Abstract With rapid urbanization and expanding infrastructure, construction contract disputes are increasing in volume and complexity, challenging traditional adjudication. This study proposes a domain-specific legal artificial intelligence (AI) system for construction contract disputes via hybrid knowledge integration based on the retrieval-augmented generation (RAG) paradigm, integrating five core legal texts and 500 adjudication cases within a dual-engine architecture. The knowledge base encodes legal concepts, relations, and rules to enable structured semantic inference. The DeepSeek-R1 reasoning engine analyzes case facts and legal logic via constrained generation, while the BGE-M3 retrieval module matches legal provisions and precedents using multivector indexing. A tripartite evaluation framework—semantic similarity, legal provision citation accuracy, and issue prediction F1 score—validates system performance. The hybrid knowledge model outperforms single-source models, achieving scores of 0.736, 0.952, and 0.937, respectively, while significantly reducing judicial document generation time. This study offers a theoretical and empirical basis for legal AI in Chinese construction disputes, demonstrating how integrating diverse legal knowledge enhances intelligent judicial assistance within China’s jurisdiction. It also provides a scalable methodological reference for the advancement of smart justice, with explicit recognition of its current jurisdictional limitations.
The digital transformation of criminal justice systems is reshaping investigations, prosecutions and court proceedings by changing how evidence is generated, preserved, verified and evaluated. This structured review examines the admissibility of digital evidence and the prospects for integrating blockchain technologies into law enforcement practice through a methodologically transparent synthesis of legal, forensic, governance, and computer-science literature. The analysis covers electronic case management, digital evidence lifecycle controls, blockchain-enabled chain-of-custody systems, smart-contract-assisted workflows, privacy-preserving architectures and cross-border evidentiary recognition. The review develops a blockchain-enabled evidentiary governance framework that links digital evidence generation, chain-of-custody management, blockchain verification, legal admissibility, and judicial trust outcomes. It also distinguishes established findings, such as the operational value of electronic case management and cryptographic verification, from emerging pilot evidence and future hypotheses concerning automated enforcement, cross-jurisdictional ledgers and autonomous justice systems. The synthesis shows that blockchain can strengthen evidentiary integrity when deployed as a governed verification layer combining permissioned architecture, off-chain evidence storage, on-chain metadata, validated consensus rules and auditable institutional oversight. However, its implementation remains constrained by governance failures, smart contract vulnerabilities, oracle and data-quality risks, scalability limits, privacy conflicts, legal uncertainty, institutional resistance and cost-benefit trade-offs. The review concludes that criminal justice digitalisation requires socio-technical governance rather than technological substitution, with legal reform, professional capacity, interoperability standards and rights-preserving design forming the foundation for trustworthy digital proceedings.
In the context of information technology deeply embedded in social interactions and transactional activities, online chat records have become a representative and frequently used type of electronic evidence in civil litigation. However, such evidence relies on specific technical environments and is easily edited and tampered with, leading to long-standing issues of scattered standards and unclear paths in judicial practice regarding evidence collection, examination, and evaluation of probative value. The current system still shows deficiencies in notarization preservation, judicial authentication, platform assistance obligations, and technical assistance identification, making it difficult to match the highly technological development trend of electronic evidence. Accordingly, it is possible to achieve a structural reshaping of authenticity identification rules by optimizing notarization and authentication mechanisms, clarifying the scope of assistance and procedural obligations of chat software operators, and introducing trusted technical means such as blockchain.
Legal systems governed by rule of law are, structurally, rule systems. Like any rule system, they contain gaps between specification and intent, concentrated in the deliberately under-specified provisions that legal philosophers call "open texture." Those gaps have always been exploitable, but exploitation was rate-limited by the cost of legal expertise and the size of the corpus to be searched. That rate-limit is now collapsing. This paper introduces the governance patch-gap: the ratio between the rate at which AI accelerates the discovery of exploitable legal ambiguities and the rate at which legislatures, courts, and treaty bodies can repair them. Using the Highly Optimized Tolerance (HOT) framework from complex-systems theory, we map legal systems onto designed artifacts whose optimization against anticipated disputes concentrates fragility at the boundaries of the specification. We define the patch-gap as a ratio of discovery rate to repair rate, identify a threat taxonomy (corporate optimizer, state actor, misaligned autonomous agent), distinguish exploit discovery from exploit execution as separate governance problems, and examine three defensive strategies and the structural limits that prevent any defense from closing the gap entirely. The paper closes with three falsifiable predictions for 2027 to 2028. TL;DR summaries (five audiences) For the SME (legal theory / AI safety / complexity). Legal systems are HOT artifacts: drafters optimize against anticipated disputes, so residual fragility concentrates in Hart's penumbra (open texture), not in the core. The paper's object is a rate ratio G = $R_d/R_p$ and a stock S with $dS/dt$ = $R_d − R_p$; G is a definition, not a fitted dynamical model. Regime labels (G ≈ 2, 10², 10³+) are heuristics. SocioHack is an unreplicated sandbox (κ = 0.55); A1/VERITE is 36 already-vulnerable contracts. Rice / FLP / attestation in §6.4 are analogical extensions, not a derivation that courts instantiate those models. The load-bearing claim that survives if SocioHack fails is the work-factor collapse in adjacent formal systems plus the discovery/execution split. For the practitioner (counsel / CISO / compliance). Treat "AI found a loophole" and "an agent filed on it" as different problems. Discovery is a tool-governance issue (access, disclosure, audit of comment corpora). Execution is an agency-and-liability issue (who is the principal; human-in-the-loop above a dollar / classification / cross-border threshold). Disclosure mandates reach corporate repeat players and miss unsupervised agents. Do not spend the policy budget on formalizing "reasonable" or "public interest"; Catala-class work shrinks the core, not the penumbra. Immediate moves: require AI-use disclosure in filings and litigation; log agent actions that change regulatory classification. For the lay person. Laws have always had gray zones on purpose; words like "reasonable" so judges can handle new cases. Finding those gray zones used to be slow and expensive (years of lawyers). AI can search the whole tax code and regulation pile cheaply and flag gaps nobody has noticed. Passing a fix still takes months to years. The paper names that mismatch the governance patch-gap: machines find holes faster than legislatures and courts can close them. The holes were always there. What changed is the cost to find them. For the decision-maker (executive / funder / board). This is not a model-refusal problem and will not be closed by a better system prompt or a voluntary commitment letter. The asset at risk is the stock of known-but-unpatched legal ambiguities, which grows whenever discovery outruns repair. Adjacent formal systems (smart-contract exploit agents at USD 0.01 – USD 3.59 / attempt; attacker break-even ~USD 6k vs defender ~USD 60k) already show the cost collapse. Do not wait for SocioHack to replicate before treating discovery-versus-execution as two budget lines. Near-term: rate-limit execution (human-in-the-loop, disclosure). Do not buy "formally verified law" as a complete close. For governance (legislatures / agencies / treaty bodies). Every new AI rule written in open-textured natural language is another search surface. The EU AI Act Art. 6 "significant risk to fundamental rights" is the same kind of term as "undue burden." Three defenses, all bounded: (1) AI red-team of draft text before enactment .. useful, not exhaustive; (2) formal methods core only; (3) rate-limits buy time, do not close G. Conflating corporate optimizers, state arbitrage, and unsupervised agents produces the wrong instrument. The paper's falsifiers are public: AI-authored substantive rulemaking comments by end-2027; an attributed in-production exploit by end-2027; two governments or the EU publishing legislative red-team reports by mid-2028. Non-claims. G is a definition, not a fitted dynamical model. Regime magnitudes are order-of-magnitude heuristics. The SocioHack result is an unreplicated preprint treated as suggestive. Rice / FLP / attestation are analogical extensions, not a formal derivation that legal institutions instantiate those models. v1.1. Adds §4.5, an illustrative software companion (concept 10.5281/zenodo.21918091): a toy that generates Rd; G and the stocks are outputs, not legal measurements. No figures in the PDF.
