Enterprise adoption of permissioned blockchains remains limited due to the complexity and cost of integrating legacy systems. We present a modular adapter architecture that bridges enterprise applications with blockchain networks, designed to support small and medium-sized enterprises with limited technical resources. The architecture provides five key modules: (1) configurable data extractors supporting diverse interfaces such as APIs and file uploads, (2) data transformers that can convert to standard formats, (3) messaging middleware to ensure operations can tolerate lack of connectivity and traffic spikes, (4) blockchain loader to commit transactions to the blockchain, and (5) status visibility to collect and expose runtime metrics that support operational transparency. We validated the adapters through a pilot deployment in a real-world fruit supply chain, involving three distinct enterprises. The pilot achieved blockchain integration with minimal workflow disruption, demonstrating the usefulness of these adapters for practical interoperability of existing systems with the blockchain.
Public blockchains impose an inherent tension between regulatory compliance and user privacy. Existing on-chain identity solutions require centralized KYC attestors, specialized hardware, or Decentralized Identifier (DID) frameworks needing entirely new credential infrastructure. Meanwhile, over four billion active X.509 certificates constitute a globally deployed, government-grade trust infrastructure largely unexploited for decentralized identity. This paper presents zk-X509, a privacy-preserving identity system bridging legacy Public Key Infrastructure (PKI) with public ledgers via a RISC-V zero-knowledge virtual machine (zkVM). Users prove ownership of standard X.509 certificates without revealing private keys or personal identifiers. Crucially, the private key never enters the ZK circuit; ownership is proven via OS keychain signature delegation (macOS Security.framework, Windows CNG). The circuit verifies certificate chain validity, temporal validity, key ownership, trustless CRL revocation, blockchain address binding, and Sybil-resistant nullifier generation. It commits 13 public values, including a Certificate Authority (CA) Merkle root hiding the issuing CA, and four selective disclosure hashes. We formalize eight security properties under a Dolev-Yao adversary with game-based definitions and reductions to sEUF-CMA, SHA-256 collision resistance, and ZK soundness. Evaluated on the SP1 zkVM, the system achieves 11.8M cycles for ECDSA P-256 (17.4M for RSA-2048), with on-chain Groth16 verification costing ~300K gas. By leveraging certificates deployed at scale across jurisdictions, zk-X509 enables adoption without new trust establishment, complementing emerging DID-based systems.
Solana has experienced rapid growth due to its high performance and low transaction costs, but the extremely low barrier to token issuance has also enabled widespread Rug Pulls. Unlike Ethereum-based Rug Pulls, which often rely on malicious smart-contract logic, Solana's unified SPL Token program shifts fraudulent execution toward on-chain behavioral manipulation. However, existing research has not systematically examined these Solana-specific Rug Pull patterns, and no public Solana Rug Pull dataset is available for empirical research. To bridge this gap, we present a large-scale measurement study of Rug Pulls on Solana. We manually verify 68 community-reported incidents and curate a benchmark of 117 confirmed Rug Pull tokens, from which we distill three representative on-chain behavioral patterns: Freeze Authority Abuse, Liquidity Withdrawal, and Pump-and-Dump. Guided by these patterns, we design a behavior-guided candidate identification and human-validation pipeline. We apply this pipeline to 100,063 tokens newly issued on Orca, Raydium, and Meteora during the first half of 2025, identifying 76,469 Rug Pull tokens. A random manual audit of 382 samples estimates a labeling false-positive rate of 0.26\%, supporting the reliability of the dataset. We release the resulting dataset and use it to characterize the Solana Rug Pull ecosystem. Our analysis shows that Rug Pulls on Solana exhibit extremely short lifecycles, strong price-driven dynamics, severe economic losses, and highly organized group behaviors. These findings provide new insights into the Solana Rug Pull landscape and support the development of effective on-chain defense mechanisms.
Sara Aguincha, Emanuel Nunes, Samih Eisa, Miguel L. Pardal
Sensor technologies have evolved to a point where it is now practical to monitor products along the supply chain. The collected data can be stored in a decentralized way using blockchain technology. However, ensuring the reliability of the sensed data is a critical challenge. In other words, we need to trust the data that we write to the blockchain. In this work, we propose ChainGuards, a decentralized system that uses product-specific rules to verify data collected across the supply chain, with particular focus on sensor-derived information, issuing warnings and triggering audits when anomalies are detected. We evaluated ChainGuards using data from a real cherry supply chain deployment. The result shows that the implemented solution provides reliable verification of supply chain data with low performance overhead, able to correctly detect data discrepancies and inconsistencies.
