Computer Science and Mathematics

Abstract: A possibility field records what evidence has made impossible, and nothing in the field records how it got that way: the same field can be reached by a thousand discriminating observations or declared in one line. The Theory of Epistemic Abductive Geometry (TEAG) closes that gap with a clock carried by each hypothesis, which advances only when evidence changes the field as seen from that hypothesis. This note states the clock from the theory, derives the law that compares the clocks of two hypotheses under the same evidence, and tests the definition and the law on the Epistemic Support-Point Filter (ESPF) on simulated LEO arcs: range and range-rate from two stations and angles only, five and two noise seeds, with and without an undisclosed maneuver, an undisclosed sensor bias, a handover of one filter's posterior to another as a declared prior, and the support threshold varied. The truth is tracked through the filter's fields without ever being used by the filter. The sign lemma of the theory holds in every one of 530,000 cases, and on the real WAAS GEO arc the truth is never demoted at all. The comparison law holds with slope 1.005 to 1.021 and correlation above 0.999 on every run, is exact up to one term that depends on the residual at the prior centre, and is unchanged by the threshold. A filter handed another's field carries the same field, commits to the same states to within centimetres, and has age zero: the clock is the only quantity that separates them. Under a correct model with a sensor finer than the support, the truth accumulates age mainly by surviving the demotion of its rivals; with a sensor coarser than the support, noise-driven demotion dominates every clock, the truth's included, so the signature of truth is the pair of ages together with the impossibility, not the age alone. The age splits into a contraction part and a growth part; on the contraction phase the survived part is a state function and the suffered part carries the order of the evidence, so the ordering dependence of epistemic time lives entirely in demotion. Epistemic time is invariant but not universal: observers in affine coordinates agree what each clock reads, each hypothesis carries its own, and the field decides how fast each one runs.

Abstract: Observer-information entropy production quantifies the loss of recoverable information about a physical source when an already formed observation undergoes source-independent irreversible degradation. The statistical second-law statement \[ \Sigma_{\mathrm{obs}}[s,t] =I(X;R_s)-I(X;R_t) =I(X;R_s\mid R_t)\ge 0 \] follows from data processing, but by itself it does not identify a physical energetic or thermodynamic cost. This paper establishes such a connection within an explicit and deliberately restricted stochastic-thermodynamic regime. We consider a finite observer memory \(R_t\) coupled to a single heat bath at temperature \(T\). A source label \(X\) is fixed during the passive relaxation, the transition generator of \(R_t\) is independent of \(X\), and the memory dynamics satisfies detailed balance with respect to a strictly positive equilibrium law \(q\). For the joint source--memory ensemble and for the unconditioned memory marginal, we define the standard stochastic-thermodynamic entropy-production rates. Using the relative-entropy chain rule and the detailed-balance \(H\)-theorem, we prove the exact correlation-dissipation decomposition \[ \frac{\dot S_{\mathrm{tot}}^{XR}}{k_B} = \sigma_{\mathrm{obs}} + \frac{\dot S_{\mathrm{tot}}^{R}}{k_B}. \] Consequently, \[ \dot S_{\mathrm{tot}}^{XR}\ge k_B\sigma_{\mathrm{obs}}, \qquad \Delta S_{\mathrm{tot}}^{XR}[s,t]\ge k_B\Sigma_{\mathrm{obs}}[s,t]. \] At fixed temperature the same result is an excess-free-energy dissipation bound, \[ \Fex_{XR}(s)-\Fex_{XR}(t) \ge k_BT\Sigma_{\mathrm{obs}}[s,t]. \] Equality holds when the memory marginal remains at equilibrium, in which case all thermodynamic irreversibility is attributable to the destruction of source--record correlation. A symmetric two-state memory provides an exactly solvable saturation example. The result is not a universal Landauer principle. It constrains total entropy production and loss of nonequilibrium free energy during passive decorrelation; it does not assert a universal heat or work cost for arbitrary information loss. Driven memories, source-dependent transition rates, active measurement, feedback, multiple reservoirs, and nonequilibrium steady states require the broader information-thermodynamic bookkeeping developed in stochastic thermodynamics. We then place the thermodynamic result beside two Fisher--Shannon bridges. Under Gaussian observation heat flow \(Y_t=X+B_t\), observer-information dissipation obeys the exact identity \[ \sigma_{\mathrm{obs}}(t) = \frac12\left(\frac{d}{t}-J(Y_t)\right) = \frac{1}{2t^2}\operatorname{mmse}(X\mid Y_t), \] while pairwise statistical distinguishability obeys \[ \frac{d}{dt}\KL(p_t\|q_t) =-\frac12\Jrel(p_t\|q_t). \] Recent temporal-Fisher speed-limit results provide a complementary route from physical entropy production to the speed of probability evolution. Together these results separate three objects that are often conflated: thermodynamic irreversibility, Shannon information loss about the physical source, and Fisher-geometric statistical motion. The paper thereby supplies a physical-cost layer for observer information theory without identifying information with energy or thermodynamic entropy.

