We study binary decision-making in governance councils of Decentralized Autonomous Organizations (DAOs), where experts choose between two alternatives on behalf of the organization. We introduce an information structure model for such councils and formalize desired properties in blockchain governance. We propose a mechanism assuming an evaluation tool that ex-post returns a boolean indicating success or failure, implementable via smart contracts. Experts hold two types of private information: idiosyncratic preferences over alternatives and subjective beliefs about which is more likely to benefit the organization. The designer's objective is to select the best alternative by aggregating expert beliefs, framed as a classification problem. The mechanism collects preferences and computes monetary transfers accordingly, then applies additional transfers contingent on the boolean outcome. For aligned experts, the mechanism is dominant strategy incentive compatible. For unaligned experts, we prove a Safe Deviation property: no expert can profitably deviate toward an alternative they believe is less likely to succeed. Our main result decomposes the sum of reports into idiosyncratic noise and a linearly pooled belief signal whose sign matches the designer's optimal decision. The pooling weights arise endogenously from equilibrium strategies, and correct classification is achieved whenever the per-expert budget exceeds a threshold that decreases as experts' beliefs converge.
As blockchain continues to saturate into a technological infrastructure for decentralized trust and cost-effective data processing, existing consensus mechanisms remain burdened by immutable issues with scalability, energy inefficiency, and adaptive security. This is an alternative to conventional Proof of Work (PoW), Proof of Stake (PoS), and Byzantine Fault Tolerance (BFT) algorithms that only provide deterministic agreement, but are still expensive, inflexible, and weak under dynamically stable networks. Over the recent years, there has been a lot of development in Machine Learning (ML) and Artificial Intelligence (AI), which have introduced intelligent self-learning consensus mechanisms to increase adaptability, efficiency, and resilience. It highlights the architectural aspects and the operational entities of various ML-powered and hybrid consensus protocols, including PoW–PoS, DPoS–PBFT, and PoCASBFT, as well as their performance implications. Supervised, unsupervised, reinforcement, and federated learning methods are surveyed to provide insights for predictive validation, anomaly detection, energy optimization, and node trust management in blockchain networks [12]. It then compares the performance of its state-of-the-art protocols, demonstrating that ML-assisted hybrids provide 15–40% throughput gains and up to 35% energy savings over the corresponding protocols when trained with traditional models. Ultimately, the paper notes scalability, interpretability, and adversarial ML as the main risks of research in this space, and glances at future directions toward cognitive consensus architectures, also noting that these architectures should be self-healing, context-aware, and able to balance decentralization, performance, and security themselves.
The development of network-type organizations is accompanied by the transformation of traditional management approaches, particularly the shift from centralized to distributed responsibility within business processes. Such transformation necessitates a reconsideration of management architecture, integrating responsibility into interconnected processes, roles, and digital environments. The study’s relevance stems from the need to enhance organizational flexibility, adaptability, and resilience in dynamic, uncertain environments. The purpose of the study is to identify mechanisms for implementing distributed responsibility in the architecture of business processes in modern network-type organizations, substantiate approaches to integrating it, and analyze its impact on the effectiveness of managerial decision-making and on interaction among process participants. The study applies systemic and process-based approaches, structural-functional analysis, business process modeling, comparative analysis of modern management practices, and the generalization of theoretical provisions on organizational design and decentralized management. It has been established that implementing distributed responsibility involves decomposing business processes into autonomous yet interconnected elements with clearly defined roles and areas of responsibility. The effectiveness of such a model is ensured through the use of digital platforms, horizontal coordination mechanisms, and transparent tools for monitoring task execution. It is substantiated that integrating decentralization principles leads to faster decision-making, greater employee engagement, and reduced managerial risk. The implementation of distributed responsibility in the architecture of business processes forms a new management paradigm focused on flexibility, adaptability, and collaborative interaction. The combination of a process-based approach with network principles of organizational activity enhances the efficiency of modern organizations and lays the groundwork for their sustainable development in the context of digital transformation.
