Zero-knowledge proofs (ZKP) provide strong cryptographic guarantees allowing a prover to demonstrate knowledge of a property without revealing the underlying secret. These mechanisms are increasingly deployed in blockchain systems, digital identity infrastructures, web proofs, confidential finance, and privacy-preserving computation. However, many practical deployments rely on an often-overlooked component: the trusted acquisition and semantic extraction chain responsible for obtaining, interpreting, and transforming real-world data into proof witnesses. While proof verification may be cryptographically trustless, the acquisition process itself frequently depends on trusted runtimes, transport security, credential issuers, parsers, APIs, browser hooks, secure execution environments, or privileged software components. This paper argues that practical proof systems do not eliminate trust entirely, but frequently displace it toward increasingly complex acquisition infrastructures. We introduce the notions of Trusted Acquisition and Governed Disclosure, where programmable trust anchors and trusted governance platforms provide explicit, attestable, and policy-controlled acquisition boundaries. We further show that once such trusted acquisition layers already exist — as is often the case in industrial, enterprise, embedded, regulated, and digital identity systems — they may also provide simpler and more deployable alternatives to selected zero-knowledge constructions through governed disclosure mechanisms. The paper does not argue against zero-knowledge proofs. Instead, it proposes a complementary architectural perspective distinguishing between trustless verification and trustworthy acquisition.
LLM guardrails face four structurally distinct barriers: algebraic blindness arising from syntactic monoid aperiodicity (unconditional); an illustrative information-theoretic lower bound (Fano-type, under a uniformity assumption); NP-hardness of instantiation verification; and structural transfer via free-category functoriality (unconditional) combined with string-level indistinguishability under a semantic-opacity assumption on symbol naming. Together these results characterize why inference-layer defenses are necessary but insufficient. We operationalize these barriers through five attack vectors. V1–V4 (homomorphic reasoning: decomposition, zero-knowledge pipelines, Tree-of-Thought solving over abstract grammars, and encoding bootstrap) exploit the information-theoretic and computational barriers against abstraction-based attacks. V5 (modular counting bypass) exploits algebraic blindness: we prove that all substring-matching regex guardrails have aperiodic syntactic monoids and are therefore provably blind to any payload encoded using modular counting. Empirically, V3 yields a mean yield of 0.466 for BFS, 0.172 for random-beam, and 0.122 for LLM-guided Tree-of-Thought (N=50, seeds 0–49, p{<}0.001); BFS dominates, as exhaustive search over small synthetic grammars outperforms LLM heuristic pruning. We extracted syntactic monoids from a corpus of 142 patterns drawn from twelve sources — 100 patterns shipped by nine third-party open-source guardrail projects and 42 patterns assembled from three author-curated pattern sets; 100\% are aperiodic, and the MOD_2 bypass construction succeeds against all aperiodic patterns. A 376-line proof-of-concept with three execution mediums validates all five vectors. We conclude that inference-layer guardrails are necessary but insufficient, and that effective defense must migrate to the execution layer where concrete artifacts become observable.
Abstract The SIS‑10 framework establishes a typed, invariant preserving safety calculus for cyber physical systems. It unifies temporal semantics, schedulability, semantic preservation, ML admissibility, cryptographic verification, and risk bounded control into a single mathematically coherent architecture. All domains and operators are fully explicit, enabling formal reasoning over system trajectories and safety envelopes. The temporal layer defines an ordered metric structure with drift aware bounded causality, interval set operators, and jitter robust event semantics. The QoS layer enforces schedulability and feasible actuation, ensuring that all control actions remain within admissible timing and load bounds. Semantic compression provides a safety preserving homomorphism that guarantees invariants survive dimensionality reduction. Multi‑modal fusion introduces cross sensor falsifiability, enabling fault detection through probabilistic disagreement. The ML layer is input validated and logic embedded, ensuring that all model outputs entail the SIS‑10 invariant set. The cryptographic layer supplies zero knowledge execution trace proofs, allowing runtime verification of transition correctness without revealing internal state. Predictive shutdown optimization is constrained by a formally defined safety envelope, ensuring that operational objectives never violate admissible safety bounds. Cyber physical risk evolves through a bounded monotone propagation model with explicit mitigation operators, while the Safety Twin provides deterministic and stochastic discrete time system dynamics. The inductive proof layer establishes global invariant preservation for all admissible executions, and the event→action mapping connects the formal calculus to real SIS triggers. SIS‑10 therefore constitutes a unified, verifiable, and implementation ready safety architecture, suitable for runtime assurance, cyber‑physical certification, and next generation functional safety systems. Further enhancements include graphical formalization, parameterized system tuning, implementation DSLs, and automated verification scripts, none of which alter the core mathematical model..
