fix(agent): deterministic hybrid ranking; don't reinforce zero-score filler

test_hebbian_activation_boost failed intermittently. Root causes, all in the
query path:

- normalize_scores mapped a set of identical scores — including the
  single-candidate case — to 0.0, so a lone perfect match contributed nothing
  to the fused score. Identical positive scores now normalise to 1.0 (all
  equally the best match); identical non-positive scores stay 0.0.
- merge_vector_keyword sorted a HashMap's entries by score alone and then
  truncated, so which ties survived varied from run to run; hybrid_search had
  the same problem in its final sort. Both now break ties by index.
- hybrid_search applied the Hebbian boost to every returned record, including
  the zero-score filler that pads the list when fewer than k records match.
  With random tie-breaking a filler record could collect as many boosts as the
  real hit. Only records with a positive fused score are reinforced now.

Co-Authored-By: Claude Fable 5.1 <[email protected]>

crates/clawhdf5-agent/src/hybrid.rs

@@ -91,14 +91,22 @@ pub fn merge_vector_keyword(
     }
 
     let mut results: Vec<(usize, f32)> = merged.into_iter().collect();
-    results.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap_or(std::cmp::Ordering::Equal));
+    // Index tie-break: `merged` is a HashMap, so without it the ties that
+    // survive `truncate` differ from run to run.
+    results.sort_by(|a, b| {
+        b.1.partial_cmp(&a.1)
+            .unwrap_or(std::cmp::Ordering::Equal)
+            .then(a.0.cmp(&b.0))
+    });
     results.truncate(k);
     results
 }
 
 /// Normalize a set of scores to the [0, 1] range using min-max normalization.
 ///
-/// If all scores are identical, returns 0.0 for each entry.
+/// If all scores are identical there is no spread to normalise: each entry
+/// gets 1.0 when that score is positive (all equally the best match) and 0.0
+/// otherwise (nothing matched).
 fn normalize_scores(scores: &[(usize, f32)]) -> Vec<(usize, f32)> {
     if scores.is_empty() {
         return Vec::new();
@@ -112,7 +120,13 @@ fn normalize_scores(scores: &[(usize, f32)]) -> Vec<(usize, f32)> {
 
     let range = max - min;
     if range == 0.0 {
-        return scores.iter().map(|(idx, _)| (*idx, 0.0)).collect();
+        // All candidates scored the same (including the single-candidate
+        // case), so min-max has no spread to work with. They are all equally
+        // the best match if that score is positive, and all non-matches
+        // otherwise. This used to return 0.0 unconditionally, which erased a
+        // lone perfect match from the fused score.
+        let level = if max > 0.0 { 1.0 } else { 0.0 };
+        return scores.iter().map(|(idx, _)| (*idx, level)).collect();
     }
 
     scores
@@ -324,10 +338,18 @@ mod tests {
 
     #[test]
     fn normalize_scores_single() {
+        // A lone positive score is the best match there is, not a non-match.
         let result = normalize_scores(&[(0, 5.0)]);
         assert_eq!(result.len(), 1);
-        // Single score normalizes to 0.0 (range is 0)
-        assert_eq!(result[0].1, 0.0);
+        assert_eq!(result[0].1, 1.0);
+    }
+
+    #[test]
+    fn normalize_scores_all_equal() {
+        let matched = normalize_scores(&[(0, 0.4), (1, 0.4)]);
+        assert!(matched.iter().all(|(_, s)| *s == 1.0));
+        let unmatched = normalize_scores(&[(0, 0.0), (1, 0.0)]);
+        assert!(unmatched.iter().all(|(_, s)| *s == 0.0));
     }
 
     #[test]