give pass 4 raw values, batch deferrable commits, fix two known bugs

Pass 4's plausibility review only ever saw normalized scores, never the
raw physical estimate behind them -- confirmed via live testing this was
exactly what caused a real misfire (gemma2:27b cited a real cyclist's
correct 5 W/kg, log-normalized to "0.159" against a car's power scale, as
grounds for rejecting an ordinary bicycle). review_plausibility now takes
raw_metrics + normalized_scores + metric units, and the prompt explicitly
instructs reasoning from the raw value first. Verified live against phi4:
it now cites the actual raw number and correctly explains why a low
normalized score doesn't mean the estimate or concept is bad.

Repository write methods used in the pipeline's hot path now take an
optional commit=False, and Pipeline defers commits during the fast/
deterministic passes (1, 3, and 2 without an LLM), flushing every 200
combos and on any exit path (finally block covers normal completion,
cancellation, and any other exception). LLM-involving calls (pass 2 with
an LLM, all of pass 4) still commit immediately -- those are slow and
crash-prone and worth protecting per-write; the deterministic passes
aren't, and recomputing them is now measured at under a second for the
full domain rather than worth 8,000+ individual fsync'd commits. Full
2,970-combination domain run: multiple minutes -> 0.91s. Test suite:
~70s -> ~15s.

Also fixes two more issues found while auditing the estimator for a real
run: CARGO_KG_PER_STRUCTURAL_KG was 500 (no real vehicle carries 500x its
own structural mass in cargo -- a magnitude bug, not a modeling choice),
corrected to 2.5. And space/rocket platforms' range_fuel now reports the
domain's ceiling instead of an arbitrary placeholder constant -- vacuum
coast isn't resistance-limited, so "distance before running out of fuel"
isn't a meaningful question for these the way it is for ground/air/water
vehicles; the real constraint is delta-v budget, a different metric this
pass doesn't model.

Validated with a live full-domain run (phi4, real Ollama calls): 115
reviewed, 0 malformed/null reviews, 0 verdict-vs-status mismatches.

Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>

src/physcom/db/repository.py

@@ -20,6 +20,14 @@ class Repository:
         self.conn = conn
         self.conn.row_factory = sqlite3.Row
 
+    def commit(self) -> None:
+        """Explicit flush point, for callers batching writes with commit=False
+        below (see Pipeline.run: instant/deterministic passes defer commits
+        and flush in bulk, since a crash there just means cheap recompute;
+        LLM-call results still commit immediately, since those are slow/
+        expensive to redo)."""
+        self.conn.commit()
+
     # ── Dimensions ──────────────────────────────────────────────
 
     def ensure_dimension(self, name: str, description: str = "") -> int:
@@ -422,7 +430,7 @@ class Repository:
         key = ",".join(str(eid) for eid in sorted(entity_ids))
         return hashlib.sha256(key.encode()).hexdigest()[:16]
 
-    def save_combination(self, combination: Combination) -> Combination:
+    def save_combination(self, combination: Combination, commit: bool = True) -> Combination:
         entity_ids = [e.id for e in combination.entities]
         combination.hash = self.compute_hash(entity_ids)
 
@@ -446,11 +454,12 @@ class Repository:
                 "INSERT INTO combination_entities (combination_id, entity_id) VALUES (?, ?)",
                 (combination.id, eid),
             )
+        if commit:
             self.conn.commit()
         return combination
 
     def update_combination_status(
-        self, combo_id: int, status: str, block_reason: str | None = None
+        self, combo_id: int, status: str, block_reason: str | None = None, commit: bool = True
     ) -> None:
         # Don't downgrade from higher pass states — preserves human/LLM review data
         if status in ("scored", "llm_reviewed") or status.endswith("_fail"):
@@ -470,6 +479,7 @@ class Repository:
             "UPDATE combinations SET status = ?, block_reason = ? WHERE id = ?",
             (status, block_reason, combo_id),
         )
+        if commit:
             self.conn.commit()
 
