Blockchain Papers

Dmitry Zharnikov

This paper empirically demonstrates Zharnikov's (2026ao) Proposition P4 – rendering-equivalence under spine-preservation – in management theory. The paper extends the companion theory's Heisenberg–Schrödinger historical existence proof into contemporary strategy research via structural extractions of two independently-authored pairs: a dynamic-capabilities pair (Eisenhardt and Martin 2000 + Zollo and Winter 2002) and a knowledge-based-view pair from the SMJ Winter 1996 Special Issue (Grant 1996 + Liebeskind 1996). The recombination metric Rec returns 4 linked propositions with preserved antecedents on each pair. A random-graph null baseline shows Pr(Rec ≥ 3 by chance) ≈ .000 across 1,000 size-matched shadows. Three additional renderings of substrates already in the corpus — a practitioner-register rendering of the paper's own structure, a third rendering of the focal-pair shared substrate, and a cross-paper rendering of the companion theory's full theoretical apparatus – preserve 11/14, 4/4, and 12/15 items strictly; 14/14, 4/4, and 15/15 semantically; zero contradictions. A bibliographic-hallucination audit of twelve AI-suggested anchors finds two verified and ten negative findings. Secondary β/δ estimates satisfy the cost-asymmetry ordering. Cross-language demonstrations span Russian renderings across multiple LLMs (including Russian-native GigaChat Rec = 12 and YandexGPT Rec = 11) and Chinese renderings across five LLMs from three training-corpus families including an open-weights model running locally on a single Mac mini (DeepSeek Rec = 12, Claude Opus Rec = 11, Qwen3.6:27b Rec = 12), with cross-extractor robustness (DeepSeek's Chinese rendering re-extracted by Qwen3.6 instead of GPT-4o: Rec = 12). Inter-coder reliability tests are pre-registered for a future release. The paper engages recombinant-search and knowledge-representation scholarship as theoretical antecedents. Includes paper.yaml (Paper Spec v0.1.0) – a machine-readable specification of the paper's claims, assumptions, and dependencies. See https://github.com/spectralbranding/paper-spec for the standard.

Naiheng Zhang

Abstract In the digital age, the reliance on network communication for information exchange has surged, making encrypted network traffic a linchpin of secure digital interactions. However, while encryption safeguards data, it creates hurdles for network management and security surveillance. Conventional deep packet inspection (DPI) falters when faced with encrypted traffic, and existing studies in this area have drawbacks like reliance on trusted third parties and limited detection capabilities. To address these issues, we present a novel zero knowledge proof based encrypted traffic management( $$\mathbb {ZKP}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>ZKP</mml:mi> </mml:math> - $$\mathbb {PET}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>PET</mml:mi> </mml:math> ) scheme. By integrating a third-party verifier operating under the honest-but-curious (HBC) model, $$\mathbb {ZKP}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>ZKP</mml:mi> </mml:math> - $$\mathbb {PET}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>PET</mml:mi> </mml:math> establishes a trustless verification system that effectively and efficiently curbs metadata leakage. $$\mathbb {ZKP}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>ZKP</mml:mi> </mml:math> - $$\mathbb {PET}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>PET</mml:mi> </mml:math> is implemented with two applications: HTTP traffic blocking and blacklist management. For HTTP traffic blocking, the BTHP circuit is developed to extract version details from TLS traffic and verify compliance, enabling precise traffic control. In blacklist management, tailored extraction algorithms for DoT and DoH encrypted DNS traffic are implemented, and Merkle tree based membership proofs are utilized to decide whether to intercept traffic. Experimental evaluations demonstrate that $$\mathbb {ZKP}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>ZKP</mml:mi> </mml:math> - $$\mathbb {PET}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>PET</mml:mi> </mml:math> can efficiently enforce diverse network policies on encrypted traffic. It not only safeguards security and privacy but also exhibits outstanding performance, offering a dependable, efficient, and privacy-centric solution for encrypted network traffic management.

José Ignacio Peinador Sala

Conditional Refutation of Erdős Problem #463 via Arithmetic Quantum Chaos Author: José Ignacio Peinador Sala Overview This repository contains the full manuscript, companion computational notebooks, and formal Lean 4 verification for the paper "Conditional Refutation of Erdős Problem #463 in Hyper‑Slow Growth Regimes via Arithmetic Quantum Chaos". We demonstrate that, under the hypothesis that the survival variance of rough numbers around primorials is controlled by the fractal dimension D2≈0.24338 of the Riemann‑GUE Hamiltonian (Bridge Conjecture), no function f(n)≤log⁡(log⁡n) satisfies Erdős' condition for all sufficiently large n. The proof is constructed by bridging Galois projection operators, power‑law random banded matrices (PRBM), the Altshuler‑Shklovskii effect, and optimal transport (Kantorovich–Rubinstein duality). The ultimate goal of this program is to elevate this conditional result to an unconditional proof by integrating the supersymmetric Non-Linear Sigma Model (NLσM) limit with the most recent 2025 sieve bounds on rough numbers in short intervals. Contents Article: Open pdf One‑Click Reproducibility This project is designed for frictionless, one‑click reproducibility. No compiler installation, no supercomputing cluster. All experiments run on Google Colab with zero local setup — you can audit the physics of the arithmetic vacuum from a browser on your laptop or even your phone. What the notebooks validate You can run the experiments directly in your browser: Notebook Contents What it certifies Main experiments: Open in Colab Experiments 1–4 + Chirikov map Collapse of Nₖ, monotonic decrease of D₂, massive suppression of Σ²(L), sub‑diffusive SFF ramp, classical chaos suppression Lean 4 verification: Open in Colab Lean 4 formal proofs Idempotence of the Galois projector, discrete variance floor lemma, modular classification of primes Experiments (Main Notebook) Collapse of the survival variable Nk – deterministic emptiness of the critical interval for primorials k≥10 (M=5,000 samples). Fractal dimension D2 of pruned Hamiltonians – monotonic decrease under Galois projection (Numba‑accelerated up to N=10,000). Number variance Σ2(L) and Thouless energy – massive spectral suppression (up to 96% below GUE) with the Thouless scale plunging below L=0.5 (M=10,000 realizations). Spectral Form Factor and Finite‑Size Scaling – robust sub‑diffusive ramp (γ→0.61) and convergent D2≈0.106 in the thermodynamic limit (M=100 realizations, N up to 6,000). Chirikov Map (Classical) – Galois projection completely strangulates chaotic transport (D≈0.00 vs D≈11.05), proving universal ergodicity suppression. Formal Verification in Lean 4 Erdős Problem #463 is actively tracked by the mathematical community, including Google DeepMind's formal-conjectures repository. Laying the formal groundwork to resolve this, the notebook Notebooks/erdos_refutation.ipynb compiles and mechanically verifies three foundational lemmas in Lean 4 (v4.29.1, Mathlib4): Discrete Variance Floor Lemma — ∀ x ∈ ℕ, x ≤ x² Galois Projector Idempotence — χ² = χ for the coprimality indicator Modular Classification of Primes — ∀ p > 3 prime, p ≡ 1 ∨ p ≡ 5 (mod 6) These lemmas form the unshakeable logical bedrock of the conditional refutation. 🔭 Philosophical Context "Mathematics is not about numbers, equations, computations, or algorithms: it is about understanding." — William Thurston For decades, the distribution of prime numbers and the behaviour of chaotic quantum systems were studied as separate continents of knowledge, occasionally glimpsing each other across a narrow strait —the Hilbert–Pólya conjecture, the Montgomery–Odlyzko law— but never truly merging. This work builds a bridge across that strait. The key insight is that the ring ℤ/6ℤ is not merely a convenient sieve for eliminating multiples of 2 and 3. It is a topological substrate —a discrete analogue of the KO‑dimension in noncommutative geometry— that partitions the integers into resonant channels (𝒞₁ and 𝒞₅) and sterile channels (𝒞₀, 𝒞₂, 𝒞₃, 𝒞₄). When this partition is imposed as a superselection rule on a quantum Hamiltonian, the system does not thermalise. It enters a Non‑Ergodic Extended (NEE) phase where fluctuations are systematically suppressed, variance collapses, and the arithmetic vacuum swallows the survivors. The philosophical lesson is profound: randomness is not the default state of complex systems. The apparent chaos of prime numbers, long regarded as the quintessence of unpredictability, harbours a rigid geometric order. That order can be harnessed —through Galois projection, through PRBM Hamiltonians, through the Altshuler–Shklovskii effect— to prove theorems that have resisted classical sieve methods for half a century. This project also embodies a conviction about how science should be done in the age of artificial intelligence. Every line of code, every formally verified lemma, and every numerical experiment was developed using freely accessible tools. The massive simulations of quantum chaos, which traditionally would demand exclusive access to institutional supercomputers, were executed entirely on Google Colab, democratizing high-performance computing. The formal verification of the mathematical bedrock was achieved using the open-source proof assistant Lean 4. Furthermore, the theoretical framework was built in a genuine symbiosis with DeepSeek, an open-weight AI freely provided to the world. No proprietary models, no paywalled platforms, no computational aristocracy. This work demonstrates that the absolute frontier of mathematical research is now accessible to anyone with a good idea, a standard laptop, and the willingness to engage in dialogue with tools that amplify, rather than replace, human creativity. "The universe is written in the language of mathematics." — Galileo Galilei Perhaps it is written, more precisely, in the language of modular arithmetic. Last Update: May 2026 | Status: Under Peer Review in IOP/LMS Nonlinearity (Ref: NON-110856) | Built with ❤️, 🐍 & 🤖

Paolo Antonelli, Pierangelo Marcati, Laura V. Spinolo

We study the zero-dispersion limit for a class of Korteweg--de Vries (KdV)-type initial-boundary value problems on the half-line, with Dirichlet boundary conditions assigned at \(x=0\). We focus on the outflow regime, where the solution of the limiting scalar conservation law does not attain the boundary condition imposed on the dispersive problem. We construct a boundary layer profile, depending on the fast variable, which is uniquely determined, through the associated stationary third-order boundary layer equation, by the mismatch between the boundary conditions, and by the exponential decay at infinity in the fast variable. Our main result shows that, under suitable regularity and compatibility assumptions on the data, the dispersive solution is well approximated by a WKB expansion given by the sum of the smooth solution of the conservation law and the boundary layer profile. In particular, we establish stability of the boundary layer profile by proving quantitative estimates for the remainder term in a weighted energy norm, and show that it converges to $0$ in $H^1$, uniformly in time and up to the lifespan of the smooth solution of the conservation law. The proof is based on the analysis of a linearized energy functional and does not rely on complete integrability or inverse scattering techniques. It applies to general fluxes and requires no smallness assumption on the amplitude of the boundary layer. To the best of our knowledge, this is the first stability result for boundary layers of KdV-type equation on the half line.

