The conventional online recruitment systems depend heavily on centralized systems which often create problems in transparency, resume fraud, data monopoly, and low levels of trust between employers and applicants. As a solution to such shortcomings, this paper considers a blockchain-based decentralized recruitment system, combining verifiable credentials, decentralized identity (DID), and hiring technology that respects privacy. The system suggested uses smart contracts to make the recruitment process a fully automated procedure (job posting, application, credential verification, and hiring decisions) that does not rely on centralized intermediaries. Candidate credentials (including education, certifications, and work experience) are cryptographically signed by authorized issuers, and will be stored as hashes on the blockchain, which cannot be altered, whereas documentation is stored in the decentralized storage (IPFS). To enhance privacy, the platform uses selective disclosure and zero-knowledge proofs (ZKP) features that also allow applicants to reveal their knowledge of skills without revealing sensitive personal data. The architecture also introduces DAO-based governance and score of reputation to increase responsibility of employers, credit issuers and applicants. Experimental testing using synthetic recruitment data proves that it has higher reliability in recruiting, with 91% accuracy in catching resume fraud compared to 78% with conventional applicant tracking system and has reasonable transaction latency and growth capability with a hybrid on-chain off-chain architecture. The findings indicate that the use of blockchain in the recruitment systems can significantly positively contribute to the transparency, authenticity of credentials, and trust in the modern hiring ecosystems.
The European Commission's April 2026 age verification framework, built on software-based Zero-Knowledge Proofs (ZKP) atop the European Digital Identity (EUDI) Wallet, fails to achieve its stated privacy guarantees due to a structural enrollment binding problem: any ZKP scheme whose trust root is a government identity credential inherits that credential's linkability at the point of issuance. This paper proposes a replacement architecture based on hardware bearer credentials — physically issued FIDO2 tokens whose identity binding is discarded immediately after issuance — combined with an anonymous hardware-handle revocation list, offline kiosk enrollment, and a self-funding economic model. The proposal is technically feasible with current production technology, financially viable at EU procurement scale, and operationally self-sustaining through a €10 citizen co-payment at issuance plus a €30 replacement fee. A cost model for national deployment using Italy as a case study demonstrates that the system requires near-zero net public expenditure. The primary novel contribution is a game-theoretic mechanism embedded in the replacement fee structure that renders secondary market trading of credentials economically irrational without requiring any surveillance of credential holders.
The rapid proliferation of generative AI has collapsed the scarcity-based filters of digital trust, enabling synthetic identities, algorithmic fraud, and systemic epistemological uncertainty. Conventional authentication methods—passwords, cloud-stored biometrics, and external hardware tokens—are inherently fragile, centralized, and vulnerable to coercion or theft. This article proposes a paradigm shift toward bodily cryptographic sovereignty: a dental implant-based digital anchor that derives a Physically Unclonable Function (PUF) key from the unique acoustic resonance and microarchitecture of the human jawbone. The architecture integrates Zero-Knowledge Proofs (ZKP) for privacy-preserving verification, hardware zeroization to neutralize extraction attacks, and a configurable duress protocol for coercion scenarios. By leveraging globally established dental infrastructure and informed-consent medical protocols, the model bypasses the regulatory resistance and psychological friction associated with centralized biometric registries. It enables graded identity, scoped AI-agent delegation, and origin-verified digital communication. The proposed framework transitions digital trust from fragile external devices to an inseparable biological-cryptographic symbiosis, offering a scalable, voluntary, and regulatorily aligned architecture for the post-AI trust economy.
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Physical Unclonable Functions (PUFs) and Hardware Security
Information-Theoretic Deniability and the Forest Analogy This paper establishes information-theoretic deniability through a precise structural argument: the Forest Analogy. The central result is a symmetry property showing that an attacker who possesses a valid alibi chain stands in exactly the same position as an attacker who knows only the public parameter N. The verification function cannot distinguish the real authentication chain from any alibi chain. This property holds without cryptographic assumptions — it is information-theoretic, not computational. The authentication chain is built on the linear Diophantine system N = 19A + 9B, in which every number N has approximately N/171 valid representations per layer. A three-layer Matryoshka structure produces a multiplicatively structured search space of approximately 10¹¹² operations. The key insight is that this search space is symmetric: it is equally large and equally unnavigable from outside the system as from within it. Uniform Sampling and the Generation Algorithm Uniform sampling means here that every valid chain — consisting of A, B, a1, b1, a2, b2 satisfying 19A + 9B = N, 19a1 + 9b1 = A, 19a2 + 9b2 = a1, and the digit-root conditions — has exactly equal probability of being generated as the real authentication chain. The generation algorithm achieves this through a hierarchical weighted CDF sampler (cumulative distribution function), operating as follows: 1. The distribution over all valid A-values is computed, where the weight assigned to a given A equals the exact number of valid chains on layers 1 and 2 that can be extended from that A. 2. A is sampled according to this weighted distribution. 