     def get_combination(self, combo_id: int) -> Combination | None:
@@ -558,6 +568,7 @@ class Repository:
         combo_id: int,
         domain_id: int,
         scores: list[dict],
+        commit: bool = True,
     ) -> None:
         """Save per-metric scores. Each dict: metric_id, raw_value, normalized_score, estimation_method, confidence."""
         for s in scores:
@@ -569,6 +580,7 @@ class Repository:
                 (combo_id, domain_id, s["metric_id"], s["raw_value"],
                  s["normalized_score"], s["estimation_method"], s["confidence"]),
             )
+        if commit:
             self.conn.commit()
 
     def save_result(
@@ -581,6 +593,7 @@ class Repository:
         llm_review: str | None = None,
         human_notes: str | None = None,
         domain_block_reason: str | None = None,
+        commit: bool = True,
     ) -> None:
         self.conn.execute(
             """INSERT OR REPLACE INTO combination_results
@@ -590,6 +603,7 @@ class Repository:
             (combo_id, domain_id, composite_score, novelty_flag,
              llm_review, human_notes, pass_reached, domain_block_reason),
         )
+        if commit:
             self.conn.commit()
 
     def get_combination_scores(self, combo_id: int, domain_id: int) -> list[dict]:
@@ -807,7 +821,7 @@ class Repository:
         return row["pass_reached"] if row else None
 
     def save_raw_estimates(
-        self, combo_id: int, domain_id: int, estimates: list[dict]
+        self, combo_id: int, domain_id: int, estimates: list[dict], commit: bool = True
     ) -> None:
         """Save raw metric estimates (pass 2) with normalized_score=NULL.
 
@@ -822,6 +836,7 @@ class Repository:
                 (combo_id, domain_id, e["metric_id"], e["raw_value"],
                  e["estimation_method"], e["confidence"]),
             )
+        if commit:
             self.conn.commit()
 
     def get_existing_result(self, combo_id: int, domain_id: int) -> dict | None:

src/physcom/engine/pipeline.py

@@ -69,8 +69,12 @@ INFRASTRUCTURE_AVAILABILITY: dict[tuple[str, str], float] = {
     ("fuel_infrastructure", "xenon_propellant"): 0.05,
 }
 
-# Crude freight-capacity proxy: kg of cargo per kg of vehicle structural mass.
-CARGO_KG_PER_STRUCTURAL_KG: float = 500
+# Crude freight-capacity proxy: kg of cargo per kg of vehicle structural
+# mass. Was 500 -- a magnitude error (500x cargo-to-structure has no real
+# vehicle analog). Real cargo ships run deadweight/lightship ratios of
+# roughly 1.5-4x depending on class; 2.5 is a reasonable general-cargo
+# midpoint for this domain-agnostic proxy.
+CARGO_KG_PER_STRUCTURAL_KG: float = 2.5
 
 # How mechanically proven/predictable an energy form is in practice — distinct
 # from safety (risk when something goes wrong) and thrust_profile (delivery
@@ -367,9 +371,13 @@ class Pipeline:
         combos = generate_combinations(self.repo, dimensions)
         result.total_generated = len(combos)
 
-        # Save all combinations to DB (also loads status for existing combos)
+        # Save all combinations to DB (also loads status for existing combos).
+        # Deferred commit -- registering combos is instant/deterministic, so a
+        # crash here just means re-running the (cheap) registration loop, not
+        # losing anything worth protecting with a commit per row.
         for combo in combos:
-            self.repo.save_combination(combo)
+            self.repo.save_combination(combo, commit=False)
+        self.repo.commit()
 
         if run_id is not None:
             self.repo.update_pipeline_run(run_id, total_combos=len(combos))
@@ -378,9 +386,20 @@ class Pipeline:
         bounds_by_name = {mb.metric_name: mb for mb in domain.metric_bounds}
 
         # ── Combo-first loop ─────────────────────────────────────
+        # Deterministic passes (1, 3, and 2 without an LLM) defer commits and
+        # get flushed periodically + in `finally` below -- a crash there costs
+        # a cheap recompute, not lost work worth committing per write. Pass 4
+        # (and pass 2 with an LLM) commit immediately after each call: those
+        # are slow and crash-prone (see the QwQ timeout saga), so that result
+        # is worth protecting the moment it lands.
+        combos_since_commit = 0
         try:
             for combo in combos:
                 self._check_cancelled(run_id)
+                combos_since_commit += 1
+                if combos_since_commit >= 200:
+                    self.repo.commit()
+                    combos_since_commit = 0
 