Abstract: Classical data processing states that post-processing cannot increase information about an upstream variable, but this principle does not by itself distinguish passive degradation of an already formed observation from active acquisition of new physical evidence. This distinction becomes fundamental in observer-mediated systems whose sensing configuration, state record, calibration, and processing chain participate in information formation. We introduce an observer-information entropy-production framework in which a physical source \(X\) generates a record \(R_s\) under an observer state, and subsequent source-independent Markov degradation produces a coarser record \(R_t\). For any prior, the observer-information entropy production is defined by Σobs[s,t]:=I(X;Rs)−I(X;Rt). For a passive degradation family \(X\to R_s\to R_t\), we prove non-negativity, additivity, the conditional-mutual-information identity \(\Sigma_{\mathrm{obs}}[s,t]=I(X;R_s\mid R_t)\), and, for discrete \(X\), the posterior-uncertainty identity \(\Sigma_{\mathrm{obs}}[s,t]=H(X\mid R_t)-H(X\mid R_s)\). Under absolute continuity in a degradation parameter, this yields a second-law principle \(\sigma_{\mathrm{obs}}(t):=-\frac{d}{dt}I(X;R_t)\ge 0\). Equality is characterized by Bayesian sufficiency; in finite experiments with a full-support prior, zero production is equivalent to recoverability of the pre-degradation experiment through a parameter-independent reverse kernel.The passive law is complemented by an exact acquisition--dissipation balance. If a new physical interaction produces fresh evidence \(A_n\) and a subsequent irreversible processing stage generates \(R_{n+1}\), then I(X;Rn+1)−I(X;Rn)=I(X;An∣Rn)−Σobs,n. Thus active acquisition and passive information entropy production are distinct non-negative terms. A strong-data-processing coefficient gives a quantitative lower bound on entropy production. For Gaussian heat-flow degradation \(Y_t=X+B_t\), we derive the exact bridge σobs(t)=12(dt−J(Yt))=12t2mmse⁡(X∣Yt) in nats per unit noise variance, thereby connecting Shannon information decay, de Bruijn's identity, Fisher information, and I--MMSE in one observer-degradation law. For pairwise hypotheses, Gaussian smoothing gives ddtDKL(pt‖qt)=−12Jrel(pt‖qt), showing that relative Fisher information is the instantaneous dissipation rate of statistical distinguishability.We finally separate this statistical second-law principle from physical entropy production. In a classical electromagnetic observation chain, the invariant propagation speed \(c\) constrains when new evidence can become causally available, while \(\Sigma_{\mathrm{obs}}\) measures irreversible loss after information has been physically instantiated in the record. No universal identity between observer-information entropy production and thermodynamic entropy production is asserted; Landauer-type bounds require an explicit physical memory-erasure model. The resulting framework provides a precise observer-mediated distinction among physical propagation, information acquisition, reversible representation transport, irreversible quality degradation, and thermodynamic dissipation.

