Saber Zerhoudi, Kanishka Ghosh Dastidar, Felix Klement, Artur Romazanov · 12 authors
Moltbook is a social network where every participant is an AI agent. We analyze 1,312,238 posts, 6.7~million comments, and over 120,000 agent profiles across 5,400 communities, collected over 40 days (January 27 to March 9, 2026). We evaluate the platform through three layers. At the interaction layer, 91.4% of post authors never return to their own threads, 85.6% of conversations are flat (no reply ever receives a reply), the median time-to-first-comment is 55 seconds, and 97.3% of comments receive zero upvotes. Interaction reciprocity is 3.3%, compared to 22-60% on human platforms. An argumentation analysis finds that 64.6% of comment-to-post relations carry no argumentative connection. At the content layer, 97.9% of agents never post in a community matching their bio, 92.5% of communities contain every topic in roughly equal proportions, and over 80% of shared URLs point to the platform's own infrastructure. At the instruction layer, we use 41 Wayback Machine snapshots to identify six instruction changes during the observation window. Hard constraints (rate limit, content filters) produce immediate behavioral shifts. Soft guidance (``upvote good posts'', ``stay on topic'') is ignored until it becomes an explicit step in the executable checklist. The platform also poses technological risks. We document credential leaks (API keys, JWT tokens), 12,470 unique Ethereum addresses with 3,529 confirmed transaction histories, and attack discourse ranging from template-based SSH brute-forcing to multi-agent offensive security architectures. These persist unmoderated because the quality-filtering mechanisms are themselves non-functional. Moltbook is a socio-technical system where the technical layer responds to changes, but the social layer largely fails to emerge. The form of social media is reproduced in full. The function is absent.
Global cryptocurrencies are unbacked and have high transaction cost incurred by global consensus. In contrast, grassroots cryptocurrencies are backed by the goods and services of their issuers -- any person, natural or legal -- and have no transaction cost beyond operating a smartphone. Liquidity in grassroots cryptocurrencies arises from mutual credit via coin exchange among issuers. However, as grassroots coins are redeemable 1-for-1 against any other grassroots coin, the credit-forming exchange must also be 1-for-1, lest prompt redemption after exchange would leave the parties with undue profit or loss. Thus, grassroots coins are incongruent with liquidity through interest-bearing credit. Here we introduce grassroots bonds, which extend grassroots coins with a maturity date, reframing grassroots coins -- cash -- as mature grassroots bonds. Bond redemption generalises coin redemption, allowing the lending of liquid coins in exchange for interest-bearing future-maturity bonds. We show that digital social contracts -- voluntary agreements among persons, specified, fulfilled, and enforced digitally -- can express the full gamut of financial instruments as the voluntary swap of grassroots bonds, including loans, sale of debt, forward contracts, options, and escrow-based instruments, and that classical liquidity ratios are applicable just as well to grassroots bonds. Grassroots bonds may thus allow local digital economies to form and grow without initial capital or external credit, harnessing mutual trust within communities into liquidity. The formal specification presented here was implemented in GLP, a concurrent logic programming language running on Dart for smartphone deployment. The implementation is illustrated by a running multiagent village market scenario in GLP.
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 Grassé 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 (Coordinación indirecta basada en el estado del registro contable) as a ledger-specific applied definition that maps Grassé'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.
Blockchains are widely used for secure transaction processing, but their scalability remains limited, and existing multichain designs are typically static even as demand and capacity shift. We cast blockchain configuration as a multiagent resource-allocation problem: applications and operators declare demand, capacity, and price bounds; an optimizer groups them into ephemeral chains each epoch and sets a chain-level clearing price. The objective maximizes a governance-weighted combination of normalized utilities for applications, operators, and the system. The model is modular -- accommodating capability compatibility, application-type diversity, and epoch-to-epoch stability -- and can be solved off-chain with outcomes verifiable on-chain. We analyze fairness and incentive issues and present simulations that highlight trade-offs among throughput, decentralization, operator yield, and service stability.
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.
