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ARXIV:2603.11560 · MULTI-AGENT SYSTEMS · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.11560MULTI-AGENT SYSTEMSSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
A theoretical framework for understanding adaptive coordination in multi-agent systems.
Opportunity summary
Pain A theoretical framework for understanding adaptive coordination in multi-agent systems.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
A theoretical framework for understanding adaptive coordination in multi-agent systems. Rather than analyzing coordination through equilibrium optimization or agent-centric learning alone, the framework models agents, incentives, and environment as a recursively closed feedback architecture.
This paper develops a dynamical theory of adaptive coordination in multi-agent systems. Rather than analyzing coordination through equilibrium optimization or agent-centric learning alone, the framework models agents, incentives, and environment as a recursively closed…
ScienceToStartup currently rates this 2.0/10 on the public viability pass. The paper establishes three structural results.
Multi-Agent Systems moved forward this cycle; last verified April 2026. Public score 2.0/10.
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A theoretical framework for understanding adaptive coordination in multi-agent systems.
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10.48550/arXiv.2603.11560A theoretical framework for understanding adaptive coordination in multi-agent systems.
Abstract
This paper develops a dynamical theory of adaptive coordination in multi-agent systems. Rather than analyzing coordination through equilibrium optimization or agent-centric learning alone, the framework models agents, incentives, and environment as a recursively closed feedback architecture. A persistent environment stores accumulated coordination signals, a distributed incentive field transmits those signals locally, and adaptive agents update in response. Coordination is thus treated as a structural property of coupled dynamics rather than as the solution to a centralized objective. The paper establishes three structural results. First, under dissipativity assumptions, the induced closed-loop system admits a bounded forward-invariant region, ensuring viability without requiring global optimality. Second, when incentive signals depend non-trivially on persistent environmental memory, the resulting dynamics generically cannot be reduced to a static global objective defined solely over the agent state space. Third, persistent environmental state induces history sensitivity unless the system is globally contracting. A minimal linear specification illustrates how coupling, persistence, and dissipation govern local stability and oscillatory regimes through spectral conditions on the Jacobian. The results establish structural conditions under which intelligent coordination dynamics emerge from incentive-mediated adaptive interaction within a persistent environment, without presuming welfare maximization, rational expectations, or centralized design.
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PROBLEM
A theoretical framework for understanding adaptive coordination in multi-agent systems. Rather than analyzing coordination through equilibrium optimization or agent-centric learning alone, the framework models agents, incentives, and environment as a recursively closed feedback...
METHOD
This paper develops a dynamical theory of adaptive coordination in multi-agent systems. Rather than analyzing coordination through equilibrium optimization or agent-centric learning alone, the framework models agents, incentives, and environment as a recursively closed feedback...
RESULT
ScienceToStartup currently rates this 2.0/10 on the public viability pass. The paper establishes three structural results.
WHY NOW
Multi-Agent Systems moved forward this cycle; last verified April 2026. Public score 2.0/10.
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A theoretical framework for understanding adaptive coordination in multi-agent systems. Rather than analyzing coordination through equilibrium optimization or agent-centric learning alone, the framework models agents, incentives, and environment as a recursively closed feedback architecture.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
This paper develops a dynamical theory of adaptive coordination in multi-agent systems. Rather than analyzing coordination through equilibrium optimization or agent-centric learning alone, the framework models agents, incentives, and environment as a recursively closed feedback architecture.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 2.0/10 on the public viability pass. The paper establishes three structural results.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Multi-Agent Systems moved forward this cycle; last verified April 2026. Public score 2.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A theoretical framework for understanding adaptive coordination in multi-agent systems.
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