In this paper, a novel security framework for industrial internet of things (IIoT) and vehicular networks is proposed, integrating blockchain technology with advanced encryption and data classification mechanisms to enhance data integrity, confidentiality and trustworthiness. The work employed ChaCha20-Poly1305 encryption to safeguard the data transaction to local cluster nodes. A private blockchain gateway then processes the encrypted data, classifying it based on confidentiality levels, and directing storage either to cloud servers or the interplanetary file system (IPFS). To ensure data integrity, a proof of authority consensus mechanism within the blockchain is incorporated, while zero knowledge proof (ZKP) methods are used for authentication and secure data access. Empirical evaluations demonstrate that our framework achieves a data transmission security rate of 97.5%, with an average encryption and decryption latency of 150 milliseconds, significantly improving over traditional methods. The proof of authority consensus mechanism exhibits a transaction validation speed of 300 transactions per second, showcasing enhanced efficiency compared to standard blockchain models. Furthermore, the integration of ZKP challenges results in a 30% reduction in unauthorised access attempts, indicating a substantial improvement in overall security. This work emphasises the need for continuous innovation in addressing the various security issues in IoT, ultimately advancing the operational efficiency and security of these systems.
Conglomerates are among the most complex organizational forms in capitalism. They own multiple businesses, often across different industries, geographies, operating models, and risk profiles. Some conglomerates own insurance companies, manufacturers, railroads, energy utilities, retailers, banks, technology firms, industrial businesses, media assets, and consumer brands under one corporate umbrella. The central challenge is not only how to operate these businesses, but how to allocate capital among them. A conglomerate must answer a difficult question: Who decides where the money goes? Should capital allocation be centralized at head office, where senior executives and finance committees compare business units and approve investments? Or should capital decisions be decentralized, allowing subsidiary managers to operate autonomously while headquarters focuses only on major capital deployment? Should internal cash flows remain inside business units, or should they be swept to corporate headquarters and redeployed across the group? Should acquisitions be initiated by subsidiaries, by corporate development teams, or by the CEO? Should capital budgeting follow rigid committee processes or owner-oriented judgment? These questions define the capital allocation architecture of the conglomerate. Berkshire Hathaway represents one of the most successful decentralized conglomerate models in modern business history. Warren Buffett and Charlie Munger built Berkshire around autonomy, trust, permanent ownership, strong subsidiary managers, and centralized capital allocation at the highest level. Berkshire’s headquarters remains small, and its operating subsidiaries are largely left alone. Yet the most important capital allocation decisions—large acquisitions, major equity investments, cash deployment, and insurance float allocation—have historically been handled centrally by Buffett and, increasingly, Berkshire’s designated capital allocation successors. By contrast, many corporations use centralized capital committees. These structures often include formal budgeting processes, investment review boards, hurdle rates, discounted cash flow models, divisional competition for capital, strategic planning cycles, and executive approval layers. Centralization can improve control, risk management, consistency, and capital discipline. However, it can also create bureaucracy, slow decisions, distort incentives, and separate capital decision-makers from operating reality. This chapter compares decentralized and centralized capital allocation models in conglomerates. It argues that neither model is universally superior. The right model depends on business quality, management trust, governance, capital intensity, complexity, regulatory risk, and the competence of headquarters. However, the Berkshire model demonstrates a powerful lesson: decentralization can compound value when paired with exceptional managerial selection, strong culture, conservative financing, and disciplined central capital allocation.
The growing adoption of the Electronic Health Records (EHR) has revolutionized healthcare information management. However, seamless and secure interoperability between different healthcare organizations continues to be a hard challenge. Data silos, centralized trust model, and lack of scalability are common impairments of traditional systems in care delivery which limit ‘patient centric’ way of care delivery. While blockchain technology offers decentralized trust and immutability, current solutions tend to be closed on a single blockchain platform, and thus not able to provide cross network interoperability and accessing data. To address this gap, this research introduces a Cross Chain EHR Sharing Framework that may be leveraged for the secure, bi-directional synchronization of EHR between Hyperledger Fabric (private blockchain) and Ethereum Sepolia Testnet (public blockchain) via decentralized storage by IPFS with AES 256 encryption. To facilitate interoperability the research introduces a smart middleware layer that autonomously monitors the blockchain events, processes encrypted Content Identifier (CID)s, enforces real time cross chain consistency and smart contract-based access control. The experimental evaluation shows that proposed framework achieves low synchronization times (< 195 ms), efficient blockchain operations with low gas and latency costs, small encryption overhead (< 4–5 KB), robust file storage and retrieval through IPFS. It also provides scalability, security and real-world applicability for the cross-chain healthcare interoperability.
