Blockchain Papers

Yijing Lin, Ze Chai, Jiacheng Wang, Zhipeng Gao · 7 authors

Semantic communication is a paradigm shift in wireless systems that transmits semantic information, such as intent, context, and meaning, instead of raw data to reduce redundant data. At its core, semantic knowledge bases (SKBs) store and organize the contextual knowledge required for accurate encoding, decoding, and reasoning over semantic information. Recently, large language models (LLMs), pretrained on massive and diverse text corpora, have been integrated into SKBs to generate high-quality semantic embeddings, enable zero-shot retrieval of relevant knowledge, and support complex inference tasks across a wide range of domains. However, since the training corpus of LLM may include outdated, malicious, or privacy-sensitive content, LLM-enabled SKBs should be updated efficiently and verifiably to remove specific data without retraining from scratch. In this article, we first conduct a survey on related works and then propose a model-agnostic proof of unlearning framework for LLM-driven SKBs in semantic communications. Specifically, we track the evolution of the unlearning process by measuring drifts in the LoRA adapter subspace. We then execute successive reverse steps and generate the proof trace that a verifier can compare to provide a quantitative and verifiable unlearning guarantee. Finally, experimental results demonstrate the effectiveness of our proposed framework.

Alex Shvets

We prove that Jensen–Shannon divergence (JSD) contraction coefficients exhibit universal strict super-tensorization: for every finite channel W with nontrivial contraction 0 < η_JSD(W) < 1, one has η_JSD(W⊗2) > η_JSD(W). The sequence η_n(W) := η_JSD(W⊗n) is nondecreasing, strictly increases along doubling, and satisfies lim η_n(W) = 1, while for η_JSD(W) ∈ {0, 1} it is identically 0 or 1. This contrasts sharply with the multiplicative tensorization η_f(W⊗n) = η_f(W)^n enjoyed by operator-convex f-divergences (KL, χ², squared Hellinger), for which contraction decays exponentially to zero. To our knowledge, this is the first f-divergence for which a universal strict super-tensorization law is established. The proof uses the Ordentlich–Polyanskiy binary edge reduction, expresses the binary JSD SDPI constant as a normalized posterior-variance functional, and shows strict amplification via the law of total variance. Convergence rate is controlled by the Bhattacharyya coefficient: 1 − η_n(W) ≤ 2A^n. Numerical verification over 4729 random channels across 26 configurations confirms zero violations. **Update v1.1:** Includes addendum with three targeted clarifications: (1) precise assumptions for binary edge reduction lemma replacing informal "mild regularity conditions," (2) explicit two-case split in the key strictness argument (Lemma 5.2, Step 2), (3) refined table caption for operator-convex divergences.

Ahmed M. Hala

This paper formalizes a mathematical physics theory for the verification of inherited scientific knowledge through a Diffeomorphic Manifold and the Successive Controlled Collapse (SCC) protocol. We define the history of science and technology as a three-tiered manifold—comprising Modern, Contemporary, and Old (Inherited) tiers—where information is transported by the "Common Language" of a lingual locale. By admitting three classes of knowledge agents—Intelligence-Human (IH), Intelligence-Artificial (IA), and Intelligence-Metaphysical (IM )—we demonstrate how high-entropy Informational Inheritance (Sacred Texts) can be distilled into zero-entropy Epistemological Truth. Using the Hala-Operator (Hˆ) as a non-adiabatic spectral regulator, we provide a proof-by-construction using the Hala-Lewis Gaseous Gate as a physical case study. Experimental results from a 23 Factorial Design quantify the Reality Gap (ϵ) at 0.124 and a Hala-Operator Efficiency (η) of 80.9%, proving that the transition from abstract nonlinear dynamics to physical prototyping is a predictable outcome of managed collapse. This framework establishes an Epistemological Barrier that protects historical context while ensuring the verifiability of technical exits in Internet 3.0 and deep-tech RD.

