Streaming inference precision budget model

Family 140: Memory–sample lower bounds for noiseless Gaussian regression. First review, 8 October 2026.

Problem and potential new use

Embedded-learning researchers can use a restricted Gaussian-regression lower bound to reason about persistent memory and sample requirements.

Applicability and commercial boundary

Noiseless Gaussian observations, angular error and terminal-state-only output are specific; the theorem does not bound arbitrary noisy or neural learners.

Initial business decision

Conditional research. Buyer demand and profitability remain hypotheses. This first pass does not establish a validated commercial market.

Next verification action

Read the memory model and test a concrete constrained learner against its applicable regime.

Evidence scope

Catalog statement reviewed; inspect linked opportunity dossier for any deeper source review. This record alone does not establish full manuscript or proof verification.

Source revision fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb. See source metadata for the exact manuscripts and available scope notes.