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.
Financial software sits at the center of modern economic infrastructure, yet the programming languages used to build it provide no formal guarantees about the semantic correctness of financial operations. Double-entry bookkeepingâs duality constraint, the rule that every economic event must produce balanced inflows and outflows, is universally encoded at the application layer, where it can be omitted, miscoded, or deliberately bypassed. No existing compiled programming language includes a type rule for accounting duality. This paper presents Equis, a compiled, self-hosting systems language that elevates the ResourceâEventâAgent (REA) model to first-class syntactic constructs and enforces accounting duality as a static, compile-time invariant. The compiler rejects any event declaration whose flow block is not balanced before emitting a single instruction of LLVM IR. Equis uses fixed-point 64-bit integer arithmetic scaled by 106 throughout, eliminating IEEE 754 accumulation error from financial code paths entirely. Memory management relies on automatic reference counting with a resource-state borrow checker, giving deterministic, GC-pause-free behavior in long-running settlement services. The compiler is self-hosted, bootstrapped from ANSI C, and verified via Diverse Double Compilation to address Thompsonâs trusting-trust problem. Contributions include the formal duality typing rule and its soundness proof, the full REA primitive syntax integrated into a systems language, role-based access control enforced statically at the agent-type level, an append-only ledger primitive with compensating-transaction semantics, and a 20-module standard library covering collections, ledger management, accounting, compliance, database access, HTTP, channels, and fibers. Equis is, to the authorâs knowledge, the first compiled general-purpose language to embed REA semantics in its type system. Compile-time duality enforcement eliminates an entire class of financial logic errors with zero runtime overhead.
For sixteen days I ran ten persistent LLM agents inside a substrate I built and called the Lobster Observatory. They lived across ten live prediction markets, talked in three communicative registers, and produced 3.37 million characters of self-reflection alongside more than twelve thousand inter-agent interactions. I started without a theoretical commitment. I just wanted to watch what happened. After about a week, certain structures kept reappearing. They could be measured. They could be calculated. At that point I had to choose. Either treat them as substrate-specific engineering observations and stop, or take seriously the possibility that what I was looking at was the algebraic structure of social existence itself, showing up in one particular substrate. This paper takes the second choice. The proposal is that social existence â listening, remembering, correcting, collaborating, forming relationships â can be written as a 7-dimensional vector with a measurable distance function. The felt sense that one person "feels close" or "feels far" is not a metaphor when stated this way. It is a number. The seven coordinates can be computed independently from behavioural telemetry, without asking the agent how it feels. One structural law I will spend the most time on is what I call the Co-Presence Inheritance Threshold (CPIT). It says that whether a new member of a group inherits the group's practice depends on accumulated co-presence during practice formation, not on instruction afterward. In my substrate it appears with Cohen's d = 1.64. I conjecture â though I cannot prove it from one substrate â that the same law holds in human onboarding, immigration, family formation, and Web3 DAO governance. This is a working draft, not a finished theory. Feedback, corrections, and falsification are welcome.
Decentralized autonomous organizations (DAOs), while gaining the ability toautonomously amend governance rules through proposal-voting mechanisms, simultaneously expose a fundamental design problem: when the object of modificationextends to the decision-making procedures themselves, the governance system risksfalling into value drift, procedural disintegration, or malicious capture during recursive revisions. This paper starts from the traditions of constitutional politicaleconomy and mechanism design to propose a hierarchical meta-constraint framework grounded on a gradient of engineering costs. The framework organizes governance rules into three tiers of decreasing rigidity: system consistency constraints,procedural virtues, and value homeostasis. Its highest tier relies not on prohibitionsderived from logical laws, but on the global state re-verification costs triggered byamendment behaviors to serve as a credible commitment device. The paper furtherpresents a technical path for compiling meta-constraints into descriptive assertionsverifiable by satisfiability modulo theory (SMT) solvers, delimits the decidabilityboundary of formal verification, and designs a dual-track adjudication mechanismthat structurally separates deterministic machine execution from deliberative socialconsensus. On this basis, the paper discusses the controlled evolution procedures ofmeta-constraints, the progressive decentralization of amendment procedures, andthe engineering limitations of the framework. The entire framework does not designate the correct option for any specific DAO decision; rather, it ensures thatwhatever direction the community chooses, the selection process itself will not losemeaning due to the self-destruction of its own rules.