                 # Check existing progress for this combo in this domain
                 existing_pass = self.repo.get_combo_pass_reached(
@@ -399,7 +418,7 @@ class Pipeline:
                         combo.status = "p1_fail"
                         combo.block_reason = "; ".join(cr.violations)
                         self.repo.update_combination_status(
-                            combo.id, "p1_fail", combo.block_reason
+                            combo.id, "p1_fail", combo.block_reason, commit=False
                         )
                         # Save a result row so failed combos appear in results
                         self.repo.save_result(
@@ -407,13 +426,14 @@ class Pipeline:
                             domain.id,
                             composite_score=0.0,
                             pass_reached=1,
+                            commit=False,
                         )
                         result.pass1_failed += 1
                         self._update_run_counters(run_id, result, current_pass=1)
                         continue  # p1_fail — skip remaining passes
                     else:
                         combo.status = "valid"
-                        self.repo.update_combination_status(combo.id, "valid")
+                        self.repo.update_combination_status(combo.id, "valid", commit=False)
 
                         # Domain constraint check (per-domain block only). combo.status
                         # stays "valid" here on purpose: it's domain-agnostic and the
@@ -431,6 +451,7 @@ class Pipeline:
                                     domain_block_reason="; ".join(
                                         dc_result.violations
                                     ),
+                                    commit=False,
                                 )
                                 result.pass1_failed += 1
                                 self._update_run_counters(
@@ -482,9 +503,13 @@ class Pipeline:
                                 "estimation_method": "llm" if self.llm else "stub",
                                 "confidence": 1.0,
                             })
+                    # LLM-produced estimates commit immediately (slow/crash-
+                    # prone, worth protecting); stub estimates are instant
+                    # and defer, same as the rest of the deterministic passes.
+                    used_llm = self.llm is not None
                     if estimate_dicts:
                         self.repo.save_raw_estimates(
-                            combo.id, domain.id, estimate_dicts
+                            combo.id, domain.id, estimate_dicts, commit=used_llm
                         )
 
                     # Check for all-zero estimates → p2_fail
@@ -492,11 +517,12 @@ class Pipeline:
                         combo.status = "p2_fail"
                         combo.block_reason = "All metric estimates are zero"
                         self.repo.update_combination_status(
-                            combo.id, "p2_fail", combo.block_reason
+                            combo.id, "p2_fail", combo.block_reason, commit=used_llm
                         )
                         self.repo.save_result(
                             combo.id, domain.id,
                             composite_score=0.0, pass_reached=2,
+                            commit=used_llm,
                         )
                         result.pass2_failed += 1
                         self._update_run_counters(run_id, result, current_pass=2)
@@ -534,7 +560,7 @@ class Pipeline:
                                 "confidence": s.confidence,
                             })
                     if score_dicts:
-                        self.repo.save_scores(combo.id, domain.id, score_dicts)
+                        self.repo.save_scores(combo.id, domain.id, score_dicts, commit=False)
 
                     # Preserve existing human data
                     novelty_flag = (
@@ -550,6 +576,7 @@ class Pipeline:
                             sr.composite_score, pass_reached=3,
                             novelty_flag=novelty_flag,
                             human_notes=human_notes,
+                            commit=False,
                         )
                         combo.status = "p3_fail"
                         combo.block_reason = (
@@ -557,7 +584,7 @@ class Pipeline:
                             f"below threshold {score_threshold}"
                         )
                         self.repo.update_combination_status(
-                            combo.id, "p3_fail", combo.block_reason
+                            combo.id, "p3_fail", combo.block_reason, commit=False
                         )
                         result.pass3_failed += 1
                         result.pass3_scored += 1
@@ -571,8 +598,9 @@ class Pipeline:
                         pass_reached=3,
                         novelty_flag=novelty_flag,
                         human_notes=human_notes,
+                        commit=False,
                     )
-                    self.repo.update_combination_status(combo.id, "scored")
+                    self.repo.update_combination_status(combo.id, "scored", commit=False)
 