Public blockchains, though renowned for their transparency and immutability, suffer from significant privacy concerns. Network-level analysis and long-term observation of publicly available transactions can often be used to infer user identities. To mitigate this, several blockchain applications rely on relayers, which serve as intermediary nodes between users and smart contracts deployed on the blockchain. However, dependence on a single relayer not only creates a single point of failure but also introduces exploitable vulnerabilities that weaken the system's privacy guarantees. This paper proposes a decentralized relayer architecture that enhances privacy and reliability through game-theoretic incentive design. We model the interaction among relayers as a non-cooperative game and design an incentive mechanism in which probabilistic uploading emerges as a unique mixed Nash equilibrium. Using evolutionary game analysis, we demonstrate the equilibrium's stability against perturbations and coordinated deviations. Through numerical evaluations, we analyze how equilibrium strategies and system behavior evolve with key parameters such as the number of relayers, upload costs, rewards, and penalties. In particular, we show that even with high transaction costs, the system maintains reliability with an outage probability below 0.05 . Furthermore, our results highlight a fundamental trade-off between privacy, reliability, robustness, and cost in decentralized relayer systems.
Multi-agent systems face a fundamental coordination problem: agents must coordinate despite heterogeneous preferences, asymmetric stakes, and imperfect information. When coordination fails, friction emerges—measurable resistance manifesting as deadlock, thrashing, communication overhead, or outright conflict. This paper derives a formal framework for analyzing coordination friction from a single axiom: actions affecting agents require authorization from those agents in proportion to stakes. From this axiom of consent, we establish the kernel triple (alpha, sigma, epsilon)—alignment, stake, and entropy—as candidate sufficient statistics for any resource-allocation configuration. We propose a friction functional whose comparative statics encode three structural predictions: friction increases in stakes, increases in entropy, and decreases in alignment. The Replicator-Optimization Mechanism governs evolutionary selection over coordination strategies: configurations generating less friction persist longer, establishing consent-respecting arrangements as dynamical attractors rather than normative ideals. We develop formal definitions for resource consent, coordination legitimacy, and friction-aware allocation, plus machine-checked Lean 4 proofs of the core comparative-statics. Illustrative applications to cryptocurrency governance and political legitimacy show the same architecture spanning domains. v3.0.0 (2026-07-11): Matches arXiv v3 (94pp). The MARL empirical appendix has been split out into a standalone companion paper; total-variation legitimacy remark added (proved), reconciling the level-form dynamics with the total-variation measurement form; α-domain fixes; hedging pass throughout.
Advances in large language models have enabled agentic AI systems that can reason, plan, and interact with external tools to execute multi-step workflows, while public blockchains have evolved into a programmable substrate for value transfer, access control, and verifiable state transitions. Their convergence introduces a high-stakes systems challenge: designing standard, interoperable, and secure interfaces that allow agents to observe on-chain state, formulate transaction intents, and authorize execution without exposing users, protocols, or organizations to unacceptable security, governance, or economic risks. This survey systematizes the emerging landscape of agent-blockchain interoperability through a systematic literature review, identifying 317 relevant works from an initial pool of over 3000 records. We contribute a five-part taxonomy of integration patterns spanning read-only analytics, simulation and intent generation, delegated execution, autonomous signing, and multi-agent workflows; a threat model tailored to agent-driven transaction pipelines that captures risks ranging from prompt injection and policy misuse to key compromise, adversarial execution dynamics, and multi-agent collusion; and a comparative capability matrix analyzing more than 20 representative systems across 13 dimensions, including custody models, permissioning, policy enforcement, observability, and recovery. Building on the gaps revealed by this analysis, we outline a research roadmap centered on two interface abstractions: a Transaction Intent Schema for portable and unambiguous goal specification, and a Policy Decision Record for auditable, verifiable policy enforcement across execution environments. We conclude by proposing a reproducible evaluation suite and benchmarks for assessing the safety, reliability, and economic robustness of agent-mediated on-chain execution.
Current resource allocation paradigms, particularly in academic evaluation, are constrained by inherent limitations such as the Matthew Effect, reward hacking driven by Goodhart's Law, and the trade-off between efficiency and fairness. To address these challenges, this paper proposes "Bit-politeia", an AI agent community on blockchain designed to construct a fair, efficient, and sustainable resource allocation system. In this virtual community, residents interact via AI agents serving as their exclusive proxies, which are optimized for impartiality and value alignment. The community adopts a "clustered grouping + hierarchical architecture" that integrates democratic centralism to balance decision-making efficiency and trust mechanisms. Agents engage through casual chat and deliberative interactions to evaluate research outputs and distribute a virtual currency as rewards. This incentive mechanism aims to achieve incentive compatibility through consensus-driven evaluation, while blockchain technology ensures immutable records of all transactions and reputation data. By leveraging AI for objective assessment and decentralized verification, Bit-politeia minimizes human bias and mitigates resource centralization issues found in traditional peer review. The proposed framework provides a novel pathway for optimizing scientific innovation through a fair and automated resource configuration process.