** Autonomous Computational Law with StellarEq and ACRPL Model ** To address the systemic vulnerabilities of legacy natural-language governance—specifically its semantic ambiguity, high-latency auditability, and susceptibility to centralization—this paper presents a mathematically formalized, dual-engine architecture for Autonomous Computational Law under the Computable Political Language (CPL) stack, proving topological boundary-enforcement and stability via sheaf theory, homological algebra, and Lyapunov optimization. Systemic resource allocation and dynamic authority routing are governed by the Stellar Causal Power Flow (SCPF) engine, which proves state-transition convergence strictly based on the fundamental axiom of political energetics: $$\text{Power}(t) = \text{Contribution}(t) \times \text{AdoptionRate}(t)$$ Within this architecture, the mathematically rigorous constraints of our formal legal framework continuously generate decentralized trust, naturally shielding the vulnerable systemic core from coercive, extractive authority. By harnessing these parameters, the fluid and dynamic flow of distributed contributions cultivates a sprawling forest of policy proposals, smoothly transforming raw physical effort into radiant social energy to illuminate civilizational evolution. To maintain absolute structural integrity, an uncompromised cryptographic protocol strictly curtails the unchecked growth of algorithmic outputs, preventing the multidimensional essence of human rights from collapsing into scalar tradeable variables. Furthermore, persistent algorithmic decay systematically cools the high-temperature transactional friction of the marketplace, recursively returning accumulated power back to the common reservoir of collective sovereignty. Discrete logic boundary enforcement is handled ex-ante by the ACRPL, which defines non-negotiable constitutional safeguards as mathematical predicates over a non-convex feasible solution space, ensuring that no optimization gradient from the SCPF engine may enter the ledger unless the security gates are strictly satisfied, thus completely hiding compilation mechanics and specific variable transitions from unauthorized reconstruction.
CyberProtocol AI Trust Standard, Version 1.0 Artificial intelligence now writes, decides, and transacts at global scale, yet the world has no shared way to answer four simple questions about any AI output: who made it, where it came from, whether it is safe, and whether it obeys the law. CyberProtocol is built to answer all four. CyberProtocol is a neutral, open, cryptographic framework for verifying AI Identity, Provenance, Safety, and Compliance across all jurisdictions. It is published as a global public good, aligned with United Nations principles, and is controlled by no nation, corporation, or bloc. The timing is decisive. Three converging mandates now demand verifiable AI: EU AI Act enforcement, the founding of WAICO, and the Rome Declaration by Nobel Laureates. Each requires proof of origin, safety, and compliance, yet no harmonized, cross-border verification standard exists today. CyberProtocol is designed to fill exactly that gap, and to do so immediately, because the building blocks already exist. The Standard defines four verifiable layers that work as one system: AI and Human Identity, using Decentralized Identifiers for AI agents and W3C Verifiable Credentials for people. Provenance and Output Certification, an immutable cryptographic seal on every output, with an optional zero-knowledge mode that proves origin without exposing trade secrets. Safety and Risk Compliance, with metadata mapped to the EU AI Act, NIST AI RMF, and ISO/IEC 42001. Cross-Border Verification, a neutral seal format anyone can validate, tied to no national scheme. CyberProtocol invents no new cryptography. It unifies proven, mature standards into one coherent, interoperable framework, which is why it can be adopted now rather than years from now. The Standard is published and stewarded by One Planet One Earth Foundation Inc., a non-profit holding United Nations ECOSOC Special Consultative Status since 2025 (esango.un.org, profile 695078), (UNDESA Civil Society Database; SEC Registration CN202004649; DSWD-FO III-L-00002-2023). This accreditation gives CyberProtocol a neutral, internationally recognized home, positioned to engage UN member states, regulators, and the Global South on equal terms. As a public good, the Standard is free to all in perpetuity. Advancing it to a working reference implementation, pilot integrations with AI laboratories, and multi-stakeholder governance requires support. The Foundation invites funders, philanthropies, standards bodies, and industry partners to help make verifiable AI a global default. Together we can ensure the AI era is built on trust that anyone, in any country, can verify. Version 1.0, Initial Proposal. Specification under Creative Commons Attribution 4.0 International (CC BY 4.0); reference code under Apache License 2.0. Official reference: https://cyberprotocol.io. Repository: https://github.com/ryanpaulpillas/cyberprotocol-ai-trust-standard. Steward: One Planet One Earth Foundation Inc., holder of UN ECOSOC Consultative Status since 2025.
Direct user-specified research topic: Autonomous agent wallets spend under English mandates like 'only stablecoin swaps under $200 daily, never bridge, never touch unaudited pools', yet deployed policy engines (Safe Transaction Guards, ERC-7579 modules, session-key allowlists) enforce only stateless numeric and selector limits and cannot express 'unaudited' or 'per day', while a naive base-model prompt over raw hex calldata cannot recover function, recipient or token flow and confabulates verdicts. Evaluate a tool-augmented structured-decoding LLM judge that fetches ABIs from Sourcify and Etherscan, decodes calldata including multicall and Permit2 payloads, simulates via eth_call state overrides for token-flow and approval deltas, attaches counterparty features (contract age, verification), and emits constrained JSON: in_policy, violated_clause quoted verbatim, offending_calldata_field. Read Ethereum and Base: ERC-20 Transfer/Approval logs, Uniswap/1inch routers, Across/Stargate bridges, Permit2 at 0x000000000022D473030F116dDEE9F6B43aC78BA3. Measure macro-F1 and clause-attribution precision on 600 hand-labeled mandate/transaction pairs plus replay accuracy on transactions whose approvals owners later revoked, beating a naive raw-hex prompt and a Safe Guard numeric-allowlist baseline. Deliver as the prototype a minimal runnable Python MCP server (stdio) exposing the priced AI tool screen_transaction_against_mandate that invokes a language or ML model over onchain data to produce its output, with a typed input/output schema, an x402-style pay-per-call metering stub that records a per-call price in USDT and emits a settlement receipt, and one smoke test that exercises the tool end to end.. Investigate this topic end-to-end: survey the state of the art, identify a concrete tractable research question within it, design and run an experiment, and report results.