                     result.pass3_scored += 1
                     result.pass3_above_threshold += 1
@@ -608,16 +636,21 @@ class Pipeline:
                                 for s in db_scores
                                 if s["normalized_score"] is not None
                             }
+                            raw_dict = {
+                                s["metric_name"]: s["raw_value"]
+                                for s in db_scores
+                                if s["raw_value"] is not None
+                            }
                             review_result: tuple[str, bool] | None = None
                             try:
                                 review_result = self.llm.review_plausibility(
-                                    description, score_dict
+                                    description, raw_dict, score_dict, domain.metric_bounds
                                 )
                             except LLMRateLimitError as exc:
                                 self._wait_for_rate_limit(run_id, exc.retry_after)
                                 try:
                                     review_result = self.llm.review_plausibility(
-                                        description, score_dict
+                                        description, raw_dict, score_dict, domain.metric_bounds
                                     )
                                 except LLMRateLimitError:
                                     pass  # still limited; skip, retry next run
@@ -664,6 +697,13 @@ class Pipeline:
                 )
             result.top_results = self.repo.get_top_results(domain.name, limit=20)
             return result
+        finally:
+            # Flush any batched deterministic writes -- runs on normal
+            # completion, cancellation, and any other exception propagating
+            # out of the loop, so nothing deferred above is ever silently lost
+            # on a clean exit path (a hard process crash is a different story
+            # and is exactly what the immediate LLM-call commits protect).
+            self.repo.commit()
 
         # Mark run as completed
         if run_id is not None:
@@ -842,15 +882,22 @@ class Pipeline:
             raw["power_density"] = (k_act * power_mass) / physics_denom if physics_denom else 0.0
 
         if "range_fuel" in raw:
-            if storage_energy_form in AMBIENT_ENERGY_FORMS:
+            if storage_energy_form in AMBIENT_ENERGY_FORMS or k_med is None:
+                # Ambient sources aren't a depletable store (see module note
+                # above). Space/rocket platforms (k_med undeclared for
+                # "space") are the same conclusion from different physics:
+                # in vacuum coast there's no resistance to fight, so a
+                # working engine covers arbitrary distance given enough
+                # time -- "range" isn't fuel-quantity-limited the way it is
+                # for a vehicle fighting drag. The real constraint for a
+                # rocket is its delta-v budget (maneuvering capability),
+                # which isn't a distance and isn't what this metric asks --
+                # reporting the domain's ceiling is the honest answer, not
+                # the old magic-constant guess (e_dens * 2.78) it replaces.
                 mb = bounds_by_name.get("range_fuel")
                 raw["range_fuel"] = mb.norm_max if mb else 0.0
-            elif k_med is not None and floor_total > 0:
+            elif floor_total > 0:
                 raw["range_fuel"] = min((e_dens * storage_mass) / (k_med * floor_total), 1e13)
-            else:
-                # space/rocket platforms: resistance-based formula doesn't
-                # apply (see module note) -- old placeholder, not a claim.
-                raw["range_fuel"] = min(e_dens * 2.78, 1e13)
 
         if "cost_efficiency" in raw:
             structural_cost = p_rep * STRUCTURAL_COST_PER_KG_BY_MEDIUM.get(medium, STRUCTURAL_COST_PER_KG_BY_MEDIUM["ground"])

src/physcom/llm/base.py

@@ -36,8 +36,20 @@ class LLMProvider(ABC):
 
     @abstractmethod
     def review_plausibility(
-        self, combination_description: str, scores: dict[str, float]
+        self,
+        combination_description: str,
+        raw_metrics: dict[str, float],
+        normalized_scores: dict[str, float],
+        metrics: list[MetricBound],
     ) -> tuple[str, bool]:
-        """Given a combination and its scores, return a (text, is_plausible)
-        tuple: natural-language assessment and whether the concept is plausible."""
+        """Given a combination, its raw physical estimates, and their
+        normalized scores, return a (text, is_plausible) tuple:
+        natural-language assessment and whether the concept is plausible.
+
+        Both raw_metrics and normalized_scores are given (not just the
+        normalized score) so the review can reason from the actual physics
+        rather than only a compressed 0-1 number, which can look
+        deceptively bad for a metric whose scale was built for a different
+        kind of vehicle. `metrics` carries each metric's unit for
+        formatting the raw value meaningfully."""
         ...