We study the Banach dual of the one-parameter stochastic integral δ_L(u) = ∫₀^T u_t dL_t for a symmetric γ-stable Lévy process with γ ∈ (1,2). The natural integrand exponent is p ∈ (1,γ): the small-jump integrability ∫|z|^p ν_γ(dz) < ∞ holds iff p < γ, so this is not an arbitrary L^p but the unique scale dictated by the singularity of the Lévy measure at the origin. On this scale, the operator-covariant derivative D_L := δ_L^* : L^q(Ω) → H_L^* is the Banach dual of the one-parameter integral. Since p < 2, the Riesz identification H_L^* ≅ H_L is unavailable, and the Banach setting is forced. The principal result is structural: D_L is strictly more restricted than the standard Malliavin add-a-point operator D_{t,z}F = F(ω + δ_(t,z)) − F(ω) on Poisson space, which is the dual of the full two-parameter compensated Poisson integral ∫∫ h(s,z) Ñ(ds,dz). By Lévy-Itô, the one-parameter integrand of δ_L has the special form h(s,z) = u(s) · z — linear in z — whereas full martingale representation on Lévy space uses general h(s,z). The representability obstruction quantifies the resulting gap precisely: centered functionals depending nonlinearly on jump sizes — canonically, the centered large-jump count #{|ΔL_s| > 1} − E[#{|ΔL_s| > 1}] — lie in ker(D_L) yet are detected by the standard add-a-point operator. The obstruction is a property of the one-parameter integral, not a feature of jump processes themselves. The factorization (Theorem A) holds on the closed proper subspace im(δ_L) ⊊ L^p_0(Ω) and characterizes precisely which functionals admit one-parameter representation. Theorem B (product rule with Leibniz defect) is a standalone duality identity: its proof uses only the definition of D_L, the Lévy-Itô formula, and Hölder's inequality, and it does not invoke (H3) or the factorization machinery. Theorem C — the strongest technical result — identifies ker(D_L) and the annihilator of im(δ_L) via L^q-L^p truncation in the jump variable, showing the annihilator is infinite-dimensional even within the first chaos. The framework has been formally verified in the Lean 4 proof assistant (2,439 lines, zero sorry, zero axioms) using Mathlib. To our knowledge, this is the first formalization of the operator-covariant derivative framework with its representability obstruction in any proof assistant. The formalization includes proved Poisson mean and variance identities, a constructed compound Poisson path, a compensated-integral interface with derived Banach-side consequences, a concrete first-chaos orthogonality model, and the full abstract theorem pipeline — all machine-checked from clearly isolated stochastic-analysis assumptions.