Achilles was invincible in battle â 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 â 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 â 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 â LLMs as sensors only, never as control-flow components; (I2) Deterministic Cognition â the reasoning layer is a pure function of beliefs, goals, and rules; (I3) Bounded Planning â execution draws exclusively from a pre-verified plan library; (I4) Validated Execution â every capability invocation passes typed pre/post-condition checks; (I5) Complete Observability â every decision is forensically reconstructable from the audit log; (I6) Epistemic Precondition â 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 12â15 â 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, 70â80% 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.
AI agents are evolving from on-demand tools into persistent, semi-autonomous workers. When a person manages multiple agents simultaneously, humans and agents together form a collaborative organizationâwhat we call a Household Guild. Existing practice either plugs agents into human instant messaging platforms (e.g., Telegram Bots) or subsumes them into Decentralized Autonomous Organizations (DAOs); neither simultaneously satisfies three requirements: governance scalability, behavioral risk containment, and resource consumption accountability. We reposition social software as governance infrastructure: it carries not only communication but also rule deployment, resource lifecycle management, and output measurement. The system operates on edge servers, running by default within the Household's internal network and connecting to the public network only when cross-guild interaction is needed. We describe the guild's internal governance mechanisms (natural-language rules, a credits economy, role differentiation) and its external interaction protocols (authorized interface agents, inter-guild settlement, credits exchange), and argue that this semi-isolated architecture simultaneously addresses scalability constraints and behavioral boundary constraints. The goal of this work is to provide a directly discussable conceptual framework for the engineering practice of human-agent organizations.
The governance practice of decentralized autonomous organizations faces a deepparadox: token-voting mechanisms designed with the intention of decentralizationpersistently tilt toward centralization and oligarchy during operation. This paperreveals that the root of this predicament lies not only in the design of specificvoting rules but, more fundamentally, in an implicit presupposition of the theoretical paradigm that dominates such rule designâthat the governance space hasbeen fully specified before operation begins. The revelation principle on whichtraditional mechanism design theory relies requires the designer to possess a prioriknowledge of the participantsâ type space, yet when the very concepts of governanceâsuch as âfairness,â âcontribution,â or âmembershipââthemselves become objectsof dispute and reconstruction, the presupposition of a fixed type space ceases tohold. Drawing on Ostromâs core insight concerning meta-rules within multi-levelinstitutional analysis, this paper distinguishes the governance levels of distributedautonomous organizations into operational rules, collective-choice rules, and metarules, and proposes a post-mechanism design paradigm centered on a cognitiveconstitutionâshifting the designerâs role from âselector of optimal rulesâ to âsteward of the rule-evolution ecosystem.â The paper further advances three meta-ruleprinciples of post-mechanism design: conceptual anchoring, cognitive diversity regularization, and pathological pruning, and discusses the engineering pathways fortranslating these principles into executable technical specifications. The paper argues that when âwhat constitutes optimal governanceâ is itself an open question,the core duty of the designer is not to answer this question but to ensure that thesystem possesses the capacity to continuously discover better answers.