src/physcom/llm/prompts.py

@@ -20,6 +20,35 @@ def format_metrics_for_prompt(metrics: list["MetricBound"]) -> str:
     return "\n".join(lines)
 
 
+def format_scores_for_prompt(
+    raw_metrics: dict[str, float],
+    normalized_scores: dict[str, float],
+    metrics: list["MetricBound"],
+) -> str:
+    """Render each metric with BOTH its raw physical value and its
+    normalized score, so the reviewing pass can reason from the actual
+    physics instead of only ever seeing a compressed 0-1 number.
+
+    A real, correct estimate can still look damning once log-normalized
+    against a scale built for a different kind of vehicle (a cyclist's
+    real ~5 W/kg reads as "0.159" next to a car's 2000 W/kg ceiling) --
+    a reviewer that only sees the 0.159 has no way to notice that. See
+    the labeled-set calibration note on PLAUSIBILITY_REVIEW_PROMPT below.
+    """
+    lines = []
+    for mb in metrics:
+        normed = normalized_scores.get(mb.metric_name)
+        if normed is None:
+            continue
+        raw = raw_metrics.get(mb.metric_name)
+        unit = mb.unit or "dimensionless"
+        raw_str = f"{raw:g} {unit}" if raw is not None else "unknown"
+        lines.append(
+            f"- {mb.metric_name}: raw estimate {raw_str} — normalized score {normed:.3f}"
+        )
+    return "\n".join(lines)
+
+
 PHYSICS_ESTIMATION_PROMPT = """\
 You are a physics estimation assistant. Given the following transportation concept, \
 estimate the requested metrics using order-of-magnitude physics reasoning.
@@ -44,15 +73,22 @@ match that magnitude, don't guess a generically "reasonable-looking" decimal.
   {{"some_metric": <number>, "another_metric": <number>}} — no explanatory text.
 """
 
-# ponytail: pass 4 only sees pass 2's raw numbers, not its reasoning. Sharpened
-# prompts on both sides closed most of the gap (a bad safety estimate went from
-# 0.95 to 0.80 on the same combo once pass 2 was told to consider combination-
-# specific hazards). Upgrade path if this isn't good enough in practice: have
-# estimate_physics() also return a short per-metric reason, persist it
-# alongside raw_value (new nullable column), and feed it into this prompt so
-# pass 4 has something concrete to agree or disagree with. Deferred because it
-# needs a schema/interface change across LLMProvider + both providers +
-# pipeline + scorer + repository, and more generated tokens per combo.
+# ponytail: pass 4 used to see only pass 2's normalized scores, not the raw
+# physical numbers or any reasoning behind them. Fixed the raw-value half of
+# that gap: format_scores_for_prompt() now shows both, since a correct raw
+# estimate can look damning once log-normalized against a scale built for a
+# different kind of vehicle (a cyclist's real ~5 W/kg reads as "0.159" next
+# to a car's 2000 W/kg ceiling) -- gemma2:27b did exactly this on a real
+# bicycle combo, citing "extremely low power density (0.159)" as grounds for
+# IMPLAUSIBLE while never reasoning from the actual (correct) 5 W/kg. The
+# reasoning-text half of the gap is still open: estimate_physics() doesn't
+# return a per-metric rationale, so pass 4 still can't see WHY pass 2 landed
+# on a number, only what the number is. Upgrade path if the raw value alone
+# isn't enough in practice: have estimate_physics() also return a short
+# per-metric reason, persist it alongside raw_value (new nullable column),
+# and feed it into this prompt. Deferred because it needs a schema/interface
+# change across LLMProvider + both providers + pipeline + scorer +
+# repository, and more generated tokens per combo.
 #
 # If we plan to LLM-review every p2 pass then maybe p2 and p4 should be combined.
 #
@@ -84,10 +120,22 @@ is NOT the question.
 {description}
 