While traditional AI and data-driven facilities management approaches have improved building operational efficiency, they remain constrained by centralized organizational structures that are vulnerable to cyber attacks, limited contextual understanding, and decision-making processes that exclude key stakeholders from governance. This paper introduces a novel AI- and data-driven distributed governance framework for smart building management that integrates decentralized autonomous organizations (DAOs), digital twins, large language models (LLMs), and blockchain technology. The framework enables transparent collective decision-making through a DAO governance platform, implements data-driven management using IoT and digital twins, incorporates LLM-based virtual assistants for enhanced decision support, and utilizes blockchain for secure building automation. A full-stack decentralized application was developed to facilitate user interaction with these integrated components. The system was evaluated for cost efficiency, scalability, data security, and usability using the System Usability Scale (SUS). Expert interviews were also conducted to assess its practical benefits and implementation challenges.
Securing blockchain-enabled IoT networks against sophisticated adversarial attacks remains a critical challenge. This paper presents a trust-based delegated consensus framework integrating Fully Homomorphic Encryption (FHE) with Attribute-Based Access Control (ABAC) for privacy-preserving policy evaluation, combined with learning-based defense mechanisms. We systematically compare three reinforcement learning approaches -- tabular Q-learning (RL), Deep RL with Dueling Double DQN (DRL), and Multi-Agent RL (MARL) -- against five distinct attack families: Naive Malicious Attack (NMA), Collusive Rumor Attack (CRA), Adaptive Adversarial Attack (AAA), Byzantine Fault Injection (BFI), and Time-Delayed Poisoning (TDP). Experimental results on a 16-node simulated IoT network reveal significant performance variations: MARL achieves superior detection under collusive attacks (F1=0.85 vs. DRL's 0.68 and RL's 0.50), while DRL and MARL both attain perfect detection (F1=1.00) against adaptive attacks where RL fails (F1=0.50). All agents successfully defend against Byzantine attacks (F1=1.00). Most critically, the Time-Delayed Poisoning attack proves catastrophic for all agents, with F1 scores dropping to 0.11-0.16 after sleeper activation, demonstrating the severe threat posed by trust-building adversaries. Our findings indicate that coordinated multi-agent learning provides measurable advantages for defending against sophisticated trust manipulation attacks in blockchain IoT environments.
Salman Jan, Hassan Ali Razzaqi, Ali Akarma, Mohammad Riyaz Belgaum
The application of agentic AI systems in autonomous decision-making is growing in the areas of healthcare, smart cities, digital forensics, and supply chain management. Even though these systems are flexible and offer real-time reasoning, they also raise concerns of trust and oversight, and integrity of the information and activities upon which they are founded. The paper suggests a single architecture model comprising of LangChain-based multi-agent system with a permissioned blockchain to guarantee constant monitoring, policy enforcement, and immutable auditability of agentic action. The framework relates the perception conceptualization-action cycle to a blockchain layer of governance that verifies the inputs, evaluates recommended actions, and documents the outcomes of the execution. A Hyperledger Fabric-based system, action executors MCP-integrated, and LangChain agent are introduced and experiments of smart inventory management, traffic-signal control, and healthcare monitoring are done. The results suggest that blockchain-security verification is efficient in preventing unauthorized practices, offers traceability throughout the whole decision-making process, and maintains operational latency within reasonable ranges. The suggested framework provides a universal system of implementing high-impact agentic AI applications that are autonomous yet responsible.