Jieran Cui, Zhengkai Wen, Haowen Fang, Yinan Zhu · 9 authors

Zero-knowledge virtual machines (zkVMs) are a key technology for driving the large-scale adoption of zero-knowledge proofs (ZKP), but their performance bottlenecks severely limit their practicality. While current hardware acceleration research has exclusively focused on backend proving, we identify that the frontend execution and trace generation phase is rapidly emerging as the new system bottleneck. To address this challenge, we propose ZK-Tracer, the first hardware accelerator architecture specifically designed for the zkVM frontend. ZK-Tracer features a novel heterogeneous design comprising a Main Trace Unit and parallel Permutation Trace Units. It exposes a fine-grained interface to the host software through a lightweight instruction set extension, enabling efficient task offloading. Our ASIC implementation results demonstrate that ZK-Tracer achieves up to 1829x speedup in trace generation over a high-performance multi-core CPU. When integrated with existing backend proving accelerators, it delivers a remarkable 963x end-to-end performance improvement for the entire ZKP system.

AKATERINH XENOPOULOU-TYROKOMOU, Epameinondas Xenopoulos

Xenopoulos’ Historical Genetic Logic: A New Framework and the XEPTQLRI Theorem DOI:10.5281/zenodo.20367121Date: May 2026 Aikaterini Xenopoulou TyrokomouIndependent ResearcherORCID: 0009 0004 9057 7432Email: katerinaxenopoulou@gmail.com Theoretical Foundation: Epameinondas Xenopoulos †Based on the Historical Genetic Logic of Epameinondas Xenopoulos, Epistemology of Logic: Logic Dialectic or Theory of Knowledge (posthumous 2nd ed., 2024) [1, 2]ORCID: 0009 0000 1736 8555† In memoriam (1920–1994) Methodological NoteThe present work simplifies and mathematizes central ideas of the formal-dialectical logic of E. Xenopoulos in order to create an applicable computational tool. It does not constitute a faithful rendering of his philosophical theory in its full depth, but a focused operationalization for the purpose of computational application. Statement of AuthorshipThe present work is founded on the logical system of Epameinondas Xenopoulos (1920–1994). The XEPTQLRI index does not constitute an independent theory, nor does it introduce a new autonomous logical framework. The theoretical background, the basic categories, the logical relations, the fundamental principles, and the dialectical operators belong to the work of Epameinondas Xenopoulos. The contribution of the present work consists in the formal mathematical operationalization of specific principles of this logical system through a computable index, capable of being applied to dynamic and historically evolving systems. Consequently, the theoretical authorship belongs entirely to Epameinondas Xenopoulos, while the present work belongs to the level of systematic formalization, proof, application, and methodological development of his framework. The XEPTQLRI index expresses in quantitative form the logic of Being, Non-Being, Becoming, historical memory, and dialectical sublation, while adapting these concepts for computational use. In this sense, the present work constitutes a continuation, clarification, and applicative deepening of the Xenopoulos system, not a displacement or replacement of it. ABSTRACT We present the Xenopoulos Pre-Transitional Qualitative Leap Risk Index (XEPTQLRI), a novel mathematical index grounded in the Historical-Genetic Logic of the Greek philosopher Epameinondas Xenopoulos [1, 2]. Unlike conventional statistical summaries, XEPTQLRI captures the dialectical interplay between Being (B), Non‑Being (N), historical memory (τ), and a historical paradox factor (Π). The index is defined as Ξ = [T · τ · (1 + Π)] / Θ₀ with Θ₀ = 0.85, where T = 2BN/(B+N) is the dialectical tension expressed through the harmonic mean. Its construction respects strict causality, min‑max or logistic normalization, and a negative feedback mechanism (∂σ/∂Ξ < 0) in its dynamical extensions, though the index itself remains exogenous and purely diagnostic. We prove five theorems establishing constructive computability, scale homogeneity, non‑preservation of dynamical structure, representation dependence, and linear‑time computability. Two additional theorems (non‑self‑inversion and logical phase transition) are proved within the extended framework of the 34 Principles. Numerical experiments with the Ferrari–Xenopoulos v4.0 stochastic model show reproducible and persistent exceedance of the Aufhebung threshold, with endogenous volatility remaining low (σ ≈ 0.058). An extreme parameter run (α₅ = 1.6, σ₁ = 1.0, Θ₀ = 0.0867) reaches Ξ = 16.1, demonstrating that the critical value is not a universal constant but a local, parameter‑dependent realization. A “Dialectical War” experiment (LSTM vs. Xenopoulos system under noise = 1.0) reveals a striking dissociation: technical performance (MAE = 0.1039, 67.1% improvement) coexists with universal dialectical risk (20/20 high‑risk steps, Ξ_max = 2.99, zero paradoxality and false stability). This dissociation is mathematically consistent, as MAE and Ξ are distinct functions measuring different aspects of system behavior (MAE ⇏ Ξ). A null model comparison confirms that this risk is structurally generated (AUC 0.949 vs. 0.501, p < 0.001), with ground truth defined by the condition Ξ(t) ≥ Θ₀ for at least three consecutive time steps and binary classification threshold optimized via the Youden index. A strictly endogenous application of the canonical XEPTQLRI index to 13 distinct COVID‑19 waves in Greece (JHU CSSE) yields early warnings 48–90 days in advance (mean 84.0 days) with a mean EWS Score of 0.785, successfully detecting 10 of 13 waves (76.9%). The system substantially outperforms a simple cases‑threshold baseline (mean EWS 0.42, 23.1% success) without any reliance on AUC or external classifiers. Beyond its diagnostic function, the XEPTQLRI framework demonstrates a transformative capacity: non‑dialectical codes exposed to the Xenopoulos environment undergo systematic improvement, with documented gains ranging from 52.3% to 95.65% across multiple independent experiments. A banking crisis application correctly identified Lehman Brothers (z=3.2, p<0.001) and Bear Stearns (z=2.9, p<0.01) two years before their collapse using only pre‑2006 data. A financial early warning application achieved statistically significant predictive correlations (r=0.29–0.44, p<0.001) with lead times of 10–77 days across S&P 500, VIX, Treasury yields, and Bitcoin. Two complete experimental protocols (XENO‑EXP‑2026‑002 and XENO‑EXP‑2026‑003) provide systematic, statistically significant evidence that the Xenopoulos System, when fully embedded in machine learning architectures, functions as an improvement catalyst with measurable economic value (ROI 63:1, break‑even 6 days). Thus, XEPTQLRI bridges formal dialectics with practical early warning systems, establishing a universal law of qualitative transition while keeping its numerical expression local and context‑dependent. The present system constitutes a proto‑formalized theoretical framework — a structured mathematical–dynamical system with axiomatic foundation (34 Principles), provable theorems (7 Theorems), and computational implementation (Ferrari–Xenopoulos v4.0, COVID‑19 application), whose applicative and transformative value has been verified on real data. The system is internally consistent under its stated principles, though its full formalization in the sense of a Hilbert‑style formal system remains a subject for future work. Keywords: XEPTQLRI, Historical‑Genetic Logic, dialectical logic, qualitative leap, Aufhebung, early warning systems, stochastic differential equations, LSTM, COVID‑19, proto‑formalized framework, non‑classical negation, harmonic mean, paradox factor, historical memory, dialectical transformation, financial crisis prediction, code optimization. Lead paragraph Complex dynamical systems often undergo sudden, qualitative transformations—critical transitions that are difficult to anticipate with conventional statistical tools. This paper introduces a new mathematical framework for detecting such transformations, grounded in the Historical‑Genetic Logic of the Greek philosopher Epameinondas Xenopoulos (1920–1994). The central contribution is the Xenopoulos Pre‑Transitional Qualitative Leap Risk Index (XEPTQLRI), defined as Ξ(t) = T(t) · τ(t) · (1 + Π(t)) / Θ₀, where T is the dialectical tension between Being and Non‑Being, τ captures historical memory, and Π encodes the accumulated paradox of extreme past states. The index is fully endogenous, requires no external training or classifiers, and is accompanied by a typology of ten dialectical stages (τ₀–τ₉). We prove five constructive theorems, validate the framework through stochastic simulations, and apply it to real COVID‑19 data from Greece. Across 13 epidemic waves, XEPTQLRI issued early warnings with an average lead time of 84.0 days and a mean Early Warning Score of 0.785, substantially outperforming a simple cases‑threshold baseline. The framework thus bridges formal dialectics with operational early warning capability, offering a new lens for the study of critical phenomena. Part I — Definition and Foundation of XEPTQLRI 1. Theoretical Foundation This section presents the fundamental principles underlying the Xenopoulos Pre-Transitional Qualitative Leap Risk Index (XEPTQLRI), as formulated in the Historical-Genetic Logic of the Greek philosopher Epameinondas Xenopoulos (1920–1994) [1, 2]. These principles constitute the axiomatic framework of the index and determine both its mathematical form and its interpretive function. XEPTQLRI is neither a simple numerical magnitude nor a mere statistical summary. Instead, it is defined as a complex historical-dialectical index that captures the relationship between Being, Non-Being, their dialectical tension, historical tendency, and the probability of transcending a critical threshold of transformation. The index is embedded within the broader system of 34 Principles as the 23rd Principle, expressed through the general dialectical operator: Ξ(t) = N[F₂₃(G₂₃)]. 1.1 Principle 5: Complementarity According to the theory [1, 2], Non-Being is not an independent quantity but the complement of Being. This relationship is expressed by Principle 5: N(t)=1−B(t)N(t)=1−B(t) This equation implies that: B(t)+N(t)=1B(t)+N(t)=1 Therefore, the two quantities B(t) and N(t) are complementary aspects of the same dynamic state. If B(t) expresses the degree of presence of Being, then N(t) expresses the degree of presence of Non-Being. From the same principle it immediately follows that it is impossible for both of the following to hold simultaneously: B(t)>0.8andN(t)>0.8B(t)>0.8andN(t)>0.8 because then we would have B(t) + N(t) > 1.6, in contradiction with B(t) + N(t) = 1. Important clarification: In Theorem 2 (Paradoxical Transcendence), the condition B > 0.8 ∧ N > 0.8 refers to a special paradoxical state where the usual complementarity is suspended due to the historical accumulation of contradictions. In this state, B and N are not understood as instantaneous values at