The escalating energy consumption of blockchain networks has intensified concerns regarding their environmental sustainability, particularly in consensus protocols derived from Proof of Work. Although Proof of Stake improves efficiency, existing mechanisms remain static and lack responsiveness to dynamic network and energy conditions. This paper presents an Energy-Adaptive Consensus Mechanism (EACM) that integrates real-time workload awareness with energy-sensitive validator selection to optimize power utilization without compromising security. The proposed model introduces a multi-factor adaptive control layer that adjusts validation intensity based on transaction throughput, node availability, and energy profiles. A carbon awareness incentive function is incorporated to prioritize validators operating on renewable or low-carbon energy sources. Prototype implementation is developed on a private Ethereum-based test network, and comparative experiments are conducted against conventional Proof of Stake under variable workloads. Results indicate measurable reductions in energy consumption while maintaining competitive throughput, latency, and fault tolerance. The findings demonstrate that adaptive consensus design can enhance blockchain sustainability and provide a viable pathway toward carbon-efficient distributed ledger infrastructures.
Ryan Babbush, Adam Zalcman, Craig Gidney, Michael Broughton · 9 authors
This whitepaper seeks to elucidate implications that the capabilities of developing quantum architectures have on blockchain vulnerabilities and mitigation strategies. First, we provide new resource estimates for breaking the 256-bit Elliptic Curve Discrete Logarithm Problem, the core of modern blockchain cryptography. We demonstrate that Shor's algorithm for this problem can execute with either <1200 logical qubits and <90 million Toffoli gates or <1450 logical qubits and <70 million Toffoli gates. In the interest of responsible disclosure, we use a zero-knowledge proof to validate these results without disclosing attack vectors. On superconducting architectures with 1e-3 physical error rates and planar connectivity, those circuits can execute in minutes using fewer than half a million physical qubits. We introduce a critical distinction between fast-clock (such as superconducting and photonic) and slow-clock (such as neutral atom and ion trap) architectures. Our analysis reveals that the first fast-clock CRQCs would enable on-spend attacks on public mempool transactions of some cryptocurrencies. We survey major cryptocurrency vulnerabilities through this lens, identifying systemic risks associated with advanced features in some blockchains such as smart contracts, Proof-of-Stake consensus, and Data Availability Sampling, as well as the enduring concern of abandoned assets. We argue that technical solutions would benefit from accompanying public policy and discuss various frameworks of digital salvage to regulate the recovery or destruction of dormant assets while preventing adversarial seizure. We also discuss implications for other digital assets and tokenization as well as challenges and successful examples of the ongoing transition to Post-Quantum Cryptography (PQC). Finally, we urge all vulnerable cryptocurrency communities to join the ongoing migration to PQC without delay.
This paper presents SAYMAN, a production-architected educational blockchain framework designed to enable controlled decentralization for learning ecosystems and digital academic infrastructure. Traditional blockchain systems prioritize full decentralization, often introducing scalability, governance, and usability challenges that limit adoption in education-focused environments. SAYMAN proposes a hybrid architectural model combining permissioned governance layers with selectively decentralized components to balance transparency, institutional control, and operational efficiency. The framework introduces modular consensus orchestration, identity-anchored participation, and configurable trust boundaries, allowing institutions, developers, and learners to interact within a verifiable yet manageable distributed system. Unlike conventional public chains, SAYMAN emphasizes educational deployment readiness, low operational overhead, and adaptable governance policies suitable for academic credentialing, collaborative research environments, and decentralized learning platforms. This work outlines the architectural principles, system design considerations, and implementation roadmap of the SAYMAN blockchain, positioning it as a practical foundation for next-generation educational Web3 infrastructure. The proposed model demonstrates how hybrid blockchain architectures can bridge centralized institutional requirements with decentralized innovation, enabling scalable and secure digital education ecosystems.
