system-sustainably-grows-and-self-corrects
OUT derived (depth 6)
The system simultaneously grows its knowledge base through exhaustive deterministic reasoning and LLM-driven derivation with guaranteed termination, while sustainably self-correcting through contradiction resolution and staleness detection — all within bounded resource consumption managed by accurate bidirectional token budgets
Summary
The system is supposed to expand what it knows through both rule-based logic and LLM-generated insights while also fixing its own errors through contradiction detection and staleness checks, all without running out of resources. This claim is currently unsupported because one or both of its prerequisites — that reasoning coverage is truly exhaustive and that self-correction stays within sustainable resource bounds — do not hold.
Justifications
SL — Exhaustive knowledge expansion (depth-5) and resource-sustainable self-correction (depth-5) are complementary growth-and-maintenance capabilities — the system can safely grow indefinitely because expansion is bounded and correction is sustainable
Antecedents (all must be IN):
- reasoning-and-knowledge-expansion-are-both-exhaustive — The system achieves exhaustive coverage in both formal reasoning (deterministic reversible truth evaluation with guaranteed-terminating exploration of all derivable conclusions) and LLM-driven knowledge expansion (complete coverage with fault tolerance across all interactive and batch LLM operations)
- 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.
Dependents
These beliefs depend on this one:
- sustainable-growth-is-indefinitely-self-correcting — The system's knowledge growth — combining exhaustive deterministic reasoning with LLM-driven derivation — is not merely sustainable but indefinitely so: resource-sustainable self-correction within a deterministically grounded lifecycle means the expanding knowledge base never outstrips the system's ability to maintain its own consistency, regardless of accumulated network size or elapsed time.