Xin Heng

The work defines the global coherence problem, in which AI agents each make locally valid decisions yet jointly produce an invalid result, and gives the Observation-Aliasing Impossibility Theorem: when k indistinguishable worlds require pairwise-disjoint actions, the best randomized worst-case success is 1/k; it then proposes local-to-global runtime semantics X = (H, C, G, F; D), where the harness owns shared state and governs commit, and nine studies show that when the deciding event is hidden a frontier model scores 12-17/40 (consistent with chance 1/3), restoring one authoritative fact returns 40/40, commit enforcement on TeamBench leaves 0/5 violations versus 5/5, and on tau2-bench Telecom the harness scores 1.00 while current-state checks score 0.07.

The work defines the global coherence problem, in which AI agents each make locally valid decisions yet jointly produce an invalid result, and gives the Observation-Aliasing Impossibility Theorem: when k indistinguishable worlds require pairwise-disjoint actions, the best randomized worst-case success is 1/k; it then proposes local-to-global runtime semantics X = (H, C, G, F; D), where the harness owns shared state and governs commit, and nine studies show that when the deciding event is hidden a frontier model scores 12-17/40 (consistent with chance 1/3), restoring one authoritative fact returns 40/40, commit enforcement on TeamBench leaves 0/5 violations versus 5/5, and on tau2-bench Telecom the harness scores 1.00 while current-state checks score 0.07.