Abstract Law firms and corporate legal departments hold large volumes of privileged text that could train superior legal AI models, but attorney-client privilege sharply constrains data sharing across organizational boundaries. Standard federated learning frameworks target statistical privacy rather than the stricter operational requirement that privileged communication content remain inaccessible to non-privileged parties. We present a federated learning architecture designed for multi-firm collaborative model training under explicit privilege constraints. The architecture integrates six components: a privilege classification engine that categorizes documents by privilege type before training; privilege-calibrated differential privacy where noise scales with sensitivity; homomorphic encryption of sanitized gradients with zero-knowledge sanitization proofs; trusted execution environment (TEE)-enclosed aggregation that combines encrypted updates without exposing individual contributions; a privilege boundary graph that models joint defense agreements with dynamic conflict detection and model rollback; and cryptographic audit trails designed for later judicial review. We evaluate the design through formal privacy analysis with composed R\'{e}nyi differential privacy budget bounds, a worked four-entity deployment scenario with conflict detection, and comparative security analysis against baseline federated configurations.
Recent large language models (LLMs) incorporate reasoning capabilities that allow them to perform well in predicting whether a smart contract respects a certain property, suggesting a complementary approach to traditional formal-methods-based techniques for smart contract verification. However, the application of LLMs in such context has two major issues: 1) properties expressed in natural language are intrinsically ambiguous, and 2) answers returned by LLMs have no guarantee of correctness. In this paper, we address both issues simultaneously by: 1) introducing a new formal specification language that extends Solidity with abstract types, and 2) designing a workflow that combines LLMs with type checking and concrete execution to generate and validate violation witnesses (i.e., counterexamples). The key idea is to represent a specification as a Solidity test with (existentially quantified) variables of abstract type; finding an instantiation of these variables to concrete values (of the correct type) concretizes the test into an executable counterexample (PoC) for the target property. We implemented our procedure in the tool Neuroforger, experimentally evaluating it on a smart-contract verification dataset drawn from literature, obtaining promising results that demonstrate its potential applicability in the wild.
Machine Law / immo.quick Core v2.3.0 defines the public technical proof surface for consequence-boundary governance and deterministic institutional enforcement. This record establishes the public-facing evidence base for immo.quick Core v2.3.0: a nine-layer deterministic enforcement architecture designed to prove, at the moment of formation, whether a transaction, decision, or institutional action is legally admissible before any protected consequence can bind. The central problem addressed by this specification is the Boundary-Behavior Gap: the difference between documenting that a process occurred and proving that an impermissible movement could not have produced a consequence. Traditional compliance systems, workflow tools, audit logs, blockchain records, and post-hoc monitoring infrastructures can document process, sequence, signatures, and records. They do not, by themselves, prove that an inadmissible transaction was structurally prevented from becoming effective. immo.quick Core v2.3.0 is specified as a closed-world enforcement architecture: blocked unless formally permitted. Every transaction must satisfy the required admissibility conditions at T=0. If proof does not exist, the system refuses execution and produces a Deny Path Artifact (DPA). If all conditions are satisfied, the system produces an Execution Proof Artifact (EPA), a cryptographically bound proof object designed for institutional, regulatory, forensic, and judicial review. This DOI record contains two complementary documents: 1. Public Technical Proof Surface A sanitized technical proof document describing the public verification model, proof-object structures, deterministic refusal logic, EPA/DPA schemas, admissibility predicates, bi-temporal evidence model, zero-knowledge proof doctrine, governance divergence logic, and verification methodology. 2. Institutional Specification A broader institutional architecture document describing the full technical, legal, sectoral, geopolitical, and economic framing of immo.quick Core v2.3.0, including the NDA-gated access model for qualified institutions, regulators, governments, central banks, auditors, and authorized examiners. Together, these documents define the public proof surface and the protected institutional verification boundary. The public proof surface is intentionally designed to be sufficient for public category evaluation, architectural understanding, and regulatory-facing explanation without disclosing the protected production substrate. It explains what is proven, how proof objects are structured, how refusal is represented, how replay and verification are conceptually performed, and why public proof does not require public leakage. This record does not disclose production keys, private cryptographic material, customer payloads, live system endpoints, operational credentials, production node topology, exact quorum configuration, productive registry locations, enforcement adapter logic, institution-specific policy bundles, proprietary source code, or security-sensitive implementation details. All hash values, Merkle roots, PCR values, BFT quorum parameters, epoch identifiers, attestation objects, and proof samples included in the public technical document are illustrative structural examples derived from synthetic test payloads. They demonstrate the schema, format, and verification posture of production artifacts without exposing exact production values or operationally exploitable infrastructure details. The distinction is deliberate: Public proof is not public leakage. The public receives the proof surface. Qualified institutions receive the verification layer. The protected production substrate remains available only under lawful institutional standing, binding NDA, and institutional verification. The architecture specified in this record includes: - deterministic consequence-boundary governance;- Prior Admissibility Space (PAS);- Deny Path Artifact (DPA);- Execution Proof Artifact (EPA);- Deterministic Execution Proof Engine (DEPE);- Bi-Temporal Ledger (BTL);- Exogenous Anchor Protocol (EAP);- Sensor/Oracle Trust Bridge (SOTB);- Machine Law Engine (MLE);- Regulatory Intent Preservation (RIP);- Cross-Jurisdictional Portability Layer (CJPL);- Autonomous Regulatory Examination Engine (AREE);- Governance Logic Divergence Engine (GLD);- post-quantum signature posture;- zero-knowledge proof based selective disclosure;- identity-first access and refusal semantics;- institutional verification without public system exposure. The public proof surface is designed to satisfy the legitimate public interest in understanding how consequence-boundary governance works while preserving the confidentiality, resilience, and security obligations expected under DORA, NIS2, the EU AI Act, GDPR, and comparable cybersecurity, operational-resilience, and institutional-risk regimes. The purpose of this record is therefore not to expose a live system. It is to anchor the public technical proof surface for a new institutional category: Machine Law. Machine Law means that admissibility is not merely reviewed, monitored, or documented after the fact. It is compiled, evaluated, enforced, refused, attested, and proven before consequence. This record establishes the public evidence base for that architecture. The live enforcement system, production artifacts, regulator-grade examination packages, cryptographic materials, node infrastructure, and protected execution substrate remain NDA-gated and available only to qualified institutional parties under verified access. Public proof surface, not production substrate.