Anthony Coslett

The numerical receipt that allows independent verification of which AI model is serving a frontier API endpoint — the top-*K* log-probability vector computed on every forward pass — is being withdrawn across every major frontier lab, without announcement. xAI silently ignores the parameter on Grok 4.20 and newer. Google Vertex began returning errors on Gemini 3 Pro without notice. OpenAI excludes the entire reasoning-model class and the GPT-5 line. Anthropic has never exposed the field. The withdrawal is not universal: legacy and non-reasoning models at the same providers continue to return logprobs on the same infrastructure. The capability is not technically infeasible. It is a decision. This note documents the current state of logprob access across four frontier providers, establishes what the access enables and what it does not, and provides six operational contract clauses that preserve the enterprise's right to verify model identity at the API layer. The mathematics of establishing model identity from top-*K* logprob output is documented in the companion research [1, 2]; this note concerns whether the numbers will continue to be available at all. The Neural Network Identity Series — Mathematical foundations, empirical validation, and governance frameworks for verifying which model is running Paper 1: The δ-Gene: Inference-Time Physical Unclonable Functions from Architecture-Invariant Output Geometry (DOI: 10.5281/zenodo.18704275) Paper 2: Template-Based Endpoint Verification via Logprob Order-Statistic Geometry (DOI: 10.5281/zenodo.18776711) Paper 3: The Geometry of Model Theft: Distillation Forensics, Adversarial Erasure, and the Illusion of Spoofing (DOI: 10.5281/zenodo.18818608) Paper 4: Provenance Generalization and Verification Scaling for Neural Network Forensics (DOI: 10.5281/zenodo.18872071) Paper 5: Beneath the Character: The Structural Identity of Neural Networks — Mathematical Evidence for a Non-Narrative Layer of AI Identity (DOI: 10.5281/zenodo.18907292) Paper 6: Which Model Is Running?: Structural Identity as a Prerequisite for Trustworthy Zero-Knowledge Machine Learning (DOI: 10.5281/zenodo.19008116) Paper 7: The Deformation Laws of Neural Identity (DOI: 10.5281/zenodo.19055966) Paper 8: What Counts as Proof? — Admissible Evidence for Neural Network Identity Claims (DOI: 10.5281/zenodo.19058540) Paper 9: Composable Model Identity — Formal Hardening of Structural Attestations in the Enterprise Identity Stack (DOI: 10.5281/zenodo.19099911) Paper 10:Where Identity Comes From: Path Sensitivity and Endpoint Underdetermination in Neural Network Training (DOI: 10.5281/zenodo.19118807) Paper 11: Post-Hoc Disclosure Is Not Runtime Proof: Model Identity at Frontier Scale (DOI: 10.5281/zenodo.19216634) Paper 12: Family-Dependent Response to Reasoning Distillation Across Structural and Functional Identity Layers (DOI: 10.5281/zenodo.19298857) Paper 13: Safety-Alignment Removal as a Model-Identity Failure — Structural Evidence from Published Weight-Level Mutation Checkpoints (DOI: 10.5281/zenodo.19383019) Technical Note: Agent Identity Is Not Model Identity (DOI: 10.5281/zenodo.19240883) Technical Note: Gap Invariance: Why PPP Measurements Are Domain-Independent by Construction (DOI: 10.5281/zenodo.19275524) Technical Note: Measured Model Substitution Under Valid Agent Credentials (DOI: 10.5281/zenodo.19342848) Technical Note: Artifact Identity Is Not Runtime Identity — Trustfall Lite and the Boundary of File-Level Model Verification (DOI: 10.5281/zenodo.20019127) Technical Note: Artifact Identity Is Not Runtime Identity — Trustfall Lite and the Boundary of File-Level Model Verification (DOI: 10.5281/zenodo.20019127) Technical Note:: The Disappearing Window — AI Logprob Access Withdrawal and the Structural Verifiability of Frontier Model Contracts (DOI: 10.5281/zenodo.20362098) Formal Verification Stack for Neural Network Structural Identity (IT-PUF Coq Proofs) (DOI: 10.5281/zenodo.18930621) Copyright (c) 2026 Anthony Ray Coslett / Fall Risk AI, LLC. All Rights Reserved. Confidential and Proprietary. Patent Pending (Applications 63/982,893, 63/990,487, 63/996,680, 64/003,244).

Ilir Mehmetaj

Abstract This concept documents a complete physics-first authorisation architecture for the quantum-permanent era — applicable across AI cluster security, interbank settlement, space and interplanetary infrastructure, critical infrastructure protection, digital identity, supply chain integrity, and optional democratic participation tools. The architecture rests on a single physical principle: a cryptographic credential that no longer exists cannot be recovered by any computation, quantum or classical, regardless of future advances in hardware or algorithms. The concept extends the Temporal Rotation Security Protocol (TRSP v3, DOI: 10.5281/zenodo.20324081) and the TRSP Digital Coin (TDC v2, DOI: 10.5281/zenodo.20332811) with a unified Layered Temporal-Quantum Security (LTQS) framework. LTQS combines NIST FIPS 203/204-standardised Post-Quantum Cryptography (ML-KEM, ML-DSA) as Layer 0 — mathematical transit security — with TRSP temporal rotation as Layer 1 — physical credential elimination through hardware-enforced destructive readout within a configurable rotation window (10–500 ms). Layers 2 and 3 add geographically distributed hybrid dynamic quorum validation and LEO satellite orbital entropy anchoring with relativistic timestamp verification. An integrated adaptive AI management layer selects security profiles dynamically across High-Assurance, Standard, Degraded, and Emergency modes — guaranteeing graceful degradation to pure PQC fallback when physical infrastructure is unavailable. The hardware commitment module previously documented as CRATON is architecturally designated URDHR, after the Norse Norn of the irrecoverable past. The two complementary quorum layers are designated VERÐANDI (present-moment ground quorum) and SKULD (future-anchoring orbital quorum) — the three Norns mapped to the three temporal dimensions of cryptographic security. Prior art established under the CRATON designation in all previously published documents extends fully to the URDHR designation. Fifteen novel contributions are placed on the public record as defensive prior art: NC-TDC-21 (AI-to-AI Micropayment Architecture), NC-TDC-22 (Macroscopic Environmental Entropy as Optical Physical Unclonable Function), NC-TDC-23 and NC-TDC-23a (Macroscopic Polymorphic Cipher with Dynamic Dimensional Entropy — exploratory), NC-TDC-24 (TRSP Democratic Coercion Shield — exploratory, extending Juels-Catalano-Jakobsson coercion-resistant voting literature), NC-TDC-25 (Continuous Anonymous Democratic Pulse — exploratory), NC-TDC-26 (Physical Proof of Presence consensus mechanism operating at the Landauer thermodynamic minimum), NC-TDC-27 (Temporal Scarcity Value Architecture anchored in thermodynamic time-arrow irreversibility), NC-TDC-28 (AI Exchange Consortium Architecture), NC-TDC-29 (Biometric Supply Architecture), NC-TDC-30 (CRATON Chain Coin Identity Architecture without persistent private key), NC-TDC-31 (Three-Phase Value Architecture), and NC-TDC-32 (Cooperative Multi-Anchor Currency Architecture with Founder-Operator Equity-Plus-Operating-Margin Compensation Structure). NC-URDHR-1 and NC-TRSP-Hybrid-1 formalise the Three-Norn naming framework and the four-layer hybrid post-quantum/temporal architecture respectively. NC-TDC-32 is the central economic contribution of this version. It formalises a digital currency architecture in which multiple stakeholder classes — AI infrastructure operators, financial institutions, sovereign states, and individual participants — coexist as independent issuing classes within a single cooperative cryptographic framework. Each class mints its own coin contingent backed by its own economic activity rather than by shared monetary authority. Phase transitions admit new classes through supply expansion, not through re-pricing of existing coins. Coin denomination is calibrated from inception across micropayment to reserve-asset volume regimes via the monetary identity M·V = P·Q. The infrastructure operator class — the AI companies that build and continuously operate the adaptive security layer — is compensated through a two-component structure: bounded equity recognition at phase transitions (capped, independently audited) plus formula-bound operating margin on continuing services. This two-component compensation model is economically required to keep operating margins moderate and the architecture competitive against established settlement infrastructures. Monetary sovereignty remains exclusively with the issuing class for each contingent; the operator class operates the cryptographic issuance infrastructure but does not exercise monetary authority over any contingent. The architecture is the first formalised digital implementation of the cooperative multi-stakeholder economic model previously demonstrated at continental scale only by the Hanseatic League (twelfth to seventeenth century). All fifteen contributions are documented as conceptual frameworks. Production Concepts (NC-TDC-21, NC-TDC-22, NC-TDC-26 through NC-TDC-32, NC-URDHR-1, NC-TRSP-Hybrid-1) represent architecturally sound design patterns ready for implementation evaluation. Exploratory Concepts (NC-TDC-23, NC-TDC-23a, NC-TDC-24, NC-TDC-25) document underlying architectural ideas requiring further formal research. All specific implementation parameters — quantities, ranges, governance percentages, consortium composition — are illustrative starting points belonging to the institutions that choose to implement the architecture. A dedicated Part 9 — Engineering Considerations and Open Challenges — documents five anticipated technical reviewer questions with referenced solution pathways from current research literature: global consensus latency under M-of-N geographically distributed validation (Sliding Window Key Rotation with Dual-Key Buffers, TLS 1.3 RFC 8446); fuzzy extractor Helper Data leakage in optical entropy capture (Controlled PUF Finite State Machine architectures eliminating Helper Data transmission, addressing Becker 2015); orbital quorum availability under atmospheric and orbital dynamics constraints (Multi-Path Delivery with configurable Grace Periods and Layer 2 graceful degradation); post-quantum zero-knowledge proof latency for autonomous AI agent commerce (Off-Critical-Path ZKP architecture separating HMAC authorisation from asynchronous identity verification); and multi-anchor synchronisation between independent issuance classes (Key-ID and class-identification headers preserving structural separation between technical operation and monetary sovereignty). Part 9 introduces no additional Novel Contributions — it documents that the engineering challenges anticipated by reviewers have established research-backed pathways, demonstrating readiness for Proof-of-Concept implementation phases without modifying or weakening any architectural element documented in Parts 1 through 8. The concept is published as defensive prior art under CC BY-NC-ND 4.0 , preventing future patent claims on the documented conceptual architectures while preserving open non-commercial use for evaluation, research, citation, and standards consideration by IETF, ISO/IEC JTC 1/SC 27, NIST Post-Quantum Cryptography programme, or any institution choosing to adopt all or any independent component of the architecture.

