A Conservation Law for Commitment in Language Under Transformative Compression and Recursive Application
Authors/Creators
Description
== V.04 DESCRIPTION (Structural Revision) ==
This paper presents a conservation law for commitment in language: the claim that the identity-preserving core of a signal—its commitment content—remains invariant under lossy compression and recursive application, provided enforcement constraints are met. We formalize commitment as a measurable semantic invariant distinct from Shannon information, define conservation conditions over transformation and recursion, and provide a falsifiability framework with concrete failure criteria.
The work situates itself against semantic information theory (Bar-Hillel & Carnap, Floridi, Tishby), transformation fidelity and drift (Bianchi et al., Xu et al., Yeh et al.), and conservation principles in computation (Atkey, Kunin et al.). We demonstrate that existing probabilistic and agent-based architectures violate commitment conservation under recursion, leading to drift and identity loss. A minimal enforcement architecture (MO§ES™) is introduced as proof-of-concept for preserving commitment invariance without model-specific assumptions.
Preliminary empirical results from compression-based stress tests on a limited corpus show patterns consistent with the conservation predictions. The framework is model-agnostic, extends beyond text to structured signals such as code and speech, and is designed for adversarial replication.
This is a structural revision incorporating peer-review preparation: reordered sections (falsification at §4), expanded related work (10 new references), scoped claims (Theorem → Proposition), and 13 technical strengthening items applied throughout.
Timestamped public disclosure. arXiv endorsement pending.
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V.04Tech_Struct_Depth.pd.pdf
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Additional details
Dates
- Updated
-
2026-02-26Technical_Structure_Depth
Software
- Repository URL
- https://github.com/SunrisesIllNeverSee/commitment-conservation
- Development Status
- Active
References
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