Learning cooperative policies for multi-agent systems is often challenged by partial observability and a lack of coordination.

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.

The convergence of blockchain and artificial intelligence (AI) has led to the emergence of AI-based tokens, which are cryptographic assets designed to power decentralized AI platforms and services. This paper provides a comprehensive review of leading AI-token projects, examining their technical architectures, token utilities, consensus mechanisms, and underlying business models. We explore how these tokens operate across various blockchain ecosystems and assess the extent to which they offer value beyond traditional centralized AI services. Based on this assessment, our analysis identifies several core limitations. From a technical perspective, many platforms depend extensively on off-chain computation, exhibit limited capabilities for on-chain intelligence, and encounter significant scalability challenges. From a business perspective, many models appear to replicate centralized AI service structures, simply adding token-based payment and governance layers without delivering truly novel value. In light of these challenges, we also examine emerging developments that may shape the next phase of decentralized AI systems. These include approaches for on-chain verification of AI outputs, blockchain-enabled federated learning, and more robust incentive frameworks. Collectively, while emerging innovations offer pathways to strengthen decentralized AI ecosystems, significant gaps remain between the promises and the realities of current AI-token implementations. Our findings contribute to a growing body of research at the intersection of AI and blockchain, highlighting the need for critical evaluation and more grounded approaches as the field continues to evolve.

This paper presents a comprehensive refutation of the so-called "blockchain trilemma," a widely cited but formally ungrounded claim asserting an inherent trade-off between decentralisation, security, and scalability in blockchain protocols. Through formal analysis, empirical evidence, and detailed critique of both methodology and terminology, we demonstrate that the trilemma rests on semantic equivocation, misuse of distributed systems theory, and a failure to define operational metrics. Particular focus is placed on the conflation of topological network analogies with protocol-level architecture, the mischaracterisation of Bitcoin's design--including the role of miners, SPV clients, and header-based verification--and the failure to ground claims in complexity-theoretic or adversarial models. By reconstructing Bitcoin as a deterministic, stateless distribution protocol governed by evidentiary trust, we show that scalability is not a trade-off but an engineering outcome. The paper concludes by identifying systemic issues in academic discourse and peer review that have allowed such fallacies to persist, and offers formal criteria for evaluating future claims in blockchain research.

As artificial intelligence (AI) systems become increasingly complex and autonomous, concerns over transparency and accountability have intensified. The "black box" problem in AI decision-making limits stakeholders' ability to understand, trust, and verify outcomes, particularly in high-stakes sectors such as healthcare, finance, and autonomous systems. Blockchain technology, with its decentralized, immutable, and transparent characteristics, presents a potential solution to enhance AI transparency and auditability. This paper explores the integration of blockchain with AI to improve decision traceability, data provenance, and model accountability. By leveraging blockchain as an immutable record-keeping system, AI decision-making can become more interpretable, fostering trust among users and regulatory compliance. However, challenges such as scalability, integration complexity, and computational overhead must be addressed to fully realize this synergy. This study discusses existing research, proposes a framework for blockchain-enhanced AI transparency, and highlights practical applications, benefits, and limitations. The findings suggest that blockchain could be a foundational technology for ensuring AI systems remain accountable, ethical, and aligned with regulatory standards.

Current approaches to AI governance often fall short in anticipating a future where AI agents manage critical tasks, such as financial operations, administrative functions, and beyond. As AI agents may eventually delegate tasks among themselves to optimize efficiency, understanding the foundational principles of human value exchange could offer insights into how AI-driven economies might operate. Just as trust and value exchange are central to human interactions in open marketplaces, they may also be critical for enabling secure and efficient interactions among AI agents. While cryptocurrencies could serve as the foundation for monetizing value exchange in a collaboration and delegation dynamic among AI agents, a critical question remains: how can these agents reliably determine whom to trust, and how can humans ensure meaningful oversight and control as an economy of AI agents scales and evolves? This paper is a call for a collective exploration of cryptoeconomic incentives, which can help design decentralized governance systems that allow AI agents to autonomously interact and exchange value while ensuring human oversight via progressive decentralization. Toward this end, I propose a research agenda to address the question of agent-to-agent trust using AgentBound Tokens, which are non-transferable, non-fungible tokens uniquely tied to individual AI agents, akin to Soulbound tokens for humans in Web3. By staking ABTs as collateral for autonomous actions within an agent-to-agent network via a proof-of-stake mechanism, agents may be incentivized towards ethical behavior, and penalties for misconduct are automatically enforced.