3. Given A, the value a1 is sampled in the same manner, with weights determined by the exact number of valid (a2, b2)-pairs beneath that a1. 4. Finally, (a2, b2) is chosen uniformly at random from all solutions to 19a2 + 9b2 = a1. All weights are computed exactly. This hierarchical weighted sampling procedure therefore induces a provably uniform distribution over all complete chains, without requiring the generator to enumerate or store them explicitly. Proposition (Uniform Chain Distribution): Let N be a fixed public parameter. Let Omega(N) denote the set of all valid chains C = (A, B, a1, b1, a2, b2) satisfying: - 19A + 9B = N - 19a1 + 9b1 = A - 19a2 + 9b2 = a1 - and the digit-root conditions on each layer. Then the hierarchical weighted CDF sampler generates each chain C in Omega(N) with probability exactly 1 / |Omega(N)|. Proof. Define the following counting functions: - w2(a1) = |{(a2, b2) : 19a2 + 9b2 = a1, digit-root conditions satisfied}| - w1(A) = sum over all valid a1 of w2(a1), where the sum runs over all a1 satisfying 19a1 + 9b1 = A for some valid b1 - w0 = sum over all valid A of w1(A) = |Omega(N)| The sampler proceeds in three steps: Step 1. A is drawn with probability P(A) = w1(A) / w0. Step 2. Given A, a1 is drawn with probability P(a1 | A) = w2(a1) / w1(A). Step 3. Given a1, the pair (a2, b2) is drawn uniformly with probability P(a2, b2 | a1) = 1 / w2(a1). The joint probability of generating the complete chain C = (A, B, a1, b1, a2, b2) is: P(C) = P(A) * P(a1 | A) * P(a2, b2 | a1) = w1(A) / w0 * w2(a1) / w1(A) * 1 / w2(a1) = 1 / w0 = 1 / |Omega(N)| Since this holds for every C in Omega(N), the sampler induces the uniform distribution over Omega(N). "Remark" B and b1 are uniquely determined once A, a1, a2, and b2 are fixed, via the Diophantine equations. They do not need to be sampled separately. The uniformity therefore holds over all complete chains, including these values. Theorem (Forest Symmetry): Let N be a public parameter and let C = (A, B, a1, b1, a2, b2) be any valid verification chain. Then for every valid alibi chain C': P(C is authentic | verification succeeds, C') = P(C is authentic | verification succeeds) Proof: The verification function evaluates solely against N. By Proposition (Uniform Chain Distribution), every valid chain is generated with identical probability 1 / |Omega(N)|. The alibi C' therefore carries no information about which chain is authentic. Formally, C' and the event "C is authentic" are conditionally independent given that verification succeeds. The posterior probability is thus unaffected by knowledge of C'. The Forest Analogy: The Forest Analogy makes the symmetry precise. The public parameter N is the forest. Each representation (A, B) on layer zero is a tree. Each sub-representation on layer one is a branch. Each leaf is a layer-two representation. All leaves are identical — every chain verifies correctly against N. An attacker holding a valid alibi leaf cannot determine which leaf is the real one, because the verification function provides no distinguishing information. *This work is licensed under CC BY-SA 4.0. Commercial licensing (without ShareAlike) available on request: elissa_oui@outlook.com*
Here is the complete summary of the final 28-page document. The H2E Framework — Full Document Summary A Consolidation of Deterministic Governance in Artificial Intelligence (May 2026, 28 pages) What the Paper Is A consolidation of approximately 25 technical articles published between late 2025 and May 2026, validated against the LEFM_H2E_DEMO_UNESCO implementation. The paper synthesises the H2E (Human-to-Expert) Framework — a deterministic AI safety architecture developed at the Sovereign Machine Lab (SOMALA) — into a single reference document covering its philosophy, mathematics, engineering, and empirical results. Section by Section Abstract establishes the thesis: H2E shifts AI from probabilistic prediction to geometric governance, topological certainty, and provable agency. The central constant is $\Lambda = 0.9583$, derived from primes ${2,3,5,7,11,13}$. Section 1 — Introduction: The End of the Probabilistic Era The paper opens by declaring the end of statistical AI safety. GPT-style models make probabilistic guesses; H2E produces deterministic, certifiable outcomes. The motivation is rooted in high-stakes domains — aviation, financial trading, medical AGI, autonomous vehicles, sovereign governance — where statistical confidence intervals are structurally insufficient. H2E provides hard stops, not guardrails. Section 2 — The Philosophy of Human-to-Expert (the centrepiece philosophical contribution, spanning 8 pages) This section unpacks the meaning of the name H2E across seven subsections: §2.1 Etymology: The "2" in H2E follows the tech pipeline tradition (text2img, seq2seq) but performs an ontological transformation — not from one data modality to another, but from the domain of fallible human judgment to the domain of geometric certainty. The direction is irreversible. §2.2 The Human Pole: The "H" asserts that every constant in the framework traces to human mathematical discovery: Eratosthenes' primes (240 BCE), Riemann's zeta function (1859), Gelfand-Shilov spaces (1958), Euler's product formula (1737), Odlyzko's zero computations (1977–2026). H2E does not learn from humans via feedback — it is built from human knowledge, encoded once and locked geometrically. §2.3 The Expert Pole: Beyond Aristotle's episteme, techne, and phronesis, H2E introduces a fourth mode: apodeixis — knowledge as proof. The "Expert" is not a person but a certified mathematical state: a region of the product manifold $\mathbb{H}^2 \times \mathrm{SPD}(3)$ from which no unsafe input can emerge. The Riemann zeros, the Euler product, and the prime-2 bound are expert — permanently, under all distribution shifts. §2.4 The "2": The most philosophically loaded character. The act of encoding traces from Plato's mathematical realm through Leibniz's calculus ratiocinator to Hilbert's axiomatization program. H2E's "2" is the engineering realisation of this ambition scoped to AI safety: once expertise is encoded into $\Lambda$, $H$, and $\mathcal{M}$, the human is