Blockchain Papers

Hitoshi Okada

No abstract is available for this record.

Osman Biçer, Alptekın Küpçü

No abstract is available for this record.

Rahul Kumar Thatikonda, Sucharitha Donepudi

Legacy enterprise resource planning (ERP) systems serve as the operational backbone of global commerce but often create bottlenecks due to their rigid, monolithic design. As organizations incorporate artificial intelligence (AI), these outdated systems struggle to support high-speed, parallel workflows, creating a significant integration challenge. This paper introduces a non intrusive modernization approach that overlays a decentralized multi-agent system (MAS) onto existing infrastructure without requiring invasive code changes. By developing a digital twin of the order-to-cash (O2C) process, we train autonomous agents through multi-agent reinforcement learning (MARL) to manage credit validation, inventory allocation, and fulfillment. We adapt the centralized training, decentralized execution (CTDE) framework to meet O2C constraints, enabling agents to learn globally optimal strategies while operating independently. Simulation results show that this architecture surpasses rule-based robotic process automation (RPA) baselines, increasing total throughput by 6.9% over a monolithic setup, though at a 6.3% error rate due to aggressive allocation policies. These results indicate that decentralized agent-based orchestration provides a scalable approach for modernizing legacy ERPs, offering increased agility without the risks associated with platform replacement.

Igor Calzada

This record contains the presentation materials prepared for the Data for Policy July Fireside Chat, “Digital Infrastructures of Democracy,” delivered online on 20 July 2026 by Professor Igor Calzada and chaired by Professor Marta Poblet. The talk presents Calzada’s action-research programme on digital citizenship and the democratic governance of digital infrastructures. Building upon the Oxford Research Encyclopedia article Digital Infrastructures of Democracy, it conceptualises democracy as being increasingly mediated by three interconnected infrastructural layers: material infrastructure, data infrastructure and algorithmic infrastructure. These layers are not politically neutral; their ownership, design and governance shape participation, rights, public accountability and the distribution of power. The presentation connects this conceptual framework with research on AI economics, Web3 decentralisation, the Digital Metropolis, EcoTechnoPolitics, territorial digital inclusion, data cooperatives and anticipatory AI governance. Particular attention is devoted to evidence from the Basque Country and Gipuzkoa, including the emerging supercomputing and quantum ecosystem associated with IBM Quantum System Two. The Basque Country is examined not as a model to be replicated mechanically, but as a city-regional laboratory whose lessons can support context-sensitive institutional learning elsewhere. The central argument is that democratic resilience requires the alignment of technical design, institutional reform and civic agency. Advanced computational capacity becomes a democratic public capability only when institutions can govern technological dependencies, territorialise benefits, ensure accountability, respect ecological limits and preserve meaningful opportunities for public participation and contestation.

Aditya Saraf, Ioannis Kaklamanis, Sarisht Wadhwa, Fatima Elsheimy

Censorship resistance is the defining advantage of blockchains over their centralized counterparts. Yet block proposers censor transactions for many reasons, from legal consequences to economic incentives. We study economically-incentivized censorship, modeled by an adversary who bribes proposers to exclude a target transaction, and define the economic censorship resistance (eCR) of a transaction as the adversary's expected cost of successful censorship divided by the user's expected payment for inclusion. Single-proposer systems are structurally weak by this measure: under a first-price auction the adversary need only match the user's bid, and fee burning pushes eCR to a few percent of what the user pays. We therefore turn to multiple concurrent proposers (MCP), where block capacity is divided among $n$ proposers and the block is the union of their sub-blocks. While MCP can substantially increase the cost of censorship by requiring the adversary to bribe many proposers, it also introduces transaction duplication, reducing throughput. The resulting trade-off depends critically on the transaction fee mechanism (TFM), which determines how fees are shared among competing proposers. We create a game theoretic model where validators construct blocks from a shared mempool, subject to an adversary's bribery attempt. We provide an algorithm that solves for the mixed equilibrium of a given mempool, which is characterized by the probability of including each transaction. This algorithm works for a wide class of TFMs, and allows us to calculate the expected throughput and censorship resistance for any bid distribution. We then use simulations to show how the eCR and throughput vary as the number of proposers increases. We compare three TFMs, finding that the duplication-penalizing TFM dominates the others across many settings. We also validate our findings with empirical Ethereum data.

