
  arXiv  Detail & Related papers  (2026-05-01T04:35:21Z)
• minAction.net: Energy-First Neural Architecture Design -- From Biological Principles to Systematic Validation [0.0]
  Modern learning optimize for accuracy without explicit treatment of internal computational cost.<n>We evaluate energy-aware learning across 2203 experiments spanning vision, text neuromorphic, and physiological datasets.
  arXiv  Detail & Related papers  (2026-04-27T06:26:36Z)
• Kirchhoff-Inspired Neural Networks for Evolving High-Order Perception [51.28469959548178]
  We propose the Kirchhoff-Inspired Neural Network (KINN), a state-variable-based network architecture constructed based on Kirchhoff's current law.<n>KINN derives numerically stable state updates from fundamental ordinary differential equations, enabling the explicit decoupling and encoding of higher-order evolutionary components.
  arXiv  Detail & Related papers  (2026-03-25T06:17:31Z)
• OMNIFLOW: A Physics-Grounded Multimodal Agent for Generalized Scientific Reasoning [51.33849811496781]
  Large Language Models (LLMs) have demonstrated exceptional logical reasoning capabilities but frequently struggle with the continuous dynamics by Partial Differential Equations (PDEs)<n>We propose OMNIFLOW, a multimodal-symbolic architecture designed to ground frozen LLMs in fundamental physical laws without requiring domain-specific parameter updates.<n>We evaluate it on a benchmark spanning microscopic turbulence, theoretical Navier-Stokes, and macroscopic global weather forecasting.
  arXiv  Detail & Related papers  (2026-03-16T18:29:01Z)
• Hard-Constrained Neural Networks with Physics-Embedded Architecture for Residual Dynamics Learning and Invariant Enforcement in Cyber-Physical Systems [0.5735035463793009]
  We formalize the Hybrid Recurrent Physics-Informed Neural Network (HRPINN), a general-purpose architecture that embeds known physics as a hard structural constraint within a recurrent integrator to learn only residual dynamics.<n>Second, we introduce the Projected HRPINN (PHRPINN), a novel extension that integrates a predict-project mechanism to strictly enforce algebraic invariants by design.<n>We validate HRPINN on a real-world battery prognostics DAE and evaluate PHRPINN on a suite of standard constrained benchmarks.
  arXiv  Detail & Related papers  (2025-11-28T16:06:24Z)
• Unified Spatiotemporal Physics-Informed Learning (USPIL): A Framework for Modeling Complex Predator-Prey Dynamics [0.0]
  Ecological systems exhibit complex multi-scale dynamics that challenge traditional modeling.<n>New methods must capture temporal oscillations and emergent patterns while adhering to conservation principles.<n>We present the Unified Stemporal Physics-Intemporal Learning framework, a deep learning architecture integrating physics-informed neural networks.
  arXiv  Detail & Related papers  (2025-09-16T18:02:23Z)
• CMPhysBench: A Benchmark for Evaluating Large Language Models in Condensed Matter Physics [71.42168240638462]
  CMPhysBench is designed to assess the proficiency of Large Language Models in Condensed Matter Physics.<n>Our results show that even the best models, Grok-4, reach only 36 average SEED score and 28% accuracy on CMPhysBench.
  arXiv  Detail & Related papers  (2025-08-25T15:32:22Z)
• Mimicking the Physicist's Eye:A VLM-centric Approach for Physics Formula Discovery [98.58830663687911]
  VIPERR-aq1 is a multimodal model that performs Visual Induction for Equation Reasoning.<n>It integrates visual perception, trajectory data, and symbolic reasoning to emulate the scientific discovery process.<n>It consistently outperforms state-of-the-art VLM baselines in accuracy and interpretability.
  arXiv  Detail & Related papers  (2025-08-24T14:34:21Z)
• Symmetry-Constrained Multi-Scale Physics-Informed Neural Networks for Graphene Electronic Band Structure Prediction [0.0]
  We present the Symmetry-Constrained Multi-Scale Physics-Informed Neural Network (SCMS-PINN) v35.<n>It directly learns graphene band structures while rigorously enforcing crystallographic symmetries.<n>The model predicts Dirac point gaps within 30.3 $mu$eV of theoretical zero and average errors of 53.9 meV (valence) and 40.5 meV (conduction) across the Brillouin zone.
  arXiv  Detail & Related papers  (2025-08-14T14:59:10Z)
• A Graph-Based Framework for Exploring Mathematical Patterns in Physics: A Proof of Concept [0.0]
  This corpus was represented as a weighted knowledge graph where a Graph Attention Network achieved 97.4% AUC in link prediction.<n>The framework's primary value emerges from its dual capability: generating hypotheses and auditing knowledge.
  arXiv  Detail & Related papers  (2025-08-07T16:18:47Z)
• PhyX: Does Your Model Have the "Wits" for Physical Reasoning? [49.083544963243206]
  Existing benchmarks fail to capture a crucial aspect of intelligence: physical reasoning.<n>We introduce PhyX: the first large-scale benchmark designed to assess models capacity for physics-grounded reasoning in visual scenarios.
  arXiv  Detail & Related papers  (2025-05-21T18:33:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.