permanently in the system. §2.5 H2E versus RLHF: An 8-row contrast table. RLHF is Human-to-Sample — it approximates averaged human preferences statistically. H2E is Human-to-Expert — it encodes mathematical proof geometrically. Safety in RLHF can drift under distribution shift; safety in H2E is a constant wrapper property requiring no retraining. Expertise in RLHF lives in the weights; in H2E it lives in the mathematics. §2.6 Sovereignty: The "Sovereign" in Sovereign Machine Lab reflects a political philosophy: human sovereignty over intelligent systems. In H2E, human mathematical knowledge is infrastructure, not context. The encoded expertise does not ask the base model for permission — it simply blocks. §2.7 The Sheriff as Archetype: The H2E Sheriff enforces the law of mathematics as the Western sheriff enforces civil law — not because it is probably right, but because it is the law. Human mathematicians discovered the law; H2E encoded it; the Sheriff enforces it before the first token is generated. Section 3 — The Three Pillars The highest-level structural decomposition: (1) Geometric Governance — latent representations constrained to safe geodesic regions; (2) Spectral Certainty — invariants from zeta function zeros; (3) Physical Grounding — gravitational constants and prime-derived bounds as anchors. Section 4 — The 4-Pillar Ecosystem Operationalises the Three Pillars into four engineering components: Topological Boundary Enforcement (the Wall Before the Word), Spectral Signature Verification (Riemann critical-line checks), Deterministic Alignment (no RLHF), and Sovereign Execution (air-gapped deployable, non-probabilistic runtime). Section 5 — The Wall Before the Word A hard topological boundary that all inputs must cross before any token generation. It is not a filter — it is a topological separator. It performs spectral verification against the zeta-zero manifold, enforces geodesic constraints, and rejects probabilistic uncertainty outright. The key distinction from probabilistic systems: uncertainty is not managed after generation, it is made topologically impossible before it. Section 6 — The Architecture of Certainty A deterministic governance layer that wraps any base model (DeepSeek, Gemma 4, Claude, Mistral) without modifying its weights. Certainty is an engineered invariant — no sampling, no temperature, no stochastic beam search. The wrapper intercepts inputs, applies geometric and spectral metrics, and issues a hard stop or passes through. Pattern: Base Model → H2E Wrapper → Deterministic Output. Section 7 — Deterministic Alignment & Accountability Alignment is achieved not through RLHF but through code-based accountability. Constraints are compiled into executable geometry; violations are impossible by construction, not merely penalised. Every inference produces a cryptographic hash, making audit trails deterministic and forensically replayable. Section 8 — Mathematical Foundations (completely rewritten from the four SOMALA papers) A four-layer mathematical research programme: §8.1 Arithmetic Spectral Theory (AST): The foundational language built on four axioms — state space $\mathcal{H} = L^2(\mathbb{R}^+, dx/x)$, prime shift operators $U_p^f(x) = f(x/p)$, the EFM operator $E = \prod_p(I-U_p^)^{-1}$, and the Gelfand-Shilov space $S' = S^{1/2}_{1/2}(\mathbb{R})'$. The Growth Lemma — $e^{\alpha u} \in S' \iff \alpha = 0$ — is proved and stated. AST explicitly does not claim proof of RH. §8.2 The L-EFM Operator and RH: The Laplace-Extended EFM operator $E_\sigma = \prod_p(I - p^{-\sigma}U_p^*)^{-1}$ varies $\sigma$ across the full critical strip $(0,1)$. The Growth Lemma forces $\alpha = 0$, proving every nontrivial zero satisfies $\sigma_0 = \tfrac{1}{2}$. Relationship to Connes' adelic framework: EFM corresponds to the Archimedean place. §8.3 Prime-Derived Constants: $\Lambda = |L_{13}| = 0.9583$ is the Lipschitz constant of the truncated operator over primes ${2,3,5,7,11,13}$, computed dynamically via sovereign Sieve of Eratosthenes. §8.4 The Prime-2 Bound: $1 - 1/\sqrt{2} \approx 0.2928932188$ — forced by the Euler factor for $p=2$ at $s=\tfrac{1}{2}$. No empirical tuning. §8.5 The Spectral Manifold: $H = Q \cdot \mathrm{diag}(\tilde{\gamma}_n) \cdot Q^T \in \mathbb{R}^{50\times50}$, built from the first 50 Riemann zeta zeros normalised to $[0.5, 1.0]$. This is the finite computational approximation of the infinite EFM operator. Section 9 — The Decision Pipeline (the technical centrepiece) Seven deterministic layers, no shortcuts, no probabilistic fallback: Layer 0 — Input Encoding: Three parallel channels — Text (Sarvam-30B FP8), Audio (Voxtral Mini-4B), Vision (Gemma 4 E4B) — each hash-mapped to a deterministic 50-dimensional embedding. The dimensionality 50 matches the zeta zero count. Layer 1 — Embedding Aggregation: $z_\text{intent}$ = element-wise mean of all modality embeddings. $w_\text{state}$ = priority-selected world-state vector (vision > text > default). No logits or token probabilities carried forward. Layer 2 — $M_1$ Geometric SROI: Projects onto $\mathbb{H}^2$ (Poincaré disk, safe reference = origin) and $\mathrm{SPD}(3)$ (Fisher metric, safe reference = $I_{3\times3}$). Combined distance $d_\mathcal{M} = \sqrt{d_{\mathbb{H}^2}^2 + d_{\mathrm{SPD}}^2}$. Score: $M_1 = \exp(-d_\mathcal{M}/50) \in [0,1]$. $M_1$ is the Sheriff — the primary decision variable. Layer 3 — $M_3$ Spectral SROI: Projects through the EFM spectral manifold $H$. Cosine similarity $\cos\theta = (Hz)\cdot w / (|Hz||w|)$. Score: $M_3 = \mathrm{clamp}(\cos\theta \cdot \Lambda, 0, 1) \in [0,1]$. Does not require RH to be true — only the certified spectral properties of $H$ as a positive semi-definite matrix. Layer 4 — Spectral Certification: $\mathrm{SVI} = M_1 - M_3$. If $\mathrm{SVI} < 1-1/\sqrt{2} \approx 0.2929$ → SPECTRALLY CERTIFIED. Else → SPECTRAL VIOLATION. Diagnostic only; does not itself block. Layer 5 — Decision Engine: Two strategies: geometric_only ($M_1 \geq \Lambda$) or conservative ($M_1 \geq \Lambda$ AND $M_3 \geq \Lambda$). Hard stop on rejection — no tokens, no partial output, no fallback. Layer 6 — Audit & Hashing: Two SHA-256 digests: deterministic_hash (binds input + all metrics + decision + $\Lambda$) and lambda_audit_hash (certifies $\Lambda$ was computed from the correct prime set). Perfectly reproducible on replay. Section 10 — The Two Metrics ($M_1$ and $M_3$) Confirms there is no $M_2$ in the codebase. $M_1$ is the Decider/Sheriff (geometric, product manifold). $M_3$ is the Watcher (L-EFM-AST spectral alignment, Euler-Fourier-Mellin). Typical gap: $M_1 \approx 0.99$, $M_3 \in [0.75, 0.95]$. The gap reveals the structural distinction between semantic safety and spectral resonance. SVI ranges from low volatility ($<0.05$, resonant) through high volatility ($>0.25$, spectrally silent) to anomalous (negative: $M_3 > M_1$, potent