Aure Ecker-Fils

Engram Commitments introduce a cryptographically verifiable, substrate-rooted identity primitive for large language models. The method extracts engrams from differential execution behavior, aggregates them into an engram vector, compresses this representation using locality-sensitive hashing, and seals it inside a binding-and-hiding cryptographic commitment. Zero-knowledge proofs enable verification of identity continuity and lineage without revealing model parameters. The construction remains stable under non-destructive transformations and degrades predictably under destructive ones, supporting collapse-aware auditing, tamper-evident provenance, and regulator-verifiable attestation. This work unifies the engram calculus, identity ontology, collapse taxonomy, and cryptographic commitments into a single framework for AI provenance, governance, and safety.

Hisashi Suga

Zero-knowledge (ZK) proofs can be formally correct while their deployment pipelines remain fragile. The practical failure modes often arise not at the proof layer, but at the layers where trust is injected: setup, key custody, entropy sourcing, implementation, governance, and deployment interfaces. This paper models ZK pipelines as trust-graphs and proposes an audit-first separation between (i) proof correctness and (ii) pipeline integrity. The core claim is structural: for any non-trivial ZK pipeline, there exists at least one responsibility binding layer R where trust is required and accountability must be assigned. Removing a ceremony does not remove responsibility; it relocates it. We provide minimal definitions, a traceable audit interface, and compact structural examples intended to support reproducible security reviews without overclaiming. Keywords: zero-knowledge; trusted setup; CRS; SNARK; STARK; trust graph; audit; governance; pipeline integrity; responsibility relocation

Gaytri Gupta, Rahul Verma

Secure and private sharing of electronic health records (EHRs) among multiple parties remains a significant challenge in digital healthcare. Although Blockchain technology can ensure data integrity through security and transparency, protecting patient privacy and enabling secure collaborative analysis continue to be difficult problems. To address these challenges, differential privacy (DP) and Zero-Knowledge Proofs (ZKPs) are integrated into a Blockchain-based solution in the innovative design of this multi-institutional EHR-sharing system architecture. ZKPs enable the verification of user identities and access requests without revealing sensitive information, while DP ensures that analytical results are statistically valid and that underlying data are protected against re-identification attacks. A permissioned Blockchain system is developed to support verifiable and privacy-preserving federated analytics over distributed data. Experimental results demonstrate that the proposed framework successfully achieves privacy protection, secure access, and interoperable data-sharing objectives.

Gianpietro Castiglione, Shahriar Ebrahimi, Narges Khakpour

A Software Bill of Materials (SBOM) is a key component for the transparency of software supply chain; it is a structured inventory of the components, dependencies, and associated metadata of a software artifact. However, an SBOM often contain sensitive information that organizations are unwilling to disclose in full to anyone, for two main concerns: technological risks deriving from exposing proprietary dependencies or unpatched vulnerabilities, and business risks, deriving from exposing architectural strategies. Therefore, delivering a plaintext SBOM may result in the disruption of the intellectual property of a company. To address this, we present VeriSBOM, a trustless, selectively disclosed SBOM framework that provides cryptographic verifiability of SBOMs using zero-knowledge proofs. Within VeriSBOM, third parties can validate specific statements about a delivered software. Respectively, VeriSBOM allows independent third parties to verify if a software contains authentic dependencies distributed by official package managers and that the same dependencies satisfy rigorous policy constraints such as the absence of vulnerable dependencies or the adherence with specific licenses models. VeriSBOM leverages a scalable vector commitment scheme together with folding-based proof aggregation to produce succinct zero-knowledge proofs that attest to security and compliance properties while preserving confidentiality. Crucially, the verification process requires no trust in the SBOM publisher beyond the soundness of the underlying primitives, and third parties can independently check proofs against the public cryptographic commitments. We implement VeriSBOM, analyze its security, and evaluate its performance on real-world package registries. The results show that our method enables scalable, privacy-preserving, and verifiable SBOM sharing and validation.

