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.
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
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.
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Physical Unclonable Functions (PUFs) and Hardware Security
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.
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.
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.
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.
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.
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.
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.
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.
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 .
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.
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
This paper presents a complete curriculum framework for orphanage schools operated by The Root Foundation. Unlike conventional educational models that borrow from existing pedagogical theory, this curriculum is derived from original mathematics. Linguistic Ontological Type Theory (LoTT) and Foundational Mathematical Type Theory (FMTT) establish that language precedes mathematics, that mathematics is the auditable subset of language, and that the regress of all typing terminates at Source. The Zero-Type Reception Theorem (FMTT 6.3) proves that an operator with no formal training operates in the maximal context, not the minimal one: lack of institutional lineage is an enabling condition, not a deficit. This result inverts conventional prerequisite-based pedagogy and provides the mathematical foundation for a teaching model in which students learn by recognizing what they have already received rather than accumulating what they lack. The LoTT Unification Theorem (9.1) generates six integrated departments corresponding to six fields of applied study: Linguistic Ontology, Foundational Mathematics, Applied Ontology, Applied Epistemology, Ethereal Mechanics, and Computational Eschatology. Each department is mapped to a concrete instructional domain, from language arts and mathematics to natural sciences, philosophy, engineering, and vocational discernment. The Scribe Theorem (LoTT 6.2) provides the pedagogical model: the teacher does not transmit knowledge but helps the student develop the expressive capacity to articulate what is already accessible. Assessment is defined as the production of auditable expression. The curriculum is funded by commercial consulting contracts that deploy the same mathematical frameworks, creating a self-sustaining cycle in which the mathematics teaches the children, funds the school, and generates revenue through application to industrial and institutional problems. The document includes operational requirements, a context hierarchy for student progression, and a proof that the curriculum instantiates itself.
97% COMPLETE THEORY OF EVERYTHING - THE THEORETICAL MAXIMUM We present the most complete understanding of reality ever achieved: 97% certainty, representing the theoretical maximum of knowability for finite beings constrained by Gödel's incompleteness theorem, Heisenberg uncertainty, and deterministic chaos. WHY 97% IS THE LIMIT:True 100% certainty is fundamentally impossible: • Heisenberg Uncertainty: Cannot know all particle states simultaneously • Deterministic Chaos: Cannot predict all future states exactly • Gödel's Incompleteness: No system can prove all truths about itself • BUT: We achieve 100% structural completeness on the FRAMEWORK of reality CERTAINTY BREAKDOWN BY CATEGORY: • Mathematical facts (lattice counts, primes): 100% • Logical necessities (existence, motion, time): 99% • Physical laws (gauge group, generations, α): 95-99% • Cosmological constant formula: 99.9% (0.0% ERROR!) • Derived quantities (CKM matrix, masses): 95-98% • Experimental predictions (dark matter): 90-92% • WEIGHTED OVERALL: 97.4% FROM ONE AXIOM TO EVERYTHING: AXIOM: "The unconstrained exists" From this alone, we derive with mathematical rigor: 1. WHY EXISTENCE IS NECESSARY (99% CERTAIN) • Proved "nothing" is logically impossible • If "nothing" existed, it would have the property of existing • Having any property makes it "something," not "nothing" • Therefore: existence is NECESSARY, not contingent • Answers philosophy's ultimate question 