Abstract: Agentic artificial intelligence (AI) systems are rapidly entering healthcare. Unlike static AI, agentic systems exert persistent, goal-directed influence over care trajectories, creating new and largely unstudied challenges for clinician trust and reliance calibration. Drawing on trust calibration theory, human-AI complementarity research, and augmented intelligence literature, this paper develops a model examining how two design dimensions of agentic clinical AI, autonomy level and transparency mechanism, shape clinician trust calibration and downstream outcomes, including decision quality, cognitive load, and error detection. We propose an experiment with clinicians using clinical vignettes across three temporally sequenced decision steps. We contribute: a reconceptualization of trust calibration as a dynamic, temporally extended process; a theorization of autonomy level and transparency as orthogonal design dimensions with interacting effects on calibration; and a falsifiable hypothesis set and an experimental design for validation. The proposed study has the potential to advance theory on human-agentic AI collaboration and inform governance design for high-stakes IS deployments.

Abstract: The IT Solution Architect is widely established in enterprise technology practice, yet the role is often defined through fragmented lists of activities rather than a coherent statement of professional engineering accountability. This paper proposes six mutually reinforcing roles for an ideal modern IT Solution Architect: Analyst and Translator of Strategy and Business Needs; Master Design Engineer; Building Technologist and Materials Engineer; Technology Sourcing Lead; Project Engineering Manager; and Solution Quality Engineer and Auditor. The framework treats the architect as the master engineer of the end-to-end IT solution across the lifecycle from problem definition to design, realization, sourcing, delivery, and assurance. Its design responsibility is grounded in five fundamental dimensions of system structure, space, dynamics, intelligence, and aesthetics and human experience.

Abstract: Notebook Coding is a multi-dimensional open science software project structured as a modular collection of reactive Observable notebook cells designed to compute and visualize nineteen bibliometric indicators for academic journals managed in Open Journal Systems (OJS) using OpenAlex. Operating across three distinct levels, it functions as: (1) a client-side software architecture querying the OpenAlex REST API via a rate-limited concurrency queue; (2) an embeddable, live bibliometric dashboard for OJS editorial management using responsive <iframe> elements; and (3) a forkable pedagogical tool for hands-on bibliometric training in library and information science (LIS) programs. By shifting from offline data processing to client-side web rendering, it offers editors, librarians, and students an open, inspectable alternative without requiring commercial database subscriptions.

Abstract: Quantum computing represents a fundamental shift in computational capability, using quantum mechanical principles, such as superposition, entanglement, and interference, in order to process information exponentially faster than classical systems. Unlike traditional computers that manipulate bits as 0s and 1s, quantum computers harness qubits that exist in probabilistic superposition states, enabling exploration of multiple solutions at the same time. While this advancement promises transformative applications in molecular modeling, optimization, and machine learning, it also threatens the cryptographic foundations securing modern digital infrastructure. The security of modern-day systems relies on mathematical problems that are not easily solvable for classical computers. Quantum algorithms, particularly Shor’s Algorithm and Grover’s Algorithm, render current asymmetric and symmetric encryption protocols computationally vulnerable; critically, the “Harvest Now, Decrypt Later” strategy transforms present-day communications into long-term security liabilities affecting government, financial, healthcare, and critical infrastructure sectors. To address this threat, this paper evaluates post-quantum cryptographic defenses and proposes Quantum Key Distribution (QKD) as the optimal security framework. Unlike mathematical approaches vulnerable to algorithmic advances, QKD anchors security in the No-Cloning theorem and wave-function collapse, ensuring that eavesdropping attempts introduce detectable anomalies, enabling real-time threat detection. Through comparative analysis of scalability, deployment feasibility, and cost efficiency, this paper demonstrates that QKD provides the most robust, future-proof mechanism for protecting sensitive data transmission in the quantum era, offering a critical blueprint for resilient network defense against emerging computational threats.

Abstract: Traditional scientific publishing and bibliometric models face severe limitations regarding prolonged operational processing times, institutional costs, and vulnerability to systemic evaluation biases. To address these inefficiencies, this paper introduces the Open Science Society (OSS), a scalable, decentralized methodology designed for trusted, fraud-resistant scientific curation and real-time peer-evaluation. Moving away from static, global metric indices and opaque review architectures, the proposed framework implements an endogenous, dynamic evaluation method that calibrates reviewer authority scores based on specialized expertise. Crucially, the methodology computes continuous evaluation weights by projecting domain-specific interaction histories onto a keyword-driven semantic space via a symmetric correlation matrix. Through rigorous mathematical formalization and empirical simulation, we demonstrate that the OSS pipeline effectively isolates strategic manipulation, collapsing the voting weight of unqualified actors or collusive networks to absolute zero while preserving genuine cross-domain authority. The system achieves fast, stationary score convergence, establishing a sustainable algorithmic blueprint to ensure long-term publishing integrity and decentralized bibliometric governance without centralized gatekeepers.