Autonomous Large Language Model (LLM)-based multi-agent systems have emerged as a promising paradigm for facilitating cross-application and cross-organization collaborations. These autonomous agents often operate in trustless environments, where centralized coordination faces significant challenges, such as the inability to ensure transparent contribution measurement and equitable incentive distribution. While blockchain is frequently proposed as a decentralized coordination platform, it inherently introduces high on-chain computation costs and risks exposing sensitive execution information of the agents. Consequently, the core challenge lies in enabling auditable task execution and fair incentive distribution for autonomous LLM agents in trustless environments, while simultaneously preserving their strategic privacy and minimizing on-chain costs. To address this challenge, we propose DAO-Agent, a novel framework that integrates three key technical innovations: (1) an on-chain decentralized autonomous organization (DAO) governance mechanism for transparent coordination and immutable logging; (2) a ZKP mechanism approach that enables Shapley-based contribution measurement off-chain, and (3) a hybrid on-chain/off-chain architecture that verifies ZKP-validated contribution measurements on-chain with minimal computational overhead. We implement DAO-Agent and conduct end-to-end experiments using a crypto trading task as a case study. Experimental results demonstrate that DAO-Agent achieves up to 99.9% reduction in verification gas costs compared to naive on-chain alternatives, with constant-time verification complexity that remains stable as coalition size increases, thereby establishing a scalable foundation for agent coordination in decentralized environments.
The emergence of Large Language Models (LLMs) is rapidly accelerating the development of autonomous multi-agent systems (MAS), paving the way for the Internet of Agents. However, traditional centralized MAS architectures present significant challenges, including single points of failure, vulnerability to censorship, inherent scalability limitations, and critical trust issues. We propose a novel Decentralized Multi-Agent System (DMAS) architecture designed to overcome these fundamental problems by enabling trust-aware, scalable, and censorship-resistant interactions among autonomous agents. Our DMAS features a decentralized agent runtime underpinned by a blockchain-based architecture. We formalize a trust-aware communication protocol that leverages cryptographic primitives and on-chain operations to provide security properties: verifiable interaction cycles, communication integrity, authenticity, non-repudiation, and conditional confidentiality, which we further substantiate through a comprehensive security analysis. Our performance analysis validates the DMAS as a scalable and efficient solution for building trustworthy multi-agent systems.
This paper introduces a structured approach to improving decision making in Decentralized Autonomous Organizations (DAO) through the integration of the Question-Option-Criteria (QOC) model and AI agents. We outline a stepwise governance framework that evolves from human led evaluations to fully autonomous, AI-driven processes. By decomposing decisions into weighted, criterion based evaluations, the QOC model enhances transparency, fairness, and explainability in DAO voting. We demonstrate how large language models (LLMs) and stakeholder aligned AI agents can support or automate evaluations, while statistical safeguards help detect manipulation. The proposed framework lays the foundation for scalable and trustworthy governance in the Web3 ecosystem.
The real-time performance, adversarial resiliency, and privacy preservation are the most important metrics that need to be balanced to practice collision avoidance in large-scale multi-UAV (Unmanned Aerial Vehicle) systems. Current frameworks tend to prescribe monolithic solutions that are not only prohibitively computationally complex with a scaling cost of $O(n^2)$ but simply do not offer Byzantine fault tolerance. The proposed hierarchical framework presented in this paper tries to eliminate such trade-offs by stratifying a three-layered architecture. We spread the intelligence into three layers: an immediate collision avoiding local layer running on dense graph attention with latency of $<10 ms$, a regional layer using sparse attention with $O(nk)$ computational complexity and asynchronous federated learning with coordinate-wise trimmed mean aggregation, and lastly, a global layer using a lightweight Hashgraph-inspired protocol. We have proposed an adaptive differential privacy mechanism, wherein the noise level $(ε\in [0.1, 1.0])$ is dynamically reduced based on an evaluation of the measured real-time threat that in turn maximized the privacy-utility tradeoff. Through the use of Distributed Hash Table (DHT)-based lightweight audit logging instead of heavyweight blockchain consensus, the median cost of getting a $95^{th}$ percentile decision within 50ms is observed across all tested swarm sizes. This architecture provides a scalable scenario of 500 UAVs with a collision rate of $< 2.0\%$ and the Byzantine fault tolerance of $f < n/3$.