Public discourse plays a critical role in shaping trust, legitimacy, and governance dynamics within decentralized Web3 ecosystems. However, existing studies often examine Web3 discourse through isolated lenses such as sentiment or topic modeling, which limits their ability to capture how emotional expression and communicative purpose jointly convey strategic intent. This study proposes a three-stage decision analytics framework that transforms unstructured Web3 discourse into diagnostic signals by jointly modeling industry domain, emotional tone, and communicative purpose. The analysis draws on 10,840 user-generated posts collected from X, Reddit, YouTube, and the ENS DAO forum, using a human-in-the-loop annotation process combined with transformer-based text classification models. The framework is evaluated using a domain-adapted language model and a general-purpose baseline, with robustness assessed through five-fold cross-validation. The results indicate that curiosity and optimism frequently align with promotional intent in infrastructure and application-oriented domains, whereas skepticism and concern are more prevalent in governance-related discourse. These findings demonstrate that emotional tone and communicative intent operate as structured, decision-relevant signals rather than incidental sentiment. The proposed framework supports systematic, diagnostic monitoring of narrative dynamics as decision support, enabling organizations, platform operators, and governance stakeholders to identify emerging legitimacy risks and shifts in community trust within decentralized environments.
Blockchain protocols often seek to procure computationally challenging work from a decentralized set of participants. While there are simple procurement auctions that result in the minimal cost of acquisition and maximal efficiency, they also lead to concentration in the provider set due to the winner-take-all market structure. We design and analyze single-good procurement auctions that balance social-cost minimization (at the extreme, a winner-take-all auction) with decentralization (at the extreme, a uniform allocation). We first give a dominant-strategy incentive-compatible (DSIC) mechanism explicitly designed to implement non-winner-take-all allocations. Our allocation rule uniquely solves an optimization with respect to a modified social-cost metric that penalizes large, single-player concentrations and is parameterized with a curvature value, $α$, with $α\rightarrow 0$ implementing the uniform allocation and $α\rightarrow \infty$ implementing the winner-take-all allocation. We further quantify the loss in social cost of this mechanism as a function of $α$. We then propose two alternative mechanisms, each addressing a limitation of the DSIC mechanism, namely a lack of Sybil-resistance and a complex payment rule. First, we examine a variation of Tullock contests to achieve a non-winner-take-all Sybil-proof procurement mechanism. Second, we consider a mechanism with the same allocation rule as the DSIC mechanism but with an alternative payment rule in which producers are simply paid proportionally to their bids. This provides a much simpler payment rule which, while not DSIC, still results in the mechanism being ex-post ``safe'' (where there exists a bidding strategy that is guaranteed to result in non-negative utility) for participating bidders. For both non-DSIC mechanisms, we characterize the equilibrium allocations and prove price of anarchy bounds.
Centralized stablecoins such as USDT and USDC enforce sanctions through contract-layer blacklist functions. Yet on public blockchains, a freeze is still an ordinary transaction competing with the sanctioned party's transfer for priority. It exposes a gap between contract-layer authority and ordering-layer enforcement: when both race for the same block, the outcome is set not by legal mandate, but by block producers' choices. Because both sides can pay for priority, sanction races create rents for block producers, which we call Sanction-Evasion MEV (SE-MEV). To measure this gap, we build the first longitudinal dataset of on-chain sanctions enforcement and evasion for Ethereum-based USDT and USDC from November 2017 to August 2025, covering more than $1.5 billion in frozen value. At least 7.3% of sanctioned USDT addresses and 18.7% of sanctioned USDC addresses had already been drained to zero before the freeze took effect. We also trace an escalation from issuer-side out-of-gas failures, to public gas auctions, private order flow, and direct payments to block producers, showing that block producers extract MEV from sanction enforcement. We then develop a game-theoretic model of stablecoin sanctions with MEV. It shows that compliant issuers cannot rationally stay outside the ordering market; fixed participation costs concentrate evasion among specialized MEV-aware adversaries; and the implicit MEV tax rises with regulatory penalties, creating incentives for vertical integration into block-building infrastructure. The problem extends beyond stablecoins. Any privileged on-chain action executed as an ordinary transaction -- emergency pauses, governance interventions, or judicial freezes -- faces the same conflict. Where ordering power follows economic incentives, ordering power is sanctioning power; contract-layer authority alone cannot guarantee enforcement.
