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code/analyze.py

@@ -139,7 +139,7 @@ def _group_annotate(ax, pts, dx=9, dy=5):
     for x, y, name in pts:
         b.setdefault((round(x, 6), round(y, 5)), []).append(name)
     for (x, y), names in b.items():
-        ax.annotate(" / ".join(names), (x, y), textcoords="offset points",
+        ax.annotate(" / ".join(sorted(set(names))), (x, y), textcoords="offset points",
                     xytext=(dx, dy), fontsize=7, color="#334155")
 
 if rows:
@@ -324,8 +324,17 @@ A("")
 # ---- headline verdict
 A("## Headline")
 A("")
-real = [r for r in rows if not r["is_control"]]
-ctlr = [r for r in rows if r["is_control"]]
+# One row per (fork, lambda); the headline counts MODELS, so collapse to the lambda=1.0 row
+# (the actual chat-vector recipe) or, failing that, the first row for that fork.
+def _canon(rs):
+    out = {}
+    for r in rs:
+        k = r["fork"]
+        if k not in out or (r["lam"] == 1.0 and out[k]["lam"] != 1.0):
+            out[k] = r
+    return sorted(out.values(), key=lambda z: z["coord_share"])
+real = _canon([r for r in rows if not r["is_control"]])
+ctlr = _canon([r for r in rows if r["is_control"]])
 if real:
     A(f"**{len(real)} real community fork(s), {len(ctlr)} ground-truth control(s).**")
     A("")
@@ -336,14 +345,14 @@ if real:
       "models out of the base model's frame, so the chat vector is already expressed in the right "
       "basis and aligning it is a no-op. The measured accuracy difference is **exactly zero on every "
       "benchmark** — the aligned and naive merges are bit-identical models.")
-    won = [r for r in real if r.get("ifeval_prompt__naive", 0) > r.get("ifeval_prompt__fork", 1)]
+    won = [r for r in real if r.get("ifeval_prompt__naive", 0) > r.get("ifeval_prompt__fork", 1) + 0.02]
     A(f"- The chat-vector recipe itself **works** on {len(won)} of {len(real)} of these forks: it lifts "
       "instruction following well above the fork it started from, i.e. the merged model beats its own "
       "parent — the bar that matters.")
     ok = sum(1 for r in real if (r["coord_share"] >= THRESH) == (r.get("ifeval_prompt__delta", 0) > 0.005))
     A(f"- The diagnostic's registered prediction was correct on **{ok}/{len(real)}** real forks.")
 if ctlr:
-    big = max(ctlr, key=lambda r: r["frac_layers_permuted"])
+    big = max(ctlr, key=lambda r: r["coord_share"])
     A(f"- On the ground-truth control (a real fork acted on by a random element of the model's own "
       f"symmetry group — functionally identical, differently parameterised), the diagnostic fires "
       f"(coordinate share **{big['coord_share']:.3f}**), the naive chat vector "