Computing with Stochastic Oracles in AI-Augmented Computation
The Stochastic-Oracle Turing Machine (SOTM) framework models AI-augmented computation as the interaction of a probabilistic Turing machine with an oracle whose responses are drawn from context-dependent distributions. This paper studies what an SOTM can achieve under two oracle-response schemes: in a cached-response oracle, each distinct query receives one response that is reused on later calls to the same query, while in a fresh-response oracle, each call returns an independent response. In both schemes, the SOTM first computes from its input and internal random source to generate its first query, then proceeds adaptively, computing from its query-response transcript (the record of queri...
| Authors | Jie Wang |
|---|---|
| Primary category | cs.CC |
| Published | 8 July 2026 |
| Last updated at source | 8 July 2026 |
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