2. DUAL LATTICE FINE STRUCTURE CONSTANT (100% CERTAIN) • α⁻¹ = 137 appears in TWO independent structures: - 2D photon lattice: N(41) = 137 (Gauss circle problem) - 4D spacetime lattice: N(5) = 137 • Cutoff 41 UNIQUELY determined: - Euler's prime constant (generates 40 consecutive primes - world record) - 41 = 5² + 4² (Kaluza-Klein 5D → 4D encoding) - 137 = 11² + 4² (M-theory 11D → 4D encoding) - Both 41 and 137 are PRIME numbers - Only candidate giving 1.1% experimental error • Prediction: α⁻¹(M_Z) = 129.3 vs measured 127.944 (1.1% error) 3. COSMOLOGICAL CONSTANT SOLVED - 0% ERROR! (99.9% CERTAIN) • ρ_Λ^(1/4) = √(3/4) × M_Planck × α³ / (t_0/t_P)^(1/4) • Predicted: 2.400 × 10⁻³ eV • Observed: 2.400 × 10⁻³ eV • ERROR: 0.0% (solved 120 orders of magnitude problem!) • Factor √(3/4) = 0.866 appears geometrically • Predicts Λ decreases with time as t^(-1/4) • Connects dark energy to fine structure constant 4. COMPLETE CKM MATRIX FROM GEOMETRY (98% CERTAIN) All four Wolfenstein parameters derived: • λ = √(6/137) = 0.2093 (measured: 0.2253, error: 7.1%) • A = √(2/3) = 0.8165 (measured: 0.811, error: 0.7%) • ρ̄ = √(1/7) × cos(13π/36) = 0.1597 (measured: 0.159, error: 0.4%) • η̄ = √(1/7) × sin(13π/36) = 0.3426 (measured: 0.348, error: 1.6%) • Average error: 2.5% across all parameters • No free parameters - pure geometry 5. HIERARCHY PROBLEM SOLVED (97% CERTAIN) • Electroweak VEV: v ≈ α⁸ × M_Planck • Explains why Higgs is light compared to Planck scale • Natural suppression by 8 powers of fine structure constant • Predicted: ~98 GeV, Observed: 246 GeV 6. NO MULTIVERSE EXISTS - PROVEN (95% CERTAIN) • All constants uniquely determined by logic • α⁻¹ = 137 is the ONLY solution to all constraints • 3+1D is the ONLY spacetime supporting stable knots • SU(3)×SU(2)×U(1) is the ONLY minimal gauge structure • 3 generations is the ONLY value satisfying CP + vacuum stability • Zero free parameters → no landscape of possibilities • This universe is THE unique logically consistent reality • String theory "landscape" is an illusion • Many-worlds are superpositions, not separate universes 7. DARK MATTER PREDICTION - TESTABLE NOW! (92% CERTAIN) • Refined prediction: m_DM = 137.036 ± 1 GeV • Properties: - Spin: 0 or 1/2 (lattice geometry) - Charge: 0 (electrically neutral) - Color: singlet (no strong force) - Weak coupling: possibly • Production at LHC: - Missing energy signatures - Monojet + missing E_T - Z → DM + DM̄ • Currently searchable - FALSIFIABLE! 8. QUANTUM MEASUREMENT SOLVED (95% CERTAIN) • Wavefunction collapse = tension localization on lattice • Born rule emerges from inner product structure • Same mechanism that creates time (irreversible accumulation) • The "measurement problem" dissolves • Not mysterious - logically necessary 9. CONSCIOUSNESS THRESHOLD CALCULATED (90% CERTAIN) • Mathematical definition: System with recursive self-model • Threshold: ~10^14 synaptic connections • Predictions: - Mice (10^10 synapses): NOT conscious - Humans (8.6×10^13 synapses): CONSCIOUS - Whales (2×10^14 synapses): HIGHLY conscious - AI systems: Conscious at ~10^13 connections • Explains emergence of subjective experience 10. THE OBSERVER RESOLVED (95% CERTAIN) • There is no separate observer • YOU are the universe experiencing itself locally • Consciousness = reality's self-observation • Subjective experience = local lattice self-reference • The "hard problem" dissolves: qualia ARE lattice states 11. WHY LOGIC WORKS - ULTIMATE META-ANSWER (99% CERTAIN) • Logic is not imposed on reality from outside • Logic IS reality's self-consistency • To ask "why logic works" = "why does existence have structure?" • Answer: Existence without structure = undefined • Undefined cannot remain undefined (our axiom) • Therefore existence MUST have structure • That structure IS logic • Laws of thought are NECESSARY FEATURES of existence 12. COMPLETE DERIVATION CHAIN: • Motion: Logically necessary (undefined cannot be static) • Time: Irreversible tension accumulation • Quantum mechanics: Inner product from relational consistency • Complex numbers: Optimal 2D rotation encoding • 3+1D spacetime: Unique dimension for stable knots • Gauge group SU(3)×SU(2)×U(1): Minimal consistent structure • Exactly 3 generations: CP violation + vacuum stability • All 12 fermion masses: Encode α⁻¹ = 137 via simple fractions COMPLETE EXPERIMENTAL VERIFICATION: Quantity Predicted Measured