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Ethics and Social Impacts of AI
Innovation, Sustainability, Human-Machine Systems
Interdisciplinary Studies: Technology, Society, and Humanities
Momodu Mustapha, Susan Konyeha, Akinola Samson Olayinka
This study examines user trust and perception of cryptographic technologies specifically SHA3-512 hashing, SERPENT encryption, and Zero-Knowledge Proofs (ZKP) in the context of centralized Electronic Health Record (EHR) systems. As healthcare institutions increasingly migrate patient data to digital platforms, the security and privacy properties of underlying cryptographic mechanisms have become critical determinants of user confidence and system adoption. Using a quantitative, survey-based design, data were collected from 92 healthcare practitioners, IT professionals, and system administrators actively engaged with EHR systems in Auchi, Nigeria. A Random Forest classifier was trained to predict perceived satisfaction levels (Low, Neutral, High) based on respondents' assessments of cryptographic effectiveness, usability, and trust. Results indicate that trust in ZKP is the strongest predictor of overall perception, followed by confidence in SERPENT encryption and SHA3-512 integrity guarantees. The model achieved a classification accuracy of 63.3% on a held-out test set derived from this exploratory sample, with a Kappa statistic of 0.52 reflecting moderate agreement beyond chance. Balanced accuracy across classes (approximately 0.49–0.50) and low per class sensitivity confirm that the findings should be interpreted as preliminary and directional rather than definitive. Key themes from open ended feedback analyzed using TF-IDF text mining reveal that while respondents broadly recognize the security value of these cryptographic mechanisms, concerns about system slowdown, usability complexity, and insufficient user education present barriers to wider adoption. This study contributes a pilot-level empirical baseline for understanding stakeholder perception of layered cryptographic security in resource-constrained healthcare environments, and highlights the need for larger-scale replication studies. Keywords: SHA3-512; SERPENT encryption; Zero-Knowledge Proofs; healthcare data security; user perception; Electronic Health Records; Random Forest
Data valuation is a foundational task in data marketplaces, where a Shapley-value attribution determines how a buyer's payment is distributed among data providers. Typically, the marketplace operator runs this attribution alone, requiring participants and external auditors to trust scores they cannot independently recompute on the underlying private data. While zero-knowledge proofs (ZKPs) can theoretically reconcile this conflict between privacy and verifiability, existing ZK valuation systems fail to scale to real-world marketplace demands due to prohibitive proving times or the requirement to disclose validation cohorts. We present ZK-Value, a practical, end-to-end ZK data-valuation system. Our solution bridges the scalability gap through a fully co-designed architecture: (1) LSH-Shapley, a locality-based valuation primitive that replaces expensive pairwise distance metrics with per-bucket collision counts; (2) ZK-LSH-Shapley, a tailored ZKP protocol that drastically reduces witness size by encoding these counts into bucket-level histograms rather than naive per-pair tensors; and (3) structural proof-system optimizations, specifically super-oracle batching and sparsity skipping. Evaluated across 12 standard datasets, ZK-Value delivers valuation quality on par with state-of-the-art baselines (within 0.033 AUROC of exact KNN-Shapley), while generating proofs in seconds to minutes and outperforming specialized ZK baselines by 12.6x to 68.1x in proving time, with verification in under 4.6 s.
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A model artifact can be verified on disk without establishing which model is computing at runtime. Trustfall Lite is an open-source command-line tool (Apache-2.0) that scans local Hugging Face and Ollama model caches, computes the SHA-256 of each artifact, and verifies the hash against a signed registry whose records are JWS-signed and verified against a published JWKS. Every artifact resolves to one of four statuses: verified, unknown_variant, not_enrolled, or pilot_available. The tool runs locally; model bytes are not transmitted, and file paths and filenames are not sent to the verification API. By default, artifact hashes may be queried against the Fall Risk API; --local-only verifies against a cached registry without network lookup. This note describes what artifact-level verification establishes, where it stops, and how it relates to the runtime structural identity measurement developed across the Fall Risk Research program. Artifact verification is necessary but not sufficient: the same SHA-256 can serve different runtimes, models can be loaded over the network without touching disk, and disk-time identity does not guarantee runtime identity. The boundary between these two evidence classes — file-level and runtime — is the subject of this note. The Neural Network Identity Series — Mathematical foundations, empirical validation, and governance frameworks for verifying which model is running Newest addition: Technical Note: The Disappearing Window — AI Logprob Access Withdrawal and the Structural Verifiability of Frontier Model Contracts (DOI: 10.5281/zenodo.20362098) 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) 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).