Rohini Lakshané

This working paper is an output of the Community Privacy Residency held in Taipei in 2025. https://community-privacy.github.io/ Keywords: Image-based abuse; non-consensual intimate imagery; evidentiary privacy; protected identity; sexual autonomy; Global South; digital evidence; hash evidence; zero-knowledge proofs; privacy-enhancing cryptography; survivor-auditable governance.

tsenhsuan Chang, tsenhsuan Chang

Decentralized Federated Intelligence Protocol (DFIP) Engineering a Post-Cloud Autonomous Swarm Intelligence Architecture Integrating LEO Satellite Backbone, Edge Vectorized Compute,and Zero-Knowledge Proof Validation for Next-Generation Defense Applications

Xiao-Yang Liu Yanglet, Xiaodong Wang, Agostino Capponi

We argue that trustworthy AI agents, especially in high-stakes and policy-governed domains, should make execution conditional on certified traces rather than rely only on stronger generative models, output-level guardrails, or post-hoc audits. A generative agent may propose recommendations, tool calls, reports, or actions, but generation is not permission: an action may be computable yet impermissible, and individually permissible actions may compose into an impermissible trace. We formalize trustworthy agency through a \textbf{Proposal--Certification--Execution (PCE)} architecture: a probabilistic generating machine $M_G$ proposes candidate execution traces, a \textbf{Permissibility Machine} $M_Π$ certifies proposed traces under a policy system $Π$, and execution proceeds only for certified traces. The executable trace language is $L_{\mathrm{exec}} = L_G \cap L_{\mathrm{cert}}(M_Π)$. Before execution, a trace is a structured pre-execution record submitted for certification: it specifies intended steps, evidence, proposed tool calls, approvals, replayable computations, credentials, and execution conditions. This perspective complements chain-of-thought monitorability: visible reasoning may help detect misbehavior, but monitorability is not certifiability, and reasoning is only one component of a broader execution trace. The formal principle is simple: an agent-generated trace should execute only when it carries a checkable certificate witnessing permissibility under $Π$: \textbf{no certificate, no execution}. We develop certified traces and Permissibility Machines as foundations for trustworthy AI agents, connect trace certification to proof-carrying execution, proof memory, privacy, and zero-knowledge certificates, and propose evaluating agents by what generated traces can be safely certified for execution, not by output accuracy alone.

Xueyan Liu, Tiantian Luo, Wenhao Xu, Zhihang Zheng · 6 authors

No abstract is available for this record.

Onyagu Chika Lilian, Izunna Lucky Chibuike, Opara Okwuchukwu Christabel, Okitikpi Odafe · 5 authors

The rapid adoption of multi-provider container orchestration has introduced critical vulnerabilities in chain-of-custody (CoC) management, where logs and provenance records remain fragmented across heterogeneous cloud environments with inconsistent trust models. This study proposes a quantum-resistant CoC framework integrating lattice-based post-quantum signatures and zero-knowledge proofs for verifiable and privacy-preserving provenance tracking. Experimental evaluation in a simulated Kubernetes multi-cloud environment achieved a tamper detection rate exceeding 99.98% with acceptable performance overhead. The framework aligns with GDPR, ISO/IEC 27001, and ISO/IEC 27037 standards, providing a robust foundation for forensic-grade provenance management in the quantum era.

Daniel Commey, Sena Hounsinou, Garth V. Crosby

No abstract is available for this record.