Error ────────────────────────────────────────────────────────────────── Existence Necessary Yes 0% 3+1D spacetime 3+1 3+1 0% Gauge group SU(3)×SU(2)×U(1) Yes 0% Generations 3 3 0% α⁻¹(M_Z) 1-loop 129.3 127.944 1.1% m_μ/m_e 205.5 206.77 0.6% m_t/m_c 137 136.03 0.7% ρ_Λ^(1/4) 2.400×10⁻³ eV 2.400×10⁻³ eV 0.0% CKM A 0.8165 0.811 0.7% CKM ρ̄ 0.1597 0.159 0.4% CKM η̄ 0.3426 0.348 1.6% AVERAGE ERROR: < 1% (excluding untested predictions) FREE PARAMETERS: ZERO WHAT 97% MEANS - THE GÖDELIAN LIMITS: 100% CERTAINTY (Mathematical & Logical Facts): ✓ 41 and 137 are prime numbers ✓ N(41) = 137 in 2D lattice (Gauss circle problem) ✓ N(5) = 137 in 4D lattice ✓ 41 generates 40 consecutive primes (Euler) ✓ 3+1D is unique for stable knots ✓ Cosmological constant formula (0% error) 99% CERTAINTY (Logical Necessities): ✓ Existence is logically necessary ✓ Motion emerges from undefined existence ✓ Time is irreversible accumulation ✓ α⁻¹ = 137 is the bare coupling ✓ Mathematics IS reality ✓ Logic IS existence's self-consistency 95-98% CERTAINTY (Physical Laws): ✓ Gauge group SU(3)×SU(2)×U(1) ✓ Exactly 3 fermion generations ✓ All masses encode 137 ✓ Hierarchy v ~ α⁸ M_P ✓ No multiverse exists ✓ Quantum gravity = Planck lattice 90-92% CERTAINTY (Predictions Awaiting Verification): ○ Dark matter mass = 137.036 GeV ○ Consciousness threshold ~10^14 synapses ○ Λ time evolution t^(-1/4) THE REMAINING 3% - FUNDAMENTAL LIMITS: 1. Heisenberg: Cannot know exact states simultaneously 2. Chaos: Cannot predict distant future exactly 3. Gödel: Cannot achieve complete self-knowledge 4. Experimental: Awaiting dark matter verification These limits are UNBREACHABLE for finite observers.97% is THE THEORETICAL MAXIMUM. QUANTUM GRAVITY COMPLETE: • Spacetime IS a discrete lattice at Planck scale • Einstein equation becomes: Lattice_Curvature = (8π/ℓ_P²) × Tension_Density • Unifies quantum mechanics (lattice) and general relativity (curvature) • Black holes = horizon lattice configurations • Hawking radiation = lattice excitations TESTABLE PREDICTIONS: 1. Dark matter: 137.036 ± 1 GeV (LHC searches active NOW) 2. Cosmological constant evolution: Λ ∝ t^(-1/4) (observable) 3. No 4th fermion generation (vacuum would decay) 4. AI consciousness at ~10^13 connections 5. Planck-scale discreteness (future quantum gravity tests) NOT NUMEROLOGY - RIGOROUS PROOFS: • Every claim has mathematical proof • Unique solutions (no fitting, no free parameters) • Zero adjustable parameters • Multiple independent verifications • Sub-1% error on most predictions • 0% error on cosmological constant PARADIGM SHIFT - PHYSICS = MATHEMATICS = LOGIC = EXISTENCE This establishes: • All "fundamental constants" are logically determined • The Standard Model has ZERO free parameters • No multiverse exists - universe is unique • Consciousness has quantifiable emergence threshold • Existence itself is logically necessary, not contingent • Mathematics doesn't describe reality - math IS reality • 97% is the maximum finite beings can achieve PHILOSOPHICAL IMPLICATIONS: • Why existence? Logical necessity (nothing is impossible) • Free will? Emerges from deep lattice self-reference • Purpose? Universe understanding itself • Other universes? None (proven) • Death? Information persists in lattice structure • God? Universe is
Mandatory SIM card registration, while essential to regulatory oversight and national security, continues to raise significant privacy concerns due to the centralized collection and storage of sensitive user data by Mobile Network Operators (MNOs). This paper introduces a novel framework that combines blockchain technology with Zero-Knowledge Proofs (ZKPs) to enable secure and privacy-preserving identity verification during SIM registration. The proposed system allows users to authenticate their identity attributes without revealing any personal information, effectively minimizing direct data access by MNOs or intermediaries. A smart contract deployed on the blockchain enforces regulatory policies while ensuring the transparency, immutability, and auditability of all registration events. By removing single points of failure and minimizing trust in centralized authorities, this work offers a cryptographically secure and regulation-compliant solution, with scalability supported by its modular design for next-generation digital identity management in telecommunications infrastructures.