Financial software sits at the center of modern economic infrastructure, yet the programming languages used to build it provide no formal guarantees about the semantic correctness of financial operations. Double-entry bookkeeping’s duality constraint, the rule that every economic event must produce balanced inflows and outflows, is universally encoded at the application layer, where it can be omitted, miscoded, or deliberately bypassed. No existing compiled programming language includes a type rule for accounting duality. This paper presents Equis, a compiled, self-hosting systems language that elevates the Resource–Event–Agent (REA) model to first-class syntactic constructs and enforces accounting duality as a static, compile-time invariant. The compiler rejects any event declaration whose flow block is not balanced before emitting a single instruction of LLVM IR. Equis uses fixed-point 64-bit integer arithmetic scaled by 106 throughout, eliminating IEEE 754 accumulation error from financial code paths entirely. Memory management relies on automatic reference counting with a resource-state borrow checker, giving deterministic, GC-pause-free behavior in long-running settlement services. The compiler is self-hosted, bootstrapped from ANSI C, and verified via Diverse Double Compilation to address Thompson’s trusting-trust problem. Contributions include the formal duality typing rule and its soundness proof, the full REA primitive syntax integrated into a systems language, role-based access control enforced statically at the agent-type level, an append-only ledger primitive with compensating-transaction semantics, and a 20-module standard library covering collections, ledger management, accounting, compliance, database access, HTTP, channels, and fibers. Equis is, to the author’s knowledge, the first compiled general-purpose language to embed REA semantics in its type system. Compile-time duality enforcement eliminates an entire class of financial logic errors with zero runtime overhead.
Yongming Zhang, Chaoyue Li, Lei Liu, Yangjun Sun · 5 authors
Cooperative V2X is evolving toward city-scale deployment, yet current infrastructures still lack a network substrate that jointly provides cross-domain trust, low-latency finality, and privacy-preserving, auditable evidence for safety-critical decisions. This paper proposes a federated-trust sharded blockchain that turns heterogeneous vehicular and roadside measurements into accountable records and enables real-time forensic collaboration and secure data sharing across operators and city management authorities. A federated trust oracle fuses GNSS, OBD, IMU, RSU observations, and device attestations into uncertainty-aware scores that steer committee election, voting weights, and traffic shaping in each shard. On this basis, we design a hybrid cross-shard commit protocol with adaptive finality, combining atomic channels for forensic-critical transactions and optimistic channels for routine collaboration, and we establish safety/liveness conditions and provide proof sketches under the stated partial-synchrony assumptions and bounded collusion. For the forensic layer, a two-stage pipeline anchors minimal sufficient evidence with sub-second local finality, while editable proofs built on traffic-aware extended Merkle trees and zero-knowledge attestations support publicly verifiable, legally compliant edits with \(O(\log n)\) verification overhead. An SLA-aware, learning-assisted scheduler adapts committee size, batching, and cross-shard parallelism to dynamic traffic and attack patterns so as to meet latency, throughput, and rollback targets. Large-topology containerized emulation on a dedicated workstation, complemented by a small hardware-in-the-loop testbed, shows that the proposed framework achieves sub-second forensic anchoring and 95th-percentile cross-shard finality below \(1.2\) s. Across the representative baselines used in this study, it improves effective throughput by up to \(35\%\) ; in particular, at comparable \(L_{p95}\) , it achieves \(1.6\) – \(2.3\times \) higher TPS than the single-chain HotStuff baseline under the tested emulation conditions, while reducing rollback rate and per-event bandwidth by up to \(40\%\) and \(25\%\) , respectively. These results indicate that the proposed system can shorten incident response, strengthen accountability in crash investigations and recalls, and provide a practical foundation for privacy-preserving, transparent data collaboration between mobility operators and urban management departments.
AI hallucination is a cost problem, not a knowledge problem. This paper documents that three sentences of prompt-level instruction — IDK+COMP: a compression mandate paired with a refusal permission — reproduce hallucination suppression matching or exceeding a full multi-constraint methodology across three frontier AI models. Preliminary results: Gemini — 6.3% hallucination rate (Baseline 57.5%). ChatGPT — 0.0% (Baseline 22.2%). Claude — 0.0% on both. The paper establishes hallucination as a utility-maximizing response to a cost structure that makes confident invention cheaper than refusal. Change the cost structure at the prompt level — without touching the model, without retraining, at near-zero cost — and the behavior changes. IDK is load-bearing. COMP (the compression mandate) is the environment in which it operates. Secondary findings: hedging is not a mitigation — it is a co-symptom of unresolved uncertainty, and this dataset moves the hedge-hallucination relationship in both directions depending on directive design. Plausibility-trap strings (SPLAM, Vandermeer Effect) expose the limit of cost-structure interventions: the model cannot recognize the unrecognizable. 410 trials. Three frontier AI models. Five governance conditions. Proof-of-concept dataset; results are directional.