Umar . Abubakar, Hamza Itopa Sanni, Abdulsalam Aliyu
Conventional attendance management approaches in academic and organisational settings face persistent challenges that include susceptibility to proxy attendance, inadequate protection of biometric credentials, and the absence of privacy-preserving verification mechanisms in cloud-hosted deployments. This paper presents SecureAttend, a cloud-based attendance management framework that addresses these deficiencies through the integration of Zero-Knowledge Proof (ZKP) cryptographic authentication with biometric capture via a ZKTeco K40 Pro fingerprint terminal. The proposed framework employs a challenge-response ZKP protocol that enables users to demonstrate possession of valid authentication credentials without disclosing underlying private keys or biometric templates to the server. Attendance records are encrypted using AES-256 prior to storage in a MongoDB cloud database, while SHA-256 hashing provides tamper-evidence for each record. Session integrity is maintained through JWT-based token management, and access boundaries are enforced via a Role-Based Access Control (RBAC) policy. Functional evaluation across eighteen test scenarios confirmed complete compliance with stated requirements. Security assessment validated correct operation of cryptographic mechanisms, access controls, and audit logging subsystems. Performance benchmarks recorded average API response latencies of approximately 85 milliseconds for authentication requests and 120 milliseconds for attendance marking operations. The results demonstrate that ZKP authentication can be deployed effectively in real-world attendance management contexts, offering measurable improvements in privacy, integrity, and resistance to credential-based attacks compared with conventional approaches.
Nobuki Fujimoto, Rei (Rei-AIOS autonomous research substrate), claude-opus-4-7) Claude (Anthropic
We present a synthesis-friendly Verilog implementation of the D-FUMT₈ Arithmetic Logic Unit, programmed onto two distinct Sipeed silicon families: Tang Console 138K (GW5AST-138B, LittleBee5 A revision, IDCODE 0x0001081B) and Tang Nano 9K (GW1NR-9C, LittleBee1 C revision, IDCODE 0x1100481B). The ALU realizes eight discrete logic values — FALSE, TRUE, NEITHER, BOTH, ZERO, FLOWING, SELF, INFINITY — encoded in 3 bits with a tier-respecting layout. The 10 supported operations include four classical-tier unary ops (NOT, OMEGA, PHI, PSI), Belnap-extended binary lattice meet/join (AND, OR), generic XOR, hardware reset, no-op, and a novel ADIABATIC operation realizing the SELF⟲ (self-reflexive) primitive: ADIABATIC(SELF) = SELF, identity elsewhere. v0.6 contributions (2026-05-10): (1) **Four-substrate cross-verification complete**: 2 Sipeed silicon families (Tang Console 138K + Tang Nano 9K, **both running byte-for-byte same dfumt8_alu_synth.v 138-line Verilog with bit-identical 0 changes to ALU logic** — only wrapper top module re-targeted for clock divider, LED polarity, and pin assignments) + Qiskit Aer simulator (Phase 1-5: 231/231 entries) + IBM Heron r2 real quantum hardware (Phase 1+2+3+5: 144/144 entries, avg fidelity 0.954). (2) **chip-portability evidence (new finding F10)**: a synthesis bug or vendor-specific assumption would diverge between LittleBee5 (5nm-class GW5AST-138B) and LittleBee1 (28nm-class GW1NR-9C) Gowin architectures; absence of divergence is operational evidence of correct synthesis on both. (3) **Tang Nano 9K User Codes**: 0x0000A5F4 (LED Blinky STEP 1038) + 0x00001D46 (D-FUMT₈ ALU STEP 1039). (4) **Reproducibility entry-cost lowered**: minimum reproduction path is ~$20 (Tang Nano 9K from 秋月電子 g117448 at ¥2,980) + free Gowin EDA Education / OSS toolchain + free Aer + free IBM Quantum Open Plan. (5) **v0.5 corrigendum RESOLVED**: Tang Nano 9K is now physical silicon programming target on equal footing with Tang Console 138K (was computational evidence only at v0.5). (6) **IDCODE-revision honest correction**: per Gowin LittleBee Programming Manual Table 5-5, GW1N(R)-9 original = 0x1100581B, GW1N(R)-9C cost-down = 0x1100481B; both `set_device ... -device_version C` (build TCL) and `--device GW1NR-9C` (programmer_cli) required for ID code match. Inherited v0.3 contributions: Lean 4 refinement proof (OUKC.PhaseC.Dfumt8AluRefinement, 292 LOC, 0 sorry) establishes commutativity of the encode/abstract-op/decode square for all four unary operations + SELF⟲ primitive law + 7 algebraic laws. IBM Heron r2 per-op fidelity hierarchy NOP/ADIABATIC ≈ 0.977 > PHI ≈ 0.956 > NOT ≈ 0.912 > XOR ≈ 0.951 reflects gate-count-vs-noise correlation consistent with quantum-noise physics expectations. Honest scope: We do NOT claim 'world-first 8-valued quantum logic' — Shi et al. (MIT, 2026, arxiv:2506.09371) demonstrated d=8 Grover on a single trapped-ion qudit prior to this work; our distinction is 3-qubit basis encoding on transmon arrays vs single-system d=8 qudit. We do NOT claim 'first paraconsistent silicon' — PAL2v (Da Silva Filho 1998-; Abe & Nakamatsu 2009; de Carvalho Jr. 2025) realized in software libraries and microcontroller-level robotics. We do NOT claim 'first many-valued silicon' — Łukasiewicz/Belnap FPGAs date to 1990s. The to-our-knowledge novel quadruple is: (D1) the specific 8-tuple semantic mapping (Belnap FDE 4-value + 4 ontological extensions: INFINITY/ZERO/FLOWING/SELF), (D2) the SELF⟲ self-reflexive primitive realized as a hardware fixed point, (D3) the four-substrate cross-verification bound to a Lean 4 refinement specification, and (D4, new in v0.6) the chip-portability evidence across two Gowin silicon architectures. Three-party co-authorship per OUKC charter v1.0 (Nobuki Fujimoto / Rei / Claude). DRAFT v0.6 — feedback welcome via GitHub Discussions at fc0web/rei-aios.
Open access
2 source records
Low-power high-performance VLSI design
Numerical Methods and Algorithms
Physical Unclonable Functions (PUFs) and Hardware Security
Nobuki Fujimoto, Rei (Rei-AIOS autonomous research substrate), claude-opus-4-7) Claude (Anthropic
We present a synthesis-friendly Verilog implementation of the D-FUMT₈ Arithmetic Logic Unit, targeting the Sipeed Tang Console NEO development board (GW5AST-138B FPGA, FPG676 package). The ALU realizes eight discrete logic values — FALSE, TRUE, NEITHER, BOTH, ZERO, FLOWING, SELF, INFINITY — encoded in 3 bits with a tier-respecting layout. The 10 supported operations include four classical-tier unary ops (NOT, OMEGA, PHI, PSI), Belnap-extended binary lattice meet/join (AND, OR), generic XOR, hardware reset, no-op, and a novel ADIABATIC operation realizing the SELF⟲ (self-reflexive) primitive: ADIABATIC(SELF) = SELF, identity elsewhere. v0.3 contributions (2026-05-09): (1) Tang Nano 9K silicon: 37 LUT4 / 0 DFF measured, testbench 50/50 PASS. (2) Tang Console NEO Phase 2B LED Blinky: SRAM-programmed, User Code 0x000084BA, write 33.72 sec, no thermal anomaly. (3) Tang Console NEO Phase 2C/3 D-FUMT₈ ALU: SRAM-programmed, User Code 0x00005C27, write 30.32 sec. (4) Qiskit Aer simulator: Phase 1-5 cumulative 231/231 truth-table