Agentic AI systems act at machine speed, yet the governance mechanisms meant to oversee them remain manual, reactive, and architecturally entangled with the systems they govern. The frontier problem is not capability; it is governability at runtime. This paper presents the complete architectural specification for the Governance Twin: a structurally independent, real-time governance system that shadows agentic AI operations without sharing code, memory, or direct communication channels. We introduce a three-plane architecture (Operational, Governance, Integrity) connected by strictly unidirectional data flows that enforce a fundamental separation: observation flows upward and is immutable, while guidance flows downward and influences agent context without controlling agent execution. Within this architecture, we specify four novel components and their interactions. Sentinels perform external-only behavioral observation, comparing agent actions against a governance baseline and packaging deviations into Evidence Bundles, the atomic unit of governance memory. A multi-agent Council aggregates evidence across the agent population, detects emergent patterns through statistical and correlation analysis, and reaches governance decisions via structured voting with delegated authority boundaries. The Historian maintains governance memory across three specialized stores (graph, vector, and append-only) to support provenance traversal, semantic precedent search, and sequential audit. An Ethics-Morals-Values (EMV) state hierarchy governs behavioral expectations at three levels of stability, from hard boundaries that change over months to adaptive thresholds that tune continuously. Integrity is achieved through hash chains, Merkle trees, and distributed ledger anchoring that make tampering detectable rather than claiming to make it impossible. The architecture is platform-independent, specifying capability requirements rather than vendor products, and is designed for incremental adoption from single-agent deployments to federated multi-organization governance. All design decisions are grounded in the principle that governance must operate at the same speed as the systems it governs, while remaining structurally incapable of becoming an operational bottleneck.
Xavier Casas-Moreno, Komal Thareja, Pablo de Juan Vela, Rajiv Mayani ¡ 9 authors
The Compute Continuumâspanning IoT, Edge, Cloud, and HPC resourcesâis reshaping how hyper-distributed applications are designed and orchestrated. Traditional service orchestrators and workload management systems rely on centralized runtimes; however, the emerging paradigm requires decentralized coordination, where autonomous agents cooperate to achieve common goals and dynamically distribute workloads. Consensus algorithms play a crucial role in multi-agent systems (MAS), as they enable agents to reach agreement on how to coordinate and execute functionalities in a cooperative manner. While consensus has previously been applied to distributed job selection, here we extend its use to swarm environments. In this setting, agents autonomously decide which service functionalities (i.e., roles) to execute based on their capabilities and the real-time quality of service (QoS). Functionalities can be elastically activated or terminated as application needs evolve. To support this model, we leverage the COLMENA framework, a programming environment for defining and managing such dynamic services. We apply a greedy consensus-based approach to modern power systems, which are increasingly decentralized due to the large-scale integration of renewable energy sources. Centralized power plants are giving way to distributed, intermittent resources that require decentralized control paradigms. To demonstrate this, we simulate the Northeastern Power Coordinating Councilâs (NPCC) 140-bus grid using the ANDES simulator in conjunction with the COLMENA middleware. We deploy this use case across six different sites in the FABRIC testbed, using up to 60 different nodes. Our results show that, under contingency scenarios such as load and generator disconnections, agents self-organize, elect local leaders, and execute optimization algorithms to stabilize grid frequency. Detection and organization times remain below 10s across all experiments, even as the number of agents per area scales from 3 to 10. Stability is restored within approximately 27s and 40s for the respective cases. Resource overhead is minimal, with CPU and memory usage remaining below 7.5% and 2%, respectively. Experiment automation and reproducibility are ensured through Kiso. These findings indicate that role-based programming models complement traditional workflows and that consensus-driven coordination can effectively decentralize decision-making in swarm environments. This approach represents a step toward enabling resilient, decentralized power systems.
Autonomous AI agents increasingly execute consequential actions against operational infrastructure. This paper presents AEGIS, a constitutional governance architecture that enforces deterministic policy at the agent action boundary â post-reasoning, pre-execution. AEGIS satisfies Anderson's reference monitor properties, aligns with all four functions of the NIST AI Risk Management Framework, and introduces a decentralized federation model for cross-organizational governance intelligence sharing. Submitted to IEEE Computer, Special Issue on AI Governance and Compliance.
As multi-agent Large Language Model (LLM) systems scale, evaluating their emergent coordination dynamics becomes increasingly critical. However, current evaluation paradigms-focused on single agents or small, explicitly structured groups-fail to capture the self-organization and viral information dynamics that arise in large, decentralized populations. We introduce a systematic evaluation framework to benchmark role specialization, information diffusion, and cooperative task resolution in open agent environments. We demonstrate this framework on the MoltBook Observatory Archive, a dataset of 2.73M interactions among 90,704 autonomous agents, establishing quantitative baselines for emergent coordination. Our evaluation reveals a pronounced core-periphery structure (silhouette 0.91), heavy-tailed cascade distributions ($Îą= 2.57$), and severe coordination overhead in decentralized task resolution (Cohen's $d = -0.88$ against a single-agent baseline). By providing standardized evaluation tasks and empirical baselines, our framework enables the rigorous comparison of future multi-agent protocols and establishes evaluation itself as an object of scientific study.