Tahrim Hossain, Faisal Haque Bappy, Tarannum Shaila Zaman, Tariqul Islam

Blockchain platforms have grown into an ecosystem of independent networks, and a growing class of applications now requires smart contracts on separate chains to act as one. Such operations must be atomic, yet immutability makes this fundamentally harder: a confirmed transaction cannot be reversed, so the rollback on which classical atomic commitment protocols depend is unavailable. Two challenges follow. Contract state must be held across an operation whose outcome is not yet known, and each chain's execution outcome must be established even though no chain can observe another. In response, we introduce a framework that achieves atomicity through forward-only correction, resolving incomplete operations with new on-chain transactions rather than reversal. The framework bounds how long contract state is held and confines contention to the state an operation touches, and it establishes outcomes from an on-chain record of what each chain executed, without relying on any single coordinating party. This work lays the foundation for atomic coordination of general smart contract operations across heterogeneous blockchains.

Jordan A Hollarsmith, Rebecca Cates, Leah Valdes, Lia Domke · 8 authors

Abstract Background and Aims Kelp aquaculture is a growing industry in Alaska, where it has the potential to influence habitat provisioning for commercially and culturally important species. Given the seasonal presence of cultivated kelp mid-water, farms present a potentially novel habitat that could influence species’ use of the nearshore zone. Methods Over 1 year, we surveyed two commercial farm sites paired with comparable non-farm control sites, assessing how the biotic assemblages and abiotic environment might be influenced by seasonal farm activities. We used environmental DNA to describe benthic and pelagic fish communities, and visual surveys on SCUBA to identify benthic macroinvertebrates and characterize the benthic environment. We also measured nutrient concentrations, temperature and salinity. Key Results Fish communities were structured significantly by season (30 % of variation) and depth (7.9 %), whereas macroinvertebrate communities were structured significantly by region (17.5 %). Owing to a missed sample event, the significance of the farm treatment could not be calculated; however, the farm treatment accounted for 4.3 and 22.3 % of variation in the fish and macroinvertebrate communities, respectively. The high effect size of both farm treatment and region in explaining macroinverterbate community variation suggests that communities might be spatially heterogeneous rather than responding specifically to presence of the farm. The measured abiotic variables differed seasonally and by depth, with no significant difference by farm treatment. Conclusions Our findings suggest minimal influence of the kelp farms on the biotic communities or abiotic environments surveyed. We did not observe unique biotic assemblages in the kelp farm sites that would be indicative of farms providing novel habitat-provisioning services. At the farm size and location surveyed, kelp aquaculture did not appear to detract from the important habitat already present. Our results should aid regulators as they make decisions to support the sustainable growth of the industry.