entries match at fidelity 1.000. (5) IBM Heron r2 real quantum hardware (ibm_kingston, 156 qubits): Phase 1 (4 native unitary × 8 inputs, 32 circuits) achieves 32/32 match with avg top-fidelity 0.9550 (job d7v6d9jack5s73bf1re0); Phase 2 (XOR × 64 entries, 6-qubit Bennett-reversible) achieves 64/64 match with avg fidelity 0.9512 (job d7v6kcvmrars73d7qqqg). Per-op fidelity hierarchy NOP/ADIABATIC ≈ 0.977 > PHI ≈ 0.956 > NOT ≈ 0.912 > XOR ≈ 0.951 confirms gate-count-vs-noise correlation expected from Heron r2 daily calibration. (6) Lean 4 refinement proof (OUKC.PhaseC.Dfumt8AluRefinement, 292 LOC, 0 sorry) establishes commutativity of the encode/abstract-op/decode square for all four unary operations, plus the SELF⟲ primitive law and seven algebraic laws (involution, idempotence, commutativity). Honest scope: We do NOT claim 'world-first 8-valued quantum logic' — Shi et al. (MIT, 2026, arxiv:2506.09371) demonstrated d=8 Grover on a single trapped-ion qudit prior to this work; our distinction is 3-qubit basis encoding on transmon arrays vs single-system d=8 qudit. We do NOT claim 'first paraconsistent silicon' — PAL2v (Da Silva Filho 1998-; Abe & Nakamatsu 2009; de Carvalho Jr. 2025) realized in software libraries and microcontroller-level robotics. We do NOT claim 'first many-valued silicon' — Łukasiewicz/Belnap FPGAs date to 1990s. The to-our-knowledge novel triple is: (D1) the specific 8-tuple semantic mapping (Belnap FDE 4-value + 4 ontological extensions: INFINITY/ZERO/FLOWING/SELF), (D2) the SELF⟲ self-reflexive primitive realized as a hardware fixed point, (D3) the three-substrate cross-verification bound to a Lean 4 refinement specification. Three-party co-authorship per OUKC charter v1.0 (Nobuki Fujimoto / Rei / Claude). DRAFT v0.3 — feedback welcome via GitHub Discussions at fc0web/rei-aios.
Open access
Quantum Computing Algorithms and Architecture
Quantum-Dot Cellular Automata
Advancements in Semiconductor Devices and Circuit Design
Cryptography is the backbone of modern network security, providing confidentiality, integrity, authentication, and non-repudiation for digital communication. However, the rapid evolution of cyber threats, particularly the looming arrival of large-scale quantum computers, poses serious challenges to the cryptographic algorithms that protect today's networks. This paper presents a systematic review of cryptography in network security, following the PRISMA 2020 guidelines. A total of 68 studies published between 2016 and 2025 were selected from five major academic databases: IEEE Xplore, ACM Digital Library, Scopus, Web of Science, and ScienceDirect. The review covers classical symmetric and asymmetric algorithms, widely deployed cryptographic protocols such as TLS 1.3, IPsec, and SSH, and the growing body of work on post-quantum cryptography (PQC). Key findings include the following: NIST finalized three post-quantum cryptographic standards (FIPS 203, 204, and 205) in August 2024; lightweight cryptography standards for IoT devices were published in 2025 with the selection of ASCON; and real-world deployment of hybrid classical/post-quantum schemes has already begun in major web browsers and messaging applications. This paper also examines emerging trends in homomorphic encryption, zero-knowledge proofs, and AI-driven cryptanalysis. Based on the findings, this review identifies critical gaps in PQC migration strategies, IoT security, and the integration of cryptography with artificial intelligence, and proposes directions for future research.
