Position Paper: Towards Open Complex Human-AI Agents Collaboration Systems for Problem Solving and Knowledge Management

• URL: http://arxiv.org/abs/2505.00018v2
• Date: Thu, 09 Oct 2025 13:19:01 GMT
• Title: Position Paper: Towards Open Complex Human-AI Agents Collaboration Systems for Problem Solving and Knowledge Management
• Authors: Ju Wu, Calvin K. L. Or,
• Abstract summary: We propose a technology-agnostic, collaboration-ready stance for Human-AI Agents Collaboration Systems (HAACS)<n>Reading empirical patterns through a seven-dimension collaboration spine and human-agent contrasts, we identify missing pieces.<n>We show interoperability with emerging agent protocols without ad hoc glue and sketch bio-cybernetic extensions.
• Score: 0.15039745292757667
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a technology-agnostic, collaboration-ready stance for Human-AI Agents Collaboration Systems (HAACS) that closes long-standing gaps in prior stages (automation; flexible autonomy; agentic multi-agent collectives). Reading empirical patterns through a seven-dimension collaboration spine and human-agent contrasts, we identify missing pieces: principled budgeting of initiative, instantaneous and auditable reconfiguration, a system-wide knowledge backbone with an epistemic promotion gate, capacity-aware human interfaces; and, as a prerequisite to all of the above, unified definitions of agent and formal collaborative dynamics. We respond with (i) a boundary-centric ontology of agenthood synthesized with cybernetics; (ii) a Petri net family (colored and interpreted) that models ownership, cross-boundary interaction, concurrency, guards, and rates with collaboration transitions; and (iii) a three-level orchestration (meta, agent, execution) that governs behavior families via guard flips. On the knowledge side, we ground collaborative learning in Conversation Theory and SECI with teach-back gates and an evolving backbone; on the problem-solving side, we coordinate routine MEA-style control with practice-guided open-ended discovery. The result is the Hierarchical Exploration-Exploitation Net (HE2-Net): a policy-controlled stance that splits provisional from validated assets, promotes only after tests and peer checks, and budgets concurrent probing while keeping reuse fast and safe. We show interoperability with emerging agent protocols without ad hoc glue and sketch bio-cybernetic extensions (autopoiesis, autogenesis, evolving boundaries, synergetics, etc). Altogether, the framework keeps humans central to setting aims, justifying knowledge, and steering theory-practice dynamics, while scaling agents as reliable collaborators within audited governance.

Related papers

• Agentic Reasoning for Large Language Models [122.81018455095999]
  Reasoning is a fundamental cognitive process underlying inference, problem-solving, and decision-making.<n>Large language models (LLMs) demonstrate strong reasoning capabilities in closed-world settings, but struggle in open-ended and dynamic environments.<n>Agentic reasoning marks a paradigm shift by reframing LLMs as autonomous agents that plan, act, and learn through continual interaction.
  arXiv  Detail & Related papers  (2026-01-18T18:58:23Z)
• Designing The Internet of Agents: A Framework for Trustworthy, Transparent, and Collaborative Human-Agent Interaction (HAX) [0.0]
  We present the Human-AI-Experience (HAX) framework, a comprehensive, three-phase approach that establishes design foundations for trustworthy, transparent, and collaborative agentic interaction.<n>HAX integrates behavioral SDKs, a schema-driven SDK enforcing structured and safe outputs, and a behavioral proxy concept that orchestrates agent activity to reduce cognitive load.<n>Grounded in Time, Interaction, and Performance (TIP) theory, HAX reframes multi-agent systems as colleagues, offering the first end-to-end framework that bridges trust theory, interface design, and infrastructure for the emerging Internet of Agents.
  arXiv  Detail & Related papers  (2025-12-12T19:04:40Z)
• InterAgent: Physics-based Multi-agent Command Execution via Diffusion on Interaction Graphs [72.5651722107621]
  InterAgent is an end-to-end framework for text-driven physics-based multi-agent humanoid control.<n>We introduce an autoregressive diffusion transformer equipped with multi-stream blocks, which decouples proprioception, exteroception, and action to cross-modal interference.<n>We also propose a novel interaction graph exteroception representation that explicitly captures fine-grained joint-to-joint spatial dependencies.
  arXiv  Detail & Related papers  (2025-12-08T10:46:01Z)
• Towards 6G Native-AI Edge Networks: A Semantic-Aware and Agentic Intelligence Paradigm [85.7583231789615]
  6G positions intelligence as a native network capability, transforming the design of radio access networks (RANs)<n>Within this vision, Semantic-native communication and agentic intelligence are expected to play central roles.<n>Agentic intelligence endows distributed RAN entities with goal-driven autonomy, reasoning, planning, and multi-agent collaboration.
