The primary method proof, offline — a deliberate, cost-driven substitution
for målbilde §11.8's real-model run (the operator runs MAF against no real
model; API for both repos is too costly privately).
`portfolio_optimiser.simulation` drives `run_project` with a scripted
synthetic chat client across two runs separated by a promotion, and shows
the learning loop close end to end:
- ScriptedChatClient subclasses the LAYERED OpenAIChatCompletionClient (not
bare BaseChatClient — else the always-attached BudgetMiddleware no-ops),
constructs offline (loopback url + dummy key), role-keys proposer/checker
replies, and records every prompt into a shared sink.
- simulate_learning_loop: Run A (fresh wiki) -> validated, persona-approved
verdict carrying a realization marker absent from the bundle -> promote_verdict
into the OKF wiki -> seed_store_from_bundle re-reads it -> Run B's hypothesis
prompt carries the marker. An empty-wiki control on Run A proves causality.
- `python -m portfolio_optimiser.simulation` prints an honest trace.
Honesty (§1): this proves the plumbing, the deterministic spine, and that the
learning dataflow closes — NOT that a live LLM would produce the proposal or
verdict (scripted stand-ins). The genuine model-behaviour comparison lives on
the Claude-SDK side (a minimal API run); the scripted client is MAF-side
scaffolding, not part of the framework-neutral shared/ core.
Load-bearing: tests/test_simulation_loadbearing.py goes red when promotion is
detached (the marker never crosses into Run B). Suite 148->149.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01MHR8iKxJRxDiDfNw8HZmWE
Kjell Tore Guttormsen
a9144cb9bb