This paper develops BU76 AAI-08|Institutional Real-Time Closure Operations as the eighth file in the B_U-based Agentic AI series. Its central claim is that the next stage of Agentic AI should not be limited to single-enterprise automation, departmental coordination, or workflow orchestration. The decisive transition is toward a same settlement surface for enterprises, institutions, industrial clusters, infrastructure systems, and multi-flow real-world operations. In this frame, Agentic AI becomes a real-time closure interface for social-scale coordination, not merely a productivity layer inside software. The paper begins by reframing institutional operation as a multi-flow reality system. Enterprises and institutions do not operate through isolated tasks. They continuously coordinate people, goods, places, capital, information, time, permissions, responsibilities, risks, and feedback. Meetings, medical services, dining, travel, procurement, production, logistics, finance, legal review, customer service, and public services are not separate events. They are scenario windows in which multiple flows must enter the same state ledger and settlement window. When these flows remain fragmented across departments, firms, platforms, or infrastructure layers, the system generates hidden residuals: timing mismatch, resource conflict, responsibility ambiguity, logistics delay, budget misalignment, and operational bottlenecks. BU76 upgrades this analysis from a single enterprise to enterprise clusters, industrial clusters, and social infrastructure. A firm usually cannot see its future throughput capacity clearly because its real production chain is distributed across multiple companies, suppliers, logistics nodes, financial windows, labor pools, public services, and spatial infrastructures. Therefore, the true settlement surface is not inside one company. It emerges when enterprise clusters, industrial clusters, infrastructure networks, financial systems, logistics systems, public-service systems, and social demand enter a shared settlement window. This is the level at which future capacity, bottlenecks, risks, and deployment gaps become visible. The paper introduces all-factor co-temporality as the operating condition of this settlement surface. All-factor co-temporality means that people, goods, places, capital, information, time, permissions, responsibilities, risks, and feedback enter the same state ledger and settlement window within a shared time range. This condition applies at multiple nested scales: an individual user, a single enterprise, enterprise-to-enterprise coordination, industry-to-industry coordination, and the alignment between enterprise or industrial capacity and social demand. These layers form a multi-respiratory-system structure, in which demand flow acts as oxygen, production flow supplies output, logistics flow transports, capital flow circulates, information flow signals, human flow provides meaning and service interaction, responsibility flow identifies boundaries, infrastructure forms organ-like carrying capacity, and the same settlement window records the metabolic rhythm. BU76 further defines pre-feedback and preloading as institutional operating capacities. Preloading is not completed settlement. It is the feasibility loading of future demand matrices into the same settlement surface. It produces feasible-throughput readouts, bottleneck exposure, and pre-deployment signals before action occurs. Pre-feedback therefore differs from real-time feedback: real-time feedback corrects ongoing deviation, while pre-feedback exposes future capacity pressure under current constraints, resources, time windows, spatial capacity, responsibilities, and risks. Its confidence interval must be assessed through the B_U development chain: background clearing, admissible carrier, directional amplification, unified settlement, and resolution ascent. The final judgment is that institutional Agentic AI must evolve into a social-scale closure operation system. Its value lies in aligning demand and production at higher granularity, synchronizing multiple real-world flows, exposing bottlenecks before failure, stabilizing resource deployment, and enabling higher-order amplification and civilizational development through a shared settlement surface.
Context: Legal contracts have served as the bedrock of business transactions for millennia. They are core to modern supply chains, and their execution can now be automated through the use of i) smart contracts, supported by blockchain technology that safeguards data integrity, and ii) Internet-of-Things technologies to support their monitoring functions. Symboleo is a specification language used to formalize legal contracts, enable property analysis, and generate smart contracts for a permissioned blockchain platform (Hyperledger Fabric). However, automation around resulting smart contracts poses security challenges, particularly regarding who should have access to operate on contract elements. Additionally, how such smart contract should interact with their Cyber-Physical System (CPS) environment, including IoT devices, remains challenging. Purpose: The thesis proposes an architecture to integrate smart contracts, Complex Event Processing (CEP), message brokers, and a blockchain platform (namely Hyperledger Fabric) to support end-to-end Cyber-Physical Smart Contracts (CPSCs). This architecture makes it possible to connect IoT devices with smart contracts (generated using Symboleo) through a CEP engine and a message broker. Additionally, this thesis proposes an access control model, treating all contract elements as resources and ensuring regulated access by designated parties. This model extends the Symboleo ontology and language for legal contracts with new modeling concepts inspired by Role-Based Access Control (RBAC), tailored for the legal contract domain, resulting in SymboleoAC (Symboleo Access Control). SymboleoAC also extends the Symboleo language to handle dynamic contract execution scenario. Methodology: This research follows a Design Science Research methodology, which guides the development and evaluation of the research artifacts. This research is conducted in several iterative steps that are divided into two main phases, one that focuses on theoretical aspects and the other on the design, demonstration, and evaluation of the research artifacts. Contributions: The contributions of this thesis are: • An architectural framework for CPSCs that leverages complementary aspects of CPS and smart contracts; • SymboleoAC, an access control ontology for Symboleo; • An extension of the current Symboleo specification language (syntax and semantics) that supports smart contract requirements, including automation and control actions, access control, and CPS components; • An implementation of the SymboleoAC ontology and semantics into a reusable JavaScript library (SymboleoACJS), together with a tool, SymboleoAC2SC, that generates JavaScript smart contract code with security aspects for a designated platform (Hyperledger Fabric); and • A secure and event-driven SymboleoAC Application Programming Interface (API) that orchestrates the runtime ecosystem connecting IoT sensors, the message broker, the CEP engine, and the blockchain platform. Through extensive and the evaluation of multiple variations of two contract case studies, SymboleoAC (architecture, ontology, and language), along with its associated tools, is shown to be an effective environment for CPSCs, simplifying the design of secure smart contracts and their connections to message brokers, CEP engines, and IoT devices.
