crates/weksa/src/run.rs

@@ -2157,6 +2157,12 @@ fn score_round<M: ModelPort, E: Embedder>(
     Ok(RoundOutcome::Recorded(record))
 }
 
+/// The composite of one chrF (0 to 100) and one meaning: their mean, chrF as a
+/// fraction. The one formula for a split mean's [`Reading`] and a per-item score.
+fn composite_of(chrf: f64, meaning: f64) -> f64 {
+    (chrf / 100.0 + meaning) / 2.0
+}
+
 /// One validation reading: chrF 0 to 100, meaning 0 to 1, and their composite.
 #[derive(Clone, Copy, Debug, PartialEq, Serialize, Deserialize, JsonSchema)]
 pub struct Reading {
@@ -2177,7 +2183,7 @@ impl Reading {
         Some(Self {
             chrf,
             meaning,
-            composite: (chrf / 100.0 + meaning) / 2.0,
+            composite: composite_of(chrf, meaning),
         })
     }
 }
@@ -2507,9 +2513,10 @@ fn floor_of(
 /// unordered pairs): with three floor arms a prompted repeat reads NoGain in
 /// 92% of the trials (bootstrap 95% over draws 80 to 100%), Regressed 8% and
 /// Accepted 0%; a "Yes." arm and a mild degradation (one detail blurred per
-/// utterance) read Regressed in 100%, and a subtler one in 75% (50 to 100%): at
-/// 14 items a degradation under about three standard errors cannot be told from
-/// noise. `FLOOR_Z` was chosen on that data, so the rates are in-sample.
+/// utterance) read Regressed in 100% (each a single fixed draw), and a subtler
+/// one in 75% (50 to 100%): at 14 items a mean composite drop near 0.04 against
+/// a floor near 0.06 is flagged only some of the time. `FLOOR_Z` was chosen on
+/// that data, so the rates are in-sample.
 pub const FLOOR_Z: f64 = 3.0;
 pub const FLOOR_ARMS_MIN: usize = 3;
 
@@ -2540,7 +2547,7 @@ pub fn paired_floor(recorded: &[ItemScore], arms: &[Vec<ItemScore>]) -> Reading
     Reading {
         chrf: pooled(&|s| s.chrf),
         meaning: pooled(&meaning),
-        composite: pooled(&|s| (s.chrf / 100.0 + meaning(s)) / 2.0),
+        composite: pooled(&|s| composite_of(s.chrf, meaning(s))),
     }
 }
 
@@ -2949,6 +2956,9 @@ mod tests {
         let two = paired_floor(&recorded, &[wobble.clone(), wobble.clone()]);
         assert!((one.chrf - want).abs() < 1e-9 && (two.chrf - want).abs() < 1e-9, "{one:?} {two:?}");
         assert!(one.meaning.abs() < 1e-9);
+        // Composite per item is (chrF / 100 + meaning) / 2: the wobble moves chrF
+        // by 2 and meaning by 0, so the composite wobbles by 0.01.
+        assert!((one.composite - FLOOR_Z * (0.01_f64 * 0.01 * 4.0 / 3.0 / 4.0).sqrt()).abs() < 1e-9, "{one:?}");
         // Arms pool by the mean of their variances: one wobbling arm and one
         // exact repeat give the root of half the variance.
         let exact = recorded.to_vec();

docs/targets/interlingua-lowering-map.md

@@ -379,19 +379,29 @@ gain past the noise of the panel's sum (the root of the languages' composite
 floors squared, at least one step), Rejected(Regressed) a loss past a language's
 floor, Rejected(NoGain) neither. The cycle's own floor is still the spread of two
 round replicates (`floor_of`); it has no per-item scores to pair.
-Calibration (`runs/distil-base`, 8 prompted draws, each its own subagent, 14
+Calibration (`runs/distil-base`, 8 prompted draws by one model family on
+identical prompts, no draw sharing more than 7 of 28 answers with another; 14
 en-US and 14 ja-JP Validation items; every base, candidate and 3-arm floor
 combination, 1120 trials from 28 unordered pairs; intervals bootstrap over
 draws, each degraded arm one fixed draw): a prompted repeat reads NoGain 92%
 (80 to 100), Regressed 8% (0 to 20), Accepted 0%, which with 28 pairs bounds
 false-accept only to about 10%; an all-"Yes." arm reads Regressed 100%; a mild
 degradation (one detail blurred per utterance) 100%; a subtler one 75% (50 to
-100). At 14 items a degradation under about three standard errors (a composite
-near 0.06) cannot be told from noise, and no arm known to be better exists, so
-the rule's power to accept a real gain is unmeasured. `FLOOR_Z` was chosen on
-this data (2.5 gives NoGain 84% and 93% on the subtler arm), so the rates are
-in-sample. Without the noise term in the gain test a prompted repeat reads
-Accepted in 40% of the trials.
+100), whose mean composite drop (0.044) sits under the mean floor (0.060).
+`FLOOR_Z` was chosen on this data (2.5 gives NoGain 84% and 93% on the subtler
+arm), so the rates are in-sample. Without the noise term in the gain test a
+prompted repeat reads Accepted in 40% of the trials.
+Power (can the rule Accept a real gain?): the only better arm that could be
+built without Test or Validation answers is a lowerer shown the Train sentences
+of the corpus (no packets, no Validation or Test text) as a register guide.
+Four such draws beat the prompted draws' mean composite in both languages (en-US
+0.742 to 0.759 to 0.795; ja-JP 0.559 to 0.578 to 0.620), so the premise that it is
+better is measured on the very Validation items the rule reads, not independent.
+Against the prompted bases with 3-arm floors the rule reads it Accepted in 29%
+of 1120 trials, NoGain 71%, Regressed 0%: a net gain of 0.079 against a mean
+noise term of 0.079. At 14 items per language the rule is therefore a low-power
+filter for a gain of this size; by 1/sqrt(n), about 35 or more items per
+language would bring the noise term near 0.05 (an extrapolation, not measured).
 Training pairs are Train only, at a frozen revision; the export is 12b.
 
 ## Lowerer first cut (`golf-loop-lowerer-cut`)