This Large language models (LLMs) have shown remarkable approach is orthogonal to other optimization techniques, capabilities across a wide range of tasks, and could be applicable as LLMs continue from natural language understanding to creative to grow in size and capabilities. We also propose content generation. However, the computational an algorithm to check and define the applicability cost of inference and the associated energy consumption of this technique in different domains, selecting present significant challenges. As the demand the most proper function words set, and analyzing for AI applications continues to grow, these the lose in performance as the percentage of costs are expected to escalate, raising concerns saved tokens increases. Additionally, we provide about sustainability and accessibility (Wu et al., an experimental evaluation in the context of general 2022).

Learning cooperative policies for multi-agent systems is often challenged by partial observability and a lack of coordination. In some settings, the structure of a problem allows a distributed solution with limited communication. Here, we consider a scenario where no communication is available, and instead we learn local policies for all agents that collectively mimic the solution to a centralized multi-agent static optimization problem. Our main contribution is an information theoretic framework based on rate distortion theory which facilitates analysis of how well the resulting fully decentralized policies are able to reconstruct the optimal solution. Moreover, this framework provides a natural extension that addresses which nodes an agent should communicate with to improve the performance of its individual policy.

In swarm robotics, decentralized control is often proposed as a more scalable and fault-tolerant alternative to centralized control. However, centralized behaviors are often faster and more efficient than their decentralized counterparts. In any given application, the goals and constraints of the task being solved should guide the choice to use centralized control, decentralized control, or a combination of the two. Currently, the tradeoffs that exist between centralization and decentralization have not been thoroughly studied. In this paper, we investigate these tradeoffs for multi-robot coverage, and find that they are more nuanced than expected. For instance, our findings reinforce the expectation that more decentralized control will provide better scalability, but contradict the expectation that more decentralized control will perform better in environments with randomized obstacles. Beginning with a group of fully independent ground robots executing coverage, we add unmanned aerial vehicles as supervisors and progressively increase the degree to which the supervisors use centralized control, in terms of access to global information and a central coordinating entity. We compare, using the multi-robot physics-based simulation environment ARGoS, the following four control approaches: decentralized control, hybrid control, centralized control, and predetermined control. In comparing the ground robots performing the coverage task, we assess the speed and efficiency advantages of centralization -- in terms of coverage completeness and coverage uniformity -- and we assess the scalability and fault tolerance advantages of decentralization. We also assess the energy expenditure disadvantages of centralization due to different energy consumption rates of ground robots and unmanned aerial vehicles, according to the specifications of robots available off-the-shelf.

This research examines the polycentric governance of digital assets in blockchain-based Decentralized Autonomous Organizations (DAOs). It offers a theoretical framework and addresses a critical challenge facing decentralized governance by developing a method to identify sybils, or spurious identities. Sybils pose significant organizational sustainability threats to DAOs and other, commons-based online communities, and threat models are identified. The experimental method uses graph deep learning techniques to identify sybil activity in a DAO governance dataset (snapshot.org). Specifically, a Graph Convolutional Neural Network (GCNN) learned voting behaviours and a fast k-means vector clustering algorithm (FAISS) used high-dimensional embeddings to identify similar nodes in a graph. The results reveal that deep learning can effectively identify sybils, reducing the voting graph by 2-5%. This research underscores the importance of sybil resistance in DAOs and offers a novel perspective on decentralized governance, informing future policy, regulation, and governance practices.