Thanks, this is a good addition. Copying files can change modification time, and a late worker can finish A after B is accepted, so wall-clock order and acceptance order can diverge. A small current-design pointer holding the accepted revision, with each proposed update naming the revision it started from, states the acceptance rule directly. Stale proposals can stay as history beside the pointer. This fits SAR’s accept or reject step while keeping the files-first approach.
That split between generation time signals and verifier time disagreement is useful. I would treat the first as a routing signal and the second as an observability signal, then keep both in the task trace so the blended cost can be attributed without turning confidence into a proxy for correctness. This also makes it possible to compare models on escalation cause, not only on which one eventually resolved the task.
The three cuts in the essay are pedagogical. Same history under one named constraint at a time so the difference is easy to see. In the runtime the packet is not a single lens winner. It is a small set of tagged facts, and each fact keeps its own use rule. For the Alice scene you mentioned the cheap stage should return an active constraint set, not one enum. Keep who knows what on the tail. Keep the relationship guard with Bob. Keep the task move that the surveillance forces. The strong stage then extracts only sourced facts that satisfy that set. Soft axes that do not bind this turn can wait. Hard constraints that make the next action legal or illegal cannot. If who knows what drops out while Alice still has to move under the tail, you get the rediscovery failure again. That is why the validator sits after the packet. Even a partial compression pass is not allowed to commit a draft that rediscovers a known fact or leaks a fact Alice does not yet hold. So the short answer is yes, the packet can and should carry more than one constraint when the next action needs them jointly. The cheap model is choosing which constraints are live for this turn, not crowning one dominant story beat.
Agreed. The concrete wiring for viewpoint compression and KFI injection lives in my GitHub project ai-bios-narrative-agent under sunnyspot114514. It has the state baseline, dynamic prompt compression, labeled memory injection, and validator-gated commits in working form. Happy to walk through any part if useful.
That instrumentation is clean. Tagging the resolving tier and charging every attempt in the chain makes the blend accountable, and tracking escalation rate beside it is the right daily control. One distinction I would keep is between escalation forced by capability limits and escalation forced by verifier disagreement. The first is a routing cost you can tune with the cheap tier. The second is a measurement cost that a single success label will hide even after you split by tier. Curious how you separate those two in your Forge workflow.
Thanks. This is the right correction. Cost per successful task only works if success is comparable, and aggregate verifier scores hide which slice fails. In production a miss isn’t free. It escalates, so the number that matters is the blended cost across the fallback tier, not a single-model average. I treated the announcements as a unit-of-account shift, not a finished measurement. Happy to hear how you’d instrument that blend in practice.
I’d treat the summary as a derived cache rather than canonical state: the runtime should govern it whenever later actions may rely on it as evidence. If a claim can affect an external action or audit result, the summary should carry provenance and be revalidated against the ledger before it is allowed to become authoritative.