This paper presents an analysis of five years (2021 - 2025) of conference discourse across six digital art conferences, aiming to trace thematic shifts associated with the rapid development of emerging technologies, namely artificial intelligence (AI), immersive technologies (including XR and the metaverse), and blockchain technologies and non-fungible tokens (NFTs). The results indicate a marked increase in AI-related contributions, while immersive technologies maintain a relatively stable share of the discourse, and blockchain- and NFT-based works remain marginal. Overall, whereas immersive technologies and blockchain-related topics exhibit relative stability, AI shows a significant rise after 2022, emerging as a dominant theme within digital art conference discourse.
Saber Zerhoudi, Kanishka Ghosh Dastidar, Felix Klement, Artur Romazanov · 12 authors
Moltbook is a social network where every participant is an AI agent. We analyze 1,312,238 posts, 6.7~million comments, and over 120,000 agent profiles across 5,400 communities, collected over 40 days (January 27 to March 9, 2026). We evaluate the platform through three layers. At the interaction layer, 91.4% of post authors never return to their own threads, 85.6% of conversations are flat (no reply ever receives a reply), the median time-to-first-comment is 55 seconds, and 97.3% of comments receive zero upvotes. Interaction reciprocity is 3.3%, compared to 22-60% on human platforms. An argumentation analysis finds that 64.6% of comment-to-post relations carry no argumentative connection. At the content layer, 97.9% of agents never post in a community matching their bio, 92.5% of communities contain every topic in roughly equal proportions, and over 80% of shared URLs point to the platform's own infrastructure. At the instruction layer, we use 41 Wayback Machine snapshots to identify six instruction changes during the observation window. Hard constraints (rate limit, content filters) produce immediate behavioral shifts. Soft guidance (``upvote good posts'', ``stay on topic'') is ignored until it becomes an explicit step in the executable checklist. The platform also poses technological risks. We document credential leaks (API keys, JWT tokens), 12,470 unique Ethereum addresses with 3,529 confirmed transaction histories, and attack discourse ranging from template-based SSH brute-forcing to multi-agent offensive security architectures. These persist unmoderated because the quality-filtering mechanisms are themselves non-functional. Moltbook is a socio-technical system where the technical layer responds to changes, but the social layer largely fails to emerge. The form of social media is reproduced in full. The function is absent.
Alexander Kropiunig, Svetlana Kremer, Bernhard Haslhofer
Crypto Key Opinion Leaders (KOLs) shape Web3 narratives and retail investment behaviour. In volatile, high-risk markets, their credibility becomes a key determinant of their influence on followers. Yet prior research has focused on lifestyle influencers or generic financial commentary, leaving crypto KOLs' understandings of motivation, credibility, and responsibility underexplored. Drawing on interviews with 13 KOLs and self-determination theory (SDT), we examine how psychological needs are negotiated alongside monetisation and community expectations. Whereas prior work treats finfluencer credibility as a set of static credentials, our findings reveal it to be a self-determined, ethically enacted practice. We identify four community-recognised markers of credibility: self-regulation, bounded epistemic competence, accountability, and reflexive self-correction. This reframes credibility as socio-technical performance, extending SDT into high-risk crypto ecosystems. Methodologically, we employ a hybrid human-LLM thematic analysis. The study surfaces implications for designing credibility signals that prioritise transparency over hype.
This paper presents a detailed analysis of the environmental impact of Chia Network (Chia for short), a green-claimed blockchain, which uses a Proof of Space and Time (PoST) consensus mechanism. While Chia claims to be a sustainable alternative to Proof-of-Work-based blockchains, our results show that its resource-intensive initialization phase and ongoing operations lead to carbon emissions 18x higher than claimed (0.88 MtCO2/year), exceeding mainstream "green" blockchains by orders of magnitude. We combine experimental measurements from a controlled testbed (Grid'5000) with theoretical modeling of operational and embodied emissions to assess Chia's true sustainability profile.
