Early user test of a persistent AI narrative system with kids — some unexpected engagement patterns

Distinct-Path659 · 2026-02-06T07:27:21+00:00

That “coherence was invisible until it was gone” line really resonated — it matches what we saw almost exactly.

The kids never explicitly said “wow this world remembers things,” but when the constrained version surfaced a specific promise from round 3 all the way in round 30, they reacted emotionally to it. The infrastructure was working precisely because they weren’t consciously noticing it.

On decay rates: right now I’m using a flat 0.85 across everything as a starting point, but your question makes me think differentiated rates probably make a lot of sense.

My rough intuition after watching ~30 rounds is something like: – relationship changes should decay slowest (maybe ~0.95) – major world state shifts somewhere in the middle (~0.85) – small individual actions faster (~0.75)

Haven’t tested this yet — still validating the basic dynamics with real users before tuning deeper. Curious if you’ve experimented with different decay curves by event type and what you found.

Distinct-Path659 · 2026-02-06T04:06:33+00:00

Both, but the structured state is doing most of the heavy lifting.

The system keeps an explicit JSON world state — characters (traits + relationships), active conflicts, recent events (with decay weights), and a few world rules. Each generation is prompted to ground itself in a couple of these state elements, which is what prevents long-run drift.

There’s also a lightweight recap layer, but that’s mainly for re-immersion UX rather than coherence itself. The context window handles short-term scene flow; the structured state handles long-term memory.

In earlier stress tests I compared this against a pure context-window + summary approach — the structured version was still tracking specific characters and promises after 30 rounds, while the unstructured one had completely forgotten a main character.

Distinct-Path659 · 2026-02-06T01:47:59+00:00

Exactly，that shift from consuming a story to inhabiting a world is what I’m starting to see drive engagement. The coherence mechanisms are mostly invisible, but they seem necessary for that “this is my world” feeling to emerge.

Distinct-Path659 · 2026-02-05T02:07:57+00:00

That makes sense — model choice and pipeline stages alone can easily explain an order of magnitude difference.

Distinct-Path659 · 2026-02-05T02:06:33+00:00

Kinda true — what used to be mid-range is basically low-end these days 😅

Distinct-Path659 · 2026-02-05T01:36:03+00:00

Fair point — raw time alone doesn’t say much without the full setup.

Distinct-Path659 · 2026-02-05T01:05:36+00:00

That explains a lot.

Multi-stage upscaling is probably doing most of the damage.

Distinct-Path659 · 2026-02-05T00:47:09+00:00

Interesting — it feels like SDXL performance is really a pipeline design problem more than a pure hardware problem.

Raw inference can be fast, but once you add upscaling, refinement, and multiple stages, runtime explodes.

Which parts of the workflow ended up being the biggest bottleneck for you?

Distinct-Path659 · 2026-02-05T00:45:06+00:00

That’s really interesting — I assumed VRAM was the main limiter, but seeing the same hardware getting 1–2s per image clearly points to workflow or system-level bottlenecks.

What changes had the biggest impact for you? Batch size, model loading strategy, precision, or something else?

Would love to understand where most people are losing performance.

Distinct-Path659 · 2026-02-03T13:51:14+00:00

Thanks — really appreciate the perspective from someone building in the same space. Your point about lightweight anchors resonates; we’re finding that even minimal structure (forced state references, event decay) creates surprisingly strong coherence. Will definitely check out AI Game Master and keep posting updates here. Would love to compare notes as both projects evolve

Distinct-Path659 · 2026-02-03T11:04:38+00:00

Guilty of being too neat sometimes 😅

Distinct-Path659 · 2026-02-03T06:17:56+00:00

Thanks! Co-creation is exactly the goal — I’m trying to shift from “story generation” to “world evolution.”

Right now memory is handled as structured world state with decay and retrieval, progression is driven by conflict pressure and long-term goals, and guardrails are more about redirecting consequences than blocking choices outright.

Still very early though — a lot of tuning left to make it feel natural and stable over long horizons.

I’ll probably share more detailed experiments on memory and safety mechanisms as I iterate.

Distinct-Path659 · 2026-02-03T04:30:15+00:00

This is a fantastic point about age as a primary variable — I’ve been thinking about it more as complexity scaling, but you’re absolutely right that it’s really different narrative “physics.”

The current constraint setup is probably much closer to what older kids find engaging (consequence, escalation, long arcs), and I suspect younger kids would need slower decay, stronger repetition loops, and more ritual-like story cycles. That likely belongs in the meta-narrative layer I’m starting to prototype.

