Errata and Corrections: Entropic Ledger Framework Papers (December 2025)

Errata and Corrections For: Entropic Ledger Framework Papers (December 2025) Wayne A. Satz and Ryan M. Gow December 2025 1 Summary This document provides corrections and clarications to the Entropic Ledger Framework papers published on Zenodo in December 2025. The primary correction concerns the LogVAMS empirical validation claims, which were based on synthetic data that did not generalize to production environments. Key correction : Large-scale validation on the BGL production dataset revealed that physics-only log anomaly detection achieves F1 = 3.05%, not the performance implied by synthetic data results. The original claims of empirical validation through LogVAMS are retracted and replaced with honest assessment of both successes and failures. 2 Aected Publications Table 1: Original Publications and Their Status Paper Original DOI Status Spacetime as Entropic Capacity 10.5281/zenodo.17808118 Revised Implications of ComplexityDependent Gravitation 10.5281/zenodo.17808243 Minor update From Selection to Dynamics 10.5281/zenodo.17808347 Unchanged What Survives Is What Exists 10.5281/zenodo.17808395 Unchanged LogVAMS 10.5281/zenodo.17808421 Superseded 3 Detailed Corrections 3.1 Paper 1: Spacetime as Entropic Capacity Original DOI : https://doi.org/10.5281/zenodo.17808118 Correction Type : Major revision (Version 2.0) 3.1.1 Abstract Original claim : The framework is validated empirically through LogVAMS, an anomaly detection system applying entropic capacity mathematics to software systems. On synthetic log data with 162 injected failures, LogVAMS achieves 35.9±12.3 observation mean lead time (95% CI: 11.760.1) with recall of 1.00 and AUROC of 0.847, signicantly outperforming baseline methods... 1 Corrected claim : We investigate empirical applicability through LogVAMS, an anomaly detection system applying entropic capacity mathematics to software systems. Initial results on synthetic data (162 injected failures) showed promising lead time ( 35.9±12.3 observations). However, large-scale validation on production data (BGL supercomputer logs, 4.7M entries) yielded F1 = 3.05%, indicating that physics-only detection does not generalize without integration with conventional machine learning methods. A hybrid architecture (ML detection + physics features) achieves F1 = 7580%, suggesting physics-inspired methods excel at interpretability enhancement rather than standalone detection. This negative result is scientically valuable and reported in full. 3.1.2 Section 6 (LogVAMS) The entire section has been rewritten to include:  Production validation results (BGL dataset)  Root cause analysis of failure modes  Honest assessment of what works and what doesn't  Recommended hybrid architecture 3.1.3 Conclusion Original claim : Is validated empirically through LogVAMS with quantied performance metrics Corrected claim : Is investigated empirically through LogVAMS (though large-scale validation revealed signicant limitations) 3.2 Paper 5: LogVAMS Original DOI : https://doi.org/10.5281/zenodo.17808421 Correction Type : Complete rewrite (Version 2.0 supersedes original) 3.2.1 Core Thesis Change Original thesis : Physics-inspired methods detect anomalies better than baselines Corrected thesis : Physics-inspired methods fail as standalone detectors but provide interpretable features that enhance ML systems 2 Table 2: Original vs. Corrected Results Metric Original (Synthetic) Corrected (Production) Dataset size 50,000 lines 4,747,963 lines Anomaly count 162 348,460 Physics-only F1 ∼ 85% implied 3.05% Lead time claim 35.9 obs Not measurable at 3% F1 Recommended approach Physics-only Hybrid (ML + Physics) 3.2.2 Results Summary 4 What Remains Valid The following claims are not aected by this correction: 1. Theoretical framework : The mathematical derivations of entropic capacity, emergent gravity, and information-theoretic foundations remain unchanged 2. Physics predictions : Falsiable predictions for holographic noise, complexity-sensitive gravitation, and GRB dispersion are unaected by LogVAMS results 3. Critical slowing down mathematics : The underlying mathematics is well-established in ecology and climate science (Scheer et al. 2009, Dakos et al. 2012) 4. Papers 3 and 4 : From Selection to Dynamics and What Survives Is What Exists do not depend on LogVAMS validation 5. 4MSB experiment : Cross-domain validation through evolution experiments remains ongoing 5 What Is Invalidated 1. LogVAMS as standalone validation : The claim that LogVAMS validates the entropic capacity framework is retracted 2. Physics-only detection ecacy : The claim that physics methods outperform ML baselines on production data is false 3. Universality (as applied to log detection) : Direct transfer of critical slowing down mathematics to log detection fails without domain adaptation 6 Scientic Rationale We publish this correction because: 1. Scientic integrity requires it : Overclaiming empirical validation undermines the theoretical work 2. Negative results are valuable : Documenting what doesn't work prevents others from wasting eort 3. The hybrid nding is useful : ML + Physics interpretability is a viable production approach 4. The theory stands independently : Physics predictions (holographic noise, complexitygravitation) don't require log detection to work 3 7 Version History Table 3: Version History Date Version Changes Dec 3, 2025 1.0 Original publication (all 5 papers) Dec 10, 2025 2.0 Major correction: LogVAMS production validation results; Papers 1 and 5 revised 8 Updated DOIs Version 2.0 papers will be uploaded to Zenodo as new versions, linked to original DOIs:  Paper 1 v2: https://doi.org/10.5281/zenodo.17808118 (new version)  Paper 5 v2: https://doi.org/10.5281/zenodo.17808421 (superseded)  Validation paper: New DOI (companion paper with full methodology)  This errata: New DOI 9 Contact For questions regarding these corrections: Wayne A. Satz, MD Temple University Health System Email: wa[email protected] ORCID: 0000-0003-3090-3852 4