How do archive systems handle knowledge that changes with each generation?

This explores what happens to a knowledge store when the AI's own outputs feed back into it — when the corpus isn't fixed but grows and shifts with every cycle of generation. The corpus has a surprisingly direct answer, and it centers on a single tension: letting a system learn from itself is how knowledge accumulates, but it's also how errors compound.

The cleanest treatment is bidirectional RAG, where a system writes its own generated answers back into the retrieval corpus — but only through a gate. Outputs have to pass entailment checks, source attribution, and novelty detection before they're allowed to join the archive, precisely so that a hallucination from one generation doesn't quietly poison every future retrieval Can RAG systems safely learn from their own generated answers?. The same instinct shows up defensively in noisy archives: when sources degrade (OCR errors, language drift in historical newspapers), the system is built to refuse rather than guess, trading coverage for integrity so each generation doesn't manufacture confident fiction Can RAG systems refuse to answer without reliable evidence?. Both notes share a thesis — accumulation is safe only when generation is constrained at the moment of write-back.

A second, lateral framing treats the changing knowledge not as a corpus but as a living document. The ACE framework handles evolving context as an incrementally edited 'playbook,' applying small curated updates through generation-reflection-curation loops instead of rewriting the whole thing each pass — which protects against the quieter failure mode where each regeneration compresses away detail until the knowledge collapses into uselessness Can context playbooks prevent knowledge loss during iteration?. Here the enemy isn't false additions but erosion: knowledge that changes by shrinking.

What's striking is that the corpus also contains the opposite philosophy. Some systems keep a persistent memory workspace across retrieval cycles specifically to detect and resolve contradictions as new evidence arrives Can reasoning systems maintain memory across retrieval cycles?, and others use each partial answer to reveal what to retrieve next, so generation itself drives what enters the working store Can a model's partial response guide what to retrieve next?. But there's a contrarian voice: memoryless, Markov-style reasoning argues that carrying accumulated history is baggage, and that contracting each step to depend only on the current state preserves coherence without the bloat — i.e., the safest way to handle knowledge that changes each generation is to deliberately not accumulate it Can reasoning systems forget history without losing coherence?.

The thing worth taking away: 'archiving' generated knowledge isn't one problem but a fork between two failure modes — contamination (bad outputs entering the record) and erosion (good detail compressing away) — and the field hasn't agreed on whether the cure is a gated, verified memory that grows or a disciplined forgetting that never lets the archive drift at all.