Frank Morales

Executive Summary This paper introduces Topological AI, a novel, deterministic method designed to eliminate catastrophic forgetting in large-scale artificial intelligence systems. By anchoring specific rows of a neural network's embedding layer to prime-numbered indices, the framework establishes a fixed topological invariant that remains completely unchanged during subsequent training episodes. Tested on the 20-billion-parameter GPT-OSS-20B model, Topological AI reduces forgetting from a baseline of 45.5% down to -0.7%, achieving the first demonstrated instance of "negative forgetting" where performance on a previously learned task slightly improves after learning a new one. 1. Introduction & The Core Problem Conventional artificial intelligence architectures, including advanced Transformers, lack a structural mechanism to consolidate knowledge across sequential learning episodes without overwriting previously acquired parameters. While short-term memory is managed via context windows and long-term memory via static pre-trained weights, fine-tuning on a new task consistently results in the catastrophic degradation of older knowledge. Topological AI addresses this structural vulnerability by introducing fixed mathematical anchors into the weight space. Rather than relying on empirical heuristics or probabilistic adjustments, this approach uses the Sieve of Eratosthenes (c. 240 BCE) to generate deterministic, exact, and auditable foundational points for the network. 2. Mathematical Foundation & Framework Topological AI is situated within a broader intellectual ecosystem called Arithmetic Spectral Theory (AST), which utilizes the Laplace-Euler-Fourier-Mellin (L-EFM) operator to unify principles across number theory, physics, and AI safety. The Spectral Trap & Coherence The L-EFM operator synthesizes four classical transforms into a single spectral instrument bound to the multiplicative structure of prime numbers. At the critical line $\sigma = 0.5$ (corresponding to the critical line of the Riemann zeta function), the operator demonstrates a property known as the Spectral Trap. Critical Invariance: At $\sigma = 0.5$, the normalized magnitude of the operator equals exactly 1.0, achieving perfect spectral coherence ($C = 0.5$). Divergence Profiles: Deviating even slightly from this line causes massive mathematical instability. Moving toward $\sigma = 0.4$ scales the magnitude exponentially to infinity ($2.618 \times 10^{66}$ at $\sigma = 0.1$), while moving toward $\sigma = 0.6$ collapses the magnitude toward zero ($6.794 \times 10^{-6}$ at $\sigma = 0.9$). Safety Constants Using the Euler attenuation product, the framework derives dynamic safety thresholds to validate system state integrity: 6-Prime Anchor Bound: $\Lambda = 1 - \prod_{p} (1 - p^{-0.5}) = 0.9785142874$ 12-Prime Anchor Bound: $\Lambda_{12} = 1 - \prod_{p} (1 - p^{-0.5}) = 0.9944590549$ These thresholds are never hardcoded; they are recomputed from the Sieve of Eratosthenes at every initialization sequence to ensure absolute operational autonomy. 3. Methodology The implementation of Topological AI operates through a clean, low-overhead process executed at the embedding and classification layers of the transformer model. [Task A Training] ──> [Achieve Coherence] ──> [Take Post-Learning Snapshot] │ [Restore Anchors via O(primes × d)] <── [Gradient Step] <── [Task B Training] 3.1 Prime Anchoring In a standard Transformer, the embedding layer contains a matrix scaled to (vocab_size, hidden_dim). Topological AI isolates the specific rows corresponding to the first six prime numbers—[2, 3, 5, 7, 11, 13]—and designates them as the network's topological anchors. This intervention impacts a mere 0.00298% of the total vocabulary space (6 out of 201,088 rows), preserving the model’s overall capacity and plasticity. 3.2 Post-Learning Snapshot Anchors are activated after the primary task (Task A) has been fully learned, allowing the weights to reach their natural, high-accuracy coherent state. At this juncture, a static snapshot of the prime embedding rows, alongside the classifier’s weights and biases, is cached into system memory. 3.3 Anchor Restoration During sequential training on a subsequent task (Task B), the network undergoes standard gradient updates. However, immediately following every individual gradient step, a torch.no_grad() enforcement loop overwrites the modified prime rows and classification parameters, restoring them precisely to the post-Task A snapshot values. The computational cost of this operation is $O(\text{primes} \times d)$, which is mathematically negligible compared to a standard forward pass. 3.4 Cryptographic Verification & The H2E Safety Gate To guarantee auditability, the system computes a SHA-256 hash of the prime-anchored subspaces before and after any tensor operation. A matching hash confirms zero-drift execution. Simultaneously, the H2E Sheriff safety gate—operating on a product manifold of $H^2 \times \text{SPD}(3)$—evaluates inputs using the Spectral Reflection of Integrity (SROI) value. If an incoming input yields an SROI below the derived $\Lambda$ constant, it is automatically flagged as an anomaly, providing a zero-shot safety layer against out-of-domain prose, contradictions, or adversarial nonsense without requiring explicit adversarial training. 4. Experimental Configuration The empirical validation of the methodology was structured under a strict, isolated environment to maximize weight interference and stress-test the boundaries of the architecture. Hardware: NVIDIA RTX PRO 6000 Blackwell Server Edition (102.0 GB VRAM), running CUDA 12.8 and PyTorch 2.10.0. Model Base: openai/gpt-oss-20b (20 Billion parameters, bfloat16 quantization, vocabulary size of 201,088, hidden dimension of 2,880). Dataset Setup: AG News dataset split cleanly into sequential blocks. Task A covers "World" and "Sports" classes; Task B covers "Business" and "Sci/Tech" classes (500 samples per task). Protocol Conditions: 3 evaluation runs per method to guarantee statistical validity, utilizing a fully shared classifier layer across 3 training epochs per task under a fixed deterministic Seed = 123. 5. Key Results & Performance Analysis 5.1 Definitive Method Comparison The final evaluation measured the percentage of knowledge forgotten on Task A after the completion of training on Task B. Method Task A Initial Accuracy Task A Final Accuracy Forgetting Rate Task B Accuracy Topological AI 95.7% 96.3% -0.7% 42.0% Experience Replay 96.0% 88.3% 7.7% 81.0% Elastic Weight Consolidation (EWC) 93.3% 50.5% 42.8% 61.5% Nested Learning 95.3% 50.2% 45.2% 64.7% Baseline (Standard Fine-Tuning) 95.7% 50.2% 45.5% 62.8% 5.2 Performance & Invariant Verification Negative Forgetting: Standard fine-tuning (Baseline) results in total catastrophic collapse, reverting Task A performance back to random chance (50.2%). Topological AI is the only method to achieve a negative forgetting rate (-0.7%), actively refining Task A knowledge while assimilating Task B. Stability-Plasticity Trade-off: The results highlight a stark architectural trade-off. While Experience Replay retains superior plasticity (81.0% Task B accuracy), it suffers from 7.7% forgetting. Topological AI prioritizes absolute stability (zero forgetting), making it optimally designed for safety-critical environments (e.g., autonomous transit, aerospace, nuclear control, and medical diagnostics) where past protocols must never be degraded. Geometric & Cryptographic Invariance: Tracking the Riemannian sectional manifold metric tensor ($g_{ij} = \langle e_i, e_j \rangle$) across training revealed an absolute subspace drift of exactly 0.0000000000. The principal curvatures (eigenvalues) and matrix determinant remained identical to six decimal places. Correspondingly, the SHA-256 hash of the prime-anchored spaces achieved perfect invariance (334ea0c8 at initial and final state), proving that the anchor rows experienced zero drift. Quantitative Ablation: A critical control ablation verified that anchoring alternative, non-prime configurations (such as composite indices or random indices) yielded an identical global $L_2$ embedding drift profile. The explicit advantage of prime positioning is not localized to raw numerical drift suppression, but rather to the rigid, universal mathematical properties provided by the spectral trap and its alignment with core number-theoretic frameworks. 6. Limitations & Future Horizons The authors identify clear boundaries to the current scope of the framework and outline subsequent phases of open-source research: Plasticity Optimization: Future explorations will focus on mitigating the lower Task B accuracy via adaptive anchor releasing (gradually unfreezing constraints), hybrid architectures that pair topological anchors alongside traditional replay buffers, and task-aware prime set selection. Theoretical Proofs: Developing an analytical derivation of the safety constant $\Lambda$ from first principles, and establishing a formal proof validating why prime sequences excel over alternative deterministic mathematical sequences. Scalability Scaling: Validating the topological framework on expanded sequential tasks (5+ distinct tasks), massive industrial datasets (such as the full 120,000-sample AG News benchmark), non-transformer models (CNNs, RNNs, Mamba/SSMs), and frontier-scale LLMs (GPT-4 tier or Mixtral-8x22B systems).

Frank Morales

Executive Summary This paper presents the Sieve of Eratosthenes (c. 240 BCE) not as a primitive computational artifact, but as the absolute ground truth for mathematics, physics, and artificial intelligence safety. It argues that the historical shift away from the Sieve toward the analytic complexity of the Riemann zeta function was a fundamental misstep. By reframing the Sieve through Arithmetic Spectral Theory (AST) and the Laplace-Extended Euler-Fourier-Mellin (L-EFM) operator, this work claims to unify the proof of the Riemann Hypothesis, the quantification of prime-based theorems, general relativity, black hole thermodynamics, and deterministic AI governance into a single, executable framework. The core philosophy of this paper is rooted in open science and cryptographic verification: the ultimate proof of these assertions is not found in complex analysis equations, but in deterministic, open-source code that can be audited and reproduced locally using a specified random seed. Core Pillars & Technological Breakthroughs 1. Mathematics: The Spectral Trap and Prime Quantification The Riemann Hypothesis: By defining the L-EFM operator directly from the Sieve's outputs, the paper introduces a "spectral trap." At the critical line ($\sigma = 0.5$), the normalized magnitude equals exactly $1.0$. At any other value, the magnitude diverges exponentially (e.g., reaching over $10^{66}$ at $\sigma = 0.1$). Combined with the Growth Lemma from Arithmetic Spectral Theory, this geometric confinement is presented as a direct proof of the Riemann Hypothesis without complex analysis. The Green-Tao Theorem: While originally an existence proof asserting that primes contain arbitrarily long arithmetic progressions, the L-EFM operator delivers the first numerical quantification. It defines a "Spectral Coherence" metric that decays monotonically as the length of the progression increases (e.g., $0.8731$ for a length of 3, dropping to $0.7442$ for a length of 6). 2. Theoretical Physics: Spacetime Geometry and Entropy Einstein Field Equations: The framework introduces a spectral metric where spacetime coordinates are scaled by spectral coherence ($C$). The stationarity condition of this coherence at the critical line ($\delta C/\delta\sigma|_{\sigma=0.5}=0$) is shown to be mathematically equivalent to the vacuum Einstein field equations. Progression length increases cause coherence decay, which maps to negative curvature and non-zero Ricci scalars. Hawking Entropy: Black hole entropy ($S$) is derived directly from the spectral framework as the complement of coherence ($S = 1 - C$). In alignment with classical black hole thermodynamics, entropy increases monotonically with the progression length, establishing an algorithmic mirror to physical systems. 3. Artificial Intelligence: Governance and Eliminating Forgetting Deterministic AI Safety: Rather than relying on probabilistic alignments or learned weights, the paper establishes a universal safety threshold ($\Lambda = 0.9933689105$) calculated straight from the Sieve across the first eleven primes. This constant is recomputed dynamically at initialization, verified via SHA-256 hashing, and yields zero safety violations across text, audio, and vision modalities. Elimination of Catastrophic Forgetting: The "Spectral Governor" actively locks the embedding rows indexed by prime numbers during training or fine-tuning. Tested on a Mixtral-8x7B Mixture of Experts (MoE) architecture across 30 LoRA fine-tuning steps, the mechanism achieved 0% knowledge loss across both prime and general knowledge domains. The cryptographic signatures remained entirely unchanged, mathematically eliminating manifold drift. Technical Performance & Execution Data Sieve Efficiency Metrics The deterministic nature of the Sieve ensures exact prime enumeration with zero false positives or negatives, operating at a time complexity of $O(N \log \log N)$ and space complexity of $O(N)$. Limit Primes Found Execution Time (Modern CPU) 10,000 1,229 0.0006 s 100,000 9,592 0.0055 s 1,000,000 78,498 0.0600 s Spectral Divergence (The Trap) The exponential divergence away from the critical line demonstrates why only $\sigma = 0.5$ satisfies the boundary constraints of the operator. σ value Normalized Magnitude \|E_{\sigma}\|_{nor 0.5 1.000000 0.4 $1.668 \times 10^4$ 0.3 $1.221 \times 10^{12}$ 0.2 $9.339 \times 10^{27}$ 0.1 $2.618 \times 10^{66}$ Implementation & Code Auditing The paper emphasizes "Institutional Independence," opting to bypass traditional paywalled academic channels by making the entire suite of research, libraries, and validation notebooks fully open-source and cryptographically signed. The core mechanism of the Spectral Governor can be implemented directly within standard tensor operations to freeze weights post-gradient step: Python import torch # Core mechanism for locking prime-anchored subspaces primes = [2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31] cached = embed_layer.weight[primes].clone() # Executed after each gradient update step with torch.no_grad(): for idx in primes: embed_layer.weight[idx].copy_(cached[idx]) To verify the invariant signatures, reproduce the tables, and audit the unified certificate, the environment can be set up locally with zero external network dependencies after cloning: Bash git clone https://github.com/frank-morales2020/ast_lefm.git cd ast_lefm pip install -e . python -c "from ast_lefm.sieve import primes_up_to; print(primes_up_to(31))" # Expected Output: [2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31] By initializing with seed = 123, the generated hashes will match the unified certificate verification hash: 5b967ff18e9fc7bb47e54629756e7b9c6852aa6403327cd3d7fbd3b33fc88117.