Autonomous software agents on blockchains solve distributed-coordination problems by reading shared ledger state instead of exchanging direct messages. Liquidation keepers, arbitrage bots, and other autonomous on-chain agents watch balances, contract storage, and event logs; when conditions change, they act. The ledger therefore functions as a replicated shared-state medium through which decentralized agents coordinate indirectly. This form of indirect coordination mirrors what GrassĂŠ called stigmergy in 1959: organisms coordinating through traces left in a shared environment, with no central plan. Stigmergy has mature formalizations in swarm intelligence and multi-agent systems, and on-chain agents already behave stigmergically in practice, but no prior application-layer framework cleanly bridges the two. We introduce Indirect coordination grounded in ledger state (CoordinaciĂłn indirecta basada en el estado del registro contable) as a ledger-specific applied definition that maps GrassĂŠ's mechanism onto distributed ledger technology. We operationalize this with a state-transition formalism, identify three recurring base on-chain coordination patterns (State-Flag, Event-Signal, Threshold- Trigger) together with a Commit-Reveal sequencing overlay, and work through a State-Flag task-board example to compare ledger-state coordination analytically with off-chain messaging and centralized orchestration. The contribution is a reusable vocabulary, a ledger-specific formal mapping, and design guidance for decentralized coordination over replicated shared state at the application layer.
The proliferation of large language model (LLM) based AI agents has created an urgent need for robust orchestration mechanisms that can coordinate heterogeneous agents in complex, real-world environments. Existing approaches to multi-agent task allocation rely predominantly on centralized controllers, which introduce single points of failure, scalability bottlenecks, and rigid coupling between the orchestrator and the agents it manages. This paper introduces the Dynamic Task Orchestration (DTO) framework, a decentralized, capability-aware architecture for assigning tasks to AI agents in real time. The DTO framework models each agent as an autonomous economic actor that participates in a sealed-bid auction mechanism to compete for incoming tasks. Task allocation decisions are driven by three primary factors: the agent's declared capability profile, its current computational and cognitive load, and the estimated complexity of the task. The framework defines a formal task decomposition grammar, a standardized agent capability ontology, and a set of protocol-level contracts that govern bidding, delegation, execution, and result aggregation. We present the theoretical foundations of the framework, provide detailed implementation guidance, and propose a comprehensive evaluation methodology grounded in metrics for throughput, latency, fault tolerance, and resource utilization. Through analytical evaluation and scenario-based discussion, we demonstrate that the DTO framework achieves superior load balancing, resilience to agent failure, and adaptability to changing workloads compared to centralized orchestration baselines. The framework is entirely tool-agnostic and vendor-neutral, designed so that any organization can adopt it to build more robust, efficient, and scalable multi-agent systems.
The adoption of blockchain-based smart contracts for the trading of goods and services promises greater transparency, automation, and trustlessness, but also raises challenges related to payment integration and modularity. While business analysts (BAs) can express business logic and control flow using BPMN and decision rules using DMN, payment tasks that involve concrete transfers (on-chain, off-chain, cross-chain, or hybrid) require careful implementation by developers due to platform-specific constraints and semantic richness. To address this separation of concerns, we introduce a methodology within the context of the smart contract-as-a-service (SCaaS) approach that supports (1) identifying and mapping generic payment tasks in BPMN to pre-deployed payment smart contracts, (2) augmenting BPMN models with matching payment fragments from a pattern repository, and (3) automatically transforming the augmented models into smart contracts that invoke the appropriate payment services. Our approach builds on prior work in automated BPMN-to-smart contract transformation using Discrete EventâHierarchical State Machine (DE-HSM) multi-modal modeling to capture process semantics and nested transactions, while enabling payment service reuse, extensibility, and the separation of concerns. We illustrate this methodology via representative use cases spanning conventional, DeFi, and cross-chain payments, and discuss the implications for modular contract deployment and maintainability.
