Context Is a Bottleneck

Mon, 14 Sep 2026 05:00:14 +0100

Chapter 10 took selection as far as a frozen run has taken it: which memories the present work needs, admitted by an explicit, traceable policy at a fixed budget. This chapter shows selection is insufficient, because admission assumes the admitted memories fit. They increasingly do not — and the Chapter 10 run puts a number on the shortfall, audited for fairness in planning/chapter-14-oracle-audit.md rather than taken on trust. Its ledger oracle reaches perfect required-evidence recall on a mean of 587 estimated tokens; the best real condition spends 1,161 to reach 0.901 recall. Rendered with the source headers and validity marks the reader actually sees, the figures become roughly 805 against 1,505 — the gap survives rendering. The auditable oracle defined in this chapter — content plus the disagreements, licence bearers, and marks a trustworthy system must carry — stands near 696 counted (1,060 rendered) tokens, still roughly 40% below the practical bundle. Roughly half the admitted budget, under a correct frame and over a good candidate pool, goes to evidence the work does not need: the practical bundle’s distractor admission sits near 0.45. That gap is not a selection failure. Selection chose those items for defensible reasons; what is missing is everything that happens between choosing a set and handing a model a context. The system now holds events, beliefs, provenance chains, validity intervals, intentions, derived loops, rules, and prior failures — and the obvious solution, putting all relevant memory into the prompt, fails in ways that are structural rather than incidental. Context is a bottleneck, and what passes through it must be assembled, not merely selected.

Token Budget on Programmer.ie: Modern AI programming

Context Is a Bottleneck