Published January 12, 2026 | Version V.05
Preprint Open

A Conservation Law for Commitment in Language Under Transformative Compression and Recursive Application

Description

This deposit constitutes Version V.05 of the Conservation Law of Commitment program. A foundational work proposing that commitment, operationalized as the identity-bearing content of a linguistic signal, admits formal treatment as a conserved invariant under lossy transformation, recursive self-application, and compression. The contribution is offered as a candidate physical principle for information-bearing language systems, rather than as a heuristic or empirical generalization.

V.05 advances three substantive contributions beyond prior versions. First, it formalizes the conservation claim through definitions of the commitment kernel, compression as a structural regime, and recursive stability, accompanied by accompanying theorems and a falsification protocol with explicit refutation conditions, oracle-specification requirements, and adversarial Goodhart-resistance provisions. Second, it introduces MO§ES™  a stateless compression gate, cryptographically-anchored lineage DAG, and external observable layer.  As a model-agnostic enforcement architecture demonstrating that invariance can be preserved without dependence on architecture-specific alignment mechanisms. Third, it integrates the EXP-001 through EXP-007 controlled experimental record (3,950 archived runs spanning recursive paraphrase, mechanism isolation, adversarial variation, self-application, and negation edge cases), which sharpens rather than displaces the falsification framework: apparent fidelity loss in the follow-on series consistently localizes to extraction or proxy-measurement bottlenecks rather than to disappearance of the underlying commitment. 

V.05 supersedes V.04 (Technical Structure Depth, February 2026) and incorporates pre-deposit revisions completed May 2026, including correction of a citation collision, removal of unsupported quantitative claims in §8.5, an explicit oracle-specification requirement in the falsification protocol (§4.3), an i.i.d. idealization disclosure in the recursive-failure proof (Theorem 6.2), and integration of the empirical companion archive citation. The complete five-version lineage, with corresponding DOIs, is recorded inline in the manuscript. 

The empirical regimes observed across EXP-001 through EXP-007 indicate that the conservation principle described here constitutes one law within a wider research program concerned with signal integrity under transformation across institutional, legal, archival, and computational domains. The present manuscript isolates the law itself. This work is the founding instrument for the forthcoming prospectus formalizing the broader program with definitional scaffolding, research agenda, and downstream deposits.

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Additional details

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Dates

Updated
2026-05-04
Testing_Addendum

Software

Repository URL
https://github.com/SunrisesIllNeverSee/commitment-conservation
Programming language
Python
Development Status
Active

References

  • Shannon, C. E. (1948). A mathematical theory of communication. Bell System Technical Journal, 27(3), 379-423
  • Turing, A. M. (1936). On computable numbers, with an application to the Entscheidungsproblem. Proceedings of the London Mathematical Society, s2-42(1), 230-265.
  • Schmidhuber, J. (2008). Driven by Compression Progress: A Simple Principle Explains Essential Aspects of Subjective Beauty, Novelty, Surprise, Interestingness, Attention, Curiosity, Creativity, Art, Science, Music, Jokes.
  • Goertzel, B., et al. (2014). A cognitive architecture based on cognitive synergy.
  • Looks, M. (2006). Meta-optimizing semantic evolutionary search.
  • Looks, M. (2009). Scalable meta-optimization: A case study with the distributed hierarchical genetic algorithm.
  • Corrêa, C., Schmid, P., Goyal, K., Kim, J., et al. (2025). Iterative Deployment Improves Planning Skills in LLMs. arXiv preprint arXiv:2512.24940.
  • Xie, Z., Ma, Y., Zhou, Y., et al. (2025). mHC: Manifold-Constrained Hyper-Connections for Stable Scaling. arXiv preprint arXiv:2512.24880.
  • Chang, E. (2025). The Missing Layer of AGI: From Pattern Alchemy to Coordination Physics. arXiv preprint arXiv:2512.05765.
  • Zhang, H., Liu, A., et al. (2025). Recursive Language Models. arXiv preprint arXiv:2512.24601.
  • Guo, D., Yang, D., Zhang, H., et al. (2025). DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning. arXiv preprint arXiv:2501.12948.
  • Chen, Z., Wang, H., Li, T., et al. (2026). SimpleMem: A Simple Memory Mechanism with Structured Compression for Long-Context Language Agents. arXiv preprint arXiv:2601.02553.
  • Park, J. S., O'Brien, J. C., Cai, C. J., et al. (2023). Generative Agents: Interactive Simulacra of Human Behavior. Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology, 1–22.
  • Bai, Y., Kadavath, S., Kundu, S., et al. (2022). Constitutional AI: Harmlessness from AI Feedback. arXiv preprint arXiv:2212.08073.
  • Centelles, A. and Mendelsohn, T. (2026). ABBA: Lattice-based Commitments from Commutators. IACR ePrint 2026/148.
  • Yeh, S., Li, S., and Mallick, T. (2026). LUMINA: Detecting Hallucinations in RAG Systems with Context-Knowledge Signals. Proceedings of ICLR 2026.
  • Xu, Y., Zhang, X., Yeh, S., et al. (2026). Simulating and Understanding LLM Deceptive Behaviors in Long-Horizon Interactions. Proceedings of ICLR 2026.
  • Gaurav, S., Heikkonen, J., and Chaudhary, J. (2025). Governance-as-a-Service: A Multi-Agent Framework for AI System Compliance and Policy Enforcement. arXiv preprint arXiv:2508.18765.
  • Kunin, D., et al. (2021). Neural Mechanics: Symmetry and Broken Conservation Laws in Deep Learning Dynamics. arXiv preprint arXiv:2012.04728.
  • Atkey, R. (2014). From Parametricity to Conservation Laws, via Noether's Theorem. ACM SIGPLAN Notices, 49(1), 491–502.
  • Bianchi, F., et al. (2022). Language Invariant Properties in Natural Language Processing. arXiv preprint arXiv:2203.07628.
  • Tishby, N., Pereira, F. C., and Bialek, W. (2000). The Information Bottleneck Method. Proceedings of the 37th Annual Allerton Conference, 368–377.
  • Floridi, L. (2004). Outline of a Theory of Strongly Semantic Information. Minds and Machines, 14(2), 197–221.
  • Bar-Hillel, Y. and Carnap, R. (1953). Semantic Information. British Journal for the Philosophy of Science, 4(14), 147–157.
  • Grimmett & Stirzaker (2001), Probability and Random Processes, Oxford University Press, 3rd ed.