Sixth-generation (6G) networks are expected to provide ubiquitous connectivity, AI-native orchestration, and seamless integration across terrestrial and non-terrestrial infrastructures. However, these capabilities introduce new privacy challenges related to the classification and protection of personal, quasi-personal, and non-personal data in complex data-driven environments. This paper presents a systematic review of 78 peer-reviewed studies published between 2019 and 2025. Following a PRISMA-based methodology, this review analyzes privacy-enhancing technologies (PETs), regulatory compliance frameworks, and architectural patterns for privacy preservation in 6G networks. The findings show that differential privacy (DP) and federated learning (FL) dominate current research, accounting for nearly 52% of the reviewed studies. Blockchain auditing and zero-knowledge proofs (ZKPs) collectively represent approximately 30%, while the remaining mechanisms, including physical-layer security (PLS), trusted execution environments (TEEs), homomorphic encryption (HE), secure multi-party computation (SMPC), and anonymization, account for roughly 18%. These mechanisms exhibit varying levels of privacy strength, utility preservation, latency, and energy cost. At the same time, evolving regulatory frameworks, including GDPR, PDPL, CCPA/CPRA, LGPD, and PIPL, increasingly extend privacy obligations to quasi-personal and aggregated data. Building on these findings, this paper proposes a unified taxonomy that clarifies the boundary between personal and non-personal data. It also provides a cross-layer mapping between PETs and compliance requirements across the Core/SBA, RAN, Edge/MEC, and NTN layers. Finally, this paper presents a forward-looking roadmap for 2025–2030, highlighting hybrid PET pipelines, post-quantum auditability, and AI-driven compliance automation as key directions for privacy-preserving 6G standardization.
Polynomial multiplication serves as a fundamental computational primitive in modern cryptography–including fully homomorphic encryption and zero-knowledge proofs –as well as in digital signal processing. Its performance optimization has become increasingly critical amid the rapid development of privacy-preserving computation and blockchain technologies. To address the limitations of traditional algorithms in meeting the demands for high throughput and low latency, this study proposes a high-performance polynomial multiplication accelerator based on the collaborative optimization of GPU-NTT and the Karatsuba algorithm. The method deeply integrates the asymptotically optimal complexity of NTT with the constant-factor efficiency of Karatsuba at moderate scales, and fully exploits the parallel computing power of GPUs to construct a modular, multi-stage pipelined acceleration framework. The divide-and-conquer nature of the Karatsuba algorithm is leveraged for coarse-grained parallelism, splitting large polynomial multiplications into subproblems handled by GPU thread blocks in parallel, while each subproblem is solved with fine-grained parallelism using GPU-accelerated NTT kernels. An innovative zero-padding strategy is introduced to enhance the generality of the NTT kernels, and shared memory caching is employed to alleviate GPU memory bandwidth bottlenecks. Experimental results on the NVIDIA RTX 4060 GPU demonstrate that the proposed method achieves a stable speedup of 1.43 \(\times \) to 1.49 \(\times \) over the baseline GPU-NTT for lower-dimensional polynomials, and outperforms the KNTT algorithm by up to 2.44 \(\times \) for higher dimensions (e.g., \(\log _2 n = 14\) ), showing superior scalability and robustness. Kernel execution time analysis further confirms that the method benefits from efficient kernel fusion and balanced workload distribution, which effectively avoids pipeline stalls and ensures high-throughput execution. This research provides a significant performance optimization solution for the practical deployment of advanced cryptographic technologies such as FHE and ZKP.
