Nous: Learning and Certifying Memory Decisions Before Source Calibration
Agent memory systems update state decisions from reports whose reliability may be unknown. Existing analyses of source estimation do not determine when a policy can be learned or its improvement certified without identifying the reporting channel. We study these three tasks using the same observed records. For a specified hidden Markov family with continuous source uncertainty, decision learning and powered certification have quadratic sample complexity, whereas fixed-precision source estimation has quartic complexity. We characterize a sharp identified interval for policy gain under an unknown shared-background channel and derive finite-sample certificates under bounded history dependenc...
| Authors | Pranav Singh |
|---|---|
| Primary category | cs.LG |
| Published | 8 September 2026 |
| Last updated at source | 5 October 2026 |
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