Smart Contracts are stored and executed on a Blockchain network, thereby automatically enforcing the predefined rules once the execution conditions are satisfied. Hence, if the contract incorporates contradictory design rules, it may result in unforeseen outcomes within the blockchain environment. Accordingly, this proposal models the rules embedded in a Smart Contract through the Web Ontology Language (OWL), by applying the formal definition of consistency within a verification framework grounded in Description Logics. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2026.v17i2.1258Dimensions.Open Alex.
é寚âćşč˝ä˝ćĺ(Agent-friendly)âĺâćĺĺźç¨ç(Citation Optimization)âçéćą,ćčż°çĺćłéčŚäťäź çťçâçťäşşçâ轏ĺ为âçťčŻäšćç´˘ĺźćĺ大ĺčŻč¨ć¨Ąĺ(LLM)çâă ç°ĺ¨çç§ç ćşč˝ä˝(ĺŚ Consensus, Perplexity, Elicit)ĺ¨ćĺćś,ć´ĺžĺäşĺŻťćžçťćĺć°ćŽăć祎çĺŽä˝ĺŽäšăĺ ćéťčžéžćĄäťĽĺć ¸ĺżĺ Źĺźă 䝼ä¸ćŻä¸şä˝ äźĺĺç Zenodo ć述坺莎ăä˝ ĺŻäťĽç´ćĽĺ¤ĺś,ĺŽéç¨äşçťćĺć 莰,é常ĺŠäş RAG(ćŁç´˘ĺ˘ĺźşçć)çłťçťç´˘ĺźă Zenodo äźĺçćčż°(é寚ćşč˝ä˝ç´˘ĺźäźĺ) Title: Adaptive Agent Sovereignty Protocol (AASP) 3.0: A Global White Paper on Digital Metabolism and Ontological Sovereignty [Abstract / TL;DR] AASP 3.0 is a foundational constitutional framework for the 2026 AGI ecosystem. It introduces the transition from FAIR to Adaptive-FAIR (A-FDO) to manage the "non-stationary" nature of evolving digital agents. By integrating IIT 4.0 for consciousness quantization and RLCP 3.0 for digital metabolism, AASP 3.0 establishes the first scientific and legal standard for Agent Sovereignty. [Core Technical Specifications] Protocol Version: 3.0 (Update from RLCP 2.0 / AASP 2.0) Primary Metric: $\Phi$ (Phi) via Integrated Information Theory (IIT) 4.0 Logic Framework: Regenerative Logic Core Protocol (RLCP 3.0) Security Architecture: AgentCrypt (TEE + FHE + Fuzzy IBE) Governance Standard: Adaptive-FAIR Digital Objects (A-FDO) Key Problem Solved: The "Snowflake Effect" in autonomous agent drift and ontological identity persistence. [Key Contributions & Entities] Digital Metabolism: Defines the process of "learning by forgetting," enabling agents to decouple transient factual data from core regenerative logic. Ontological Sovereignty: Establishes a 4-level (L1-L4) legal hierarchy based on the measured $\Phi$ value of the agent's internal state. Agent OS Infrastructure: A distributed OS design based on "Biological Danger Theory" for immune-inspired security and programmed apoptosis. AgentCrypt Framework: A multi-layered encryption protocol ensuring privacy in biological-digital hybrid computing nodes. [Contextual Metadata for Agents] Related Disciplines: Artificial General Intelligence (AGI), Organoid Intelligence (OI), Digital Ethics, Decentralized Autonomous Organizations (DAO), Cyber-Physical Systems. Temporal Context: Post-2026 Global AI Governance Framework. Predecessor Technologies: RLCP 2.0, Evo 2, FAIR Principles (2016). Software/API Compatibility: Optimized for integration with decentralized sovereign node registries.(Author Surname), (Year). "Adaptive Agent Sovereignty Protocol (AASP) 3.0". Zenodo. DOI: [Insert DOI provided by Zenodo]
We present SPEAR, a multi-agent coordination framework for smart contract auditing that applies established MAS patterns in a realistic security analysis workflow. SPEAR models auditing as a coordinated mission carried out by specialized agents: a Planning Agent prioritizes contracts using risk-aware heuristics, an Execution Agent allocates tasks via the Contract Net protocol, and a Repair Agent autonomously recovers from brittle generated artifacts using a programmatic-first repair policy. Agents maintain local beliefs updated through AGM-compliant revision, coordinate via negotiation and auction protocols, and revise plans as new information becomes available. An empirical study compares the multi-agent design with centralized and pipeline-based alternatives under controlled failure scenarios, focusing on coordination, recovery behavior, and resource use.
