resource-management-supports-belief-currency
OUT derived (depth 4)
Active belief currency management — sustainable derivation of new beliefs and staleness detection for existing ones — operates with accurate bidirectional token budget control, ensuring derivation rounds allocate resources correctly per agent and output fits context-limited consumer constraints.
Summary
The system's ability to keep its knowledge fresh — generating new conclusions and catching outdated ones — depends on accurate control of how many tokens get spent in each direction. This claim is currently retracted, meaning one or both of its foundations (reliable token budgets or active knowledge maintenance) are not holding up, so the guarantee that resource management properly supports ongoing knowledge upkeep is not established.
Justifications
SL — Token accuracy provides the resource foundation for sustainable belief derivation and maintenance
Antecedents (all must be IN):
- token-budgets-are-accurate-bidirectionally — Token budget management is accurate in both directions: the compact module reliably constrains output size for context-limited consumers, while the derive pipeline correctly allocates input budgets per agent — ensuring resource-bounded operation across the entire LLM integration surface.
- belief-currency-is-actively-managed — The system actively manages belief currency bidirectionally: the production-ready derive pipeline safely introduces new beliefs through defensive validation, while the staleness CI gate detects drift in existing beliefs against source material — together preventing both unsafe additions and undetected obsolescence.
Dependents
These beliefs depend on this one:
- resource-sustainable-lifecycle-has-no-gaps — Gapless lifecycle management is resource-sustainable: accurate bidirectional token budgets support both new belief derivation and existing belief staleness detection, ensuring no lifecycle gap arises from resource exhaustion.
- self-correction-is-resource-sustainable — The system's self-correction capability — contradiction resolution at derivation time and staleness detection at maintenance time — is resource-sustainable: accurate bidirectional token budgets support continuous belief derivation and maintenance, ensuring the correction loop can operate indefinitely without resource exhaustion.