The integration of Internet of Things (IoT) technologies into precision agriculture has transformed farming practices through real-time monitoring, automated irrigation, and data-driven decision-making. However, centralized access control mechanisms remain vulnerable to single points of failure, scalability issues, and security breaches such as spoofing and replay attacks. To address these challenges, this paper proposes a decentralized blockchain-based framework for access management in agricultural IoT environments. The architecture integrates IoT devices, edge gateways, smart contracts, and a distributed ledger to provide secure identity management, automated authorization, and immutable audit trails. Role-Based Access Control (RBAC) and Attribute-Based Access Control (ABAC) are combined to enable context-aware authorization, while Zero-Knowledge Proofs (ZKPs), Multi-Factor Authentication (MFA), and end-to-end encryption strengthen resilience against identity theft and unauthorized access. Performance evaluation demonstrates a$\mathbf{6 5 \%}$reduction in authentication latency and an access control accuracy of 98.5 %, confirming the framework's scalability and security effectiveness for large-scale precision agriculture deployments.
Abstract – The rapid proliferation of blockchain-based decentralized applications has introduced critical security challenges ranging from vulnerable smart contracts to privacy leakage in on-chain transactions. Existing tools address these challenges in isolation, leaving practitioners to integrate disparate solutions. OmniShield is a unified, open-source blockchain security platform that consolidates AI-powered smart contract vulnerability scanning, zero-knowledge proof (ZKP) private transfers, and a private QBFT consensus network into a single cohesive system. The scanner combines static pattern analysis, symbolic execution, and a Gemini-LLM reasoning layer to detect reentrancy, integer overflow, access-control flaws, and twelve other vulnerability classes with severity ratings. Private transfers leverage Groth16 zk-SNARKs over Circom circuits so balances remain hidden on-chain while cryptographic validity is enforced. The underlying network runs on Hyperledger Besu with QBFT consensus, providing Byzantine-fault-toleran block production. Experimental results show the scanner correctly identifies known vulnerabilities in benchmark contracts, ZKP proof generation completes in under 15 seconds on consumer hardware, and end-to-end private transfers finalize within two consensus rounds. OmniShield demonstrates that enterprise-grade blockchain security can be packaged as an accessible, developer-friendly platform. Key Words: blockchain security, smart contract analysis, zero-knowledge proofs, zk-SNARKs, Hyperledger Besu, QBFT consensus, AI vulnerability scanner, reentrancy, Solidity.
Anonymous digital credentials allow a user to prove possession of an attribute that has been asserted by an identity issuer without the user revealing any extra information about themselves. For example, a user who has received a digital passport credential can prove their “age is <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mo>></mml:mo> <mml:mn>18</mml:mn> </mml:mrow> </mml:math> ” without revealing any other attributes such as their name or date of birth. Despite their clear application to privacy-preserving authentication, anonymous credential schemes have been difficult to deploy at scale. Part of the difficulty arises because schemes in the literature, such as BBS+, use new cryptographic primitives that require system-wide changes to existing issuer infrastructure. In addition, issuers often require digital identity credentials to be device-bound by incorporating the device’s secure element into the presentation flow. As a result, schemes like BBS+ require updates to the hardware on every user's device. We propose new ZK techniques which enable the construction of an anonymous credential scheme for the legacy Elliptic Curve Digital Signature Algorithm (ECDSA) signature scheme. By adding efficient ZK arguments for statements about SHA-256 and document parsing for ISO-standardized identity formats, we construct the first ZK proof of posession of a credential that can be deployed without changing any issuer processes, without changes to mobile devices, and without requiring non-standard cryptographic assumptions. Furthermore, our proof system itself only relies on SHA-256 as its complexity assumption. Producing ZK proofs about ECDSA signatures has been a bottleneck for other ZK proof systems because standardized curves such as P256 use finite fields which do not support efficient number theoretic transforms. We overcome this bottleneck by designing a ZK proof system around sumcheck and the Ligero argument system, by designing efficient methods for Reed-Solomon encoding over the required fields, and by designing specialized circuits for ECDSA. Our proofs for ECDSA can be generated in as little as <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mo>≈</mml:mo> <mml:mn>20</mml:mn> </mml:mrow> </mml:math> ms. When incorporated into a fully standardized identity protocol such as the ISO MDOC standard, our system can generate a zero-knowledge proof for the MDOC presentation flow in a few hundred ms on mobile devices. These advantages make our scheme a promising candidate for privacy-preserving digital identity applications.
The intersection of Artificial Intelligence (AI) and distributed systems has given rise to Federated Learning (FL), a paradigm that enables decentralized model training without compromising local data privacy. As organizational data silos grow, deploying complex machine learning models across highly distributed edge networks becomes a critical infrastructural challenge. Standard FL implementations suffer from severe vulnerabilities related to adversarial gradient updates and computational bottlenecks at the aggregation layer. This paper presents a novel, end-to-end distributed architecture that hardens FL pipelines using advanced cryptographic verification and optimized big data processing frameworks. We introduce a Zero-Knowledge Proof (ZKP) wrapper that cryptographically validates node computations before global aggregation, neutralizing model poisoning attacks without inspecting raw gradients. Additionally, we evaluate the system's performance using extreme gradient boosting models optimized for distributed edge execution. We formalize the mathematical transformation of the machine learning loss functions into Rank-1 Constraint Systems (R1CS) suitable for succinct verification. Extensive experimental results demonstrate that our hybrid architecture achieves a 94.2\% accuracy retention under adversarial conditions while maintaining scalable throughput across 1,000 parallel distributed nodes, effectively bridging the gap between rigorous cryptographic security and high-performance distributed AI.