Deep neural networks (DNNs) have emerged as a prominent approach for detecting smart contract vulnerabilities, driven by the growing contract datasets and advanced deep learning techniques. However, DNNs typically require large-scale labeled datasets to model the relationships between contract features and vulnerability labels. In practice, the labeling process often depends on existing open-sourced tools, whose accuracy cannot be guaranteed. Consequently, label noise poses a significant challenge for the accuracy and robustness of the smart contract, which is rarely explored in the literature. To this end, we propose Contrastive learning-enhanced Granular-Ball smart Contracts training, CGBC, to enhance the robustness of contract vulnerability detection. Specifically, CGBC first introduces a Granular-ball computing layer between the encoder layer and the classifier layer, to group similar contracts into Granular-Balls (GBs) and generate new coarse-grained representations (i.e., the center and the label of GBs) for them, which can correct noisy labels based on the most correct samples. An inter-GB compactness loss and an intra-GB looseness loss are combined to enhance the effectiveness of clustering. Then, to improve the accuracy of GBs, we pretrain the model through unsupervised contrastive learning supported by our novel semantic-consistent smart contract augmentation method. This procedure can discriminate contracts with different labels by dragging the representation of similar contracts closer, assisting CGBC in clustering. Subsequently, we leverage the symmetric cross-entropy loss function to measure the model quality, which can combat the label noise in gradient computations. Finally, extensive experiments show that the proposed CGBC can significantly improve the robustness and effectiveness of the smart contract vulnerability detection when contrasted with baselines.
Hasan Mahmud Rhidoy, Mahdi H. Miraz, Iftekhar Salam
The convergence of blockchain and the Internet of Things (IoT) enables secure, decentralised, and verifiable data exchange across distributed smart environments. However, traditional blockchain frameworks suffer from inherent scalability constraints, limited throughput, and high latency, which conflict with the stringent real-time requirements of IoT applications such as industrial automation, intelligent healthcare, and smart transportation. These systems demand ultra-low latency, high transaction throughput, lightweight computation, and efficient resource utilisation. This review provides a comprehensive, structured analysis of state-of-the-art scalability solutions specifically adapted to blockchain-enabled IoT. The discussion encompasses Layer 1 enhancements, Layer 2 off-chain processing, sharding-based parallelisation, integration of edge and fog computing, and hybrid consensus mechanisms. For each approach, the review highlights operational principles, performance benefits, trade-offs in decentralisation and security, and suitability for latency-sensitive deployments. Furthermore, real-time quality-of-service considerations are examined to understand how scalability strategies impact system responsiveness, energy efficiency, and data integrity. Key open challenges, including the scalability-security trade-off, privacy preservation, interoperability, and sustainable resource management, have been identified as persistent barriers to large-scale adoption. Finally, the review outlines future research directions, emphasising adaptive and AI-driven consensus algorithms, quantum-safe cryptographic models, the convergence of blockchain with 5G/6G networks, and edge intelligence. By consolidating diverse technical insights and emerging trends, this work serves as a timely reference for developing scalable, secure, and sustainable blockchain architectures for real-time IoT applications.