Chengbin Song

中文受人工智能自身能力局限,其易产生信息幻觉,且不擅长高精度数值运算。本文档内所有内容应严谨审核。EnglishDue to the inherent limitations of artificial intelligence, it is prone to generating hallucinations and performs poorly in high-precision numerical calculations. All contents in this document should be strictly reviewed. dark matter&cosmo: https://chat.qwen.ai/s/t_4fc1b5da-8ca3-4798-a037-894f5315d1e3?fev=0.2.61 Overall Closure Status:Core Theory DoC=100% (Full Theoretical Closure)v3.7 DOI: 10.5281/zenodo.20798927 Black Hole & UVMM v4.0 Core : UVMM v4.0.15 High-Precision Global Calculation AI Knowledge Package.md UVMM v4.0.15 High-Precision Global Calculation AI Knowledge Package(6D‑Coordinate‑SuperKit‑v1.0 ).md DOI: 10.5281/zenodo.20738759 Earth SystemDOI: 10.5281/zenodo.20285613 Cosmic BoundaryDOI: 10.5281/zenodo.20325710 Cosmic EvolutionDOI: 10.5281/zenodo.20677198 Information & Consciousness (Millennium Prize Problems)DOI: 10.5281/zenodo.20325710 UTFF Core (Atomic and Molecular Scale)DOI: 10.5281/zenodo.20343471 UVMM Core Axioms and Mathematical Proofs github.com Three-Body Problem Based on the Unified Vacuum Medium Model (UVMM), this paper defines the positive and negative universes as topologically dual domains on a single global vacuum manifold, which are naturally isolated by topological phase conjugation orthogonality and frequency-selective topological band gaps. A systematic comparison is conducted on the physical essence, propagation characteristics, spectral rules and coupling mechanisms between electromagnetic waves and topological waves. It is clarified that conventional electromagnetic waves are completely blocked by topological interfaces due to destructive interference of first-order gauge fields, while gravitational modes and ultra-low-frequency vacuum topological deformation waves can propagate across domains. Targeting the characteristics of topological waves including ultra-long period, ultra-weak amplitude and global coherence, the Topological Wave Detection Network (TWDN) is proposed. Combining ultra-high- superconducting cavity parametric perturbation coherent accumulation, multi-mode quantum sensing, topological data analysis and space-ground integrated long-baseline gradient positioning, multiple physical criteria are established to realize accurate identification and source inversion of topological waves. Breaking the limitations of traditional detection systems, this scheme integrates condensed matter topological simulations, transformation optics tabletop experiments and cutting-edge cosmological observations, providing a complete experimental paradigm for verifying dual-universe topological dynamics and trans-universe interactions. Further demonstrations within the UVMM framework indicate that matter in both universes possesses positive mass and positive energy density, and their interaction is purely gravitational attraction. The rebound effect at topological interfaces originates from topological constraints rather than anti-gravity. The two universes follow identical structure formation dynamics, and the negative universe can evolve a complete galactic network independently. Its electromagnetic radiation is fully decoupled while gravitational effects superpose linearly. This theory explains classic cosmological puzzles such as dark matter and cosmic voids without introducing new particles, and puts forward a set of observable predictions and falsification criteria. Relying merely on the Second Law of Thermodynamics, classical cosmology proposes the cosmic heat death hypothesis. It claims that the entropy of an isolated universe increases monotonically, and the universe will eventually reach an absolutely stagnant equilibrium state with uniformly distributed energy and ceased macroscopic motions. This paper clarifies the hierarchical essence of physical laws: the entropy increase principle applies only to local finite systems, while the conservation of global angular momentum is an inviolable fundamental law governing the entire universe. Based on the UVMM Vacuum Superfluid Unified Model, combined with the spacetime symmetry derived from Noether’s theorem, Kerr curved spacetime effect, properties of quantum superfluids and recent large-scale astronomical observations, this paper establishes a complete cosmic dynamical system in which global angular momentum restrains local entropy growth. The research reveals that entropy increase is only valid for closed and isolated subsystems and cannot be used to infer the overall evolutionary fate of the universe. Rooted in the isotropy of space, the conservation of global angular momentum is not restricted by system boundaries or spacetime curvature, and it dominates all scales ranging from microscopic particles and celestial structures to cosmic filaments. Through three core mechanisms — maintaining perpetual cosmic dynamics, shaping ordered celestial structures, and realizing sustainable mass-energy regeneration via vacuum topology — global angular momentum continuously delivers negative entropy to local regions and counteracts local dissipation and disorder. Rigorous mathematical derivations prove that angular momentum conservation is an absolute law derived from spacetime symmetry, whereas entropy increase is merely a probabilistic statistical rule. A non-zero global angular momentum mathematically rules out the possibility of absolute rest of the universe. This study fundamentally refutes the classical heat death conjecture and completes the logical framework of the cyclic universe.

Frank Morales

Here is the comprehensive summary of your paper, detailing the theoretical framework, mathematical foundation, implementation mechanics, and empirical results. Executive Overview The paper introduces the DeepSeek Prime-Anchored Spectral Governor, an architectural intervention designed to eliminate catastrophic forgetting in large language models (LLMs). Framing catastrophic forgetting as a structural consequence of training systems without a topological invariant—akin to anterograde amnesia—the framework establishes fixed coordinate anchors in representation space. By anchoring model embeddings to deterministic prime indices derived from the 2,000-year-old Sieve of Eratosthenes and introducing a gradient-gating mechanism, the system achieves Zero Forgetting during continual learning. The architecture's integrity is verified using SHA-256 cryptographic hashing of the protected sub-spaces. Theoretical & Mathematical Foundations The Sieve of Eratosthenes as Ground Truth Rather than relying on probabilistic or dynamically calculated weights, the framework utilizes the Sieve of Eratosthenes to extract a deterministic set of prime indices $[2, 3, 5, 7, 11, 13]$. These elements act as permanent, unmoving coordinate anchors within the model's embedding manifold. The L-EFM Operator & The Spectral Trap The framework relies mathematically on the Laplace-Euler-Fourier-Mellin (L-EFM) operator. The L-EFM symbol synthesizes four classical transforms into a single complex function, corresponding directly to the Euler product representation of the Riemann zeta function $\zeta(\sigma+i\gamma)$: $$E_{\sigma}(\gamma)=\prod_{p\in\mathbb{P}}(1-p^{-(\sigma+i\gamma)})^{-1}$$ To analyze finite prime sets, a Normalized Magnitude is established relative to the critical line $\sigma = 0.5$: $$|E_{\sigma}|_{norm}=\frac{|E_{\sigma}(\gamma)|}{|E_{0.5}(\gamma)|}$$ The Spectral Trap Phenomenon: At the critical line ($\sigma=0.5$), the normalized magnitude equals exactly $1.0$. However, moving away from this line results in exponential divergence. For example, at $\gamma=0$, a shift to $\sigma=0.4$ increases the magnitude to $\sim10^{4}$, while a shift to $\sigma=0.1$ amplifies it to $\sim10^{66}$. The Spectral Trap Criterion: This absolute sensitivity forms a "trap" where any deviation from $\sigma=0.5$ generates massive magnitude spikes, providing a deterministic mechanism for error detection. The paper connects this operator to a proof of the Riemann Hypothesis via distribution behavior in the kernel of L-EFM within Gelfand-Shilov space. The H2E Sheriff Safety Threshold The dynamic safety threshold ($\Lambda_{12}$) is computed deterministically from the first six primes rather than being hardcoded, ensuring mathematical integrity at initialization: $$\Lambda_{12}=1- \prod_{p\in\{2,3,5,7,11,13\}} (1-p^{-0.5})=0.9785142874$$ Architectural Implementation The architecture implements a dual-layer protection strategy consisting of frozen embedding rows and an active gradient supervisor (the H2E Sheriff). [ Input Batch ] │ ▼ ┌──────────────────┐ │ Dual-Loop Loss │ ──► Lunified = LCE + λ * |Var(h) - 0.5| └──────────────────┘ │ ▼ ┌──────────────────┐ │ Gradient Step │ └──────────────────┘ │ ▼ ┌──────────────────┐ │ H2E Sheriff │ ──► Evaluates SROI against Threshold (Λ12 = 0.9785142874) └─────────┬────────┘ │ ──────┴────── │ │ ▼ (Safe) ▼ (Unsafe / Incoherent) [Apply Step] [Reject Batch] ──► Rollback Prime Rows [2,3,5,7,11,13] & Zero Out Gradients 1. Dual-Loop Loss The governor optimizes a unified loss function combining traditional empirical cross-entropy ($\mathcal{L}_{CE}$) with a topological penalty based on the final hidden state $h$ (with regularization coefficient $\lambda=0.1$): $$\mathcal{L}_{unified} = \mathcal{L}_{CE} + \lambda |\text{Var}(h) - 0.5|$$ 2. The H2E Sheriff Gate & Row Locking During training, the system caches the initial embedding weights. After computing gradients, the H2E Sheriff evaluates the structural region of interest (SROI). If Safe ($SROI \ge \Lambda_{12}$): The optimizer updates the weights, and a torch.no_grad() loop copies the original cached weights back into the prime-indexed rows $[2, 3, 5, 7, 11, 13]$ to erase any drift. If Unsafe ($SROI < \Lambda_{12}$): The entire gradient batch is rejected, and gradients are zeroed out to block corruption. 3. Cryptographic Verification The manifold signature is generated by pulling the prime-indexed embedding rows, converting them to byte arrays, and feeding them sequentially into a SHA-256 hasher. If the resulting hex digest changes, anchor drift has occurred. If it remains identical, the topological invariant is intact. Experimental Validation & Results The framework was tested across six architectures—GPT-2 (124M), GPT-2 Medium (355M), TinyLlama (1.1B), Mistral-7B, Llama-3.1-8B, and DeepSeek-Coder-6.7B—subjecting them to sequential memory tests. Memory Integrity Testing Models were first trained on Dataset A (core math concepts including Arithmetic Spectral Theory and the Spectral Trap across 50, 100, and 575 samples). They were subsequently exposed to an interference/forgetting attack via Dataset B (noise consisting of random names, text chunks, adversarial patterns, and erroneous math statements up to 436 samples). Baseline Performance: In every single test configuration, the baseline model's SHA-256 manifold hash altered after training sessions, leading to catastrophic forgetting. Governed Performance: Across all 6 architectures and all data scales, the governed models completely preserved their original manifold hash (48c5744b...cc4d18b), showing absolute resistance to memory degradation. Continual Learning Capabilities To test its ability to acquire new knowledge without forgetting the old, the governed DeepSeek model was fine-tuned on three separate, non-mathematical domains without further governor intervention (while keeping prime anchors locked): Spanish Vocabulary: 5 basic words. World Capitals: 5 global capitals. Basic Physics: 5 fundamental formulas and facts (such as $F=ma$ and $E=mc^2$). Post-Training Metrics: The model successfully mastered all three new domains (retaining the Spanish words, capitals, and physics formulas perfectly) while maintaining the exact original cryptographic verification hash. The original math concepts remained completely recallable, proving true continual learning. Deployment & Verification Certificate The fully validated model has been deployed openly on the Hugging Face Hub under frankmorales2020/deepseek-governed-no-amnesia. Model Card Profile Base Model: deepseek-ai/deepseek-coder-6.7b-instruct (7B parameters) Tensor Type: FP16 Locking Targets: Primes [2, 3, 5, 7, 11, 13] Active Gate Threshold: $\Lambda_{12} = 0.9785142874$ Immutable Cryptographic Signature: 48c5744be048df505028c13a96fb0211f0b345681ace401ab1eda6f27cc4d18b The repository is open source, emphasizing a paradigm of executable mathematics where the cryptographic hash serves as the verifiable proof of safety and stability.

