budget-enforcement-is-efficient-across-pipeline
OUT derived (depth 2)
All budget-constrained operations — compact output distillation and derive belief allocation — achieve computationally efficient tracking with representation-safe minimum bounds, ensuring budget enforcement never becomes a performance bottleneck.
Summary
Budget enforcement across the system is designed to be fast and never slow things down, whether it is trimming output during compaction or divvying up belief slots during derivation. Both operations use lightweight math rather than expensive computation, and both guarantee reasonable minimums so nothing gets starved. This belief is currently retracted, meaning one or both of those underlying efficiency claims no longer hold.
Justifications
SL — Compact uses O(1) per-line char tracking; derive uses O(N) linear accumulation with floor-5 guarantee — both avoid quadratic costs
Antecedents (all must be IN):
- compact-budget-tracking-is-efficient-and-approximate — The compact module tracks token budgets efficiently through an approximate but computationally fast strategy: O(1) per-line budget checks via a running character count, with token estimation based on chars/4 — a lightweight approximation avoiding external tokenizer dependencies while maintaining accuracy sufficient for budget enforcement.
- derive-budget-is-efficient-and-floor-bounded — The derive pipeline's per-agent budget allocation is both computationally efficient (O(N) linear accumulation, not quadratic) and representation-safe (each agent and local group guaranteed at least 5 belief slots), ensuring proportional allocation never starves minority agents.
Dependents
These beliefs depend on this one:
- resource-efficiency-spans-footprint-through-budgets — The system achieves resource efficiency from the broadest to the narrowest scope: zero external dependencies and lazy module loading minimize the static footprint at packaging and startup, while efficient O(1) budget tracking with approximate token estimation constrains resource consumption during both compact distillation and derive belief allocation at runtime.