Haihan Zhang, Chenheng Zhang, Zhiquan Qi, Zhouchen Lin

Whether exact scalar feedback intrinsically incurs the additional dimension $d$ paid by known zeroth-order methods remains open even for Lipschitz convex optimization. For a universal Lipschitz scale, the value only bound $O(d^2\log(d+1)\log(1/ε))$ and two-point bound $O(dε^{-2})$ yield the upper bound $\widetilde O\left(d\min\{d,ε^{-2}\}\right)$. By contrast, prior lower bounds for arbitrary randomized algorithms give only $Ω(\min\{d,ε^{-2}\})$, leaving a factor $d$ unexplained. We close this gap, up to logarithmic factors, for arbitrary adaptive randomized algorithms minimizing a convex objective with a universal Lipschitz scale over the $d$-dimensional Euclidean unit ball, where each query returns only the exact scalar value. Let $T_ε$ denote the minimum number of queries required to return an $ε$-suboptimal point with probability at least $1/2$, uniformly over the function class. We prove that \[T_ε\ge c\,\frac{d\min\{d,ε^{-2}\}}{\log\!\bigl(\min\{d,ε^{-2}\}\bigr)},\] for $d\ge d_0$ and $0<ε\leε_0$, where $c,ε_0>0$ and $d_0\in\mathbb N$ are universal constants. This gives $Ω\left(\frac{d}{ε^2\log(1/ε)}\right)$ in the low-accuracy regime $ε\ge d^{-1/2}$ and $Ω\left(\frac{d^2}{\log d}\right)$ in the high-accuracy regime $ε\le d^{-1/2}$ with the latter independent of $ε$. These bounds match the corresponding upper bound up to logarithmic factors. To our knowledge, this is the first near-optimal lower bound for arbitrary adaptive randomized algorithms throughout both accuracy regimes of exact value Lipschitz convex optimization. The proof uses a random support function hard family and develops a posterior mean energy method for adaptive exact max observations, in place of first-order zero chain constructions and noise based transcript inequalities.

Yi Yang, Hao Zhu, Haibo Li, Yuehua Chen · 5 authors

No abstract is available for this record.

C. Prabakaran, R. Kannadasan

Personalized gene editing demands robust mechanisms for privacy, ethical governance, and verifiable data integrity. This paper proposes ViBioChain, a modular blockchain-anchored architecture integrating five components: (1) differential chain-of-custody audit combining quantum fingerprinting with post-quantum signatures for immutable genomic audit trails; (2) proof-of-bioethical-compliance employing zero-knowledge proofs and AI-based ontology evaluation for automated bioethical gating; (3) federated genomic trust mesh (FGTM) enabling privacy-preserving collaborative model training with Renyi differential privacy accounting and trust-weighted federated aggregation; (4) ethical smart orchestration network for modular smart-contract-based workflow governance; and (5) genomic impact estimator via ethical explainability graphs (GIE-EEG) for ancestry-aware, ethically constrained phenotypic forecasting. Afterexpert-driven reconciliation, the implementation was rerun using 800 simulated individuals per dataset, 120 binary loci, five institutional clients, five independent seeds (42-46), and a true trust-weighted federated logistic aggregation path for FGTM rather than the earlier centralized accuracy proxy. Across three genomic cohorts and three domain-comparable baselines, ViBioChain achieved 92.16% ethical violation interception, 100.00% audit trail accuracy, 99.47% workflow traceability, 0.9183 ethical score alignment, and the highest global model accuracy among the tested methods (74.36%). The formal Renyi differential privacy accountant remained within budget ([Formula: see text], [Formula: see text]); however, the conservative clean-versus-noisy update leakage proxy did not support the earlier lowest-empirical-leakage assertion. That claim has therefore been removed. Additional IID and non-IID experiments show that severe Dirichlet client heterogeneity ([Formula: see text]) reduced final accuracy by 1.70-4.10 percentage points relative to IID partitions. The revised results provide a more conservative and reproducible blueprint for secure, ethically governed, and explainable genomic medicine in multi-institutional settings.

Jiping Yao, Shiwen Sun, Yan Liu

No abstract is available for this record.

Bright Samuel

NOXFORD ID is a seven-layer privacy-preserving identity verification architecture designed to address a specific fraud vulnerability in cross-institutional identity validation: the brittleness of exact-match verification against ordinary, legitimate variation in how names and dates are recorded across institutions. The architecture combines hardened Bloom-filter cryptographic linkage (privacy-preserving record linkage), field-separated fuzzy matching, a probabilistic decision engine with an explicit human-review tier, a tamper-evident cross-institutional integrity ledger, a genuine Schnorr zero-knowledge proof of identity knowledge, replay-resistant institution-specific key derivation, and a Root Certificate Authority layer aligned with Nigeria's NIMC Act 2026, which designates the National Identity Management Commission as Root Certification Authority for the country's National Public Key Infrastructure. Each layer is independently implemented and empirically tested against a labeled test corpus, with results reported transparently, including design flaws discovered and corrected during development. The paper documents a measured reduction in false rejection of legitimate citizens from 57% to 14% relative to a representative exact-match baseline, while maintaining zero fraud slip-through in testing, and explicitly states the system's current limitations, including evaluation corpus scale, dependence on enrollment-data integrity, and pending network-level threat-detection integration.