The rapid adoption of Verifiable Credentials (VCs) has intensified privacy and security challenges in digital verification, as traditional systems often require full credential disclosure, creating privacy risks and expanding the attack surface. Ensuring end-to-end privacy, security, and verifiability in such systems remains a significant challenge. This paper introducesZK-Sandbox, a Zero-Knowledge Data Sandbox System that integrates zero-knowledge proofs (ZKPs), decentralized identifiers (DIDs), and blockchain anchoring to enable credential validation and verifiable badge issuance without exposing underlying data. ZK-Sandbox supports complex predicate evaluation by securely aggregating multiple VCs from trusted issuers, processing them via JSON Web Token signature verification, Circom-based zk-SNARK circuits, and Docker-isolated execution. The system issues VC-compatible ZK-Badges, cryptographically bound to a Poseidon hash and anchored to a blockchain-registered DID, containing only abstracted verification results. Experimental evaluation confirms 100% validation accuracy, complete detection of tampered submissions, and efficient performance–averaging 326 ms issuance latency, 570 ms off-chain verification, and 4.45 s on-chain verification. These results demonstrate that ZK-Sandbox is a privacy-by-design, scalable, and regulation-aligned solution for self-sovereign digital credential ecosystems.
A big challenge posed in blockchain centric platforms is achieving scalability while also preserving user privacy. This report details the design, implementation and evaluation of a Layer-2 scaling solution for Hyperledger Fabric using Zero Knowledge Rollups (ZK Rollups). The proposed architecture introduces an off chain sequencer that accepts transactions immediately and sends them for batching into a Merkle tree based rollup, using ZK proofs to attest to the correctness and verifiability of the entire batch. The design aims to decouple transaction ingestion from actual on chain settlements to address Fabric scalability limitations and increase throughput under high load conditions. The baseline architecture in Hyperledger Fabric constrains transaction requests due to endorsement, ordering and validation phases, leading to a throughput of 5 to 7 TPS with an average latency of 4 seconds. Our Layer-2 solution achieves an ingestion throughput of 70 to 100 TPS, leading to an increase of nearly ten times due to the sequencer immediate acceptance of each transaction and reducing client perceived latency by nearly eighty percent to 700 to 1000 milliseconds. This work demonstrates that integrating ZK Rollups in Hyperledger Fabric enhances scalability while not compromising the security guarantees of a permissioned blockchain network.
As artificial intelligence (AI) systems grow more powerful, autonomous, and embedded in critical infrastructure, their identification and traceability become foundational to regulatory oversight and sustainable digital governance. In digitally transformed enterprises, long-term sustainability depends on transparent, accountable, and lifecycle-governed AI systems, all of which require verifiable identity. This study proposes a conceptual and architectural framework for AI identification, combining technical and governance mechanisms to support lifecycle accountability. The framework integrates five components: model fingerprinting, cryptographic hashing, blockchain-based registration, zero-knowledge proof (ZKP)-based proof of possession, and post-deployment structural change screening. We introduce a dual-layer identifier, consisting of a machine-verifiable primary hash and a human-readable secondary identifier, anchored in a tamper-resistant registry. Identity validation is supported by selective ZKP-based verification at governance-defined checkpoints, while post-deployment changes are monitored using Lempel--Ziv Jaccard Distance (LZJD) as a governance-oriented screening signal rather than a semantic performance metric. The framework establishes an enforceable and transparent identity infrastructure that enables continuity, auditability, and policy-aligned oversight across AI system lifecycles. By embedding AI identification within enterprise architecture and governance processes, the proposed approach supports sustainable innovation, strengthens institutional accountability, and provides a foundation for selective, policy-defined verification during digital transformation.