Abhinav Goel, Agostino Capponi, Alfio Gliozzo, Chaitya Shah
We introduce SmartEval, a benchmark for systematically evaluating the quality of Solidity smart contracts generated by large language models (LLMs) from natural language specifications. SmartEval provides a corpus of 9,000 generated contracts paired with expert-written ground-truth implementations drawn from the FSMSCG dataset, a five-dimensional evaluation rubric covering functional completeness, variable fidelity, state-machine correctness, business-logic fidelity, and code quality, and a reproducible generation-and-evaluation pipeline. To validate the benchmark's reliability, we conduct three independent empirical studies: a five-condition ablation study (N=300 per condition) isolating the contribution of each pipeline component, a human expert evaluation by three Columbia University PhD researchers confirming automated scores align with expert judgment to within 0.34 points, and external security analysis via the Slither static analyzer confirming 79.4% agreement between the LLM auditor and a non-LLM rule-based tool. Systematic analysis of 9,000 generated contracts reveals characteristic failure modes (logic omissions at 35.3%, state transition errors at 23.4%, and complexity-driven degradation) and quantifies a +8.29 composite-score advantage of generated contracts over ground-truth implementations, attributable to LLMs' literal specification-following behavior. SmartEval establishes a reproducible, validated foundation for empirical research on LLM smart contract synthesis quality, with all data, evaluation code, and generated contracts publicly released.
Achilles was invincible in battle — except for one point of structural vulnerability that no amount of strength could compensate for. Modern LLM-based agent frameworks (LangChain, AutoGen, CrewAI, ReAct) share this property exactly: impressive capability in controlled settings, catastrophically exploitable in regulated production environments through a single architectural flaw — the language model controls the decision. Organizations in regulated sectors (finance, insurance, healthcare, legal, compliance) face a direct consequence: these frameworks cannot be deployed in workflows subject to EU AI Act, DORA, or GDPR Article 22, because they provide no structural guarantee of determinism, auditability, or equal treatment. Traditional symbolic agent systems (JADE, Jason, Jadex) satisfy regulatory requirements but cannot ingest the unstructured natural-language inputs that define real enterprise workflows. The industry needs both properties simultaneously. No existing framework provides them. AQUILES is a production architecture for AI agents in regulated domains that resolves this gap through principled separation of concerns, instantiating the HADD paradigm (Hybrid Agents with Deterministic Decisions). AQUILES organizes agent functionality into five cooperating layers: an Interface Layer converting unstructured input into typed, validated beliefs via LLM sensors; a Cognition Layer performing pure-function BDI deliberation fully determined by its inputs; a Planning Layer selecting from a pre-verified HTN plan library without runtime synthesis; an Execution Layer enforcing typed precondition and postcondition contracts on every capability invocation; and a transverse Observation Layer producing append-only audit entries synchronously with every state transition. Language models are confined strictly to the perception boundary — they parse input into beliefs, they never select goals, plans, or capabilities. The heel remains; it is simply no longer load-bearing. The HADD paradigm is codified as six architectural invariants: (I1) Typed Role Inversion — LLMs as sensors only, never as control-flow components; (I2) Deterministic Cognition — the reasoning layer is a pure function of beliefs, goals, and rules; (I3) Bounded Planning — execution draws exclusively from a pre-verified plan library; (I4) Validated Execution — every capability invocation passes typed pre/post-condition checks; (I5) Complete Observability — every decision is forensically reconstructable from the audit log; (I6) Epistemic Precondition — no belief enters the BDI cycle without satisfying freshness, non-contestation, and source triangulation, enforced by the EVR module (Epistemic Verification for RAG). Any implementation satisfying all six invariants acquires reproducibility, zero LLM hallucination in state, LLM provider independence, and structural alignment with EU AI Act Articles 12–15 — as architectural properties, not retrofitted compliance measures. AQUILES partitions agents into cognitive holons (BDI-HTN reasoning components subject to full HADD governance) and reactive holons (deterministic capability executors verified by typed contracts alone). In observed production deployments, 70–80% of holons by count are reactive, meaning governance complexity scales with the cognitive subset rather than with total component count. The AQUILES protocol is language-agnostic by design: cognitive holons are typically Python (Anthropic SDK, sentence-transformers, pypdf); endpoint-monitoring holons are Go (single-binary cross-compilation); blockchain and zero-knowledge holons are Rust (arkworks, halo2, revm). We prove a Language Neutrality property: HADD compliance is preserved across heterogeneous polyglot deployments. For autonomous field deployments, AQUILES derives MYRMIDON agents that execute a signed MissionPackage autonomously on constrained hardware, inheriting AQUILES's safety guarantees without requiring runtime connectivity. This paper makes five engineering contributions: (C1) the HADD paradigm formalized as six architectural invariants with rationale and derived operational properties; (C2) the cognitive/reactive holon distinction and its governance economy consequences; (C3) a polyglot holon model with Language Neutrality proof and domain-language affinity mapping across Python, Go, and Rust; (C4) a multi-tenant operational-cell formalism enabling cryptographically enforced tenant isolation for regulated multi-client deployments; (C5) four reusable design patterns extracted from production experience (Sensor Firewall, Belief Expiry, Capability Contract, Observation Fanout), together with measurement methodology, adoption guidance, and explicit characterization of the architecture's limits.
Web3 prediction markets, exemplified by Polymarket, have gained prominence for leveraging collective intelligence to forecast a wide range of social, political, and sports events. However, among the thousands of prediction market events, consensus disputes still arise due to imperfections in market mechanisms. On Polymarket alone, the trading volume involving disputed events has reached $972,370,804.71, underscoring the critical need for objective and efficient dispute resolution. In this study, we introduce large language models (LLMs) to: (1) evaluate whether web-enabled LLMs can reproduce the decision quality of UMA's on-chain voting process once a dispute has been raised, and (2) predict, based on event rules, which market events are likely to face future disputes before they occur. Our findings show that LLMs are unable to reliably predict which events will become disputed in advance; however, once a dispute is initiated, web-enabled LLMs achieve 89.58% agreement with UMA's final resolutions and demonstrate strong stability.