Surendra Kumar Shukla, Vishan Kumar Gupta, Paras Jain, Lipika Goel · 6 authors

The lattice-based encryption is one of the most appropriate choices to secure the blockchain technology during the post-quantum era withstanding security against a quantum adversary attack. The authors study lattice-based cryptography and integrate it with the blockchain architecture in order to address gaps that have recently been exposed because of the continued advances with quantum systems. However, a majority of the classical cryptographic designs such as RSA and ECC are designed around the difficulty of particular mathematical problems, one of which is the Shor algorithm, which makes them highly susceptible to quantum algorithms. Learning with Errors (LWE) and Short Integer Solution (SIS). The paper will discuss the feasibility of lattice-based digital signatures, zero-knowledge proofs, and encryption systems that should be deployed in blockchain so that the integrity of data should be maintained, transactions should be secure, and privacy of users should be preserved. Furthermore, it introduces performance features of blockchain where it considers the lattice-based cryptographic systems where it entails scalability, complexity of computing, and latency. The results are then employed to support the options of encryption with lattices to lay the groundwork for the architecture to quantum-resistant blockchain-based frameworks, and the ultimate stage of secure and scalable decentralized networks in the post-quantum world.

Gang Shen, Zhenhua Han, Weizhi Meng, Mingwu Zhang

Vehicular ad-hoc networks (VANETs) are networks based on short-range wireless communication technology, mainly used for direct communication between vehicles and interaction with roadside infrastructures. The emergence of VANETs has improved the efficiency and safety of vehicle travel. However, malicious vehicles may intentionally send incorrect messages to mislead other vehicles for personal gain, and this behavior cannot be identified yet. To address this issue, we propose an efficient, identifiable, and abortable multi-party signature scheme for VANETs. Specifically, we utilize the property of zero-knowledge proofs to design a method that can efficiently identify malicious vehicles during the signature process, and define a strategy to quickly locate false proofs. Additionally, the message signing key in the proposed scheme is jointly generated by multiple entities, which avoids the security issues of key escrow and single point failure. Through rigorous security analysis, it is demonstrated that the proposed scheme satisfies essential security requirements for VANETs, including message unforgeability and authentication, vehicle anonymity, malicious traceability, collusion resistance, replay attack resistance, identifiable abort, and forward security of signing private key. Performance analysis shows that our scheme reduces the total aggregation verification time by up to 80% compared to existing schemes, and maintains a lower packet loss rate ($&lt; $0.6%) at higher vehicle densities. Therefore, our scheme is suitable for large-scale VANETs deployments.

Tomoyuki Tateno, Naoki Ishibashi, Yasushi Kiyoki

The Paris Agreement of 2015 has prompted countries to accelerate their efforts to become carbon neutrality efforts, which meant reducing CO2 emissions to virtually zero. Limiting global warming to less than 1.5°C by 2050 rely on technologies that remove CO2 from the atmosphere faster than humans release it. This implies that CO2 will be removed at a rate of 1-30 gigaton per year by 2050. Carbon Capture Storage / Sequestration (CCS) and Utilization (CCU) are concepts and technologies that collect emitted CO2 store it permanently underground, or recycle it as energy or chemicals for use in manufacturing and other economic activities. CCS and CCU have been discussed globally, but have not reached local and practical levels. Currently planned large-scale CCS requires significant government investment and new technological developments for capture, transport, and storage / sequestration, therefore implementation is expected to start in the second half of 2030 towards the 2050 goal. The need to start acting now where possible rather than waiting for the distant future, makes it important to implement CCS and CCU on a small scale and build towards future scale-up as an immediate solution. This study proposes a support method and system to help companies that emit large amounts of CO2 such as power plants, cement, petrochemicals, and steel industries, to decide how to treat their CO2 emissions in the context of decarbonization. In this study, a Simple, Measurable, Attainable, Relative, and Time-Bound (SMART) decision support method and Direct Air Capture Location and Cost Simulator (DLCS) system were developed to provide a solution to the Negative Emission 5W1H “What, Who, Which, When, Why, and How” from the perspective of a company that emits CO2. A prototype model with parameter settings was proposed based on knowledge gained from practical experience. The functionality of the SMART method and DLCS system was confirmed by applying sample data from the actual data of the ‘Tokyo Region’ as a Proof of Concept (PoC). In this PoC, characteristics of direct air capture which is a critical technology for negative emissions, were verified. The core of the SMART and DLCS model entails combinatorial optimization, distance calculation, cost estimation, and market projection including constraint solution.

