Harden LLM proposal parsing

src/aituner/cli.py

@@ -141,6 +141,8 @@ def cmd_study_tune(args: argparse.Namespace) -> int:
             proposal_source = None
             proposal_text = call_llm_for_proposal(policy=study.llm, prompt=prompt)
             proposal_name = f"proposal-{state.next_trial_index:04d}"
+        raw_proposal_path = store.study_root(study.study_id) / "proposals" / f"{proposal_name}.raw.txt"
+        raw_proposal_path.write_text(proposal_text, encoding="utf-8")
         proposal = parse_proposal_text(proposal_text, study)
         store.write_proposal(study.study_id, proposal_name, proposal)
         trial, _ = store.materialize_trial(study=study, state=state, proposal=proposal)

src/aituner/spec.py

@@ -395,15 +395,20 @@ class Proposal:
 
     @classmethod
     def from_dict(cls, data: Mapping[str, Any]) -> "Proposal":
+        expected_effects = data.get("expected_effects")
+        if isinstance(expected_effects, str):
+            expected_effects_value = [expected_effects.strip()] if expected_effects.strip() else []
+        else:
+            expected_effects_value = _coerce_str_list(
+                expected_effects, context="proposal.expected_effects"
+            )
         return cls(
             observation=_require_str(data.get("observation"), context="proposal.observation"),
             diagnosis=_require_str(data.get("diagnosis"), context="proposal.diagnosis"),
             config_patch=ConfigPatch.from_dict(
                 _require_mapping(data.get("config_patch"), context="proposal.config_patch")
             ),
-            expected_effects=_coerce_str_list(
-                data.get("expected_effects"), context="proposal.expected_effects"
-            ),
+            expected_effects=expected_effects_value,
             why_not_previous_failures=str(data.get("why_not_previous_failures") or "").strip(),
         )