Abstract The one among primary source of Indian national GDP is MSME sector, presently functions under a “Compliance Paradox” though Goods and Service Tax (GST) has digitalized revenue collection, the dependence on batch-based processing and non-transparent algorithms facing major systemic inefficacy, periodic working capital lock-ups, contingent vendor liability, phantom notification burst. This article outlines a transformative roadmap powered by Autonomous Tax Administration (ATA) conceptual framework through Autonomous Jurisprudence in a real time by bridging synchronous Gateways to GSP-Edge to that of GST Suvidha Provider (GSP). ATA integrates three major cognitive layers (i) Cryptographic Invoice Provenance (for digital birthright we use Zero-Knowledge Proofs), (ii) Stability-Weighted Anomaly Detection (to mathematically distinguish clerical evasion errors) (iii) SHAP-based Explainable AI (XAI) for transparency. Finally I recommend Real-Time Credit Liquidity Protocol (RTCLP), which leverages a dynamic Autonomous Trust Index (ATI) to release Input Tax Credit (ITC) instantly upon generating invoice. This transforms a manual “sunk cost” tax compliance into real-time “liquidity assets”. 1.Introduction Background The current GST 2.0, is designed to serve for a 5 trillion economy which shows a structural maturation of a highly optimized fiscal architecture design for indirect tax regime of India. As of February 2026, the shift toward AI-powered “Agentic Automation” to harmonize India’s environment with international best practices, reform has transcended simple tax subsumption to address deep-seated inefficiencies in resources allocation, compliance equity and revenue resilience. Which insist Tech-driven fiscal transformation 2.Problem Statement MSME sector face a “Compliance Paradox “ despite successful digitalization of the tax base, Digitalization and Automation has created Aggressive Automated Compliance (widely described as “Notice Terrorism” in the trade circles) environment. Current batch-based systems trigger automated intimations, such as Form DRC-01B and DRC-01C, when deviations in tax liability or Input Tax Credit (ITC) cross prescribed risk thresholds. This retrospective type reconciliation often results in the immediate blocking of subsequent return fillings and the lock-up of critical working capital. 3.Research Objectives 1. To design a conceptual framework for an Autonomous Tax Administration (ATA) that replaces reactive enforcement with proactive facilitation 2. To develop a model that secures the digital birthright of transactions using cryptographic provenance. 3. To integrate real-time credit liquidity protocols into the existing digital public infrastructure (DPI). 4.Significance The Indian MSME sector remains the backbone of the economy, yet micro-firms have registered a lower average turnover growth (4.1%) compared to small and medium firms (8.9%) due to lower digital readiness. The ATA framework seeks to reallocate the 28.6 hours per month MSME sector currently spent on manual compliance back into productivity. Furthermore by providing “Logic Certificates” of cryptic notices, the ATA can reduce the backlog of over 14000 appeals currently pending in the nascent GST Appellate Tribunal system. 5.Research Questions 1. How can Gradient-Boosted AI differentiate between stochastic clerical errors and systematic evasion in the real-time? 2. Can an evolved GSP-led cryptographic provenance model eliminate vendor-chain liability without imposing new hardware costs on MSME sector? 6.Scope and Limitation The study focuses on the Indian MSME sector and assumes adoption of API-first ERP systems or GSP-Edge Gateways. It is limited by current legislative constraints regarding fully autonomous punitive adjudication and the digital divide in rural infrastructure. 7.Literature Review GST and MSMEs Post-GST turnover data suggests that larger SMEs are better positioned to leverage tax benefits due to professionalized digital accounting (Bhalla et al., 2023; Kumar & Kumar, n.d.). For smaller entities, ITC mismatches between GSTR-3B and GSRTR-2B remain the primary driver of administrative friction, with unresolved DRC-01C notices exposing businesses to recovery proceedings under Section 73 or 74 (Anantham, 2025; GSTR-3B & ITC Errors Leading to GST Notices, n.d.) Autonomous Tax Administration The OECD “Tax Administration 3.0” vision envisions tax compliance as seamless, automated by product of business activity. The model advocates for a “Connected once, comply everywhere” approach, where service providers manage the complexities of data transmission, allowing the tax authority to act as an invisible partner in commerce(taxguru_in & Goyal, 2023). Theoretical Framework The research introduce Autonomous Jurisprudence, which means a legal philosophy were AI act as a functional “agent” of the state, (AI handles the scale and speed of administrative interactions), but framework authority is strictly bound by a “human-in-the-loop” while human judiciary retains the moral and punitive finality. This framework prioritizes the Three pillars recognizing that particularly those of accountability and understandable by design, while AI can facilitate real-time interactions, administrative law necessitates that humans retain meaningful control over punitive adjudication to ensure fairness and avoid bias. Conceptual Framework for Autonomous Tax Administration (ATA) Core Principles of ATA framework operates on three pillars: Facilitation-First (prioritizing error correction over penalties), Real-Time Transparency (Using XAI to explain system decisions), Infrastructure Resilience (ensuring rural accessibility through store and forward logic). Key components GSP-Edge Gateway: This component leverages the existing network of GST Suvidha Providers (GSPs)- authorized intermediaries that connect taxpayers to GSTN. By evolving the GSP’s role into an “Edge Gateway,” the system performs cryptographic Zero-Knowledge Proofs (ZKP) at the GSP level rather than requiring expensive hardware at the MSME’s storefront. Gradient-Boosted Anomaly Detection: The “Intent Filter” that mathematically distinguishes stochastic clerical noise from systemic evasion patterns. Shap-based Explainable AI (XAI): The “Interpreter” that provides plain-English “logic certificates” for every system flag. Cryptography Invoice Provenance: The Cryptography Invoice Provenance performs the action of “Anchor” that digital birth right of every transaction, to regulate synthetic forgery Integration of Components The framework utilizes a Synchronous Bridge to authorized GSP hubs to connect with tax AI of MSME ERPs. Inspired by UPI Lite and Aadhar offline XML to accommodate rural infrastructure the framework adopts Store-and-Forward architecture, where Zero Knowledge Proofs is embedded with local time stamps during network drops asynchronously. Proposed workflow Event Trigger: MSME generates an invoice in an ONDC-compatible app or ERP. Integrity Anchor: The GSP-Edge Gateway performs a ZKP check to lock the digital birth right of the transaction. Cognitive Scoring: Gradient-Boosted models assign a real time ATI score Facilitation Loop: If a minor variance is found the SHAP module generates a Logic Certificate and an instant auto-correction prompt. Instant Liquidity: Once validated, the RTCLP activates, releasing ITC to the Electronic cash ledger in under 3 seconds. 8.Methodology Research Design The framework adopts a Design Science Research (DSR) methodology to create and evaluate ATA framework by tecno-legal artifact, this approach ensures the model is technically viable and legally sound through a couple of iterations. Data collection Empirical analysis of GSTN automated notice volumes (DRC-01B/C logs) and UPI transaction surges( e.g., the 14,000 cases identified in Karnataka) served as the primary data source for identifying systematic friction points. Analytical Tools Mathematical formulations were developed to simulate the Autonomous Trust Index (ATI): where V_a is the verification Authenticity, C_s is the compliance stability, L_g is the Ledger Governance and N_s is the network Stability. Expected Outcomes and impact Efficiency Gains Moving from “monthly filling” to invisible compliance, the ATA targets a near zero labour burden for MSMEs, reallocating valuable human hours back into the economy. Fiscal integrity Precision in identifying systematic evasion, thereby reducing audit load on honest tax payers. By eliminating synthetic surgery through ZKP anchors with projected 95% precision. Transparency The framework replaces black box algorithm with a citizen-centre charter that translate Jargon into clear public value: Technical term Citizen centric translation Benefit Stochastic error Simple Typo Instant correction no notice Lambda architecture Real time verification Instant ITC availability SHAP Logic Clear recent for flags Transparency in system actions ZKP Provenance Secure digital birthright Protection from vendor default 9.Discussion Theoretical implication The ATA framework redefines the “social contract” between the STATE and MSMEs through “Trust-by-Design”. By treating the governance as infrastructure, the system assumes compliance as a default state for high ATI actors. Limitations A critical legal anchor is the Guwahati High court ruling in construction catalyser Vs State of Assam (2024) which held that summary notice in DRC-01 are supplementary and cannot substitute a proper show cause notice authenticated by a proper officer. Furthermore, section 75(4) of the CGST act mandates a personal hearing before any adverse order is passed. Consequently, AI in the ATA framework act as investigator facilitator while human officers must remain in final Adjudicator for punitive actions to preserve