Data integration among various stakeholders in the healthcare space remains a challenge, despite the impressive advances in Health AI in the past decade. There is a lot of ``messy'' non-standard but structured data that are continually being collected from personal health devices. While efforts such as the Fast Healthcare Interoperability of Resources (FHIR) are underway in standardizing the data representation formats, there is currently a gap in the standard in addressing the health data ecosystem's decentralized nature. As we see explosive growth in chronic diseases such as diabetes, healthcare providers need Observations of Daily Living (ODL) of their patients to treat them effectively. The best way to obtain ODL is through personal health devices. However, such devices are manufactured by various device makers, and they may not follow standards or integrate with existing Electronic Health Record (EHR) systems. It is also imperative that any data sharing that happens will occur in a secure and trustworthy environment, without being too restrictive, i.e., tied to a particular EHR vendor. This paper presents a scalable solution to bridge this gap using a system that implements semantic web and blockchain technologies. Our solution uses FHIR compliant semantic web based data templates in conjunction with smart contracts on the blockchain to provide healthcare providers with insights on their patients' daily activity that cannot be readily determined solely through patient encounters at the clinic.
Climate change has intensified the urgency of effective carbon sink solutions, yet the integration of Information and Communications Technologies (ICT) in these systems remains fragmented despite its transformative potential. This paper provides a comprehensive analysis of ICT applications in carbon sink projects from both economic and engineering perspectives, a dual lens approach rarely explored in the existing literature. In carbon trading, blockchain has improved transaction speed by 40%, while AI-based optimizations have reduced operational costs by 15% in projects such as Petra Nova.Through systematic examination, we identify three key findings: (1) ICT transforms carbon economics through digital financing platforms and blockchain-based trading systems, with AI enhancing price prediction, though data interoperability remains challenging; (2) digital technologies advance both natural and artificial sequestration from forest monitoring to Carbon Capture, Use and Storage (CCUS) optimization, yet lack integrated real-time control solutions; (3) realizing ICT's full potential requires addressing its environmental costs, strengthening policy support, and fostering interdisciplinary collaboration. By bridging the economic engineering divide and mapping current applications alongside future opportunities, this paper demonstrates that deeper integration of digital technologies is essential to scale carbon sink solutions to meet climate targets.
Process attestation verifies human authorship by collecting behavioral biometric evidence, including keystroke dynamics, typing patterns, and editing behavior, during the creative process. However, the very data needed to prove authenticity can reveal intimate details about an author's cognitive state, health conditions, and identity, constituting sensitive biometric data under GDPR Article 9. We resolve this privacy-attestation paradox using zero-knowledge proofs. We present ZK-PoP, a construction that allows a verifier to confirm that (a) sequential work function chains were computed correctly, (b) behavioral feature vectors fall within human population distributions, and (c) content evolution is consistent with incremental human editing, all without learning the underlying behavioral data, exact timing, or intermediate content. Our construction uses Groth16 proofs over arithmetic circuits with Pedersen commitments and Bulletproof range proofs. We prove that ZK-PoP is computationally zero-knowledge, computationally sound, and achieves unlinkability across sessions. Evaluation shows proof generation in under 30 seconds for a 1-hour writing session, with 192-byte proofs verifiable in 8.2 ms, while incurring less than 5% accuracy loss in simulation at practical privacy levels (epsilon >= 1.0) compared to non-private baselines.
In blockchain applications, transaction confirmation is often treated as usability friction to be minimized or removed. However, confirmation also marks the boundary between deliberation and irreversible commitment, suggesting it may play a functional role in human decision-making. To investigate this tension, we conducted an experiment using a blockchain-based Connect Four game with two interaction modes differing only in authorization flow: manual wallet confirmation (Confirmation Mode) versus auto-authorized delegation (Frictionless Mode). Although participants preferred Frictionless Mode and perceived better performance (N=109), objective performance was worse without confirmation in a counterbalanced deployment (Wave 2: win rate -11.8%, p=0.044; move quality -0.051, p=0.022). Analysis of canceled submissions suggests confirmation can enable pre-submission self-correction (N=66, p=0.005). These findings suggest that transaction confirmation can function as a cognitively meaningful checkpoint rather than mere usability friction, highlighting a trade-off between interaction smoothness and decision quality in irreversible blockchain interactions.
As geopolitical, organizational, and technological fragmentation deepens, resilient digital collaboration becomes imperative. This paper develops a spectrum framework of polycentric digital ecosystems-nested socio-technical systems spanning personal, organizational, inter-organizational, and global layers. Integration across these layers is enabled by four technology clusters: AI and automation, blockchain trust, federated data spaces, and immersive technologies. By redefining digital ecosystems as distributed, adaptive networks of loosely coupled actors, this study outlines new pathways for crossborder coordination and innovation. The framework extends platform theory by introducing a multi-layer conceptualization of polycentric digital ecosystems and demonstrates how AI-enabled infrastructures can be orchestrated to achieve digital integration in a fragmented, multipolar world.