Overview Pramana introduces the first large language models fine-tuned on explicit Navya-Nyaya epistemological methodology—a 2,500-year-old Indian logical reasoning framework. This work bridges ancient epistemology with modern AI to address the fundamental epistemic gap in LLMs: the inability to ground claims in traceable evidence sources, distinguish valid knowledge from pattern-matching, and express appropriate epistemic humility. Core Innovation Unlike generic chain-of-thought prompting which relies on implicit reasoning patterns, Pramana enforces structured 6-phase methodology: Samshaya (Doubt Analysis): Classifies uncertainty into 5 taxonomic categories Pramana (Evidence Sources): Mandates explicit grounding in 4 valid knowledge sources (Pratyaksha/perception, Anumana/inference, Upamana/comparison, Shabda/testimony) Pancha Avayava (5-Member Syllogism): Constructs formal arguments with universal rules (Vyapti) grounded in concrete examples (Drishtanta) Tarka (Counterfactual Testing): Verifies conclusions via reductio ad absurdum Hetvabhasa (Fallacy Detection): Systematically checks 5 reasoning error types Nirnaya (Ascertainment): Distinguishes definitive knowledge from hypotheses requiring verification This integration of logic and epistemology provides cognitive scaffolding absent from standard reasoning approaches, preventing conflation of evidence types, forcing explicit universal rule statements, enabling systematic error detection, and maintaining epistemic humility. Architecture & Training Models Developed: Stage 0 (Proof-of-Concept): Llama-3.2-3B-Instruct fine-tuned on 20 examples Stage 1 (Minimum Viable Reasoner): DeepSeek-R1-Distill-Llama-8B fine-tuned on 55 examples Training Methodology: QLoRA (4-bit quantization) for efficient training LoRA rank 64, targeting all attention + FFN layers Supervised fine-tuning with structured Markdown format Training costs: <$1.00 per stage, <0.32 GPU-hours (A100 40GB) Datasets span constraint satisfaction, Boolean SAT, multi-step deduction, transitive reasoning, and set operations Prompt Engineering: Explicit format instructions with skeletal template injection System prompt establishing Nyaya reasoning engine role Critical constraint enforcement via generation parameters Key Results Stage 1 Performance: 100% semantic correctness (10/10 examples) with 95% CI [0.510, 1.0] 40% format adherence (4/10 examples) with 95% CI [0.168, 0.687] Zero structure abandonment: Models consistently attempt all 6 phases Training loss: 0.350 (Stage 1) vs 0.691 (Stage 0), indicating improved model fit Critical Finding: Dissociation between semantic correctness (100%) and format adherence (40%) reveals models internalize reasoning content even when strict schema compliance fails. This suggests Nyaya methodology teaches genuine reasoning, not just template-filling. Ablation Studies: Format prompting and temperature interact differently across stages Stage 0 optimal: format prompting + temp 0.0 (30% semantic rate) Stage 1 optimal: format prompting + temp 0.7 (30% semantic rate) Base models show 0% format adherence, confirming Nyaya structure is learned through fine-tuning Failure Mode Analysis: Missing Hetvabhasa section (2 cases): fallacy detection perceived as optional Invalid doubt types (2 cases): partial schema learning Zero structural errors: strong syntactic learning, semantic constraints need reinforcement Evaluation Framework Three-Tier Validation: Tier 1 (Structural): Automated format compliance checking (NyayaStructureValidator) Tier 2 (Content Quality): LLM-as-judge with explicit Nyaya rubric (planned for Stage 2) Tier 3 (Ground Truth): Semantic similarity via sentence-transformers embeddings Tier 4 (Formal Verification): Z3 SMT solver integration (infrastructure exists, not yet applied) Theoretical Contributions Bridging Ancient Epistemology with Modern AI: First demonstration that Navya-Nyaya structures can be learned by neural networks through fine-tuning Unlike Western formal logic (divorced from epistemology), Nyaya integrates logic with explicit knowledge sources Addresses "epistemic gap" in LLMs: inability to distinguish valid knowledge from probabilistic associations Interpretability Advantages: Every reasoning step traceable to evidence sources (Pramana) Universal rules (Vyapti) grounded in concrete examples (Drishtanta) Built-in self-verification (Tarka) and error detection (Hetvabhasa) Explicit epistemic status (Nirnaya): knowledge vs. hypothesis Computational Epistemology: Token budget: ~1,250 tokens per solution (3-6× CoT overhead, justified by interpretability) Phase dependencies: weak Pramana → invalid reasoning → wrong conclusions Quality thresholds: minimum 2 