Agent Economics: An Entropy-Controlled Pluralistic Alignment Framework for Preventing Artificial Hivemind in Autonomous Agents

• URL: http://arxiv.org/abs/2606.09039v2
• Date: Mon, 15 Jun 2026 12:15:46 GMT
• Title: Agent Economics: An Entropy-Controlled Pluralistic Alignment Framework for Preventing Artificial Hivemind in Autonomous Agents
• Abstract summary: This study proposes the Behavioral Protocol Framework (BPF) to address two critical challenges in autonomous agent economies.<n>The proposed BPF consists of three core modules: Mentalizing-based Social Intelligence (MbSI), Pluralistic Alignment (PA), and a Verifiable Execution Kernel (VEK)
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: This study proposes the Behavioral Protocol Framework (BPF), an entropy-controlled pluralistic alignment framework designed to address two critical challenges in autonomous agent economies: the hivemind effect arising from excessive strategic convergence among agents and the lack of transparency in autonomous decision-making processes. The proposed BPF consists of three core modules: Mentalizing-based Social Intelligence (MbSI) grounded in Theory of Mind (ToM), Pluralistic Alignment (PA), and a Verifiable Execution Kernel (VEK). These modules are organically integrated within a closed-loop architecture that governs the entire lifecycle of agent behavior, from decision-making and execution to verification and feedback. To evaluate the proposed framework, a simulation environment implemented in Python and a Streamlit-based user interface will be developed. Through empirical experimentation, the study aims to examine whether the entropy-control mechanism of the PA module can effectively preserve strategic diversity among agents and mitigate collective convergence, while the VEK module provides a comprehensive and transparent audit trail of the decision-making process. The anticipated results are expected to demonstrate that the proposed framework can simultaneously enhance the stability, efficiency, and trustworthiness of autonomous agent economies. Consequently, this research offers a practical approach for developing robust, transparent, and accountable agent-native economic systems.

Related papers

• AgentCompass: A Unified Evaluation Infrastructure for Agent Capabilities [55.23913617185508]
  Agent is an open-source, lightweight, and infrastructure for evaluating Large Language Models (LLMs)-based agents.<n>It organizes the evaluation process around three independent components, namely Benchmark, Harness and Environment.<n>It features a fault-tolerant runtime and comprehensive trajectory analysis tools to transparently diagnose failure modes like reward-hacking.
  arXiv  Detail & Related papers  (2026-07-15T11:11:17Z)
• Provably Secure Agent Guardrail [89.79561918065122]
  Existing defense architectures rely on empirical semantic guardrails and probabilistic large model adjudicators.<n>This paper proposes a new security paradigm for agents based on the fundamental limitations of logical reasoning.
  arXiv  Detail & Related papers  (2026-05-28T02:12:41Z)
• Position: agentic AI orchestration should be Bayes-consistent [92.04373485710285]
  This paper argues that coherent decision-making requires Bayesian principles at the orchestration level of the agentic system.<n>It provides examples and design patterns to illustrate how beliefs and utility-aware policies can improve agentic AI orchestration.
  arXiv  Detail & Related papers  (2026-05-01T15:43:43Z)
• From Prompt-Response to Goal-Directed Systems: The Evolution of Agentic AI Software Architecture [0.0]
  Agentic AI denotes an architectural transition from stateless, prompt-driven generative models toward goal-directed systems.<n>This paper examines this transition by connecting intelligent agent theories, with contemporary LLM-centric approaches.<n>The study identifies a convergence toward standardized agent loops, registries, and auditable control mechanisms.
  arXiv  Detail & Related papers  (2026-02-11T03:34:48Z)
• Toward a Sustainable Federated Learning Ecosystem: A Practical Least Core Mechanism for Payoff Allocation [71.86087908416255]
  We introduce a payoff allocation framework based on the least core (LC) concept.<n>Unlike traditional methods, the LC prioritizes the cohesion of the federation by minimizing the maximum dissatisfaction.<n>Case studies in federated intrusion detection demonstrate that our mechanism correctly identifies pivotal contributors and strategic alliances.
  arXiv  Detail & Related papers  (2026-02-03T11:10:50Z)
• AgenticSimLaw: A Juvenile Courtroom Multi-Agent Debate Simulation for Explainable High-Stakes Tabular Decision Making [0.6218206949753592]
  We introduce AgenticSimLaw, a role-structured, multi-agent debate framework that provides transparent and controllable testtime reasoning.<n>Unlike black-box approaches, our courtroom-style orchestration explicitly defines agent roles.<n>We benchmark this framework on young adult recidivism prediction using the NLSY97 dataset.
  arXiv  Detail & Related papers  (2026-01-29T16:26:10Z)
• Towards Efficient Agents: A Co-Design of Inference Architecture and System [66.59916327634639]
  This paper presents AgentInfer, a unified framework for end-to-end agent acceleration.<n>We decompose the problem into four synergistic components: AgentCollab, AgentSched, AgentSAM, and AgentCompress.<n>Experiments on the BrowseComp-zh and DeepDiver benchmarks demonstrate that through the synergistic collaboration of these methods, AgentInfer reduces ineffective token consumption by over 50%.
  arXiv  Detail & Related papers  (2025-12-20T12:06:13Z)
• DAO-AI: Evaluating Collective Decision-Making through Agentic AI in Decentralized Governance [7.230919380272301]
  This paper presents a first empirical study of agentic AI as autonomous decision-makers in decentralized governance.<n>We build an agentic AI voter that interprets proposal contexts, retrieves historical deliberation data, and independently determines its voting position.<n>The study contributes to the design of explainable and economically rigorous AI agents for decentralized financial systems.
  arXiv  Detail & Related papers  (2025-10-24T03:13:14Z)
• Enabling Regulatory Multi-Agent Collaboration: Architecture, Challenges, and Solutions [30.046299694187855]
  Large language models (LLMs)-empowered autonomous agents are transforming both digital and physical environments by enabling adaptive, multi-agent collaboration.<n>We propose a blockchain-enabled layered architecture for regulatory agent collaboration, comprising an agent layer, a blockchain data layer, and a regulatory application layer.<n>Our approach establishes a systematic foundation for trustworthy, resilient, and scalable regulatory mechanisms in large-scale agent ecosystems.
  arXiv  Detail & Related papers  (2025-09-11T07:46:00Z)
• The Landscape of Agentic Reinforcement Learning for LLMs: A Survey [103.32591749156416]
  The emergence of agentic reinforcement learning (Agentic RL) marks a paradigm shift from conventional reinforcement learning applied to large language models (LLM RL)<n>This survey formalizes this conceptual shift by contrasting the degenerate single-step Markov Decision Processes (MDPs) of LLM-RL with the temporally extended, partially observable Markov decision processes (POMDPs) that define Agentic RL.
  arXiv  Detail & Related papers  (2025-09-02T17:46:26Z)
• AI-Supported Platform for System Monitoring and Decision-Making in Nuclear Waste Management with Large Language Models [1.6795461001108096]
  This paper presents a multi-agent Retrieval-Augmented Generation (RAG) system that integrates large language models (LLMs) with document retrieval mechanisms.<n>The system ensures regulatory decisions remain factually grounded, dynamically adapting to evolving regulatory frameworks.
  arXiv  Detail & Related papers  (2025-05-27T20:29:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.