fix(learning-harness): round zero writes down every pair it has paid for

The 176-pair vfc baseline hit its four-hour timeout and wrote nothing. Scores
were accumulated in memory and persisted once at the end, so roughly four
hours of paid API calls were lost. I fixed the exception path this morning --
a provider hiccup costs one pair -- and missed that a timeout, a kill or a
machine going away cost all of them.

Each pair is now appended to `scores.jsonl` and flushed as it is measured, and
`--resume` keeps what a previous run of that exact baseline already bought.
This also gives the per-pair outcomes the round-3 review asked for, which make
the noise floor auditable after the fact instead of only in aggregate.

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

src/openultrasast/cli.py

@@ -223,6 +223,12 @@ def main(argv: list[str] | None = None) -> int:
             command.add_argument("--catalog", type=Path, default=DEFAULT_CATALOG)
             command.add_argument("--slice", choices=SLICE_NAMES, default="all")
             command.add_argument("--json", action="store_true")
+        if name == "baseline":
+            command.add_argument(
+                "--resume",
+                action="store_true",
+                help="keep the pairs a previous run of this baseline already measured and paid for",
+            )
         if name == "candidates":
             command.add_argument("--generator", choices=("ir", "ruleset"), default="ir", help="which pass enumerates the candidate sites")
         if name in {"baseline", "round", "score"}:
@@ -1352,6 +1358,7 @@ def _learning_run(args: argparse.Namespace, cases: Sequence[PairCase], taxonomy:
             slices=(args.slice,) if args.slice != "all" else DEFAULT_SLICES,
             spent_usd=spent,
             thinking=config.learning.thinking,
+            resume=args.resume,
         )
         print(f"learning baseline cost: ${report.cost_usd:.4f}")
         print(f"learning baseline {model}: {len(report.floors)} families, k={report.k_runs} -> {out / 'baseline'}")