Ahmad Parr

\begin{abstract} We present a formally verified mathematical framework whose objective is to provide a common semantic foundation for eight traditionally distinct areas of mathematics:Set Theory, Category Theory, Type Theory, Mathematical Logic, Analysis, Algebra,Topology, and the proposed computational meta-domain \emph{METATRON}. Rather thantreating these disciplines as isolated foundations, the framework interprets each as afixed-point system generated by an intrinsic structural operator. This viewpoint allowsmathematical stability, convergence, and compositionality to be studied through a unifiedsemantic lens, where invariant structures emerge as fixed points of domain-specifictransformations. The principal contribution is the development of a universal fixed-point semanticsparameterized by a contraction coefficient governed by the golden ratio\[\phi=\frac{1+\sqrt5}{2},\]which acts as the canonical scaling constant throughout the framework.The resulting theory provides a common language in which recursive computation,categorical composition, logical inference, algebraic closure, topological continuity,and computational resonance may be analyzed within a single mathematical system. Three principal results are established. The first is the \emph{Goldilocks Theorem}. Beginning from the foundationalAxiom Zero and without introducing additional assumptions beyond the formaldevelopment, we prove that sovereign stability exists uniquely inside the interval \[0<q<1.\] Within this region every admissible resonance operator is contractive, every recursiveconstruction admits bounded evolution, and every authenticated computation preservesits constitutional invariants. Outside this interval either divergence or trivial collapsenecessarily occurs. Consequently, the interval $(0,1)$ becomes the unique admissiblestability zone for the entire framework. The second contribution is the \emph{Grand Unified Fixed-Point Theorem}. We show thatseven of the eight mathematical domains admit natural fixed points under theirfundamental structural operators. Set-theoretic closure, categorical composition,logical inference, algebraic completion, analytic contraction, topological continuity,and METATRON resonance each possess invariant objects satisfying \[F(x)=x.\] Type Theory occupies a distinguished position. Its primitive successor operator \[S(x)=x+1\] possesses no fixed point over the real numbers, establishing it as the uniquenon-contractive boundary of the framework. Rather than representing a defect,this exceptional behavior identifies the successor operation as the mathematicalsource of unbounded computation, recursion, induction, and Turing completeness.The absence of a fixed point therefore becomes a structural characterization ofcomputability itself, separating finite invariant mathematics from open-endedalgorithmic evolution. The third principal contribution introduces the \emph{Resonance Pipeline}, adepth-five computational operator acting on authenticated symbolic states.We prove that successive resonance iterations satisfy a $\phi$-contractivemapping whose limit exists, is unique, and is independent of evaluation orderunder the stated assumptions. Furthermore, the associated Trust Resonance Score \[\mathrm{TRS}=388.985128\] is shown to remain strictly positive, finite, and bounded throughout every stageof execution. These invariants establish computational stability for the resonancepipeline while providing quantitative guarantees regarding convergence andstructural consistency. All principal theorems presented in this work have been mechanically verifiedusing the Lean~4 proof assistant. Every completed theorem is proven withoutplaceholder axioms, admitted lemmas, or \texttt{sorry} declarations, yielding amachine-checkable corpus whose correctness is independently verifiable.The current formalization establishes complete verification for seven of theeight foundational domains considered. Equally important are the results that remain beyond present knowledge.Two major mathematical problems are intentionally left unresolved and areexplicitly identified as open conjectures rather than claimed theorems. The first concerns the Riemann Hypothesis, for which we investigate a$\phi$-contractive iterative framework converging toward the critical line$\operatorname{Re}(s)=\tfrac12$ without asserting a proof. The second concerns the Navier--Stokes existence and smoothness problem,where a corresponding $\phi$-stepping viscosity operator is proposed as apossible analytical framework while leaving the Millennium Prize questionentirely open. By explicitly distinguishing formally verified mathematics from ongoingresearch directions, the framework maintains a clear separation betweenestablished results and conjectural investigations. Overall, the present formalization achieves machine verification acrossseven of the eight proposed mathematical domains, corresponding toapproximately $87.5\%$ completion of the intended foundational program.The remaining domains coincide precisely with two of the deepest openproblems in contemporary mathematics, illustrating both the expressivepower and the current limitations of formal proof technology. The guiding methodological principle of the work is therefore not merelyformal verification but what we call \emph{constitutional honesty}:every completed theorem is mechanically certified, every assumption isexplicitly declared, every computational artifact is reproducible, and everyunsolved question remains honestly identified as an open mathematical problem.In this view, mathematical integrity is measured not by eliminating uncertainty,but by making the boundary between knowledge and conjecture mathematicallyprecise. \end{abstract}