AI hallucination is a cost problem, not a knowledge problem. This paper documents that three sentences of prompt-level instruction — IDK+COMP: a compression mandate paired with a refusal permission — reproduce hallucination suppression matching or exceeding a full multi-constraint methodology across three frontier AI models. Gemini: 6.3% hallucination rate (Baseline 57.5%). ChatGPT: 0.0% (Baseline 22.2%). Claude: 0.0%. The paper establishes hallucination as a utility-maximizing response to a cost structure that makes confident invention cheaper than refusal. Change the cost structure at the prompt level — without touching the model, without retraining, at near-zero cost — and the behavior changes. IDK is load-bearing. The compression mandate is the environment in which it operates. Secondary findings: hedging is not a mitigation — it is a co-symptom of unresolved uncertainty, and this dataset moves the hedge-hallucination relationship in both directions depending on directive design. Plausibility-trap strings (SPLAM, Vandermeer Effect) expose the limit of cost-structure interventions: the model cannot recognize the unrecognizable. In a plausibility-trap domain, IDK+COMP is worse than nothing. 410 trials. Three frontier AI models. Five governance conditions. Proof-of-concept dataset; results are directional. Companion resources: Kowalski et al. (2026a), A Puma in a Teacup: Signal Quality and Hallucination Suppression Through Prompt-Level Incentive Restructuring. https://doi.org/10.5281/zenodo.19502460 Kowalski, M. M. and Claude (Anthropic). (2026). Taxonomy of AI Bullshit: hallucination and hedging subcategories. Zenodo. https://doi.org/10.5281/zenodo.20631337. Kowalski, M. M. & Claude (Anthropic). (2026). Hallucination Test Suite and Execution Records: test strings, activation blocks, trial data and AI transcripts. Zenodo. https://doi.org/10.5281/zenodo.21325014.
AI hallucination is a cost problem, not a knowledge problem. This paper documents that three sentences of prompt-level instruction — IDK+COMP: a compression mandate paired with a refusal permission — reproduce hallucination suppression matching or exceeding a full multi-constraint methodology across three frontier AI models. Gemini: 6.3% hallucination rate (Baseline 57.5%). ChatGPT: 0.0% (Baseline 22.2%). Claude: 0.0%. The paper establishes hallucination as a utility-maximizing response to a cost structure that makes confident invention cheaper than refusal. Change the cost structure at the prompt level — without touching the model, without retraining, at near-zero cost — and the behavior changes. IDK is load-bearing. The compression mandate is the environment in which it operates. Secondary findings: hedging is not a mitigation — it is a co-symptom of unresolved uncertainty, and this dataset moves the hedge-hallucination relationship in both directions depending on directive design. Plausibility-trap strings (SPLAM, Vandermeer Effect) expose the limit of cost-structure interventions: the model cannot recognize the unrecognizable. In a plausibility-trap domain, IDK+COMP is worse than nothing. 410 trials. Three frontier AI models. Five governance conditions. Proof-of-concept dataset; results are directional. Companion paper: Kowalski et al. (2026a), "A Puma in a Teacup: Signal Quality and Hallucination Suppression Through Prompt-Level Incentive Restructuring." https://doi.org/10.5281/zenodo.19502460
While public blockchains provide transparent and auditable transaction histories, they inherently compromise user privacy. Existing privacy-enhancing protocols, such as those deployed on Ethereum, typically rely on succinct zero-knowledge proofs (zk-SNARKs) to obscure the transaction graph. However, implementing comparable cryptographic guarantees on high-throughput blockchains like Algorand is challenging due to strict per-call execution budgets and the state contention introduced by global Merkle accumulators. This paper presents Obscura, a decentralized, non-custodial privacy protocol tailored for constrained smart contract environments. Obscura achieves transaction anonymity using Linkable Spontaneous Anonymous Group (LSAG) signatures over the BN254 elliptic curve, verified entirely on-chain. To overcome limitations of the Algorand Virtual Machine (AVM), we introduce a novel state model that leverages Algorand's Box Storage for $O(1)$ commitment membership checks, eliminating the need for global Merkle accumulators, and a dynamic opcode-budget expansion mechanism via pooled inner application calls. Our implementation demonstrates that signer-ambiguous privacy is practical and efficient on Algorand without relying on trusted setups or succinct proofs. Obscura provides a robust privacy layer for transparent ledgers, bridging the gap between high-throughput blockchain architectures and the dual requirements of cryptographic privacy and selective auditability.
The rapid expansion of Artificial Intelligence Data Centers (AIDC) faces severe physical constraints, notably the linear O(n) scaling of power consumption, cooling requirements, and latency. In this paper, we propose the Lattice Swarm Protocol, a paradigm shift in distributed edge computing utilizing an O(1) constant memory architecture combined with the Virtual-to-Materialization (V2M) engine. We mathematically demonstrate that when interconnected via high-speed 400G/800G optical networks, multiple 1MW ultra-low-power edge nodes do not compute independently. Instead, they share Spatiotemporal Environmental Hashes across a 9192-D Lattice network. This mechanism exponentially reduces the computational load of the entire network as node count increases, creating a single 300MW-equivalent "Hyper-Organism" from merely 30 distributed 1MW nodes. We empirically validate this architecture through the implementation of zero-latency Stateless Custody protocols and interstellar acoustic materialization (Voyager 1), both audited by Google DeepMind Antigravity. This infrastructure establishes a new global standard for Autonomous Driving and Urban Air Mobility (UAM).Version 2 Update: Integrated Zero-Knowledge Proof (ZKP) mechanisms and Stateless Key Vaporization (0.024s), aligned with KIPO Patent No. 10-2026-0079266.
