Corrects a common misconception: the 60s heartbeat must not call any LLM.
It runs the mood decay equation -- microseconds of float math. Giving it a
model is an architecture error, not a resource problem.
Real token spend lands in two places:
- per-turn input context (ontology + state + dyad + memory)
- background task frequency
So route by scene, not by time:
none rule/math tick, reach-out decision, fillers, avatar drive
low local small life events, memory gist, fact extract
standard mid + short speculative-response second segment
high flagship main dialogue, proactive message, crisis
Hard constraints:
- dialogue floor is LOW, never NONE (can get dumber, cannot lose lines)
- crisis never degrades (product constraint, not perf tuning)
- degradation is predictable: one rung per request, floor only at
the hard budget threshold
Cost levers, by actual payoff:
1. prompt caching (fixed prefix) -- by far the largest
2. upstream compression (small model condenses 20 memories to 3)
3. batching (one batch per 30min, not 20 single calls)
4. swapping to a smaller model -- smallest payoff, hurts persona
Also: cost degradation is narratable. 'Net error -> I zoned out' already
exists in the projection table, so being throttled is in-character and true.
- config.py: tiers / routing / budget, replacing llm+llm_fast+llm_background
- think/: scenes, budget, router, wiring
- models.yaml restructured around tiers
- SPEC gains 9.5
- 20 new tests; 78 total pass; ruff clean
mobai
8dea68a25b