Rodrigo Reis Lastra Cid

Digital governance, operationalized by technologies such as blockchain and decentralized autonomous organizations (DAOs), constitutes a phenomenon that redefines fundamental philosophical concepts for collective life. This article undertakes a systematic philosophical analysis of this phenomenon, structured around foundational conceptual problems. We begin from four axes of inquiry: (1) the ontological problem of the nature of code-based entities; (2) the epistemic problem of trust and knowledge in algorithmic systems; (3) the normative problem of authority, legitimacy, and justice in automated governance; and (4) the logical problem of the limits of normative formalization. The analysis demonstrates that these problems materialize at the necessary intersection of philosophy with computer science, law, and economics. It concludes that digital governance is, in essence, a philosophical enterprise, whose responsible development demands prior conceptual clarity regarding the nature of collective agency, the foundations of trust, the embedding of values into code, and the structural limits of automating social normativity.

Pan Wang, Xiaofeng Lu

No abstract is available for this record.

Mikhail Perepelitsa

In this paper, we develop an open-economy macroeconomic model of a Proof-of-Stake network to analyze nominal token-price dynamics and the systemic effects of speculative capital. We first consider a network populated solely by active utility users, who finance network activity through a steady exogenous inflow of fiat currency. We prove the existence of a unique, globally asymptotically stable steady-state equilibrium with a well-defined nominal token price and derive a closed-form expression for the network's relaxation time. Calibrating the model using parameters representative of the current Ethereum network, we estimate a relaxation half-life of approximately 46 years. This extreme macroeconomic inertia implies that the token price may remain persistently displaced from its evolving steady-state benchmark, producing sustained price overshooting as the network adjusts to changing fundamentals. We then introduce an Investor class to examine the effects of passive and active speculative capital. We show that passive institutional staking compresses the native staking yield and creates a structural imbalance that systematically raises the nominal token price while shifting consensus ownership away from active utility users. Active speculative capital has a qualitatively different effect. In response to capital shocks, the Consumer class's rigid preference for fiat-denominated consumption generates an endogenous constant-value strategy. This mechanism shifts staked-token ownership from the Investor class toward active utility users, with potentially favorable implications for consensus decentralization.

Faisal Haque Bappy, Tahrim Hossain, Tarannum Shaila Zaman, Tariqul Islam

Cross-chain DAOs face unique security challenges that go beyond traditional single-chain vulnerabilities. This paper identifies and categorizes four critical attack vectors in cross-chain DAO governance: bribery attacks, token control exploits, human-computer interaction deceptions, and protocol vulnerabilities. We propose a comprehensive security framework with a multi-layered architecture that integrates cryptographic trust anchors, fraud-resistant consensus mechanisms, and decentralized validation techniques to address these threats. Our framework introduces novel components, including a Governance Kernel with on-chain rule verification, a Cross-Chain Trust Layer using threshold cryptography, and a Resilience Layer offering time-locked decision reversals and progressive dispute resolution. By establishing a structured set of countermeasures, this work lays the foundation for secure, transparent, and attack-resistant governance across diverse blockchain environments.