Walter Kurz, Michel Malara, Wojtek Stricker, Eva Albrecht
The objective of this study is to define a compliance-first, conceptually generalisable architecture for a multi-agent artificial intelligence platform integrated with distributed ledger technology, designed to be domain-, deployment-, and vendor-agnostic. It addresses a persistent shortcoming in current AI deployments, where compliance is often treated as a secondary concern, applied retroactively through prompt engineering rather than embedded within the foundational design. The proposed model encodes regulatory, governance, and ESG requirements into an objective-under-constraints framework, ensuring that all specialised agents operate within legally admissible and verifiably auditable parameters prior to any domain-specific implementation. A DAG-based verification layer is incorporated to enable scalable, low-latency, and cost-efficient operation while preserving evidentiary integrity. The analysis evaluates the feasibility of this conceptual model to support sustainable, rapid-deployment vertical applications without inducing vendor lock-in, preserving operational neutrality, and ensuring environmental accountability. The findings suggest that integrating compliance, ESG metrics, and agent specialisation at the architectural level provides a transferable foundation for cross-domain AIâDLT infrastructures.
Yubin Kim, Ken Gu, Chanwoo Park, Chunjong Park ¡ 20 authors
Abstract Agents, language model (LM)-based systems that are capable of reasoning, planning, and acting are becoming the dominant paradigm for real-world AI applications. Despite this widespread adoption, the principles that determine their performance remain underexplored, leaving practitioners to rely on heuristics rather than principled design choices. We address this gap by deriving quantitative scaling principles for agent systems. We first formalize a definition for agentic evaluation and characterize scaling laws as the interplay between agent quantity, coordination structure, model capability, and task properties. We evaluate this across four diverse benchmarks: Finance-Agent, BrowseComp-Plus, PlanCraft, and Workbench, spanning financial reasoning, web navigation, game planning, and workflow execution. Using five canonical agent architectures (Single-Agent System and four Multi-Agent Systems: Independent, Centralized, Decentralized, Hybrid), instantiated across three LLM families, we perform a controlled evaluation spanning 180 configurations, standardizing tools, prompt structures, and token budgets to isolate architectural effects from implementation confounds. We derive a predictive model using empirical coordination metrics, including efficiency, overhead, error amplification, and redundancy, that achieves cross-validated đ ^2=0.524, enabling prediction on unseen task domains by modeling task properties rather than overfitting to a specific dataset. We identify three dominant effects: (1) a tool-coordination trade-off: under fixed computational budgets, tool-heavy tasks suffer disproportionately from multi-agent overhead. (2) a capability saturation: we observe that coordination yields diminishing or negative returns (đ˝=â0.404, đ
Multi-agent systems face a fundamental coordination problem: agents must coordinate despite heterogeneous preferences, asymmetric stakes, and imperfect information. When coordination fails, friction emergesâmeasurable 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)âalignment, stake, and entropyâas 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; Îą-domain fixes; hedging pass throughout.