  arXiv  Detail & Related papers  (2025-12-04T03:09:33Z)
• Emergent Coordination in Multi-Agent Language Models [2.504366738288215]
  We introduce an information-theoretic framework to test whether multi-agent systems show signs of higher-order structure.<n>This information decomposition lets us measure whether dynamical emergence is present in multi-agent LLM systems.<n>We apply our framework to experiments using a simple guessing game without direct agent communication.
  arXiv  Detail & Related papers  (2025-10-05T11:26:41Z)
• Exploring Human-AI Collaboration Using Mental Models of Early Adopters of Multi-Agent Generative AI Tools [4.382163871275696]
  We investigated how early adopters and developers conceptualize multi-agent Gen AI tools.<n>We conducted semi-structured interviews with 13 developers, all early adopters of multi-agent Gen AI technology who work at Microsoft.<n>We identified key challenges, including error propagation, unpredictable and unproductive agent loop behavior, and the need for clear communication to mitigate the layered transparency issues.
  arXiv  Detail & Related papers  (2025-09-10T05:35:38Z)
• Agentic Web: Weaving the Next Web with AI Agents [109.13815627467514]
  The emergence of AI agents powered by large language models (LLMs) marks a pivotal shift toward the Agentic Web.<n>In this paradigm, agents interact directly with one another to plan, coordinate, and execute complex tasks on behalf of users.<n>We present a structured framework for understanding and building the Agentic Web.
  arXiv  Detail & Related papers  (2025-07-28T17:58:12Z)
• Deep Research Agents: A Systematic Examination And Roadmap [79.04813794804377]
  Deep Research (DR) agents are designed to tackle complex, multi-turn informational research tasks.<n>In this paper, we conduct a detailed analysis of the foundational technologies and architectural components that constitute DR agents.
  arXiv  Detail & Related papers  (2025-06-22T16:52:48Z)
• Chain of Methodologies: Scaling Test Time Computation without Training [77.85633949575046]
  Large Language Models (LLMs) often struggle with complex reasoning tasks due to insufficient in-depth insights in their training data.<n>This paper introduces the Chain of the (CoM) framework that enhances structured thinking by integrating human methodological insights.
  arXiv  Detail & Related papers  (2025-06-08T03:46:50Z)
• A Novel Zero-Trust Identity Framework for Agentic AI: Decentralized Authentication and Fine-Grained Access Control [7.228060525494563]
  This paper posits the imperative for a novel Agentic AI IAM framework.<n>We propose a comprehensive framework built upon rich, verifiable Agent Identities (IDs)<n>We also explore how Zero-Knowledge Proofs (ZKPs) enable privacy-preserving attribute disclosure and verifiable policy compliance.
  arXiv  Detail & Related papers  (2025-05-25T20:21:55Z)
• Internet of Agents: Fundamentals, Applications, and Challenges [68.9543153075464]
  We introduce the Internet of Agents (IoA) as a foundational framework that enables seamless interconnection, dynamic discovery, and collaborative orchestration among heterogeneous agents at scale.<n>We analyze the key operational enablers of IoA, including capability notification and discovery, adaptive communication protocols, dynamic task matching, consensus and conflict-resolution mechanisms, and incentive models.
  arXiv  Detail & Related papers  (2025-05-12T02:04:37Z)
• Advancing Multi-Agent Systems Through Model Context Protocol: Architecture, Implementation, and Applications [0.0]
  This paper introduces a comprehensive framework for advancing multi-agent systems through Model Context Protocol (MCP)<n>We extend previous work on AI agent architectures by developing a unified theoretical foundation, advanced context management techniques, and scalable coordination patterns.<n>We identify current limitations, emerging research opportunities, and potential transformative applications across industries.
  arXiv  Detail & Related papers  (2025-04-26T03:43:03Z)
• Synergizing RAG and Reasoning: A Systematic Review [8.842022673771147]
  Recent breakthroughs in large language models (LLMs) have propelled Retrieval-Augmented Generation (RAG) to unprecedented levels.<n>This paper presents a systematic review of the collaborative interplay between RAG and reasoning.
  arXiv  Detail & Related papers  (2025-04-22T13:55:13Z)
• A Survey of Frontiers in LLM Reasoning: Inference Scaling, Learning to Reason, and Agentic Systems [93.8285345915925]
  Reasoning is a fundamental cognitive process that enables logical inference, problem-solving, and decision-making.<n>With the rapid advancement of large language models (LLMs), reasoning has emerged as a key capability that distinguishes advanced AI systems.<n>We categorize existing methods along two dimensions: (1) Regimes, which define the stage at which reasoning is achieved; and (2) Architectures, which determine the components involved in the reasoning process.