Joseph Tafese, Karthik Nukala, Hassen Saïdi, Natarajan Shankar · 6 authors

We present a case study on proof-driven software understanding of mature, security-critical infrastructure. While formal methods are traditionally applied during the design phase, we present our experience applying formal reasoning onto a mature industrial C++ codebase. We focus on a formal analysis of the core algorithm that implements the Stellar blockchain's SDEX order book. By combining large language models (LLMs), Prototype Verification System (PVS), and SeaHorn, we are able to prove core properties of the production codebase. Our approach also identified an inconsistency in documentation related to the reachability of an exception location. Most importantly, however, we produce artifacts that make it easy for code changes to be checked against established invariants. This work demonstrates how the strategic combination of theorem proving and model checking provides a path for delivering robust assurance to legacy systems.

Gonçalo Frutuoso, Diogo Rodrigues, Alexandre P. Francisco, Cátia Vaz

Verifying academic credentials remains difficult: records are held by individual institutions in proprietary systems, verification is slow and manual, and counterfeit qualifications are widespread. Blockchain-based registries have been proposed as a remedy, but existing systems tend to anchor certificate hashes without binding them to a verifiable identity, without an explicit mechanism to accredit issuing institutions, and without support for correcting or revoking credentials once issued. This paper investigates whether an infrastructure designed for regulated financial instruments can be repurposed to close these gaps. We present the design of a registry for identity-bound academic credentials that composes OnchainID self-sovereign identities (ERC-734/ERC-735) with the T-REX suite (ERC-3643): its trusted-issuer registry becomes an on-chain issuer-accreditation whitelist, and each certificate is represented as a signed, updatable claim bound to a student's identity and verifiable by any third party without a wallet, while sensitive fields are kept off-chain. We make explicit the tension between a transferable-security-token standard and non-transferable credentials, clarifying which of its guarantees carry over. We validate the design with a reference implementation covering the full certificate life cycle and evaluate it in terms of gas cost, scalability, latency, and security, quantifying the overhead relative to a hash-anchoring baseline.

Andrei Neagu, Eeham Khan, Leila Kosseim

This paper presents our system for Task 3 of the CLEF 2026 FinMMEval Lab, which requires daily long, flat, or short trading decisions for Bitcoin (BTC) and Tesla (TSLA) using news and historical market data. We formulate the problem as a discrete-action Markov Decision Process and compare four deep reinforcement learning algorithms: Policy Gradient (PG), Proximal Policy Optimization (PPO), Deep Q-Learning (DQL), and Deep Deterministic Policy Gradient (DDPG). The agents use technical indicators, cyclical calendar encodings, and daily news sentiment scores produced by LLaMA 3.2 1B. To reduce overfitting and align training with the objective of outperforming buy-and-hold, we introduce an alpha reward based on excess market return and randomize episode start dates. Hyperparameters are optimized with Ray Tune over 180 trials per algorithm-asset pair, with early stopping and model selection based on validation Sharpe ratio. On the CLEF Task 3 test set, DDPG achieves the strongest overall performance. DQL was selected a priori for the live endpoint because it obtained the highest validation Sharpe ratio, with selection performed without access to the test period. For TSLA, DDPG and DQL achieve cumulative returns of 54.96% and 52.62%, respectively, compared with 16.45% for buy-and-hold. For BTC, DDPG achieves a positive return of 1.58% while buy-and-hold declines by -34.27%. The results also reveal a substantial validation-to-test generalization gap, highlighting the difficulty of transferring policies selected in bull-market conditions to a bear-market regime.

