A Hierarchical Credibility Formal Deduction Framework for Physical Theories

Formal proof tools have mature applications in pure mathematics. Deduction systems represented by Lean can verify the rigor of logical derivations step by step, realizing transparency, reproducibility and verifiability of mathematical axioms and proof procedures. However, modern physical systems have long lacked corresponding standardized logical verification tools. A common industry issue exists where ad-hoc patch assumptions are artificially solidified into underlying axioms. When contradictions emerge between theoretical deductions and experimental observations, academia generally adopts the approach of adding new assumptions to resolve conflicts, rather than reviewing the self-consistency of underlying premises. This eventually leads to the proliferation of hidden assumptions, bloated theoretical systems, mixed underlying logic and solidified disciplinary paradigms. To fill this research gap, this paper proposes a hierarchical credibility formal deduction framework for physical premises, and innovatively names this general physical logical adjudication system Physics AGI (General Deduction and Verification Framework for Hierarchical Physical Credibility). In current academic contexts, "Physics AI" is fixed to refer to tools for physical simulation, numerical calculation and data fitting, which is inconsistent with the core positioning of theoretical logical verification in this paper. Therefore, this naming is abandoned. Meanwhile, this paper clearly distinguishes general AGI from the Physics AGI defined herein. Conventional AGI generally refers to the ontology of general artificial intelligence, while Physics AGI is a general logical evaluation standard and deduction system for all physical theories. It is an exclusive new academic term in physics with no universal definition in academia, belonging to the original standardized theoretical tool proposed in this paper. This research only establishes framework principles, grading rules and deduction paradigms, and does not involve software code development. Subsequent engineering teams can independently develop implementation tools according to the public specifications of this paper. Developers may define product names autonomously, but the core adjudication logic, grading criteria and traceability rules of this framework are permanently established by this paper. At the current stage, Physics AGI relies on preset transparent human rules to complete classification of physical premises, verification of logical chains, traceability of derivation dependencies, early warning of theoretical conflicts and preliminary theoretical review of academic papers. In the long run, with the maturation of general artificial intelligence technology, Physics AGI can achieve high-level capabilities such as autonomously parsing physical theories, automatically identifying axioms and hypotheses, independently assigning credibility grades, and judging the correctness of logical reasoning. Whether for manual rule-based deduction or future intelligent autonomous deduction, all judgment criteria, classification bases and grading justifications of Physics AGI must be fully disclosed in writing, traceable throughout the whole process, and free of hidden black-box operations. All grading results are open for recheck. Any researcher can reproduce the deduction process and inspect whether premises meet grading standards. Non-compliant premises will be automatically forced to downgrade, completely eliminating subjective judgment and opaque operations. This framework aims to provide a unified, transparent, recheckable and iterable standardized logical filter for physical theories, filling the blank of formal deduction in physics.