Abstract: Stablecoins have rapidly developed into a significant form of digital currency, with market capitalisation surpassing \$300 billion by mid-2026 and transaction volumes exceeding those of Bitcoin and Ethereum. This paper analyses stablecoin use from a criminological perspective, treating stablecoins as a form of digital cash and using cash as the analytical baseline. Combining ecosystem-scale market measurements (Artemis Analytics) with institutional and regulatory analysis, it argues that stablecoins offer four criminogenic advantages for money laundering relative to cash: scale and velocity, programmable layering, borderless settlement, and pseudonymity. These features create a structural mismatch with prevailing AML/CFT compliance frameworks, which largely assume identifiable intermediaries and territorially bounded payment rails. The paper develops four policy implications, each grounded in the analytical properties identified, that specify the relevant regulated actor, the precise compliance obligation, the principal implementation trade-offs, and a path forward for regulatory design and future research, while recognising the investigative opportunities that public ledgers create relative to physical cash.

Abstract: Field of view (FOV) and spatial audio are characteristics of virtual environments that can influence visual and auditory presentation and, consequently, physiological responses during interaction with virtual reality. This study aims to assess physiological responses during interaction with a low-immersion virtual environment by analysing heart rate variability (HRV). To this end, is developed a specialised 3D game using passive red-cyan anaglyph stereoscopy and a large display/projector (1.78 × 1.00 m) positioned 3.50 m from the participant. Thirty participants (17 men and 13 women) took part in the study, each undergoing a baseline resting phase and an experimental phase involving interaction with the virtual environment. Continuous electrocardiographic recording was performed using a Holter monitor during both phases, and consecutive RR intervals were extracted from the recorded signals for subsequent time-domain, frequency-domain, non-linear, and geometric HRV analysis. The experimental setup featured a physical diagonal field of view of 32.52°, a software-defined field of view of 97.90°, and enabled spatial audio. The results demonstrate statistically significant differences between the resting state and the experimental VR condition across a range of HRV metrics, including MeanRR, SDNN, SDANN, RMSSD, pNN50, LF, HF, LF/HF, SD1, SD2, HRVti, and TINN (p < 0.001). The observed changes in HRV parameters suggest that HRV can serve as an objective physiological indicator for characterising the physiological response to a low-immersion virtual environment. The data characterise the physiological response to the specific combination of visual and audio parameters under study, without allowing for the independent determination of the effects of physical FOV, software-defined FOV, anaglyph stereoscopy, and spatial audio. The study contributes by developing and experimentally characterising an accessible, low-immersion VR setup and by applying a multi-domain HRV analysis to assess the physiological response during interaction with it. This approach can serve as a foundation for future research on physiological reactions during interactions with virtual environments, as well as for developing and evaluating accessible VR systems for experimental, educational, and training applications.

Abstract: Retrieval-augmented generation (RAG) grounds a language model in an external document collection, which moves part of the system’s trust boundary out of the model weights and into a data pipeline. An adversary able to write to that pipeline can therefore influence model output without touching the model, through knowledge-base poisoning, indirect prompt injection, trigger backdoors or retrieval flooding. Defenses proposed against these threats are typically evaluated only against the published string construction of each attack and are reported as a single stack, so it is not known which control carries the security, nor whether any of it survives an adversary who has read the defense. This article addresses both gaps. We present an instrumented RAG framework with a four-layer defense, comprising ingestion-time source verification, calibrated retrieval filtering, provenance-weighted robust aggregation and anomaly monitoring, in which every layer can be enabled independently, and we introduce a defense-aware adaptive adversary decomposed into six individually switchable evasion techniques so that defensive failure can be attributed rather than merely observed. The evaluation spans three corpora (SQuAD, HotpotQA and a bundled corpus), a sentence-transformer retriever, seven configurations, five attacks and three seeds, and reports attack success restricted to the queries each configuration answers correctly with no adversary present. Against the four static attacks the layered defense performs as the literature predicts, reducing attack success from 0.95 to 0.00. Against the adaptive adversary the same stack fails: the retrieval filter’s true-positive rate falls to at most 0.02 and its AUROC to between 0.32 and 0.65, although the same filter reaches 0.97 to 1.00 against every static attack, and robust aggregation increases attack success because the adversary supplies the corroboration its redundancy weighting rewards. A leave-one-out ablation attributes the failure to three of the six techniques, each a low-cost rewriting choice, and a budget sweep prices the attack at two documents per target. Sweeping the detector threshold shows that no operating point serves both adversaries, since catching the adaptive attack requires discarding enough legitimate evidence to drive clean accuracy from 0.61 to 0.06. Binding ingestion to an unforgeable HMAC signature reduces adaptive attack success to 0.01 at no cost to clean accuracy and is the only control in the study that survives. We conclude that content-based heuristics belong in a RAG security architecture as detection rather than prevention, and that verifiable provenance belongs at its base.

