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NIOT-09 · AI-native numerical information runtime
Numerical Information Object Theory
NIOT-09 · AI-native numerical information runtime
Neo.K (as signed)
AI-Native Numerical Information Runtime: Adaptive Probing, Dynamic Expansion, Responsibility Closure, and Authoritative Commit
Specifies an AI-native numerical runtime (ANIR): an AI controller runs a gap-driven loop (probe, expand, bridge, verify, close, promote, stop) under finite action plans and the two invariants 'no silent promotion' and 'missing is not pass'. An MVP outline and pseudocode (§193) are given, but there is no running code.
An AI-controlled numerical probing loop can be specified under invariants such as 'no silent promotion'. This is a specification at the level of architecture and interfaces.
The abstract says NIOT 'formally becomes a landable AI-native numerical runtime architecture', yet the paper's own closing declaration lists an MVP repository, a minimal Lean/Coq formalization and local-runtime experiments as future work. The five 'theorem candidates' restate design invariants (for example, 'if the state machine forbids X, X cannot occur').
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This is a full-text translation of the Chinese source, which remains authoritative for its research claims. Translation is not independent mathematical verification and does not upgrade the original evidence level.