EviGraph: Proof-Carrying Selective Recommendation over Temporal Public-Service Knowledge Graphs

Abstract:Public-service recommendations require evidence that matches the requested service, scope, and date. Yet treating every missing detail as decisive can withhold useful recommendations. We introduce EviGraph, which distinguishes critical decision requirements from information that can remain unresolved. A language agent links these requirements to evidence in a temporal knowledge graph, while a deterministic checker establishes whether a recommendation is supported. Evaluation on a bilingual Hong Kong public-service benchmark with executable policy references shows that this distinction reduces unnecessary abstention. Additional verification, however, can withdraw supported recommendations without improving decision quality. These findings suggest that reliable evidence-based navigation depends on specifying what must be established for a decision, rather than simply adding more verification.

Artificial Intelligence (cs.AI)

arXiv:2610.00212 [cs.AI]

(or arXiv:2610.00212v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2610.00212 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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