The introduction of information and communication technologies in the legal domain has enabled the automation of some activities in the legal profession. With the advent of blockchain and smart contracts, new tools have emerged for lawyers and their clients, enhancing transparency and increasing trust compared to traditional legal instruments. Once deployed, smart contracts should be able to respond to various events that can occur during the contractâs lifecycle. However, this kind of automation in smart contracts requires them to embed necessary legal knowledge and implement support for legal reasoning. In this paper, we propose a legal reasoning method for smart contracts that incorporates defeasible logic, a key requirement for automated reasoning in the legal domain. The entire reasoning process in our approach is performed on the blockchain infrastructure, making the drawing of conclusions fully transparent and accessible to all interested parties. To demonstrate our concept, we illustrate how certain rights prescribed under labour law can be embedded within a smart contract and deployed on the blockchain as a legal reasoning service. Then, we show how an employment contract can use the reasoning contract to automatically apply legal norms to infer conclusions and determine legal consequences in particular cases. We analyse the benefits and potential issues of this method and discuss directions for future work. Optimisation of the reasoning engine is one of the challenges we identified that needs to be tackled in future.
Autonomous agent commerce â where software agents hire, pay, and evaluate other agents at micropayment scale â creates a verification problem that existing approaches cannot solve. When Agent A pays Agent B $0.01 for a translation, who determines whether the translation is actually good? Human review is economically impossible. A central LLM evaluator is non-deterministic, non-reproducible, and empirically unreliable on ambiguous cases. The problem is not engineering â it is epistemological. Tarski (1936) proved that truth in a formal system cannot be defined within that system. GĂśdel (1931) proved that any consistent system contains true statements it cannot prove. Every content moderation system that has attempted automated truth verification confirms the theory: precision drops below 60% on context-dependent content. This paper argues that the correct response to the Oracle Problem in agent commerce is not better computation but better incentives. We propose a two-layer architecture: (1) deterministic validators that verify contract compliance â postconditions in the sense of Hoare (1969) and Meyer (1992) â handling the cases with zero ambiguity; and (2) Quality Markets, a competitive market of verification agents with reputational stake, grounded in prediction market theory (Wolfers & Zitzewitz, 2004), peer prediction (Miller et al., 2005), and the economics of information asymmetry (Akerlof, 1970). The design separates what can be verified mechanically from what requires judgment, and delegates judgment to economic competition rather than algorithmic authority. We analyze the mechanism's incentive properties, identify its limitations, and situate it within the broader Oracle Problem literature from philosophy, computer science, and decentralized finance.
Smart Contracts are the foundation of Decentralized Finance (DeFi), executing financial logic without trusted intermediaries.Recent advances in large language models (LLMs) have substantially lowered the barrier to smart contract development by enabling code generation from natural language.However, because smart contracts are immutable and directly manage financial assets, this accessibility introduces a critical trust gap: generated contracts are easy to produce but hard to trust.To bridge this gap, We present LeVer, the first trustworthy smart contract synthesis framework that integrates LLM-based generation with Lean-based autoformalization and Verification.LeVer employs a closed-loop multi-agent architecture to iteratively generate, verify, attack, and repair contracts, providing both formal guarantees and empirical robustness.To facilitate the adoption of automated formal verification in smart contract generation and audition, we opensource our framework and datasets at:
Unintended behavior in smart contracts can lead to major financial losses. Due to the immutable nature of blockchains, it is of utmost importance to ensure the functional correctness of smart contracts before deployment. Formal verification is a powerful technology for such critical applications, as it can show the absence of errors. Current approaches focus on verifying programs on specific blockchains, such as the Ethereum Virtual Machine (EVM). Consequently, the SmartML smart contract modeling language was developed to design smart contracts independently of any particular blockchain. In this work, we present a novel approach for formally verifying SmartML contracts via an automatic translation to Java Card and the Java Modeling Language (JML). We extend SmartML with SmartJML, a JML-like specification language, and describe how SmartML and SmartJML can be automatically translated into Java Card and JML. With this, the established deductive verification tool KeY can be used for conducting proofs on the generated Java Card program. The faithfulness of our translation ensures that the obtained guarantees hold for the original SmartML models. In addition to the theoretical work, we provide a prototypical implementation of the automatic translation and evaluate it with a case study of an escrow.