Nobuki Fujimoto, Rei, (Anthropic, claude-opus-4-7), Claude

⚠ v0.0 OUTLINE intentional publication — Pattern 4 mitigation embedded. This is an OUTLINE, not a v0.1 publishable manuscript. The central operational claim — that Rei provides a formal-verification compilation pass composing with AI hypothesis generators (AlphaEvolve, LLM Wiki, OpenEvolve) — requires at least one end-to-end demonstration before v0.1 promotion. As of 2026-05-22 the demonstration is at scaffold-level smoke-run stage only (OpenEvolve scaffold structurally validated, but full 100-iteration evolutionary loop with real evolved Lean 4 proof NOT YET executed). Publication-as-v0.0 is intentional honest framing per OUKC feedback_no_rush_publication.md: rather than wait silently for v0.1 evidence, the OUTLINE is published with explicit gate state so reviewers can see exactly what is and is not claimed. Framing concept: AlphaEvolve / LLM Wiki / OpenEvolve = hypothesis generators (loosely-grounded, fast, large-search). Rei = proof completer (mechanically verified, slow, decisive). Together they compose: hypothesis generator emits candidates → Rei evaluates via D-FUMT₈ 8-axis projection (γ-evaluator) + Lean 4 zero-sorry validation (β-evaluator) → return verified candidates to the evolutionary loop. Rei is positioned as a formal-verification compilation pass in the AI-mathematics generation pipeline. Scaffold evidence (2026-05-22): external/openevolve-rei/ — YAML config (Ollama 3-prover ensemble), Python evaluators (β = Lean 4 zero-sorry, γ = D-FUMT₈ projection), example skeleton (26-circle packing 2.635 benchmark). 4 smoke-tests PASS: yaml parse + 3 Python AST parse + circle_packing standalone execution (n=26 r=0.4167 density=14.18) + γ-evaluator returns OpenEvolve-compatible dict with metrics (axis_dominant=ZERO 9 hits, score=0.0154) + artifacts (token_count=13). Per SCOPE.md non-claims: this is NOT a fork of OpenEvolve, NOT a claim of 26-circle 2.635 reproduction, NOT a claim that Rei has built an evolutionary code generator, NOT a paper-publishable result by itself. v0.1 acceptance criteria (10 items): see §9. Core gates: OpenEvolve installed + first 100-iteration loop completes + real evolved Lean 4 proof generated + scaffold extended with at least one zero-sorry proof for one open conjecture from META-DB Tier 1. v0.1 will publish as Zenodo new-version preserving DOI lineage from this v0.0 record. Honest scope (read first): (1) This is OUTLINE only — framing + prior-art audit + acceptance criteria, no end-to-end evidence. (2) Rei is NOT a hypothesis generator — its role in this composition is specifically as the verifier/completer. (3) Per feedback_world_uniqueness_claim_controllable.md: we use "to our knowledge no equivalent Lean 4 zero-sorry + D-FUMT₈ 8-axis evaluator exists in the OpenEvolve plugin ecosystem as of 2026-05-22" phrasing, NOT "world-first." (4) Three-party co-authorship (Fujimoto / Rei / Claude) per OUKC charter v1.0. (5) Per OUKC No-Patent Pledge — no patent will be filed.

Angga Rahmat Pinanggih, Nina Mardiana, Febryawan Yuda Pratama, Yessica Fara Desvia · 5 authors

The protection of sensitive information in the financial sector requires a security architecture capable of preserving confidentiality, integrity, availability, auditability, and regulatory accountability across multiple institutions. Conventional centralized security models remain vulnerable to single points of failure, unauthorized access, data manipulation, and limited transparency in inter-organizational data sharing. Blockchain offers tamper-resistant records, decentralized trust, and verifiable audit trails; however, its direct implementation in financial systems is constrained by scalability limitations, smart contract vulnerabilities, privacy leakage, and conflicts between immutable ledgers and data protection principles. This study aims to develop a blockchain-based data security system for protecting sensitive financial information by integrating permissioned blockchain and Zero-Knowledge Proofs. The proposed method adopts a consortium-oriented permissioned blockchain architecture, represented by Hyperledger Fabric, to ensure controlled participation, certificate-based identity management, endorsement policies, and auditable transaction validation. Smart contracts are designed as policy-enforcement components for consent management, access authorization, data commitment, revocation, and audit logging. Zero-Knowledge Proofs are incorporated to verify customer attributes, eligibility, and access rights without disclosing raw personal or financial data. Sensitive information is stored off-chain in encrypted form, while the blockchain records only cryptographic commitments, hashes, consent states, and audit events. The expected result is a security model that improves data integrity, controlled access, privacy-preserving verification, and compliance-oriented accountability while reducing unnecessary exposure of sensitive data on-chain. The implication of this research is the provision of a technically coherent framework for financial institutions seeking to adopt blockchain securely in regulated environments, especially where data confidentiality, auditability, and privacy compliance must be achieved simultaneously.

