Defines the global coherence problem for AI agents and states the Observation-Aliasing Impossibility Theorem that gives the exact condition for guaranteed valid joint actions.
The provided search context does not contain information regarding the "Observation-Aliasing Impossibility Theorem" or a specific framework defining global coherence through that exact theorem.
However, the context identifies related concepts in multi-agent coherence: Locally Coherent, Globally Incoherent: A 2026 paper by Anany Kotawala formalizes how multi-component LLM agents can produce probabilistic claims that violate basic probability axioms, even when individual components are locally coherent. This is bounded by the compositional residual ($\epsilon^$), which measures the L2 distance from the composed output to the joint coherent polytope. Team of Rivals: Research suggests achieving coherence by orchestrating agents with conflicting incentives (e.g., planners, executors, critics) rather than relying on a single agent, ensuring outputs remain within acceptable boundaries through opposing forces. Semantic Intent Divergence: Conflicts often arise not from data errors but from semantic contradictions (e.g., one agent approving a loan while another rejects it). Solutions involve a Semantic Intent Graph (SIG) and a Process Context Layer (PCL) to establish shared operational semantics and prevent invalid state transitions. * Decision Coherence: A formal correctness requirement stating that agents must not observe state corresponding to no valid configuration of reality, requiring transactional consistency and temporal/concurrency envelopes to ensure joint views are valid.
This paper formalizes the global coherence problem in multi-agent AI systems: each agent may act rationally or “correctly” relative to its own local observations, beliefs, and semantic interpretation, yet the resulting joint behavior can still violate a global constraint, task specification, or shared objective. It frames this mismatch as a semantic rather than merely algorithmic failure, arguing that local correctness does not automatically compose into system-level validity. The core issue is that agents can be locally consistent while being globally incoherent because their local views do not fully determine the global state of the collaboration.
Its central contribution is the Observation-Aliasing Impossibility Theorem, which identifies the precise condition under which guaranteed valid joint actions are possible from local information. The key idea is that if two globally distinct joint actions or states are observationally aliased—i.e., indistinguishable to the agents from their local perspectives—but differ in global validity, then no local-to-global mapping or coordination rule can guarantee that the team’s joint action is valid. Equivalently, a sound local selection rule exists only when the agents’ joint observation partition refines the valid/invalid distinction: no observation class may contain both a valid and an invalid joint action. This gives an impossibility boundary for distributed coordination based solely on local semantic information.
The result matters because it provides a formal diagnostic for a common failure mode in multi-agent systems, especially those built from autonomous or LLM-based agents that reason locally but must satisfy global constraints. It shifts the design focus from improving individual agent rationality to ensuring that the system’s semantic architecture prevents observation aliasing, either through richer observations, shared ontologies, explicit communication, verification layers, or stronger common-knowledge assumptions. In short, the paper argues that global coherence is a first-class semantic requirement, not an emergent property of individually competent agents.