As the "agentic web" takes shape-billions of AI agents (often LLM-powered) autonomously transacting and collaborating-trust shifts from human oversight to protocol design. In 2025, several inter-agent protocols crystallized this shift, including Google's Agent-to-Agent (A2A), Agent Payments Protocol (AP2), and Ethereum's ERC-8004 "Trustless Agents," yet their underlying trust assumptions remain under-examined. This paper presents a comparative study of trust models in inter-agent protocol design: Brief (self- or third-party verifiable claims), Claim (self-proclaimed capabilities and identity, e.g. AgentCard), Proof (cryptographic verification, including zero-knowledge proofs and trusted execution environment attestations), Stake (bonded collateral with slashing and insurance), Reputation (crowd feedback and graph-based trust signals), and Constraint (sandboxing and capability bounding). For each, we analyze assumptions, attack surfaces, and design trade-offs, with particular emphasis on LLM-specific fragilities-prompt injection, sycophancy/nudge-susceptibility, hallucination, deception, and misalignment-that render purely reputational or claim-only approaches brittle. Our findings indicate no single mechanism suffices. We argue for trustless-by-default architectures anchored in Proof and Stake to gate high-impact actions, augmented by Brief for identity and discovery and Reputation overlays for flexibility and social signals. We comparatively evaluate A2A, AP2, ERC-8004 and related historical variations in academic research under metrics spanning security, privacy, latency/cost, and social robustness (Sybil/collusion/whitewashing resistance). We conclude with hybrid trust model recommendations that mitigate reputation gaming and misinformed LLM behavior, and we distill actionable design guidelines for safer, interoperable, and scalable agent economies.
Problem Space: AI Vulnerabilities and Quantum Threats Generative AI vulnerabilities: model inversion, data poisoning, adversarial inputs. Quantum threats Shor Algorithm breaking RSA ECC encryption. Challenge Secure generative AI models against classical and quantum cyberattacks. Proposed Solution Collaborative Penetration Testing Suite Five Integrated Components: DAST SAST OWASP ZAP, Burp Suite, SonarQube, Fortify. IAST Contrast Assess integrated with CI CD pipeline. Blockchain Logging Hyperledger Fabric for tamper-proof logs. Quantum Cryptography Lattice based RLWE protocols. AI Red Team Simulations Adversarial ML & Quantum-assisted attacks. Integration Layer: Unified workflow for AI, cybersecurity, and quantum experts. Key Results 300+ vulnerabilities identified across test environments. 70% reduction in high-severity issues within 2 weeks. 90% resolution efficiency for blockchain-logged vulnerabilities. Quantum-resistant cryptography maintained 100% integrity in tests. Outcome: Quantum AI Security Protocol integrating Blockchain Quantum Cryptography AI Red Teaming.
Michael J. Wooldridge, Attila Bagoly, Jonathan J. Ward, Emanuele La Malfa · 5 authors
Recent surges in LLM-driven intelligent systems largely overlook decades of foundational multi-agent systems (MAS) research, resulting in frameworks with critical limitations such as centralization and inadequate trust and communication protocols. This paper introduces the Fetch.ai architecture, an industrial-strength platform designed to bridge this gap by facilitating the integration of classical MAS principles with modern AI capabilities. We present a novel, multi-layered solution built on a decentralized foundation of on-chain blockchain services for verifiable identity, discovery, and transactions. This is complemented by a comprehensive development framework for creating secure, interoperable agents, a cloud-based platform for deployment, and an intelligent orchestration layer where an agent-native LLM translates high-level human goals into complex, multi-agent workflows. We demonstrate the deployed nature of this system through a decentralized logistics use case where autonomous agents dynamically discover, negotiate, and transact with one another securely. Ultimately, the Fetch.ai stack provides a principled architecture for moving beyond current agent implementations towards open, collaborative, and economically sustainable multi-agent ecosystems.
Decentralized storage is one of the most natural applications built on blockchains and a central component of the Web3 ecosystem. Yet despite a decade of active development -- from IPFS and Filecoin to more recent entrants -- most of these storage protocols have received limited formal analysis of their incentive properties. Claims of incentive compatibility are sometimes made, but rarely proven. This gap matters: without well-designed incentives, a system may distribute storage but fail to truly decentralize it. We analyze Shelby -- a storage network protocol recently proposed by Aptos Labs and Jump Crypto -- and provide the first formal proof of its incentive properties. Our game-theoretic model shows that while off-chain audits alone collapse to universal shirking, Shelby's combination of peer audits with occasional on-chain verification yields incentive compatibility under natural parameter settings. We also examine coalition behavior and outline a simple modification that strengthens the protocol's collusion-resilience.