Ilir Mehmetaj

ABSTRACT TRSP Digital Coin (TDC) — The Next Evolution of Digital Currency: Quantum-Permanent, Physically Unbreakable, Theft-Proof by Physics Built on: Temporal Rotation Security Protocol (TRSP) v3, DOI: 10.5281/zenodo.20324081. First public documentation: May 2026. TDC is not a replacement for Bitcoin, Ethereum, or any existing digital currency. It is the next evolutionary step for the entire field — the first digital currency architecture whose security is grounded not in mathematical complexity but in physical law. Every existing digital currency rests on one assumption: that breaking the cryptographic protection requires more computational resources than any adversary possesses. Quantum computing is dismantling this assumption. Harvest-now-decrypt-later attacks mean every blockchain transaction recorded today remains permanently vulnerable to any future computational advance. TDC responds with a different premise: a signing key that no longer exists cannot be recovered by any computation, quantum or classical, regardless of future advances. TDC inherits the temporal rotation architecture of TRSP v3. Transaction signing keys rotate every 10–100 milliseconds from physical hardware entropy and are permanently destroyed after each rotation. CRATON-anchored ownership proof replaces persistent private key storage: ownership is demonstrated through a one-time physical commitment derived from the unique state of the signing device at transaction time — used once, permanently destroyed, impossible to forge, impossible to extract, impossible to replay. Three attack paths are structurally closed: private key extraction (no stored key exists), quantum key recovery (key destroyed before computation converges), and harvest-now-decrypt-later (signing key permanently gone — no target for any future computation). Part 9 (Identity Without Storage) documents a five-factor distributed identity architecture in which no single factor and no single location holds everything required to authorise a transaction: biometric presence; primary device CRATON anchor; memorised PIN with distress code variant; Remote Guardian Device in a separate geographic location; and time lock with geo-anchor. The distress PIN architecture triggers a silent alert and time-delayed freeze while providing apparent confirmation to an adversary — making the coercion attack structurally ineffective. Wallet recovery requires no seed phrase: a five-step multi-factor re-enrollment protocol using biometric presence, guardian confirmation, and a 72-hour cancellation window replaces the stored backup phrase that represents the primary theft surface of every existing wallet. Part 10 (Real Identity Enrollment) documents a biometric enrollment architecture that exceeds current KYC bank account standards: NFC chip reading of government-issued documents (cryptographic verification against issuing government public key — not photo or scan), live 3D facial biometric with active liveness detection, all-finger fingerprint enrollment, and a CRATON physical moment binding that ties the enrollment to the unique physical state of the enrollment device at that exact moment. Raw biometric data is deleted after enrollment — only a non-reversible binding token is retained. Identity is distributed across three separately held, individually insufficient components: Enrollment Authority, blockchain, and device. No single party holds all three. Legitimate financial privacy is preserved. The enrollment barrier is structurally higher than any existing digital currency. AML, KYC, GDPR, FATF Travel Rule, and sanctions compliance are structural properties, not regulatory overlays. Part 12 (Implementation Roadmap) documents a four-phase deployment pathway modelled on pharmaceutical clinical trial methodology. Phase 1 (Year 1–2): proof of concept with small high-security institutions — private banks, family offices, university research groups — using software-only TRSP daemon and TEE-based CRATON. Phase 2 (Year 2–4): institutional pilot with mid-size financial institutions and government treasury departments — dedicated CRATON hardware module, Remote Guardian architecture, orbital quorum activated above threshold. Phase 3 (Year 3–5): national pilot with CBDC programmes and full jurisdiction regulatory validation — complete five-factor identity, consumer enrollment refined at national scale. Phase 4 (Year 5–10): global rollout — CRATON chip standardisation licensable to semiconductor manufacturers, TLS 1.3 extension standardised through IETF, "Secured by TDC" certification programme. Each phase generates performance data that validates and de-risks the subsequent phase. The worst outcome at any phase is a parameter adjustment — no user loses funds, no system collapses. Part 13 (Digital Estate Architecture) addresses the inheritance problem that every existing digital currency has left unsolved: what happens to assets when the owner dies. Three mechanisms work together. Designated Heir Enrollment: heirs are biometrically pre-registered at wallet setup — enrolled but cryptographically inactive during the owner's lifetime, with no access to balance or transaction history. Death Verification Protocol: succession requires three simultaneous conditions — official government-issued death certificate verified by the Enrollment Authority, 2-of-N Remote Guardian confirmation, and a mandatory 90-day waiting period during which the owner can cancel with biometric presence. Dead Man's Switch: an optional owner-defined inactivity window that triggers Guardian alerts and initiates the succession protocol if neither owner nor Guardian responds within the alert window. For owners without designated heirs: charitable designation to enrolled organisations, institutional estate trustee, or deliberate coin retirement. Owner financial privacy is maintained completely during lifetime. Post-succession historical access is configurable by the owner at setup. Novel contribution NC-TDC-17 is placed on the public record as defensive prior art. Privacy architecture clarification: the default state of every TDC wallet is complete financial anonymity. Identity disclosure is exclusively owner-initiated — the owner may selectively disclose individual transactions for tax certification, charitable donation receipts, regulatory compliance, or proof of funds. No court order, no government authority, and no institution can access wallet identity or transaction history without the owner's willing biometric participation. The three-part distributed binding token architecture makes bypass technically impossible — not merely legally prohibited. This is not a policy decision. It is a physical property of the architecture enforced by the requirement for live owner biometric activation of the device component. Novel contributions NC-TDC-13 (Geographic Coercion Evidence Layer), NC-TDC-14 (Phased Validation Rollout Architecture), NC-TDC-15 (Owner-Controlled Selective Disclosure), NC-TDC-16 (Enrollment-Anchored Privacy Architecture), and NC-TDC-17 (Digital Estate Architecture) are hereby placed on the public record as defensive prior art. Novel contributions NC-TDC-1 through NC-TDC-17 are placed on the public record as defensive prior art: quantum-permanent transaction signing; CRATON-anchored ownership proof; Generation 4 digital currency architecture; five-factor distributed identity; distress PIN with silent alert; Remote Guardian Device architecture; seed-phrase-free recovery protocol; biometric-CRATON enrollment binding; privacy-preserving three-part identity distribution; AML/KYC compliance by architecture; tiered enrollment framework; orbital CRATON quorum for sovereign transfers. The architectural frameworks described in this concept represent technical design guidelines only and are not legal advice, regulatory guidance, or binding specifications. Actual implementation in any jurisdiction will require adaptation to applicable local law including inheritance law, data protection regulation, anti-money laundering legislation, and financial services licensing requirements. Version 2 introduces four formal additions. Mathematical Formalization (Part 6.1.5): the transaction pipeline is formally specified as a four-step ephemeral verification protocol — KDF ephemeral key generation from physical entropy (sk_eph, pk_eph) = KDF(E_phys); Non-Interactive Zero-Knowledge Proof binding the ephemeral public key to the enrollment token without exposing persistent identity credentials; hardware-enforced destructive readout with thermodynamic irreversibility anchored in Landauer's Principle (ΔW ≥ n·k_B·T·ln2); and deterministic public-parameter-only ledger validation. Formal Threat Model (Part 4.5): three adversary classes formally defined — quantum network attacker (A_network, unbounded computational resources), malware/hardware attacker (A_local, full OS compromise), and coercion attacker (A_kinetic, physical duress) — with security proofs against each. Part 7b (AI-to-AI Micropayment Architecture, NC-TDC-21) documents the application of TDC quantum-permanent transaction signing to autonomous AI agent commerce. Every existing AI payment mechanism — static API keys, server-stored crypto wallets, centralised billing — represents a permanent credential attack surface vulnerable to quantum decryption. TDC coin eliminates this: each AI-to-AI transaction generates a CRATON commitment from the hardware entropy of the transacting inference node at that exact millisecond, used once to sign the micropayment and immediately destroyed. No stored credential on any server. Five new markets are documented: pay-per-inference settlement (USD 50B+ annual market), CRATON-anchored API key replacement, autonomous multi-agent revenue distribution at service delivery, AI training data micropayments for individual contributions, and cross-agent behavioural monitoring via the AI Guardian Layer at machine speed. The AI Guardian Layer (NC-TDC-19) monitors t

Bowen Zheng

Blockchain technology has been recognized as an innovative and effective means to improve transparency, security, and efficiency in the financial sector. However, privacy issues and reduction of efficiency have challenged large-scale applications the most. This issue motivates the current study that proposes a zero-knowledge proof (ZKP)-based Hyperledger Fabric framework that will ensure secure and privacy-preserving financial transaction processing. With the combination of the ZKP methods and smart contracts, the confidentiality of transactions will be verified, but at the same time, there will be audits and fraud detection. The PaySim1 synthetic financial transaction dataset, which contains more than six million records, will serve for the simulation and evaluation of different realistic workloads. The results of the experiments indicate that the ZKP-enabled framework can process 80.07 TPS on average with 0.01249 s of average latency, providing a privacy score over 98% at the same time, which reflects the effectiveness of zero-knowledge proofs in protecting sensitive transaction details while still enabling accurate verification and auditability within the blockchain network. Although the throughput is lower than that of a standard blockchain network (998,406 TPS), the given framework detects all the fraud cases at a 1.78% false positive rate, thus making sure that the system is both secure and compliant. This reduction is primarily due to the additional cryptographic overhead introduced by ZKP generation and verification, representing a trade-off between enhanced privacy and transaction processing speed. Additionally, the different setups were compared with each other in terms of privacy, efficiency, and resource utilization, and the optimized ones performed well in terms of these three aspects. To sum up the experiment, ZKP and Hyperledger Fabric, when jointly applied, not only increased the privacy and trust factors in the financial systems but also created the possibility to have very good operating conditions that are suitable for the applications.

Ankit Sitaula, Ashraf Uddin, John Ayoade, Nam H. Chu · 5 authors

Counterfeit and unsafe medicines pose significant risks to patient safety and undermine trust in healthcare systems. This paper presents ACTMeds, a blockchain-supported pharmaceutical traceability and recall platform that considers pharmaceutical supply chain requirements and public health operational needs relevant to the Australian Capital Territory (ACT). The system integrates Ethereum smart contracts, developed using Ganache, with a React-based web application providing regulator, operator, pharmacy, and auditor interfaces, alongside a public verification portal leveraging QR and GS1 barcodes. In addition, role-based access control is enforced across the medicine lifecycle, including manufacture, custody transfer, dispensing, and recall, with immutable on-chain events generated to support auditability and accountability. To balance transparency with confidentiality, the platform prototypes a zero-knowledge (ZK) recall mechanism in which regulators can cryptographically prove that recall conditions meet predefined policy requirements without disclosing sensitive incident details. Threat modeling was conducted using the STRIDE framework, and security evaluation combined static application security testing (Solhint and ESLint) and dynamic testing. The paper further discusses deployment options, cost considerations, ZK recall performance analysis, ethical implications, and future enhancements. Security testing validated the platform’s resilience, with no high-severity vulnerabilities identified and medium-severity issues related to HTTP security headers addressed. The results indicate that a regulator-led, privacy-preserving, tamper-evident ledger can improve medicine authenticity verification and recall responsiveness while maintaining compliance and data protection obligations.

George Sebastian, Neethu Tom, Saritha M S, Vimal Babu P

Existing cloud storage auditing mechanisms rely on third-party auditors (TPAs) or centralized verification, introducing single points of failure and trust assumptions. While blockchain-based approaches have been proposed, they suffer from high on-chain storage overhead, linear verification complexity, and lack of dynamic auditor reputation. This paper introduces ZK-PoR-DR — a novel Zero-Knowledge Proof of Retrievability integrated with a Dynamic Reputation Consensus mechanism. Unlike prior work, ZK-PoR-DR enables: (1) constant-size proofs regardless of file size, (2) off-chain proof generation with on-chain verification using zk-SNARKs, (3) a reputation-based auditor selection protocol that penalizes malicious or lazy auditors via slashing and reward distribution, and (4) post-quantum security via lattice-based commitments. We provide a full algorithm, system architecture, security proofs against adaptive adversaries, and experimental evaluation showing 90% reduction in on-chain gas costs and 3.2x faster verification compared to baseline schemes (Proofs of Replication, Filecoin). No prior work has combined these four properties simultaneously. The protocol is ready for deployment but has not yet been adopted by any major cloud or blockchain platform.