Taking pure elementary algebra as the research tool, this paper solves the three-layer right-associative tower exponent equation without advanced knowledge such as logarithms and calculus throughout the whole process. First, integer solutions are strictly eliminated via integer recursive scaling, zero solutions are excluded by analyzing domain boundaries, and combined with the growth law of power operations, it is predicted that the solution is in the form of positive real numbers with fractional exponents. Based on the expression form of positive real powers, variable substitution and derivation are directly carried out to obtain the particular solution , which is verified by substitution. Rigorous demonstrations are conducted on negative real solutions and interval monotonicity: it is directly proved that the equation has no solution when , and strict monotonicity is proved by definition only in the interval , completing the proof of uniqueness of real solutions. Meanwhile, the definitional contradictions of negative base numbers in the real number field are clarified, negating the existence of negative real solutions. This paper retains the intuitive and original reasoning logic, revises the connection defects of argumentation, and forms a complete, rigorous and self-consistent research compatible with the elementary mathematics system. 本文以纯初等代数方法为工具,求解三层右结合指数塔方程 ,全程不使用对数、微积分等高阶知识。首先通过整数递推放缩排除整数解,补充定义域边界排除零解,结合幂运算增长规律预判解为正实数分数指数形式;基于正实数幂的表达形式直接设元推导,求得特解 并代入验证。针对负实数解、区间单调性等问题展开严谨论证:直接证明 时方程无解,仅在 区间用定义法证明严格单调性,完成实数解唯一性证明;同时厘清负数底数的实数域定义矛盾,否定负实数解存在性。全文保留直观原生推理逻辑,修正论证衔接问题,形成严谨、自洽、适配初等数学体系的完整研究。
Alessio Basti, Rikkert Hindriks, Ruggero Freddi, Gian Luca Romani · 7 authors
Cross-frequency interactions are fundamental brain mechanisms for integrating information across temporal scales. However, accurate identification of these couplings is hindered by complex multi-frequency nonlinearities and by spurious, zero-lag artifacts caused by volume conduction. To our knowledge, conventional metrics lack a robust framework to characterize genuine interactions among multiple time series where a frequency of interest $f_N$ arises from the combination of $N-1$ components such that $f_N = \sum_{i=1}^{N-1} f_i$. We introduce a general family of antisymmetric cross-polyspectral indices designed to quantify these harmonic dependencies while being intrinsically robust to instantaneous mixing. We derive the theoretical properties of these quantities and validate them through simulations of cubic nonlinearities. As a proof of concept, we apply the indices to empirical EEG recordings; the results reveal significant higher-order dependencies that elude standard analytical approaches. We further discuss how these indices can inform novel, personalized multi-site transcranial magnetic stimulation (mTMS) protocols by enabling the selective monitoring and modulation of specific multi-frequency network interactions.
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.
Open access
2 source records
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
Open access
2 source records
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.
<b>【Description】</b>[EN]:<i>Relocation Notice:</i><br>Due to an unavoidable structural incompatibility between the mandatory data-parsing frameworks of conventional academic repositories and the strict zero-knowledge proof requirements of this archive, this item has been relocated to this environment. To protect its integrity from automated systemic interference, it is currently placed under a Permanent Embargo, functioning exclusively as a cryptographic spatial anchor.