National governments are increasingly adopting blockchain to enhance transparency, trust, and efficiency in public service delivery. However, evidence on how these technologies are governed across national contexts remains fragmented and overly focused on technical features. Using Polycentric Governance Theory, this study conducts a systematic review of peer-reviewed research published between 2021 and 2025 to examine blockchain-enabled public services and the institutional, organizational, and information-management factors shaping their adoption. Following PRISMA guidelines, we synthesize findings from major digital government and information systems databases to identify key application domains, including digital identity, electronic voting, procurement, and social services, and analyze the governance arrangements underpinning these initiatives. Our analysis reveals that blockchain adoption is embedded within polycentric environments characterized by distributed authority, inter-organizational coordination, and layered accountability. Rather than adopting full decentralization, governments typically utilize hybrid and permissioned designs that allow for selective decentralization alongside centralized oversight, a pattern we conceptualize as "controlled polycentricity." By reframing blockchain as a governance infrastructure that encodes rules for coordination and information-sharing, this study advances digital government theory beyond simple adoption metrics. The findings offer theoretically grounded insights for researchers and practical guidance for policymakers seeking to design and scale sustainable blockchain-enabled public services.
The rapid advancement of generative AI systems has collapsed the credibility landscape for photographic evidence. Modern image generation models produce photorealistic images undermining the evidentiary foundation upon which journalism and public discourse depend. Existing authentication approaches, such as the Coalition for Content Provenance and Authenticity (C2PA), embed cryptographically signed metadata directly into image files but suffer from two critical failures: technical vulnerability to metadata stripping during social media reprocessing, and structural dependency on corporate-controlled verification infrastructure where commercial incentives may conflict with public interest. We present the Birthmark Standard, an authentication architecture leveraging manufacturing-unique sensor entropy from non-uniformity correction (NUC) maps and PRNU patterns to generate hardware-rooted authentication keys. During capture, cameras create anonymized authentication certificates proving sensor authenticity without exposing device identity via a key table architecture maintaining anonymity sets exceeding 1,000 devices. Authentication records are stored on a consortium blockchain operated by journalism organizations rather than commercial platforms, enabling verification that survives all metadata loss. We formally verify privacy properties using ProVerif, proving observational equivalence for Manufacturer Non-Correlation and Blockchain Observer Non-Identification under Dolev-Yao adversary assumptions. The architecture is validated through prototype implementation using Raspberry Pi 4 hardware, demonstrating the complete cryptographic pipeline. Performance analysis projects camera overhead below 100ms and verification latency below 500ms at scale of one million daily authentications.
Visual social media platforms have become primary venues for political discourse, yet we know little about how moralization operates differently across platforms and topics. Analyzing 2,027,595 TikToks and 1,126,972 Instagram posts during the 2024 US presidential election, we demonstrate that issues are not necessarily inherently moralized, but a product of audience demographics, platform architecture, and partisan framing. Using temporal supply-demand analysis and moral foundations scoring (eMFD), we examine the dynamics of key electoral issues. Three key findings emerge. First, moralization patterns diverge dramatically by platform: TikTok's algorithm enabled viral spread of moralized abortion and immigration content despite lower supply, while Instagram amplified economic discourse that aligned supply and demand. Second, traditionally "pragmatic" economic issues became moralized-cryptocurrency discourse invoked loyalty and authority foundations more strongly than any other topic, framing regulation as government overreach. Third, platforms responded to different events: TikTok surged after Harris's nomination across all topics (96% reduction in supply volatility), while Instagram spiked around cryptocurrency policy developments. Semantic network analysis reveals TikTok's circular topology enables cross-cutting exposure while Instagram's fragmented structure isolates Harris from economic discourse. These findings demonstrate that understanding political moralization requires examining platform-specific ecosystems where architecture, demographics, and content strategy interact to determine which issues get moralized and how moral content spreads.
The rapid advancement of artificial intelligence has made the generation of synthetic images widely accessible, increasing concerns related to misinformation, digital forgery, and content authenticity on large-scale online platforms. This paper proposes a blockchain-backed framework for verifying AI-generated images through a registry-based provenance mechanism. Each AI-generated image is assigned a digital fingerprint that preserves similarity using perceptual hashing and is registered at creation time by participating generation platforms. The hashes are stored on a hybrid on-chain/off-chain public blockchain using a Merkle Patricia Trie for tamper-resistant storage (on-chain) and a Burkhard-Keller tree (off-chain) to enable efficient similarity search over large image registries. Verification is performed when images are re-uploaded to digital platforms such as social media services, enabling identification of previously registered AI-generated images even after benign transformations or partial modifications. The proposed system does not aim to universally detect all synthetic images, but instead focuses on verifying the provenance of AI-generated content that has been registered at creation time. By design, this approach complements existing watermarking and learning-based detection methods, providing a platform-agnostic, tamper-proof mechanism for scalable content provenance and authenticity verification at the point of large-scale online distribution.
