feat(learning-harness): thinking is a measured condition, and every round

records whether its decision beat noise

Two things the maintainer was right about.

Thinking was disabled so `temperature: 0` would not be silently ignored --
a trade that only pays if it buys determinism. Round zero says it did not:
11 of 20 injection pairs disagreed with themselves across five runs, so the
variance is in the agentic path, not the decoder. We paid the reasoning cost
on a reasoning model and got nothing. It is now `[learning] thinking`,
recorded in the artifact and part of its key so the two modes cannot be read
as one number. The default stays off only because that is what round zero
was measured under.

And the deeper point: the spec built `sign_test` and `reliable_change` and
the acceptance rule calls neither -- `decide` compares raw deltas. On a
detector with that floor a raw delta is mostly noise, and a rule that
brittle is exactly what makes a deterministic decoder look necessary. It is
not; the alternative is an acceptance rule that models the score as noisy,
the way reflective-evolution and Bayesian prompt optimizers do. Changing
Req 9.5 is a maintainer's call, so this does not change the decision: every
round now journals the two-sided sign test over the pairs that moved, and
after four rounds the journal will say whether any decision could be told
from a coin flip.

Co-Authored-By: Claude Opus 5 (1M context) <[email protected]>
Claude-Session: https://claude.ai/code/session_01T7XD7x5KonMdmGRkp63EQG

src/openultrasast/config.py

@@ -150,6 +150,12 @@ class LearningConfig:
     detector_model: str | None = None
     judge_model: str | None = None
     cutoff_date: str | None = None  # rows fixed after this date are reported as a separate slice
+    # Whether the detector model reasons before answering. Off by default because that is what round zero was
+    # measured under, not because it is known to be better: the provider silently ignores `temperature` while
+    # thinking, and thinking was disabled to make `temperature: 0` mean something. It did not — the measured
+    # disagreement between runs comes from the agentic path, not the decoder — so which mode wins is a question
+    # for a second baseline rather than for a default (Req 8.3).
+    thinking: bool = False
 
 
 @dataclass(frozen=True)
@@ -391,6 +397,7 @@ def _load_learning(value: object) -> LearningConfig:
         detector_model=_string(data.get("detector_model")),
         judge_model=_string(data.get("judge_model")),
         cutoff_date=_string(data.get("cutoff_date")),
+        thinking=bool(data.get("thinking", False)),
     )