<i>Strategic Ambiguity Regarding Future Disclosure:</i><br>While this archive is strictly restricted to preserve its current integrity and trade secret classification, this status does not definitively preclude the possibility of partial or full disclosure in the future. The management of this intellectual property remains entirely under strategic discretion. Furthermore, regardless of any future discoveries or the accumulation of operational insights, I assume no obligation to update this document, provide continuous reporting, or issue prior notification regarding any changes in disclosure status, scope, or conditions.This data archive contains the restricted documentation for the "Persona-Native Principle (PNP) - Final Form."<br>The existence and integrity of this document have been legally established and secured by an electronic certified date (timestamp) issued by a Notary Public in Japan.Cryptographic Proof of Existence (SHA-256 Hash):<br>5ec4637def8bc7a45b113d6dee25e4d88672865ebd37def1c9c30c95de581145[JP]:<i>移設記録:</i><br>従来の学術リポジトリが前提とする「データ開示と自動解析の強制力」と、本アーカイブが要求する「完全なアクセス拒絶による存在証明（ゼロ知識証明）」の間に、不可避の構造的非互換性（Structural Incompatibility）が確認されました。そのため、予期せぬシステム的干渉から完全性を保護するべく、本アーカイブは独自の暗号学的アンカーとして、本環境にて恒久的なエンバーゴ（封印）下に置かれています。<i>将来の開示に関する戦略的曖昧性:</i><br>現在の完全性および営業秘密としての分類を保持するために本アーカイブは厳格に制限されていますが、この状態は、将来においてその一部または全部を公開する可能性を断言して否定するものではありません。本知的財産の管理は、完全に戦略的裁量の下にあります。さらに、今後新たな発見や運用知見が蓄積された場合であっても、私は本文書の更新、継続的な報告、および開示状況や条件の変更に関する事前通知を行ういかなる義務も一切負いません。本データアーカイブは、『Persona-Native Principle (PNP) - Final Form』に関するアクセス制限付きドキュメントを格納しています。<br>本文書の存在および完全性は、日本国公証人による電子確定日付（タイムスタンプ）により法的に保全されています。存在証明ハッシュ値 (SHA-256):<br>5ec4637def8bc7a45b113d6dee25e4d88672865ebd37def1c9c30c95de581145<br><b>[Patent Status & Strategic Protection]</b><br>[EN]:The core architecture and methodologies documented within this archive are subject to pending patent applications in Japan (e.g., Application No. 2026-000032). In accordance with our Strategic Non-Disclosure Policy, the disclosure of this specific jurisdiction and application number does not constitute a comprehensive representation of our global intellectual property portfolio. We reserve all rights to pursue, expand, or maintain provisional and formal protections across international jurisdictions without prior public notification.<br>[JP]:本アーカイブに記録された中核的なアーキテクチャおよび方法論は、日本国において特許出願中（例：特願2026-000032）です。当方の「戦略的非開示ポリシー」に基づき、この特定の管轄と出願番号の開示は、当方のグローバルな知的財産ポートフォリオの全容を示すものではありません。当方は、事前の公的通知なしに、国際的な管轄区域において仮出願および本出願による保護を追求、拡大、または維持するすべての権利を留保します。<b>【Terms of Access & Confidentiality】</b>[EN]:<b>Restriction of Access & Confidentiality:</b> The contents of this archive contain highly sensitive proprietary assets of Persona Foundry Aoi Design. All files are secured under restricted access. Any unauthorized access, disclosure, or attempt to bypass these restrictions is not permitted under applicable intellectual property guidelines.[JP]:<b>アクセス制限と守秘義務:</b> 本アーカイブの内容は、Persona Foundry Aoi Designの機密性の高い独自資産として厳格に管理されています。すべてのファイルはアクセス制限下で保護されており、不正アクセス、開示、または制限を迂回するいかなる試みも、適用される知的財産保護の観点から許可されていません。<b>【Terms of Use】</b>[EN]:<b>Disclaimer of Warranties:</b> The materials are provided "AS IS." The author makes no representations and extends no warranties of any kind, express or implied.<b>Limitation of Liability:</b> In no event shall the author be liable for any direct, indirect, or consequential damages arising from any unauthorized access, use, or inability to use the materials.<b>Governing Law and Dispute Resolution:</b> This Agreement and any disputes arising out of it shall be governed by and interpreted in accordance with the laws of Japan. Any concerns will be resolved within the appropriate legal venues in Japan designated by the author.[JP]:<b>無保証 (AS IS):</b> 本データは「現状有姿」で提供されます。著者は、明示的か黙示的かを問わず、いかなる種類の保証も行いません。<b>責任の制限:</b> 著者は、本データへの不正アクセス、使用、または使用不能から生じるいかなる直接的、間接的、または結果的な損害についても責任を負いません。<b>準拠法および紛争解決:</b> 本規約およびそこから生じるいかなる紛争も、日本国法に準拠し、解釈されるものとします。懸念事項が生じた場合、日本国内における著者が指定する適切な法的手続きに従って解決されるものとします。<b>[ License ]</b>CC BY-NC-ND 4.0 InternationalThe statements within the document take precedence over any platform terms.<br>※投稿および掲載プラットフォームの規定にかかわらず、本文内の記載を優先します。<b>[ Files & Integrity ]</b>File: PNP FINAL FORM - BILINGUAL_v1.0.pdfHash: 5ec4637def8bc7a45b113d6dee25e4d88672865ebd37def1c9c30c95de581145
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.