Abstract: This study investigates the dynamic relationships between Bitcoin, oil prices, and the US dollar (USD) using a Vector Autoregressive (VAR) model. Utilizing daily data from 2018 to 2023, the analysis reveals that both Bitcoin and oil prices exert significant short-term impacts on the USD, though these effects diminish over the long term. Bitcoin, characterized by its high volatility and safe-haven attributes, serves as an alternative asset during periods of economic uncertainty, while oil prices influence the dollar through trade flows and inflationary pressures. The findings highlight the transient nature of these interactions, with Bitcoin and oil acting as short-term pressure factors on the USD. These insights are crucial for investors and policymakers in managing risks and optimizing strategies in a volatile financial environment. This study contributes to the literature by providing empirical insights into the interconnectedness of cryptocurrencies, commodities, and currencies, offering valuable implications for financial decision-making. Keywords: Bitcoin, Oil Prices, US Dollar (USD), Vector Autoregressive (VAR) Model, Cryptocurrencies, Exchange Rates, Safe-Haven Assets JEL Classification Number: C32, E44, G15, Q43
Pre-registration of a structural scar class prediction for microsoft/phi-4 based on measurement-site stiffness (S = 0.0358), before the structural scar measurement is conducted. Predicts INTERMEDIATE class (1,000–4,000×ε non-max) based on the stiffness→scar ordering established across four families (Mistral, Llama, Qwen, Gemma) in Papers 1–12 and confirmed by RC-6 (DOI: 10.5281/zenodo.19305176). Designed as a hostile falsification test: Phi is trained with heavy synthetic-data distillation from GPT-4-class teachers, unlike any previously tested family. Explicit falsification criteria and hostile hypotheses defined. Part of the Fall Risk AI research program on neural network structural identity. The Neural Network Identity Series — Mathematical foundations, empirical validation, and governance frameworks for verifying which model is running Paper 1: The δ-Gene: Inference-Time Physical Unclonable Functions from Architecture-Invariant Output Geometry (DOI: 10.5281/zenodo.18704275) Paper 2: Template-Based Endpoint Verification via Logprob Order-Statistic Geometry (DOI: 10.5281/zenodo.18776711) Paper 3: The Geometry of Model Theft: Distillation Forensics, Adversarial Erasure, and the Illusion of Spoofing (DOI: 10.5281/zenodo.18818608) Paper 4: Provenance Generalization and Verification Scaling for Neural Network Forensics (DOI: 10.5281/zenodo.18872071) Paper 5: Beneath the Character: The Structural Identity of Neural Networks — Mathematical Evidence for a Non-Narrative Layer of AI Identity (DOI: 10.5281/zenodo.18907292) Paper 6: Which Model Is Running?: Structural Identity as a Prerequisite for Trustworthy Zero-Knowledge Machine Learning (DOI: 10.5281/zenodo.19008116) Paper 7: The Deformation Laws of Neural Identity (DOI: 10.5281/zenodo.19055966) Paper 8: What Counts as Proof? — Admissible Evidence for Neural Network Identity Claims (DOI: 10.5281/zenodo.19058540) Paper 9: Composable Model Identity — Formal Hardening of Structural Attestations in the Enterprise Identity Stack (DOI: 10.5281/zenodo.19099911) Paper 10:Where Identity Comes From: Path Sensitivity and Endpoint Underdetermination in Neural Network Training (DOI: 10.5281/zenodo.19118807) Paper 11: Post-Hoc Disclosure Is Not Runtime Proof: Model Identity at Frontier Scale (DOI: 10.5281/zenodo.19216634) Paper 12: Family-Dependent Response to Reasoning Distillation Across Structural and Functional Identity Layers (DOI: 10.5281/zenodo.19298857) 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) 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).