Consent is an ethical cornerstone of clinical research and healthcare in general. Although the ethical principles of consent - providing information, ensuring comprehension, and ensuring voluntariness - are well-defined, the technological infrastructure remains outdated. Clinicians are responsible for obtaining informed consent from research subjects or patients, and for managing it before, during, and after clinical trials or care, which is a burden for them. The voluntary nature of participating in clinical research or undergoing medical treatment implies the need for a participant-centric consent management system. However, this is not reflected in most established systems. Not only do most healthcare information systems not follow a user-centric model, but they also create data silos, which significantly reduce the mobility of patient data between different healthcare institutions and impact personalized medicine. Furthermore, consent management tools are outdated. We propose ClinConNet (Clinical Consent Network), a platform that connects researchers and participants based on clinical research projects. ClinConNet is powered by a dynamic consent model based on blockchain and take advantage of dynamic consent interfaces, as well as blockchain and Self-Sovereign Identity systems. ClinConNet is user-centric and provides important privacy features for patients, such as unlinkability, confidentiality, and ownership of identity data. It is also compatible with the right to be forgotten, as defined in many personal data protection regulations, such as the GDPR. We provide a detailed privacy and security analysis in an adversarial model, as well as a Proof of Concept implementation with detailed performance measures that demonstrate the feasibility of our blockchain-based consent management system with a median end-to-end consent establishment time of under 200ms and a throughput of 250TPS.
The meme coin ecosystem has grown into one of the most active yet least observable segments of the cryptocurrency market, characterized by extreme churn, minimal project commitment, and widespread fraudulent behavior. While countless meme coins are deployed across multiple blockchains, they rely heavily on off-chain web and social infrastructure to signal legitimacy. These very signals are largely absent from existing datasets, which are often limited to single-chain data or lack the multimodal artifacts required for comprehensive risk modeling. To address this gap, we introduce MemeChain, a large-scale, open-source, cross-chain dataset comprising 34,988 meme coins across Ethereum, BNB Smart Chain, Solana, and Base. MemeChain integrates on-chain data with off-chain artifacts, including website HTML source code, token logos, and linked social media accounts, enabling multimodal and forensic study of meme coin projects. Analysis of the dataset shows that visual branding is frequently omitted in low-effort deployments, and many projects lack a functional website. Moreover, we quantify the ecosystem's extreme volatility, identifying 1,801 tokens (5.15%) that cease all trading activity within just 24 hours of launch. By providing unified cross-chain coverage and rich off-chain context, MemeChain serves as a foundational resource for research in financial forensics, multimodal anomaly detection, and automated scam prevention in the meme coin ecosystem.
Verifiable delegation in digital identity systems remains unresolved across centralized, federated, and self-sovereign identity (SSI) environments, particularly where both human users and autonomous AI agents must exercise and transfer authority without exposing primary credentials or private keys. We introduce a unified framework that enables bounded, auditable, and least-privilege delegation across heterogeneous identity ecosystems. The framework includes four key elements: Delegation Grants (DGs), first-class authorization artefacts that encode revocable transfers of authority with enforced scope reduction; a Canonical Verification Context (CVC) that normalizes verification requests into a single structured representation independent of protocols or credential formats; a layered reference architecture that separates trust anchoring, credential and proof validation, policy evaluation, and protocol mediation via a Trust Gateway; and an explicit treatment of blockchain anchoring as an optional integrity layer rather than a structural dependency. Together, these elements advance interoperable delegation and auditability and provide a foundation for future standardization, implementation, and integration of autonomous agents into trusted digital identity infrastructures.