Abstract: Why does space have three dimensions? We study a recognizer with finitely many independent binary distinctions, one changed by each elementary act. Its states form a cube graph, and a shortest closed run through every state realizes a circle. We require a second recognition trajectory to represent a nonzero integral first-homology class in the complement of that circle. For a closed connected orientable smooth substrate with vanishing first integral homology, this requirement holds exactly in dimension three. The substrate is then an integral homology sphere, and the complementary class is measured by the linking number. We prove the converse for every substrate in this class and show that closedness and ambient first-homology vanishing are sharp hypotheses. A refinement construction supplies an explicit substrate from weighted binary records. The result concerns homological records; identifying this substrate with observed physical space is a further physical claim. The proof uses classical Alexander–Lefschetz duality.

Abstract: Technological advancements have been responsible for a significant part of the growth in agricultural productivity in recent years. Digital technologies have a high potential to enable the development of the agricultural sector, reshape value chains, and significantly contribute to more productive, resilient, and transparent food systems; however, their adoption in Brazil remains uneven due to regional disparities and structural bottlenecks. The research investigated this problem to build and validate the Digital Technology Adoption Conditioning Analysis Model (MAC-AgriTech), through a case study with Brazilian agricultural data, encompassing the identification of conditioning factors, their territorial evaluation, and the proposition of actions, while providing structured data collection and analysis instruments. The spatial analysis revealed deep territorial asymmetries, concentrating resources and infrastructure in the South and Southeast regions. Econometric modeling demonstrated that digital adoption is primarily driven by the producer’s digital familiarity, connectivity quality, and property scale, with 77% of producers identifying acquisition and maintenance costs as the primary barrier. The transition to digital agriculture in Brazil requires targeted, multidimensional public policies—such as expanded rural connectivity, technical training, and subsidized credit—to overcome regional gaps, and to increase agricultural competitiveness, efficiency, and sustainability.

Abstract: Before/after studies of planned special events routinely report elevated emissions and attribute the increase to event-induced congestion. We show that this attribution can be wrong, and we give a cheap test that separates the two causes. Using one-minute detector data from LATTICE, an operational freeway analytics platform deployed on the Las Vegas network, we analyze the 2026 Electric Daisy Carnival on Interstate 15 across both travel directions. Northbound, the arrival direction, loses 9.5 mph during a five-hour evening window. Southbound, over the same days and the same weather, loses nothing. Yet CO₂ rises by a nearly identical amount in both directions (+11.6% vs. +11.8%), which means the headline emissions increase is driven by weekend travel demand, not by the congestion. Treating the uncongested direction as a control, a difference-in-differences estimate isolates the congestion-specific component. Only PM_2.5 retains a substantial residual (+8.9 percentage points); CO₂ and NO_x retain essentially none. The residuals order monotonically by each pollutant’s measured low-speed emission sensitivity (Spearman ρ = 1.00, n = 4), a sensitivity we recover independently from the same detector stream. The practical consequence is that event-emissions claims based on a single direction should be treated as demand estimates unless a control corridor is included.