Pre-registration of a structural scar class prediction for google/gemma-3-12b-it based on measurement-site stiffness (S = 0.1335), before the structural scar measurement is conducted. Predicts QUIET class (100–600×ε non-max) based on the stiffness→scar ordering established across three families (Mistral, Llama, Qwen) in Papers 1–12. Explicit falsification criteria defined. Part of the Fall Risk AI research program on neural network structural identity. The Neural Network Identity Series — Mathematical foundations, empirical validation, and governance frameworks for verifying which model is running Paper 1: The δ-Gene: Inference-Time Physical Unclonable Functions from Architecture-Invariant Output Geometry (DOI: 10.5281/zenodo.18704275) Paper 2: Template-Based Endpoint Verification via Logprob Order-Statistic Geometry (DOI: 10.5281/zenodo.18776711) Paper 3: The Geometry of Model Theft: Distillation Forensics, Adversarial Erasure, and the Illusion of Spoofing (DOI: 10.5281/zenodo.18818608) Paper 4: Provenance Generalization and Verification Scaling for Neural Network Forensics (DOI: 10.5281/zenodo.18872071) Paper 5: Beneath the Character: The Structural Identity of Neural Networks — Mathematical Evidence for a Non-Narrative Layer of AI Identity (DOI: 10.5281/zenodo.18907292) Paper 6: Which Model Is Running?: Structural Identity as a Prerequisite for Trustworthy Zero-Knowledge Machine Learning (DOI: 10.5281/zenodo.19008116) Paper 7: The Deformation Laws of Neural Identity (DOI: 10.5281/zenodo.19055966) Paper 8: What Counts as Proof? — Admissible Evidence for Neural Network Identity Claims (DOI: 10.5281/zenodo.19058540) Paper 9: Composable Model Identity — Formal Hardening of Structural Attestations in the Enterprise Identity Stack (DOI: 10.5281/zenodo.19099911) Paper 10:Where Identity Comes From: Path Sensitivity and Endpoint Underdetermination in Neural Network Training (DOI: 10.5281/zenodo.19118807) Paper 11: Post-Hoc Disclosure Is Not Runtime Proof: Model Identity at Frontier Scale (DOI: 10.5281/zenodo.19216634) Paper 12: Family-Dependent Response to Reasoning Distillation Across Structural and Functional Identity Layers (DOI: 10.5281/zenodo.19298857) 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) 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).
LLMs degrade in long conversations — not because context is too long, but because contradictions accumulate. GPT-4o-mini drops from 100%% to 10%% under contradiction. Google's 1M-token window still drops 47.8pp. This project presents a metabolic architecture ("cognitive sleep") that resolves contradictions during idle time, preventing context rot. Key results: - 8 models, 11 pairs: sign test p=0.0107 - gemma3:27b (n=3): ON 73.3%% vs OFF 21.1%%, p<0.001, d=8.80 - Knowledge anchoring: ON exceeds contradiction-free baseline (73.3%% vs 56.7%%) - Frontier replication: GPT-4o, Gemini 3.1, Sonnet 4.6 — three response patterns (collapse, resistance, non-retention). delta_c is model-specific. Papers: 1. Structural Collapse as Information Loss (DOI: 10.5281/zenodo.19254667) 2. Predicting Computational Cost from delta (DOI: 10.5281/zenodo.18943573) 3. Cognitive Sleep for LLMs (DOI: 10.5281/zenodo.19322371) Code & Tools: - DeltaZero (research system): https://github.com/karesansui-u/delta-zero - delta-prune (middleware, pip install delta-prune): https://github.com/karesansui-u/delta-prune - Papers + Lean 4 proofs: https://github.com/karesansui-u/delta-survival-papers
This work presents a Quantum Measurement Units (QMU) ledger-based decomposition of the hydrogen $2\mathrm{S}$--$6\mathrm{P}$ transition, using the Aether Physics Model (APM) as a geometric framework for interpreting atomic structure. The analysis is anchored to the 2026 high-precision spectroscopic measurement yielding a proton charge radius of $r_p = 0.8406(15)\,\mathrm{fm}$. The hydrogen spectrum is reformulated as a perturbative expansion in the fine-structure constant $\alpha$ on a single base frequency scale $F_q = m_e c^2 / h$. The conventional decomposition into Dirac, radiative (Lamb shift), and finite-size contributions is translated into QMU ledger form using the Compton wavelength $\lambda_C = h/(m_e c)$ and the invariant relation $F_q \lambda_C = c$. A central result is the derivation of the proton finite-size frequency shift in QMU form:\[\Delta \nu_{\mathrm{finite}}(2S)=-\frac{\pi^2}{3}\,\alpha^4\left(\frac{m_r}{m_e}\right)^3\left(\frac{r_p}{\lambda_C}\right)^2F_q,\]where $m_r$ is the reduced mass. This expression is obtained by direct substitution from the conventional bound-state QED formulation using $\hbar = h/(2\pi)$ and $\lambda_C = h/(m_e c)$, preserving dimensional and scaling consistency. Inversion of this relation provides a direct extraction of the proton charge radius from the measured frequency shift, yielding agreement with experiment at the $10^{-3}\,\mathrm{fm}$ level when recoil is included through the factor $(m_r/m_e)^3$. Within the QMU framework, the finite-size correction is interpreted as a geometric traversal mismatch between the electron’s bound-state path and the proton’s distributed Aether structure. The proton radius emerges as a dimensionless geometric ratio $r_p/\lambda_C$, linking nuclear structure directly to the electron Compton scale without introducing additional fundamental lengths. The paper also establishes a ledger identity flow connecting the Aether unit closure relation\[A_u \cdot \mathrm{curl} = {F_q}^2 {\lambda_C}^2\]to the observed hydrogen transition frequency, demonstrating that atomic structure can be expressed as successive geometric perturbations of a single invariant frequency scale. Predictions include stability of the ratio $r_p/\lambda_C$ across hydrogenic systems, sensitivity of hyperfine structure to distributed charge anisotropy, and consistency between electronic and muonic hydrogen when reduced-mass effects are treated as a coupled inertial ledger. This work provides a geometrically unified interpretation of the proton radius within the QMU/APM framework and identifies experimental pathways for testing traversal-based effects in precision spectroscopy.
This paper presents a reformulation of the recently realized primary quantum current standard, based on the Josephson and quantum Hall effects, within the framework of Quantum Measurement Units (QMU) derived from the Aether Physics Model (APM). In conventional SI metrology, the current standard is expressed as$$I = \left(\frac{n}{p}\right) e f_J,$$where $f_J$ is the Josephson frequency and $n/p$ is determined by the quantum Hall state. While numerically accurate, this expression compresses magnetic flux geometry and charge representation into the constants $h$ and $e$. In QMU, electrical quantities are expressed in distributed charge, allowing the roles of frequency, conductance, and flux geometry to be separated explicitly. The Josephson--Hall system is shown to realize the identities$$potn = \frac{freq}{cond}, \qquad curr = \frac{potn}{resn}.$$ This leads to the central result that the quantum current standard is fundamentally a \textit{potential closure} governed by frequency and conductance geometry, rather than a direct charge-transport relation. Within this framework: The Josephson effect provides a frequency source $freq = f_J$. The quantum Hall effect defines a discrete conductance geometry. Potential emerges as $potn = freq/cond$. Current follows as $curr = potn/resn$. The resulting current relation becomes$$curr = \left(\frac{n}{p}\right) {e_\mathrm{emax}}^{2} f_J,$$which is the QMU form of the experimental result and represents a realization of the general QMU current definition$$curr = {e_\mathrm{emax}}^{2} F_q.$$ The formulation also shows that conductance is the reciprocal of magnetic flux,$$cond = \frac{1}{mflx},$$and that quantization arises from discrete geometric partitioning of flux. Because all quantities are expressed in distributed charge, no unit mismatch occurs, and the resulting relations remain real-valued. The use of complex impedance in conventional formulations is therefore interpreted as arising from combining quantities of different physical character rather than from a fundamental requirement. This work is intentionally limited to the reinterpretation of an experimentally realized system. It does not attempt to replace quantum mechanical descriptions or provide a full treatment of time-dependent circuit behavior. Instead, it demonstrates that the Josephson--quantum Hall current standard can be expressed as a consistent QMU ledger with explicit geometric meaning. The SI expression is recovered as a projection through charge conversion, while the QMU formulation foregrounds the underlying frequency--flux geometry governing the system.