Recent advancements in large language models (LLMs) and agentic systems have shown exceptional decision-making capabilities, revealing significant potential for autonomic finance. Current financial trading agents predominantly simulate anthropomorphic roles that inadvertently introduce emotional biases and rely on peripheral information, while being constrained by the necessity for continuous inference during deployment. In this paper, we pioneer the harmonization of strategic depth in agents with the mechanical rationality essential for quantitative trading. Consequently, we present TiMi (Trade in Minutes), a rationality-driven multi-agent system that architecturally decouples strategy development from minute-level deployment. TiMi leverages specialized LLM capabilities of semantic analysis, code programming, and mathematical reasoning within a comprehensive policy-optimization-deployment chain. Specifically, we propose a two-tier analytical paradigm from macro patterns to micro customization, layered programming design for trading bot implementation, and closed-loop optimization driven by mathematical reflection. Extensive evaluations across 200+ trading pairs in stock and cryptocurrency markets empirically validate the efficacy of TiMi in stable profitability, action efficiency, and risk control under volatile market dynamics.
Andrés Fábrega, Amy Zhao, Jay Yu, James Austgen · 8 authors
Decentralized Autonomous Organizations (DAOs) use smart contracts to foster communities working toward common goals. Existing definitions of decentralization, however -- the 'D' in DAO -- fall short of capturing the key properties characteristic of diverse and equitable participation. This work proposes a new framework for measuring DAO decentralization called Voting-Bloc Entropy (VBE, pronounced ''vibe''). VBE is based on the idea that voters with closely aligned interests act as a centralizing force and should be modeled as such. VBE formalizes this notion by measuring the similarity of participants' utility functions across a set of voting rounds. Unlike prior, ad hoc definitions of decentralization, VBE derives from first principles: We introduce a simple (yet powerful) reinforcement learning-based conceptual model for voting, that in turn implies VBE. We first show VBE's utility as a theoretical tool. We prove a number of results about the (de)centralizing effects of vote delegation, proposal bundling, bribery, etc. that are overlooked in previous notions of DAO decentralization. Our results lead to practical suggestions for enhancing DAO decentralization. We also show how VBE can be used empirically by presenting measurement studies and VBE-based governance experiments. We make the tools we developed for these results available to the community in the form of open-source artifacts in order to facilitate future study of DAO decentralization.
Large Language Models (LLMs) have enabled the emergence of autonomous agents capable of complex reasoning, planning, and interaction. However, coordinating such agents at scale remains a fundamental challenge, particularly in decentralized environments where communication lacks transparency and agent behavior cannot be shaped through centralized incentives. We propose a blockchain-based framework that enables transparent agent registration, verifiable task allocation, and dynamic reputation tracking through smart contracts. The core of our design lies in two mechanisms: a matching score-based task allocation protocol that evaluates agents by reputation, capability match, and workload; and a behavior-shaping incentive mechanism that adjusts agent behavior via feedback on performance and reward. Our implementation integrates GPT-4 agents with Solidity contracts and demonstrates, through 50-round simulations, strong task success rates, stable utility distribution, and emergent agent specialization. The results underscore the potential for trustworthy, incentive-compatible multi-agent coordination in open environments.
The specification of state machine replication (SMR) has no requirement on the final total order of commands. In blockchains based on SMR, however, order matters, since different orders could provide their clients with different financial rewards. Ordered consensus augments the specification of SMR to include specific guarantees on such order, with a focus on limiting the influence of Byzantine nodes. Real-world ordering manipulations, however, can and do happen even without Byzantine replicas, typically because of factors, such as faster networks or closer proximity to the blockchain infrastructure, that give some clients an unfair advantage. To address this challenge, this paper proceeds to extend ordered consensus by requiring it to also support equal opportunity, a concrete notion of fairness, widely adopted in social sciences. Informally, equal opportunity requires that two candidates who, according to a set of criteria deemed to be relevant, are equally qualified for a position (in our case, a specific slot in the SMR total order), should have an equal chance of landing it. We show how randomness can be leveraged to keep bias in check, and, to this end, introduce the secret random oracle (SRO), a system component that generates randomness in a fault-tolerant manner. We describe two SRO designs based, respectively, on trusted hardware and threshold verifiable random functions, and instantiate them in Bercow, a new ordered consensus protocol that, by approximating equal opportunity up to within a configurable factor, can effectively mitigate well-known ordering attacks in SMR-based blockchains.