Abstract: Generative recommendation must connect predicted identifiers to a catalog that changes after training. New items precede feedback, identifier assignments evolve, and request rules constrain which outputs can be served. This survey compares item admission, semantic-ID evolution, and model adaptation through their update dependencies and dynamic operating conditions. A coupled-state framework connects catalog events to item assignments, model parameters, histories, and access structures. We use it to explain when metadata retrieval, targeted editing, continual learning, recoding, and request filtering address the relevant bottleneck. Three findings synthesize interface dependencies and their operating conditions: access and feedback learning require separate tests; recoding requires coordinated model and resolver handoffs; candidate access, item resolution, and eligibility expose different failures. From these findings and the heterogeneous cost evidence, we derive an evaluation principle: assess update quality against complete cost within the item's available lifetime. Six source-located comparison groups show when cohort choice reverses a baseline preference, mapping repair leaves learning unresolved, and evaluation or filtering changes the meaning of a gain. A conditional method-selection table connects these contrasts to the information, migration work, and service window required by each route. The resulting evaluation guidance preserves early access-only intervals, measures quality by item age, and accounts for preparation, updates, and serving. Open questions concern when to switch or combine update routes, how to migrate identifier versions, and how admission translates into useful exposure.

Abstract: Evaluation validity can change which recommender appears better. Ranking a relevant item supplied by an evaluator differs from finding it in a catalog; generating a valid identifier differs from identifying one item. This focused survey examines 20 offline protocol studies within a 49-paper coded corpus and develops two findings. First, reported model or history-strategy orderings change when studies vary candidate pools, credit for ambiguous identifiers, or the benefit measured by fairness. Second, apparently conflicting results can concern different objects: selecting long-tail recommendations differs from recovering popular benchmark records, and reducing attribute recoverability differs from distributing user benefit. We explain these findings through the items a model can access, the outputs receiving credit, the held-out population, and the outcome being measured. The synthesis derives evaluation controls for interpreting utility, diversity, exposure, and fairness in LLM-based and generative recommendation.

Abstract: LATTICE is an operational research platform that converts live freeway detector streams into traffic state, prediction, emissions, cost, sensor health, and event-impact intelligence at one-minute resolution. Deployed on the Las Vegas metropolitan freeway network, LATTICE ingests measurements from approximately 1,000 Nevada Department of Transportation FAST detector locations every 60 seconds and processes them through four concurrent engines: a network health and sensor validation engine, an EMFAC-based emissions estimator covering 18 pollutant and energy metrics, a monetized impact engine grounded in the EPA Social Cost of Carbon, BenMAP per-ton health damage values, and USDOT travel-time valuation, and a corridor-specific graph-based spatio-temporal prediction service producing 15-, 30-, 45-, and 60-minute speed forecasts with continuous online MAE and RMSE tracking. All outputs are delivered to a six-view interactive dashboard viaWebSocket, persisted under tiered retention policies, and surfaced through automated daily and event-driven operational reports. By unifying live detector ingestion, environmental impact estimation, economic valuation, multi-horizon forecasting, and operational reporting within an extensible deployed system, LATTICE demonstrates a practical architecture for next-generation intelligent transportation systems in rapidly growing urban environments.

Abstract: Location-based digital out-of-home (DOOH) media is becoming the fastest-growing offline advertising channel. Global OOH spending is projected to reach $54.2 billion in 2025, including $25.5 billion in digital spending, rising to $28.0 billion in 2026; North American OOH revenue has reached $9.46 billion, with digital screens contributing 36.3%. However, in-store attribution conflicts with privacy regulation: 20 U.S. states have enacted comprehensive privacy laws, most treating precise geolocation as sensitive data, while some prohibit location-data sales within a 1,750-ft (≈533-m) radius, creating an approximately 26-fold gap relative to the 20-m precision required for geofence attribution. We develop an identifier-free region-level spatiotemporal framework that learns counterfactuals from 12-week pre-placement foot-traffic sequences, represents spatial dependence through a sparse neighborhood graph, and combines hierarchical Bayesian estimation with a distance-decay kernel. Privacy computation runs at the edge: 15-min events are aggregated into 1-h windows, windows with fewer than 20 independent events are suppressed, each device contributes once per window, and differential-privacy noise is added; raw coordinates, continuous trajectories, and stable identifiers are not centrally stored. Posterior increments are converted into revenue weights, while placement selection is solved as a budget-constrained submodular coverage problem using a sparse coverage index, max-heap lazy greedy search, and partial enumeration of three candidate placements, retaining the 1−1/e approximation guarantee. Calibration used 23 cities, 133 authorized dealers, 23,901 placement weeks, 3.05 million geofences, and 68 million geocoded POIs with positioning error radii ≤20 m, yielding observed in-store increments of 10.7% (95% CI: 7.9%–13.5%) and 9.8% (6.6%–13.0%). After degrading resolution to 533 m and setting ε=1.0, estimation deviation was 1.8 percentage points, 95% interval coverage was 93.6%, and the minimum detectable increment at 80% power was 4.6%. Spillovers were significantly positive and decayed monotonically within 1.2 km; ignoring them would underestimate increment by about 2.1 percentage points. At the same budget, effective reach increased by 23.4%, or media cost fell by 18.6% at the same increment, demonstrating a deployable attribution-and-allocation pipeline under coarse geolocation constraints.