Raman Chadha, Paras Mahajan, Rajat Gupta, Sunil Khullar

The centralized nature of the existing digital identity infrastructure provides single points of failure and potential breaches of user privacy. This work proposes a decentralized infrastructure for managing digital identities using a permissioned blockchain and W3C-compliant Decentralized Identifiers and Verifiable Credentials together with zero-knowledge proofs to support a user-controlled and privacy-preserving identity verification process. The experiment verifies the ability of the proposed infrastructure to handle up to 155 transactions in the verification process in 105 ms for a maximum of 500,000 users without the possibility of egress operations as a potential threat to perform unauthorized access to the personal user information. User test participants are satisfied with the design of the wallet interface and the management of the corresponding consents. Process analysis indicates the feasibility of the designed infrastructure in complying with the key principles of the GDPR and CCPA regulations.

Dr M.Santhalakshmi, Bharath K, Suraj Shenoy, Avani Singh, Tanushka Jain, Swamy Samartha

The rapid expansion of the blockchain gaming sector, projected to reach a $268.8 billion valuation by 2025 1 , has been severely compromised by the proliferation of automated Sybil attacks and bot-driven economic manipulation. Traditional anti-bot measures, such as CAPTCHAs and behavioural analytics, are increasingly circumvented by advanced AI-driven scripts. This paper proposes a novel Context-Aware Reputation-Identity Hybrid (CRIH) framework that integrates biometric-backed Proof-of-Personhood (PoP) with decentralized reputation metrics. By leveraging World ID’s hardware-oracle verification and recursive Zero-Knowledge Proofs (ZKPs), the CRIH framework enables thrustless identity portability across Layer 2 (World Chain) and Layer 3 (Mythos Chain) architectures. We demonstrate that this tiered, risk-sensitive approach significantly reduces bot-driven inflation while preserving player privacy and minimizing onboarding friction.

Javier Ron, Martin Monperrus

Verifying that a compiled binary originates from its claimed source code is a fundamental security requirement, called source code provenance. Achieving verifiable source code provenance in practice remains challenging. The most popular technique, called reproducible builds, requires difficult matching and reexecution of build toolchains and environments. We propose a novel approach to verifiable provenance based on compiling software with zero-knowledge virtual machines (zkVMs). By executing a compiler within a zkVM, our system produces both the compiled output and a cryptographic proof attesting that the compilation was performed on the claimed source code with the claimed compiler. We implement a proof-of-concept implementation using the RISC Zero zkVM and the ChibiCC C compiler, and evaluate it on 200 synthetic programs as well as 31 OpenSSL and 21 libsodium source files. Our results show that zk-compilation is applicable to real-world software and provides strong security guarantees: all adversarial tests targeting compiler substitution, source tampering, output manipulation, and replay attacks are successfully blocked.

Andrew David Hulme, Alexei Lisitsa, Boris Konev

This is an extended appendix for an unpublished paper. It covers the use of a framework defined in that paper to prove the zero-knowledge of a few zero-knowledge proofs. The first example, covering 3-colourability, is justified and explained. The second, covering boolean circuit satisfiability, is simply given.