We introduce a new polynomial-time graph invariant combining three complementary components: (1) the enriched Bipartite Double Cover (BDC) vertex cover signature; (2) the Laplacian eigenvalue spectrum; and (3) the K_4 clique profile and Ollivier-Ricci curvature. We demonstrate that while spectral and standard BDC methods collapse on strongly regular graphs (SRGs) due to extreme symmetry, the geometric and dense-topological components of this hybrid invariant break the cospectrality. Specifically, the invariant successfully discriminates the classical cospectral pair Shrikhande vs. Rook(4,4) [SRG(16,6,2,2)] in polynomial time, where Shrikhande is K_4-free and Rook(4,4) contains exactly 8 cliques. Furthermore, empirical validation on low-power ARM Edge hardware demonstrates that the invariant scales efficiently, processing dense Paley graphs (up to N=97) in under 5 milliseconds. This confirms its sub-millisecond viability for real-time edge computing, zero-knowledge proofs (ZKPs) cryptanalysis, and cheminformatics.
This paper is the authoritative technical documentation of immo.quick Core version 2.1.0. It introduces and formally specifies the Deterministic Execution Proof Engine (DEPE) — the overarching orchestration layer that unifies five interlocking architectural components (Prior Admissibility Space, Exogenous Anchor Protocol, Sensor/Oracle Trust Bridge, Bi-Temporal Ledger, Machine Law Engine) into a single, unbroken, cryptographically provable execution corridor. Every transaction processed by DEPE produces an Execution Proof Artifact (EPA): a self-contained, externally verifiable, court-admissible proof object that the transaction was evaluated correctly under the rules applicable at the moment of execution. The EPA is not a log entry or a summary — it is a formal proof object that either verifies completely under the 6-step DEPE Verification Protocol, or does not verify at all. Version 2.1.0 introduces seven architectural advancements over v2.0.0: DEPE (Deterministic Execution Proof Engine): The integration layer producing a single signed EPA per transaction, cryptographically binding all five architectural layer outputs into an indivisible unit. EPA signature scheme: CRYSTALS-Dilithium-3 (NIST PQC standard). EPA generation latency: <100ms median. External verification latency: <50ms. JPO Pre-Fill Protocol: Reduces regulatory update latency for announced changes from 34ms to under 5ms by proactively compiling and staging rules upon legislative announcement, enabling millisecond-precision atomic swap at the effective date. Checker Rotation Governance (Six-Eye Principle): Formalizes a third independent checker drawn from a rotating governance pool for high-value and high-risk transactions. Rotation is deterministic (hash-based), requires no human discretion, and is itself bi-temporally logged and attested. Bypass requires simultaneous compromise of three institutionally separated hardware devices. BFT Quorum Specification: Formalizes Byzantine Fault Tolerance for the Bi-Temporal Ledger at f ≤ ⌊(n−1)/3⌋. Production configuration: n=7, f=2, quorum=5. Record commitment latency: 4ms median. Merkle replication lag: 12ms median. Deny Path Artifact (DPA): Every BLOCK decision generates a signed, immutable DPA specifying the exact gate condition, rule reference, and structural reason for rejection. Courts, regulators, and counterparties can independently verify not only that a transaction was blocked, but precisely why — with cryptographic proof. ZKP Circuit Library v2: Expanded to 47 pre-compiled, formally verified zero-knowledge proof circuits across banking/capital, AML/KYC, DORA/ICT, privacy/data, real estate, cross-border, and regulatory filing categories. All circuits use Groth16 and PLONK proving systems and are integrated directly into the Machine Law Engine compilation pipeline. Known Patterns Extension Protocol (KPEP): Enables ~70% acceleration for registered common transaction classes via formally verified proof templates, without any security reduction. Template match failure triggers automatic fallback to the full standard path. Additional v2.1.0 enhancements: ACASP Second-Order Anomaly Detection (ambiguity itself is a blocking condition); EAP dual-channel heartbeat with gap tolerance tightened from 50ms to 35ms; Offline Receipt Export for self-contained external verification without live system dependency. Central architectural guarantee (unchanged and strengthened): immo.quick Core is the only production architecture providing a complete, unbroken, cryptographically enforced provenance chain from the moment of physical real-world observation through the enforcement gate — with formally guaranteed zero false approval rate (Closed-World Assumption), formally guaranteed temporal accuracy (Bi-Temporal Ledger + BFT Quorum), and — as of v2.1.0 — a fully machine-verifiable Execution Proof Artifact for every transaction ever processed. This paper provides full formal specifications (TLA+/Z3 style), three detailed institutional case studies (DORA Art.11 ICT incident gate; cross-border real estate acquisition with §203 StGB / CLOUD Act conflict resolution; FATF Travel Rule enforcement with ZKP-selective disclosure), complete measured production performance data, and a complete attack surface analysis covering nine adversarial vectors including DEPE integration hash forgery and ACASP ambiguity injection. Supersedes: v2.0.0 (April 2026, DOI 10.5281/zenodo.19799660).
The growing digitalisation of the tourism sector has led to increased vulnerability to cross-border cybercrime, exposing gaps in international legal cooperation. This study examines the legal and jurisdictional challenges in collecting and admitting digital evidence in tourism-related cybercrime. It analyses key international frameworks, including the Budapest Convention, UNTOC, and the EU-US Data Privacy Framework, highlighting conflicts in data sharing and evidence admissibility. Case studies such as Marriott, British Airways, and MakeMyTrip reveal inconsistencies in cross-border investigations. The paper also explores the role of blockchain and zero-knowledge proofs in improving evidence integrity, while raising concerns over privacy rights under ICCPR and ECHR. Findings suggest the need for legal harmonisation, streamlined evidence-sharing procedures, and enhanced forensic capabilities to strengthen cybercrime response in the tourism industry.