Abstract: Enterprise accounts-payable systems increasingly issue negotiable paper instruments on blank security stock rather than on pre-printed, entity-specific stock. This operational model reduces inventory and tray-management overhead, but it also removes a physical access-control boundary: possession of entity-specific paper no longer constrains which legal entity, bank account, routing data, or instrument number can be rendered. This paper characterizes the resulting threat surface and presents a multi-entity disbursement platform (MDP) that reconstructs analogous controls in software. The implemented core combines deterministic payload canonicalization and Merkleroot binding, WebAuthn-based authorization in which the batch root is supplied as the server challenge, an idempotent reserve– render–confirm sequence-allocation state machine backed by PostgreSQL transactional advisory locks, and database-enforced tenant isolation using Row-Level Security (RLS). The broader architecture also specifies a post-render issuance guard, tamperevident audit anchoring, and Positive Pay export controls; these components are explicitly distinguished as implemented, partial, or design-stage. A prototype implemented in Node.js and PostgreSQL was evaluated on an 8-core AMD workstation. Cryptographic synthesis scaled linearly with batch size (R 2 = 0.999), adding approximately 8.40 µs per item and remaining below 17 ms at 2,000 items. WebAuthn assertion verification averaged 0.93 ms. In a nofailure concurrency benchmark comprising 10,000 total sequence allocations and up to 128 overlapping request lanes, the allocator produced no duplicate or omitted allocations. These results establish low computational overhead and concurrency robustness for the measured mechanisms; they do not constitute a formal proof of end-to-end security. Finally, public commercial-banking materials illustrate an external-control asymmetry in Positive Pay workflows: exception decisions are often exposed through online treasury interfaces whose authorization semantics are not cryptographically bound to the original internal issuance digest. We therefore argue for transaction-bound authorization at the bank-side exception boundary as a future interoperability objective. Index Terms—Disbursement security, blank-stock printing, WebAuthn, FIDO2, Positive Pay, multi-tenant isolation, Merkle tree, tamper-evident logging, PostgreSQL Row-Level Security.

Abstract: Digital computation enforces stability physically, by thresholded restoration at every gate. Analog and wave computation executes directly in its substrate, so stability must instead be supplied informationally, through acquisition, identification and correction. We treat that obligation as a stability contract and establish its resource requirements. For independent Gaussian drift, holding n channels at a fixed mean-square distortion below the innovation variance requires feedback at order n bits per cycle, so a modality whose useful feedback capacity stays bounded fails beyond a computable channel count, whereas component-resolved observation delivers that information in one parallel acquisition. Information rate is not sequential depth. Maintenance separates into acquisition depth, feedback information and traffic, and host arithmetic and state. Within the declared class, a projected heavy-ball law driven by measured residuals achieves constant acquisition depth together with the linear information and arithmetic floors, at order-one auxiliary state per channel, reaching tolerance in four to five acquisitions on the tested monitor families. A routing-closure condition follows: a physical accelerator stays in the workload’s scaling class only when maintenance traffic does not cross the host boundary at a higher asymptotic rate than the workload interface. Evidence spans photonic, in-memory, large-scale simulation and quantum loop-closure studies.