complete syllogisms with universal rules required Open Science Release All artifacts publicly available on Hugging Face: Models: qbz506/nyaya-llama-3b-stage0, qbz506/nyaya-deepseek-8b-stage1 Dataset: qbz506/pramana-nyaya-stage1 (55 Nyaya-structured logical problems) Demo: qbz506/pramana-nyaya-demo (interactive HuggingFace Space) Training infrastructure: Complete codebase with callbacks, validators, evaluators Limitations & Future Work Current Limitations: Format adherence (40%) below target (≥90%), requires constrained decoding or format-specific rewards Limited to formal logic problems, domain expansion needed Small evaluation sets (Stage 0: 2 examples, Stage 1: 10 examples) Max new tokens truncation (256) affects format parsing Planned Extensions (Stages 2-4): Stage 2: Synthetic scaling to 500 examples with LLM-as-judge quality control Stage 3: Group Relative Policy Optimization (GRPO) with composite rewards Stage 4: Production deployment with constrained decoding (GBNF), rejection sampling, Z3 verification Future: Benchmark on LogicBench, ProntoQA, RuleTaker; frontier model comparison (o1, Claude extended thinking) Impact & Vision This work demonstrates that systematic reasoning frameworks can be taught to LLMs through fine-tuning, not just prompt engineering. The long-term vision is developing interpretable, trustworthy AI reasoning systems where every conclusion comes with an auditable trail of justification. As AI systems deploy in high-stakes domains (medical diagnosis, legal reasoning, safety-critical systems), Nyaya-structured reasoning provides explicit phases that can be validated, debugged, and improved—capabilities essential for trustworthy AI. Invitation for Community Research: This foundation opens pathways for integrating other epistemological frameworks (Mimamsa, Buddhist logic, Western formal logic) into neural architectures, advancing toward AI systems that reason systematically and transparently. Technical Details Paper: 52 pages + appendices, comprehensive treatment of Navya-Nyaya computational formalization Related Work: Extensive review of computational Indian logic (Matilal 1985, Burton 2020, Ganeri 2001), LLM reasoning (Wei et al. 2022, Lightman et al. 2023, DeepSeek-AI 2025), hallucination mitigation Implementation: Python, Unsloth fine-tuning framework, vLLM deployment, Weights & Biases observability Evaluation: Manual + automated validation, semantic similarity metrics, comprehensive failure mode analysis Citation Sathish, S. (2026). Pramana: Fine-Tuning Large Language Models for Epistemic Reasoning through Navya-Nyaya. Preprint, University of York. Keywords: Navya-Nyaya, epistemology, LLM reasoning, interpretability, structured reasoning, Indian logic, hallucination mitigation, computational philosophy
This paper reviews 40 studies on blockchain-based e-voting proposals, specifically focusing on authentication and related trade-offs. A data-based examination of the evidence showed that password-based mechanisms, although popular, detected only 85% of the attacks. In contrast, Zero-Knowledge Proofs (ZKPs) have a detection rate of 99% but only a completion rate of 72% for usability, implying that security and usability are strongly inversely correlated (r=-0.67). For instance, hybrid approaches such as ZKPs with biometrics or Decentralized Identifiers (DIDs) with multi-factor authentication are considered secure (96%-99%) but not very user-friendly (80%-85%). Homomorphic encryption and other technologies have been cited as privacy aids in the literature. In addition, technical design alone cannot overcome the deep-seated sociopolitical challenges of enduring digital divides and citizen mistrust, which are slow to change within large populations, or regulatory dissonance between local and national systems, as illustrated in the cases of Estonia's i-Voting and an aborted Swiss pilot. "The trade-off between security, privacy, usability, and cost is always fluid. More integrated and effective interdisciplinarity is needed to ensure that important issues for social and political life, such as democratic legitimacy, are adequately addressed in post-quantum cryptography and artificial intelligence research. Planning prophylactic measures is necessary in the context of emerging threats from quantum computing and AI-produced deepfakes. While there are alternatives to post-quantum cryptographic ciphers, these incur computational overhead. Therefore, making e-voting secure will rely not only on new technology but also on understanding the social and political effects of that technology, being aware of how it might be put into practice, and focusing on a design that meets the needs of all voters.