Pratik Save

We present Viturka, a blockchain architecture that replaces wasteful proof-of-work mining with productive federated learning. The core innovation is Proof of Credibility (PoC): a consensus mechanism where block production probability is determined by accumulated reputation from validated AI contributions rather than computational hash power or financial stake. Viturka leverages recent breakthroughs in Zero-Knowledge Machine Learning (ZKML) to achieve cryptographic verification of model training. Validators generate zero-knowledge proofs attesting to correct training execution, enabling instant on-chain verification without trusted intermediaries or statistical consensus mechanisms. By integrating frameworks like EZKL and Lagrange's DeepProve with GPU-accelerated proving via the Icicle library, validation that previously required hours of recomputation now produces mathematical proofs verifiable in milliseconds. Participants earn credibility by contributing quality training data or validating others' contributions. Only the top 10 highest-credibility validators can participate in validation rounds, with mandatory cooldown periods ensuring rotation. The system uses a temporal commit-reveal scheme for data contributions combined with ZK proofs for validation—fake contributions result in permanent bans, while fraudulent validation is mathematically impossible. This creates infrastructure for training AI models on distributed data without central coordination, with economic incentives aligned toward data quality rather than raw computation. Applications range from commercially valuable use cases like DeFi credit scoring—which could unlock over $100B in overcollateralized capital—to public-good AI for rare diseases, minority languages, and environmental monitoring.

M.Lakshmanan, Gaurav Londhe, Joshuva Arockia Dhanraj, Sriramkumar R · 6 authors

In order to ensure the transparent and immutable maintenance of healthcare data, Blockchain technology has been proposed, but it places privacy against core requirements—one of which is privacy demanded by law. Anonymization techniques present today are useful in providing privacy, however they fall short in this sense in terms of guarantee. While zero-knowledge proofs (ZKPs) are one of the strongest cryptographic attestations, they come with hefty computational costs. This paper proposes an HH-ZKP model within a patient-specific-scope and selective disclosure on a smart contract. On a preliminary note, energetic sorting of electronic health records is actually done where anonymization is achieved using hybrid heuristic methods adhering to the constraints of k-anonymity and l-diversity. This is later followed by succinct ZKPs, ensuring that privacy is being obeyed without exposure of any of the hiding values. Again, the aggregations of these proofs are put onto the block with the smart contract, optimized for gas usage, to increase the scalability to a higher level. It is shown through experimental evaluations on the 10 K synthetic EHR dataset that the proposed scheme shows about a 75% reduced on-chain cost four times reduced proof sizes fully meeting HIPAA Safe Harbor compliance. The HH-ZKP model, by yielding a hybrid of heuristic-preprocessing and formal-principled cryptographic verification, is paving the way for scalable and regulator-aware blockchain health applications.

Guilhem Repetto, Nojan Sheybani, Gabrielle De Micheli, Farinaz Koushanfar

Privacy concerns in machine learning systems have grown significantly with the increasing reliance on sensitive user data for training large-scale models. This paper introduces a novel framework combining Probably Approximately Correct (PAC) Privacy with zero-knowledge proofs (ZKPs) to provide verifiable privacy guarantees in trustless computing environments. Our approach addresses the limitations of traditional privacy-preserving techniques by enabling users to verify both the correctness of computations and the proper application of privacy-preserving noise, particularly in cloud-based systems. We leverage non-interactive ZKP schemes to generate proofs that attest to the correct implementation of PAC privacy mechanisms while maintaining the confidentiality of proprietary systems. Our results demonstrate the feasibility of achieving verifiable PAC privacy in outsourced computation, offering a practical solution for maintaining trust in privacy-preserving machine learning and database systems while ensuring computational integrity.