  arXiv  Detail & Related papers  (2025-04-12T01:27:49Z)
• Large Language Model Agent: A Survey on Methodology, Applications and Challenges [88.3032929492409]
  Large Language Model (LLM) agents, with goal-driven behaviors and dynamic adaptation capabilities, potentially represent a critical pathway toward artificial general intelligence.<n>This survey systematically deconstructs LLM agent systems through a methodology-centered taxonomy.<n>Our work provides a unified architectural perspective, examining how agents are constructed, how they collaborate, and how they evolve over time.
  arXiv  Detail & Related papers  (2025-03-27T12:50:17Z)
• AI-Enabled Knowledge Sharing for Enhanced Collaboration and Decision-Making in Non-Profit Healthcare Organizations: A Scoping Review Protocol [0.0]
  This protocol outlines a scoping review designed to systematically map the existing body of evidence on AI-enabled knowledge sharing in non-profit healthcare organizations.<n>The review aims to investigate how such technologies enhance collaboration and decision-making, particularly in the context of reduced external support following the cessation of USAID operations.
  arXiv  Detail & Related papers  (2025-03-10T17:09:12Z)
• Principal-Agent Reinforcement Learning: Orchestrating AI Agents with Contracts [20.8288955218712]
  We propose a framework where a principal guides an agent in a Markov Decision Process (MDP) using a series of contracts. We present and analyze a meta-algorithm that iteratively optimize the policies of the principal and agent. We then scale our algorithm with deep Q-learning and analyze its convergence in the presence of approximation error.
  arXiv  Detail & Related papers  (2024-07-25T14:28:58Z)
• Coding for Intelligence from the Perspective of Category [66.14012258680992]
  Coding targets compressing and reconstructing data, and intelligence. Recent trends demonstrate the potential homogeneity of these two fields. We propose a novel problem of Coding for Intelligence from the category theory view.
  arXiv  Detail & Related papers  (2024-07-01T07:05:44Z)
• Deconstructing Human-AI Collaboration: Agency, Interaction, and Adaptation [9.36651659099834]
  We propose a new unified set of dimensions through which to analyze and describe human-AI systems. Our conceptual model is centered around three high-level aspects - agency, interaction, and adaptation.
  arXiv  Detail & Related papers  (2024-04-18T10:12:18Z)
• Decentralized and Lifelong-Adaptive Multi-Agent Collaborative Learning [57.652899266553035]
  Decentralized and lifelong-adaptive multi-agent collaborative learning aims to enhance collaboration among multiple agents without a central server. We propose DeLAMA, a decentralized multi-agent lifelong collaborative learning algorithm with dynamic collaboration graphs.
  arXiv  Detail & Related papers  (2024-03-11T09:21:11Z)
• A Survey on Knowledge Distillation of Large Language Models [99.11900233108487]
  Knowledge Distillation (KD) emerges as a pivotal methodology for transferring advanced capabilities to open-source models. This paper presents a comprehensive survey of KD's role within the realm of Large Language Models (LLMs)
  arXiv  Detail & Related papers  (2024-02-20T16:17:37Z)
• Emergent Explainability: Adding a causal chain to neural network inference [0.0]
  This position paper presents a theoretical framework for enhancing explainable artificial intelligence (xAI) through emergent communication (EmCom) We explore the novel integration of EmCom into AI systems, offering a paradigm shift from conventional associative relationships between inputs and outputs to a more nuanced, causal interpretation. The paper discusses the theoretical underpinnings of this approach, its potential broad applications, and its alignment with the growing need for responsible and transparent AI systems.
  arXiv  Detail & Related papers  (2024-01-29T02:28:39Z)
• The Participatory Turn in AI Design: Theoretical Foundations and the Current State of Practice [64.29355073494125]
  This article aims to ground what we dub the "participatory turn" in AI design by synthesizing existing theoretical literature on participation. We articulate empirical findings concerning the current state of participatory practice in AI design based on an analysis of recently published research and semi-structured interviews with 12 AI researchers and practitioners.
  arXiv  Detail & Related papers  (2023-10-02T05:30:42Z)
• Investigating Bi-Level Optimization for Learning and Vision from a Unified Perspective: A Survey and Beyond [114.39616146985001]
  In machine learning and computer vision fields, despite the different motivations and mechanisms, a lot of complex problems contain a series of closely related subproblms. In this paper, we first uniformly express these complex learning and vision problems from the perspective of Bi-Level Optimization (BLO) Then we construct a value-function-based single-level reformulation and establish a unified algorithmic framework to understand and formulate mainstream gradient-based BLO methodologies.
  arXiv  Detail & Related papers  (2021-01-27T16:20:23Z)

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.