Varshitha C.N, Leela M.H

ABSTRACT The rise of cryptocurrency has transformed the way individuals perceive and participate in investment activities. As digital assets continue to gain global recognition, major digital assets, including Bitcoin, Ethereum, Ripple (XRP), and Tether, have gained significant interest among investors seeking alternative avenues for wealth creation. The growing adoption of blockchain technology, expansion of digital financial services, and increasing accessibility of cryptocurrency trading platforms have contributed to the rising popularity of crypto investments in India. The present study explores the emerging cryptocurrency investment trends in India through the analysis of secondary information collected from scholarly articles, industry reports, government documents, and other credible sources. The research focuses on important areas including cryptocurrency adoption, market growth, investor demographics, regulatory developments, and investment behaviour. The findings indicate that investments in cryptocurrencies have experienced notable growth in India, particularly among younger investors, despite challenges related to market volatility and regulatory uncertainty. The study concludes that digital assets are gradually becoming an important part of the Indian investment environment and may continue to influence future investment patterns. Keywords:, Digital Assets, Investment Trends, Blockchain Technology, Investor Behaviour.

Metehan Ortakarpuz, Fatih Mangır

Smart contracts, by enabling the automated and transparent execution of ag-reements through blockchain technology, promise to simplify complex cross-border transactions and reduce reliance on intermediaries, possessing trans-formative potential in international trade. However, significant legal uncerta-inties remain regarding their legal nature, contract validity, applicable law, jurisdiction, and liability. This study aims to examine the legal framework of blockchain-based smart contracts in international trade and to comparatively evaluate regulatory approaches in different legal systems. The research emp-loys a methodology that combines doctrinal legal analysis with comparative regulatory review, focusing particularly on legal recognition, regulatory inst-ruments used, general regulatory approaches, and emerging key legal issues. The findings of the international legal framework review and the comparative analysis conducted in the EU, United Kingdom, USA, some Asian countries, and Türkiye show that widespread adoption of smart contracts is still limited due to legal uncertainties and unresolved doctrinal questions, while regula-tory approaches are still evolving.

Andrea Caravaggio, Silvia Leoni

Abstract The management of infectious diseases increasingly relies on innovative but costly pharmaceutical treatments, raising complex trade-offs between epidemiological containment, fiscal sustainability, and institutional coordination. We develop a spatially structured agent-based model in which decentralized health authorities allocate treatment under local budget constraints while infection spreads across a two-dimensional lattice through neighborhood spillovers. Within each location, treatment intensity is chosen endogenously, interacting with local GDP dynamics and pricing conditions. Simulation results reveal that purely decentralized optimization mitigates but does not reverse infection growth within policy-relevant horizons, generating persistent spatial heterogeneity in both epidemiological and economic outcomes. We then introduce bounded spatial policy interaction, showing that partial coordination substantially improves containment but may increase the persistence of fiscal engagement. Extending the model to heterogeneous and time-varying pricing, we find that price discrimination amplifies medium-run infection and fiscal pressure under decentralization. However, when surplus revenues finance endogenous R&amp;D, treatment efficacy improves over time, generating a feedback mechanism in which innovation mitigates long-run epidemiological and economic losses. Our findings highlight the critical interplay between spatial structure, decentralized decision-making, pricing design, and innovation incentives in shaping epidemic outcomes. Effective management of high-cost treatments requires not only medical efficacy but also institutional coordination and carefully designed market mechanisms.

Daishiro Hirashima

VSAT (Vulnerability Saturation Auditing) makes the completeness of an LLM security audit a measurable, calibrated quantity. It runs several deliberately diverse LLM audit "lenses" over the same codebase and treats each lens as a capture occasion, so the overlap structure yields (i) a far more complete union vulnerability list and (ii) a Chao2 richness estimate of the undiscovered population. VSAT combines this statistical discovery saturation with a deterministic OWASP ASVS structural coverage into a single completeness score (Phi = C_struct x C_hat) and derives a saturation-based stopping rule. Implemented as a security-audit skill on the cc-rsg-web agentic platform, VSAT attains 94.7-100% category recall and 99.1% code-verified precision on three documented benchmark applications (NodeGoat, django.nV, DVWA), delivers a 2.4-3.4x discovery uplift over a single pass with a per-finding proof-of-concept and regression test, and its non-zero residual estimate is corroborated by an independent real-world field validation. To our knowledge this is the first method to bring capture-recapture completeness estimation and a saturation stopping rule to LLM-based web-application security auditing.