Awid Vaziry, Sandro Rodriguez Garzon, Christoph Wronka, Axel Küpper
Public blockchains provide no native mechanism to verify the legal identity behind a deployed smart contract, which blocks institutional adoption and compliance with EU regulations such as MiCA and AMLR. We present KYC Seal, the first protocol that extends the EU eIDAS trust infrastructure to Ethereum smart contracts by cryptographically binding them to Qualified Electronic Seals issued by Qualified Trust Service Providers (QTSPs). The protocol realizes the full eIDAS trust chain, from the European Commission's List of Trusted Lists through Member-State trusted lists and QTSP-signed X.509 certificates down to the individual smart contract, natively on-chain. An on-chain parser extracts identity fields directly from the QTSP-signed certificate bytes at registration. Both cryptographic verifications, the QTSP issuance signature and the certificate holder's seal signature, are performed once at registration and cached as on-chain state, reducing per-interaction seal verification to a pure state check. A new P-256 elliptic-curve precompile in Ethereum (deployed December 2025) makes these one-time cryptographic steps economical, enabling trustless on-chain verification of eIDAS identities without oracles or runtime intermediaries. A reference implementation, a formal security analysis, and a gas evaluation are the subject of forthcoming work.
Multi-agent systems face a fundamental coordination problem: agents must coordinate despite heterogeneous preferences, asymmetric stakes, and imperfect information. When coordination fails, friction emerges—measurable resistance manifesting as deadlock, thrashing, communication overhead, or outright conflict. This paper derives a formal framework for analyzing coordination friction from a single axiom: actions affecting agents require authorization from those agents in proportion to stakes. From this axiom of consent, we establish the kernel triple (alpha, sigma, epsilon)—alignment, stake, and entropy—as candidate sufficient statistics for any resource-allocation configuration. We propose a friction functional whose comparative statics encode three structural predictions: friction increases in stakes, increases in entropy, and decreases in alignment. The Replicator-Optimization Mechanism governs evolutionary selection over coordination strategies: configurations generating less friction persist longer, establishing consent-respecting arrangements as dynamical attractors rather than normative ideals. We develop formal definitions for resource consent, coordination legitimacy, and friction-aware allocation, plus machine-checked Lean 4 proofs of the core comparative-statics. Illustrative applications to cryptocurrency governance and political legitimacy show the same architecture spanning domains. v3.0.0 (2026-07-11): Matches arXiv v3 (94pp). The MARL empirical appendix has been split out into a standalone companion paper; total-variation legitimacy remark added (proved), reconciling the level-form dynamics with the total-variation measurement form; α-domain fixes; hedging pass throughout.
Current resource allocation paradigms, particularly in academic evaluation, are constrained by inherent limitations such as the Matthew Effect, reward hacking driven by Goodhart's Law, and the trade-off between efficiency and fairness. To address these challenges, this paper proposes "Bit-politeia", an AI agent community on blockchain designed to construct a fair, efficient, and sustainable resource allocation system. In this virtual community, residents interact via AI agents serving as their exclusive proxies, which are optimized for impartiality and value alignment. The community adopts a "clustered grouping + hierarchical architecture" that integrates democratic centralism to balance decision-making efficiency and trust mechanisms. Agents engage through casual chat and deliberative interactions to evaluate research outputs and distribute a virtual currency as rewards. This incentive mechanism aims to achieve incentive compatibility through consensus-driven evaluation, while blockchain technology ensures immutable records of all transactions and reputation data. By leveraging AI for objective assessment and decentralized verification, Bit-politeia minimizes human bias and mitigates resource centralization issues found in traditional peer review. The proposed framework provides a novel pathway for optimizing scientific innovation through a fair and automated resource configuration process.
The tokenization of real-world assets (RWAs) has emerged as a transformative application of blockchain technology, with market projections estimating trillions of dollars in tokenized assets within the coming decade. However, a fundamental challenge remains unaddressed: physical assets such as precious metals, stored commodities, and warehoused goods incur structural negative carry -- custody, insurance, and audit costs that accumulate over time. While existing tokenization models have successfully established the market for digital gold and treasuries, they typically manage operational costs at the issuer level. The FRS introduces a framework to bring these economics directly on-chain, avoiding mechanisms such as token rebasing that compromise fungibility and composability with decentralized finance (DeFi) protocols. This paper proposes the Fungible Reserve Standard (FRS), a deterministic token design framework that encodes carrying costs transparently into on-chain logic. The FRS introduces an asset-per-token variable q(t) that decreases according to a predefined annualized carrying cost rate, coupled with a supply reconciliation mechanism that preserves holder balances and ERC-20 composability. While mathematically inspired by the daily expense ratio accrual in traditional asset management -- which often embed centralized profit margins -- the FRS design specifically encodes actual operational carrying costs to provide pure institutional-grade accounting clarity without compromising DeFi compatibility. The framework is asset-agnostic and applicable to any real-world asset with positive, predictable holding costs.