O presente artigo analisa o estado atual da verificação formal de contratos inteligentes, com ênfase em ferramentas, métodos e limitações práticas para o ecossistema Web3. A verificação formal é compreendida como o emprego de técnicas matemáticas – entre as quais model checking, verificação baseada em SMT (Satisfiability Modulo Theories) e lógica de Hoare – para provar que propriedades especificadas são válidas para todas as execuções possíveis de um contrato, oferecendo garantias de segurança mais fortes do que aquelas proporcionadas por testes e auditorias manuais. Pesquisas recentes comparam ferramentas líderes voltadas à linguagem Solidity, a exemplo de solc-verify, SMTChecker, VeriSmart, ESBMC-Solidity e Certora Prover, destacando diferenças em expressividade de especificações, grau de automação, desempenho e taxas de falsos positivos e negativos. Surveys sistemáticos revelam ainda que, entre mais de duzentas ferramentas de análise e detecção de vulnerabilidades desenvolvidas entre 2018 e 2024, fração relevante adota métodos de verificação formal – especialmente model checking em nível de design e SMT em nível de implementação – mas que a adoção em esteiras industriais permanece limitada por fatores como complexidade de uso, custo e expertise especializada exigida. Casos práticos em protocolos de finanças descentralizadas (DeFi) demonstram que a verificação formal é capaz de detectar erros sutis, como bugs de arredondamento (rounding errors) que podem ensejar perdas de milhões de dólares, desde que invariantes de negócio e propriedades de segurança sejam corretamente especificados nas linguagens próprias de cada ferramenta. Conclui■se que, embora a verificação formal constitua peça crucial para elevar o patamar de segurança de contratos inteligentes críticos, ela enfrenta limitações de escalabilidade, cobertura de propriedades, dependência de especificações precisas e integração com ciclos ágeis de desenvolvimento, o que aponta para tendência de emprego combinado com auditoria manual, fuzzing e análise estática tradicional.
E Chen, Xuanyu Liu, Limin Jia, Bo Liang · 6 authors
The widespread adoption of smart contracts, self-executing agreements on the blockchain, is hindered by the complexity of translating real-world contracts, often written in multiple languages, into their digital counterparts. This paper addresses this challenge by introducing an innovative approach based on Contract Text Markup Language (CTML), an extensible markup language specifically designed to facilitate the automatic generation of smart contracts from multilingual contracts. CTML overcomes traditional method limitations by employing a two-stage transformation process: (1) Contract Abstraction and Markup: CTML redefines grammar rules and incorporates encoding extensions to transform multilingual contracts into structured, marked-up contracts. This process effectively abstracts the essential details of the original contract, enabling language-agnostic interpretation. (2) Domain-Specific Language (DSL) Translation and Smart Contract Code Generation: The marked-up contract is then seamlessly translated into a DSL program, capturing the legal concepts in a machine-readable format. Finally, the DSL program is automatically compiled into executable smart contract code, ready for deployment on the blockchain. The effectiveness of the proposed approach is demonstrated using a legal contract in both English and Chinese. Therefore, the CTML-based approach can automatically generate smart contracts from multilingual contracts, enabling a more inclusive and accessible smart contract ecosystem.
Machine Law Engine (MLE) v1.2.0 presents a formal computational architecture that reconceives regulatory compliance from a retrospective, documentary discipline into a pre-emptive, cryptographically enforced state property. Where classical GRC tooling observes violations after they occur, the MLE enforces legal constraints before execution — making non-compliant operations computationally impossible rather than merely detectable. The architecture introduces three original contributions to the field of computational law and applied cryptography: (1) The Admissibility Vector — a four-dimensional formal scoring function (authority α, evidence ε, context γ, transition legality τ) that evaluates every regulated operation at execution time against all applicable legal rules. The collapse axiom τ=0 → Φ=0 produces terminal refusals for legally impossible state transitions that cannot be overridden by any combination of authority or evidence. (2) The Challenger Provenance Architecture — a novel mechanism, without precedent in published GRC frameworks, that enforces structural independence of AI-assisted compliance reasoning. If a challenger input cannot demonstrate cryptographic divergence (CPD ≥ 0.70, path_overlap ≤ 0.20) from the primary reasoning path, the gate cannot achieve full institutional binding — operationalising DORA Art.15, EU AI Act Art.9(9), and BCBS 239 Principle 11 as cryptographic invariants rather than policy obligations. (3) The Seven Formal Invariants — hard computational constraints governing the MLE's correctness properties, with mathematical predicates, three-tier runtime monitoring (write-time, scheduled, continuous), and automated violation response protocols including cryptographically evidence-hashed remediation workflows. The reference implementation integrates: four hardware TEE providers (AWS Nitro Enclave, Azure Confidential Computing, Intel SGX/TDX, AMD SEV-SNP) with PCR register semantic attestation; a post-quantum cryptographic stack fully standardised under NIST FIPS 203/204/205 (CRYSTALS-Kyber-1024, CRYSTALS-Dilithium-3, SPHINCS+) providing 30-year evidence integrity against harvest-now-decrypt-later attacks; four PLONK-based Zero-Knowledge proof circuits on BLS12-381 (128-bit soundness) resolving privacy-compliance paradoxes for OFAC sanctions screening, FinCEN BSA threshold verification, DORA Art.28 vendor certification, and GDPR right-to-erasure evidence chains; a bi-temporal append-only ledger with DORA Art.11 automated retro-simulation; a seven-stage NLP-to-enforcement-code compilation pipeline with Kyber-1024 tamper detection and dual-approval protocol; a multi-framework conflict engine covering six active cross-regulatory conflict pairs (GDPR × FINMA, GDPR × FinCEN BSA, DORA × NIS2, EU AI Act × GDPR, eIDAS 2 × CCPA) with five deterministic resolution strategies; and nine Interactive Verification Layer modules enabling complete live regulatory demonstration in 35 minutes without preparation. Regulatory framework coverage spans 17 frameworks across EU, US, CH, and UK jurisdictions including DORA, GDPR, NIS2, EU AI Act, eIDAS 2, FINMA Circ.2023/1, BaFin MaRisk, FinCEN BSA, OFAC/CAATSA, FATCA, CRS, ISO 27001:2022, and SOC 2. Evidence export targets eight regulatory authorities (EBA, EDPB, ENISA, FINMA, BaFin, FCA, SEC, FinCEN) in authority-native formats (XBRL, XML, BSA E-Filing) via Dilithium-3-signed, SPHINCS+-sealed bundles with direct API transmission. The system is currently deployed in production as of 31 March 2026. Invariant status at publication: 6/7 HOLDING · INV-5 WARNING (AMD SEV-SNP PCR2 drift, remediation active, resolution within 72 hours). Keywords: machine law, pre-emptive compliance enforcement, admissibility vector, post-quantum cryptography, trusted execution environment, zero-knowledge proofs, bi-temporal ledger, DORA, GDPR, EU AI Act, cryptographic compliance, challenger provenance, regulatory technology, GRC, hardware attestation, CRYSTALS-Kyber, CRYSTALS-Dilithium, SPHINCS+, PLONK License: CC BY 4.0 Version: 1.2.0 DOI: 10.5281/zenodo.immo.quickCore.1.2.0
Technical implementation of NY Senate Bill S.7263 compliance architecture providing cryptographic enforcement of professional licensure requirements in AI chatbot systems. Presents five-layer architecture (Decision Rights Registry, Organizational Trust Graph, Accountability Ledger, Institutional Safety Net, Governance Version Control) with thirty enumerated workarounds including deepfake-based authorization simulation, Web3/DAO evasion, quantum computing threats, side-channel attacks, and legal evolution strategies. Establishes comprehensive prior art for defensive patent protection. Filed February 25, 2026, seven days before S.7263 advanced to Third Reading.