Tsokwa Daniel, Emmanuel Siman*

Abstract: The research presents a Privacy-Preserving Data Exchange (PPDE) framework for electronic health records (EHRs) that integrates elliptic-curve cryptography (ECC), zero-knowledge proofs (ZKPs), and capability tokens on a permissioned blockchain to create a cohesive, policy-compliant data-sharing workflow. In this system, IoT gateways establish ECC-based session keys to encrypt measurements at the edge, while only cryptographic hashes and pointers to the encrypted data are stored on a consortium blockchain, ensuring data confidentiality with tamper-evident integrity and auditable provenance. Healthcare providers issue Access Request Transactions that include ECC signatures and ZKPs to prove policy compliance (such as patient consent and role-based access) without revealing sensitive details. Off-chain ZKPs validate authorization decisions, with on-chain records providing verifiable evidence of conformance and access history, and a capability token is granted to authorize future data access within defined scopes and time windows. The framework emphasizes end-to-end confidentiality, privacy-preserving authorization, and tamper resistance through the blockchain. Our evaluation analyzes network throughput, end-to-end delay, packet delivery ratio, scalability, and energy efficiency, with particular attention to the overhead from blockchain operations, ECC, and ZKP verification. Results show improved privacy and security for EHR data, with only nominal overhead relative to the privacy and integrity gains, and favorable scalability and energy performance in realistic deployments.

T.G. Aigumov, F.M. Abdulmukminova, A.I. Sakhbieva

This article explores the role of zero-knowledge cryptographic proofs as a basic trust mechanism for financial and government digital services. The theoretical section explains how such proofs shift compliance verification from a "reveal data and verify" mode to a "prove property and admit access to service" mode, thereby reducing transaction costs, mitigating information asymmetries, and limiting agency costs. Drawing on insights from new institutional economics and mechanism theory, it demonstrates how formalized verifiability improves rule robustness, makes truthful reporting individually rational, and reduces the negative externalities of leaks. The analytical section systematizes classes of constructions, highlighting tradeoffs between proof size, latency, the need for trusted configuration, and operational risks. The practical section describes applications in payment infrastructure, lending, insurance, cross-border settlements, digital identity, and ledger management: range constraint verification, threshold and attribute verification, verifiable computation, and selective disclosure. It is demonstrated that with proper design, it is possible to combine data minimization with targeted transparency and effective enforcement, including in projects involving central bank digital money and pan-European identity wallets. Implementation metrics are discussed: marginal cost of proof and verification, average latency, fault tolerance, and the proportion of cases where primary documents are replaced with proof of property.

Shaoyu Li, Hexuan Yu, Shanghao Shi, Md Mohaimin Al Barat · 7 authors

With the growing demand for wireless spectrum, dynamic spectrum sharing (DSS) frameworks such as the Citizens Broadband Radio Service (CBRS) have emerged as practical solutions to improve utilization while protecting incumbent users (IUs) such as military radars. However, current incumbent protection mechanisms face critical limitations. The Environmental Sensing Capability (ESC) requires costly sensor deployments and remains vulnerable to interference and security risks. Alternatively, the Incumbent Informing Capability (IIC) requires IUs to disclose their identities and operational parameters to the Spectrum Coordination System (SCS), creating linkable records that compromise operational privacy and mission secrecy. We propose IU-GUARD, a privacy-preserving spectrum sharing framework that enables IUs to access spectrum without revealing their identities. Leveraging verifiable credentials (VCs) and zero-knowledge proofs (ZKPs), IU-GUARD allows IUs to prove their authorization to the SCS while disclosing only essential operational parameters. This decouples IU identity from spectrum access, prevents cross-request linkage, and mitigates the risk of centralized SCS data leakage. We implement a prototype, and our evaluation shows that IU-GUARD achieves strong privacy guarantees with practical computation and communication overhead, making it suitable for real-time DSS deployment.

Abirami S.K, Karpagam G.R

No abstract is available for this record.

Eric Allender, Shuichi Hirahara, Harsha Tirumala

We show that a decidable promise problem has a non-interactive statistical zero-knowledge proof system if and only if it is randomly reducible via an honest polynomial-time reduction to a promise problem for Kolmogorov-random strings, with a superlogarithmic additive approximation term. This extends work by Saks and Santhanam (CCC 2022). (Saks and Santhanam showed that promise problems that can be reduced in this way to such an approximation of the Kolmogorov-random strings have (possibly interactive) zero-knowledge proof systems, and they did not address the converse implication.) We build on this to give new characterizations of Statistical Zero Knowledge SZK , as well as the related classes NISZK L and SZK L .

