# 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](source.json) for the exact manuscripts and available scope notes.