 ## Metric Scores
-All scores below are normalized to 0-1, where HIGHER IS ALWAYS BETTER for
-every metric listed, regardless of what the metric measures (this already
-accounts for things like "lower cost is better" — you don't need to invert
-anything). A score of 1.0 means excellent, not "pegged" or "maxed out badly."
+Each metric below is given as its raw estimated physical value (in the unit
+shown) AND a normalized score from 0-1, where HIGHER IS ALWAYS BETTER for
+every metric listed regardless of what it measures (this already accounts
+for things like "lower cost is better" — you don't need to invert anything).
+A score of 1.0 means excellent, not "pegged" or "maxed out badly."
+
+Reason from the RAW value first — it's the actual physics. The normalized
+score is a summary, not a fact on its own: a real, correct estimate can
+still normalize to a low-looking number simply because the domain's scale
+was built for a different, more demanding kind of vehicle (a cyclist's real
+~5 W/kg legitimately normalizes to ~0.16 next to a car engine's 2000 W/kg
+ceiling — that low score doesn't mean the estimate is bad or the concept is
+weak, it means human power is small next to a car engine, which everyone
+already knows). If a normalized score looks alarming, check whether the raw
+value is actually reasonable for what this component fundamentally is
+before treating the score as evidence of a problem.
 
 {scores}
 

src/physcom/llm/providers/gemini.py

@@ -11,6 +11,7 @@ from physcom.llm.prompts import (
     PHYSICS_ESTIMATION_PROMPT,
     PLAUSIBILITY_REVIEW_PROMPT,
     format_metrics_for_prompt,
+    format_scores_for_prompt,
 )
 from physcom.models.domain import MetricBound
 
@@ -46,9 +47,13 @@ class GeminiLLMProvider(LLMProvider):
         return parse_metric_json(response.text, metrics)
 
     def review_plausibility(
-        self, combination_description: str, scores: dict[str, float]
+        self,
+        combination_description: str,
+        raw_metrics: dict[str, float],
+        normalized_scores: dict[str, float],
+        metrics: list[MetricBound],
     ) -> tuple[str, bool]:
-        scores_str = "\n".join(f"- {k}: {v:.3f}" for k, v in scores.items())
+        scores_str = format_scores_for_prompt(raw_metrics, normalized_scores, metrics)
         prompt = PLAUSIBILITY_REVIEW_PROMPT.format(
             description=combination_description,
             scores=scores_str,

src/physcom/llm/providers/mock.py

@@ -21,9 +21,13 @@ class MockLLMProvider(LLMProvider):
         return result
 
     def review_plausibility(
-        self, combination_description: str, scores: dict[str, float]
+        self,
+        combination_description: str,
+        raw_metrics: dict[str, float],
+        normalized_scores: dict[str, float],
+        metrics: list[MetricBound],
     ) -> tuple[str, bool]:
-        avg = sum(scores.values()) / max(len(scores), 1)
+        avg = sum(normalized_scores.values()) / max(len(normalized_scores), 1)
         if avg > 0.5:
             return ("This concept appears plausible and worth further investigation.", True)
         return ("This concept has significant feasibility challenges.", False)

src/physcom/llm/providers/ollama.py

@@ -12,6 +12,7 @@ from physcom.llm.prompts import (
     PHYSICS_ESTIMATION_PROMPT,
     PLAUSIBILITY_REVIEW_PROMPT,
     format_metrics_for_prompt,
+    format_scores_for_prompt,
 )
 from physcom.models.domain import MetricBound
 
@@ -34,9 +35,13 @@ class OllamaLLMProvider(LLMProvider):
         return parse_metric_json(text, metrics)
 
     def review_plausibility(
-        self, combination_description: str, scores: dict[str, float]
+        self,
+        combination_description: str,
+        raw_metrics: dict[str, float],
+        normalized_scores: dict[str, float],
+        metrics: list[MetricBound],
     ) -> tuple[str, bool]:
-        scores_str = "\n".join(f"- {k}: {v:.3f}" for k, v in scores.items())
+        scores_str = format_scores_for_prompt(raw_metrics, normalized_scores, metrics)
         prompt = PLAUSIBILITY_REVIEW_PROMPT.format(
             description=combination_description,
             scores=scores_str,