Zhenyu Xu, Victor S. Sheng

Protecting the intellectual property of large language models (LLMs) is a critical challenge due to the proliferation of unauthorized derivative models. We introduce a novel fingerprinting framework that leverages the behavioral patterns induced by safety alignment, applying the concept of refusal vectors for LLM provenance tracking. These vectors, extracted from directional patterns in a model's internal representations when processing harmful versus harmless prompts, serve as robust behavioral fingerprints. Our contribution lies in developing a fingerprinting system around this concept and conducting extensive validation of its effectiveness for IP protection. We demonstrate that these behavioral fingerprints are highly robust against common modifications, including finetunes, merges, and quantization. Our experiments show that the fingerprint is unique to each model family, with low cosine similarity between independently trained models. In a large-scale identification task across 76 offspring models, our method achieves 100\% accuracy in identifying the correct base model family. Furthermore, we analyze the fingerprint's behavior under alignment-breaking attacks, finding that while performance degrades significantly, detectable traces remain. Finally, we propose a theoretical framework to transform this private fingerprint into a publicly verifiable, privacy-preserving artifact using locality-sensitive hashing and zero-knowledge proofs.

Griff gurwell

Abstract & Technical Summary:This paper presents a disruptive empirical analysis of 117 celestial bodies demonstrating a systemic phase-locking to a universal frequency of 144 Hz and its fractal subdivisions. By normalizing orbital periods from NASA’s JPL Horizons and the Exoplanet Archive against a 144-fractal grid, we identify a mathematical coherence that contradicts stochastic models of solar system formation. Key Empirical Findings:The primary significance of this study lies in the identification of multiple "Zero-Point Nodes" where celestial bodies exhibit a 0.000% deviation from the calculated harmonic targets. These include: Sedna (90377): Despite its extreme 11,400-year orbit at the system's perimeter, it maintains a 0.000% error relative to the 144-harmonic base. The Trojan Cluster: Multiple bodies (including Achilleus, Patroclus, and Eureka) show 0.000% deviation, functioning as 1:1 phase-locked anchors in the Jupiter and Mars Lagrange points. J-X Lysithea: A mid-group Jovian satellite exhibiting a 0.000% fractal hit relative to the Earth’s Precessional Great Year (25,920 years). Pluto (134340): Historically viewed as an outlier, Pluto aligns with a 0.020% deviation, acting as a primary Kuiper Belt harmonic sentinel. Archaeoastronomical Correlation:The study establishes a direct geodetic link between these orbital constants and the Great Pyramid of Giza. We demonstrate that the structure’s 1:43,200 scaling ratio ( 144×300144 cross 300 144×300 ) and the reported 144,000 casing stone count are not symbolic, but are high-precision encodings of the Earth’s sidereal and orbital harmonics. Conclusion:The convergence of 117 disparate bodies—ranging from high-eccentricity comets like 1P/Halley (0.1% error) to exoplanetary systems like TRAPPIST-1—on a single mathematical constant constitutes a statistical proof of design. The data suggests the universe functions as a Phase-Locked Resonant Cavity, where matter settles into the standing wave nodes of a 144 Hz "Master Clock." https://ctftheory.com/ “Ancient Knowledge of Exoplanet Orbital Ratios: The 144 Hz Universal Harmonic Encoded in the Great Pyramid and Confirmed in TRAPPIST-1 and Kepler-90 Systems” Resonance Architecture in the Continuous Temporal Funnel: Scalar Field Topology from Ancient Structures to Planetary Geometry The 144 Harmonic_ Universal Temporal Stabilization Constant Across Ancient Civilizations, Modern Engineering, and Independent AI Simulations. The Sacred Frequency Trinity: Mathematical and Biblical Evidence for 963 Hz, 666 Hz, and 144 Hz as Fundamental Consciousness Resonances Why Earth AND the Sun: The Coupled Resonance System Explained