Open access
Ethics and Social Impacts of AI
Artificial Intelligence in Healthcare and Education
We present an end-to-end framework for systematic evaluation of LLM-generated smart contracts from natural-language specifications. The system parses contractual text into structured schemas, generates Solidity code, and performs automated quality assessment through compilation and security checks. Using CrewAI-style agent teams with iterative refinement, the pipeline produces structured artifacts with full provenance metadata. Quality is measured across five dimensions, including functional completeness, variable fidelity, state-machine correctness, business-logic fidelity, and code quality aggregated into composite scores. The framework supports paired evaluation against ground-truth implementations, quantifying alignment and identifying systematic error modes such as logic omissions and state transition inconsistencies. This provides a reproducible benchmark for empirical research on smart contract synthesis quality and supports extensions to formal verification and compliance checking.
This thesis explores the doctrinal and practical challenges of applying the principle of party autonomy (lex voluntatis) to smart contracts and transactions governed by Artificial Intelligence (AI). The decentralized and immutable nature of Distributed Ledger Technology (DLT) fundamentally disrupts traditional private international law connecting factors, such as "place of performance" or "habitual residence." The author analyzes how the Rome I Regulation and the Hague Principles on Choice of Law can be adapted to "code-is-law" ecosystems where enforcement is automated and often bypasses state judicial mechanisms. Special attention is paid to the tension between algorithmic execution and "overriding mandatory provisions" (lois de police), questioning whether an AI can recognize and apply mandatory public policy norms that usually override the chosen law. The paper proposes a hybrid regulatory approach "Lex Cryptographia" that embeds choice of law clauses directly into the smart contract's metadata to ensure legal certainty.
Prediction markets aggregate dispersed information into probabilistic forecasts that consistently outperform polls and expert panels, yet their adoption is constrained by a structural liquidity problem: providers of market depth bear adverse selection risk that deters retail participation. We present the Onix Protocol, a hybrid architecture that decouples the pricing function from the settlement function in prediction markets. By pairing automated market maker pricing-a Constant Product Market Maker (CPMM) for binary outcomes and a Logarithmic Market Scoring Rule (LMSR) for multi-outcome markets-with parimutuel (totalizator) settlement, we achieve a structural guarantee that liquidity-provider principal is never at risk from betting outcomes. We formalize the protocol's economic invariants, prove the LP principal guarantee for both market types, describe a "Lazy" liquidity pool enabling passive retail participation, analyze the dispute resolution mechanism under DAO governance, and discuss the experimental hypotheses this system is designed to test. The protocol is implemented as consensus-level operations on the VIZ distributed ledger.
This study quantifies Large Language Models (LLMs) and humanoids as a new labor force and describes the transformation of economic structures brought about by "super-fluid task allocation involving humans," facilitated by tokenized task transactions built on blockchain technology, from the perspective of statistical physics. Furthermore, we devise a constructive approach called "Legal Engineering" and discuss its governance mechanisms. First, we define the price fluctuations of tokenized tasks as "work volatility" and suggest that, within the scope where specific assumptions (existence of information friction, amplification of interactions, and introduction of approximate effective temperature) hold, phase-transition-like behaviors (rapid changes in order similar to bubbles) can occur in the market. Volatility here is interpreted not merely as a statistic but as an operational approximation of "social temperature" that emerges as a result of amplified information friction and interactions. As a governance mechanism to suppress this entropy increase, we propose the "Latent Torus," an information event horizon. The Latent Torus handles internal optimization invisible from the frontend and ensures sustainable social order by recirculating only optimized parameters to smart contracts. Here, by combining quantum optimization with "Semantic Intervention" via "Regulated LLMs," we aim for stabilization based on "semantic depth" rather than apparent liquidity. Furthermore, we propose a "Grand Unified Algorithm" to simultaneously handle economic efficiency (Hamiltonian minimization), humanity (Well-being), and social credit (Proof of Trust) within a single mathematical framework. The scope of this paper is not to advocate for immediate control of society as a whole, but rather to provide a conceptual model for optimizing and auditing trade-offs between indicators in a consistent manner under limited task spaces, participant sets, and operational rules. As a concrete model, we present "Computational Social Contract Theory (CSCT)" and confirm its behavior and limitations under various assumptions through quantitative analysis using multi-agent simulations. Notably, this theory presents a design policy for realizing "verifiable concealment" in governance under certain assumptions (circuitability, computational assumptions, and soundness of key management/operation) using cryptographic techniques such as zero-knowledge proofs (zk-SNARKs). This explores the possibility of hiding the details of internal optimization while maintaining compliance with the Constitutional Core, allowing citizens to verify legitimacy, and examining the operational requirements necessary for such a system. This paper presents a conceptual proposal for institutional design in a post-capitalist society and examines the redesignability of money and law. Note that the quantitative results of this paper are positioned as exploratory simulations and do not directly claim predictive confirmation.