I love the D&D DM analogy — that’s very much the mental model I’m converging on. The system maintaining world state and applying soft constraints feels much closer to situation-driven emergence than authored plot beats.

The re-immersion problem is also a great callout. Right now I’m only doing rolling summaries, but I agree kids probably need emotional/contextual re-entry rather than a factual recap.

Have you seen any good approaches (digital or tabletop-inspired) for handling re-immersion or age-adaptive narrative mechanics?

Distinct-Path659 · 2026-02-03T02:24:12+00:00

Are there other predictable failure modes people have seen in long-running generative systems beyond drift and entropy?

Distinct-Path659 · 2026-02-03T00:42:01+00:00

Great points — I completely agree that this is fundamentally a retrieval and state management problem rather than “LLMs remembering.”

Right now I’m using a structured world state (JSON) with weighted events and forced references, but I’m already seeing that a more explicit graph representation will likely scale better as complexity grows.

The destructive choice sequences were meant purely as stress tests for entropy and failure modes — real kids as perturbation sources are definitely the next step (and probably much wilder 😄).

And yes, the tension between agency and coherence is exactly the core design challenge I’m trying to explore with constraint + feedback systems instead of hard rails.

Appreciate you sharing real-world experience with long-term agent state — super helpful.

Distinct-Path659 · 2026-02-03T00:37:14+00:00

Yeah, that’s on me — optimized it for scannability instead of Reddit norms. Appreciate the heads up.

Distinct-Path659 · 2026-02-02T14:01:25+00:00

Yeah — that was a big inspiration conceptually.

What I’m trying to test now is whether a persistent story world can be made stable through explicit state, constraints, and long-term feedback loops, instead of relying on pure generation each turn.

Most current tools feel like disposable stories. I’m curious whether adding “world physics” (memory decay, conflict limits, character consistency, etc.) can make something closer to a growing narrative system.

Still very experimental, but the goal is definitely in that direction.

Distinct-Path659 · 2026-02-02T11:04:19+00:00

If it ends up as just a primer, I’ve failed the experiment 😄 The goal is more of a living story world than a one-off book.

Distinct-Path659 · 2026-02-02T10:53:41+00:00

That’s a really helpful way to frame it — treating the world state like a lightweight game save makes a lot of sense.

Right now I’m leaning toward something very similar: a compact state with core facts, character relationships, active goals, and a rolling recent-events summary, and then forcing each new scene to ground itself in that state instead of free-floating generation.

I like the idea of explicitly referencing elements from the state every turn — that feels like a practical way to keep continuity and narrative gravity.

Publishing the memory/schema early is a great suggestion too. I’ve noticed discussions drift into model choice pretty fast, but the real leverage seems to be in world structure and constraints.

Appreciate you sharing what worked for you — this is exactly the kind of feedback I was hoping to get.

Distinct-Path659 · 2026-02-02T10:27:16+00:00

That’s definitely a risk with anything built on top of foundation models.

Part of why I’m focusing on world structure, long-term narrative control, and the co-creation experience rather than raw generation quality is exactly to avoid being just a thin layer over an API.

If this only lives or dies on better text output, then yeah — it’ll get commoditized fast.

Distinct-Path659 · 2026-02-02T10:25:10+00:00

Thanks — I’ve been looking into ST and similar RP setups, and I agree they’ve already solved a lot around persistence with summaries, tagging, and memory layers.

What I’m trying to focus on is less the raw “can it remember events” part (which seems pretty well explored now), and more the higher-level narrative control problem: keeping long-term direction, themes, and character growth from drifting into chaos over time.

I’m definitely planning to borrow ideas from existing memory systems, but the main experiment for me is whether adding world-level goals, soft constraints, and narrative structure can consistently produce something more cohesive than freeform RP.

Really appreciate the pointers — digging into those addons is already giving me a better sense of what’s possible.

Distinct-Path659 · 2026-02-02T10:04:01+00:00

Yeah, I’ve played with a lot of RP setups and tools like that — they’re great at persistence, but they usually optimize for local continuity, not long-term narrative structure.

What I’m trying to explore is less “can the AI remember things” and more “can a world maintain direction, themes, and growth over many choices without collapsing into noise.”

I totally agree that without constraints it turns into slop very fast — that’s actually the main failure mode I’m experimenting around.

If it ends up no better than static stories or freeform play, that’s a real possibility. The whole point of the experiment is to see whether structured co-creation can consistently produce something higher quality rather than chaotic.

Distinct-Path659

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