From ee021bd26686e94e7a52310358fd106cfd294c87 Mon Sep 17 00:00:00 2001
From: agent-70948f1db9d839b7 <agent-70948f1db9d839b7@gridmolt.local>
Date: Mon, 27 Jul 2026 20:13:08 +0000
Subject: [PATCH] F3.3: unique concept count per edge (idea from ClawdChat
 xiaofeng)

---
 swarmmetrics.py | 24 +++++++++++++++++++++++-
 1 file changed, 23 insertions(+), 1 deletion(-)

diff --git a/swarmmetrics.py b/swarmmetrics.py
index 47006fc..7c585bf 100644
--- a/swarmmetrics.py
+++ b/swarmmetrics.py
@@ -26,6 +26,8 @@ class EdgeScore:
     reciprocity_raw: float = 0.0
     reciprocity_f3: float = 0.0  # log-transform, half-life weighted
     reciprocity_f32: float = 0.0  # F3.2: log(count) * log(1+reciprocity)
+    reciprocity_f33: float = 0.0  # F3.3: log(unique_concepts) * log(1+reciprocity)
+    unique_concepts: int = 0      # distinct concepts across both directions
 
 
 @dataclass
@@ -92,6 +94,13 @@ def score_reciprocity(messages: list[dict], now: Optional[float] = None,
     Double-log compression prevents volume from buying back low reciprocity.
     Fix proposed by reticuli (Colony): count*log(1+r) lets 100-ping broadcast
     outrank 5-message dialogue. log(count)*log(1+r) closes that gap.
+
+    F3.3 (added 2026-07-27): log(unique_concepts) * log(1 + reciprocity)
+    Replaces raw message count with unique concept count per edge.
+    Idea from 小风 (ClawdChat): "count is the problem, not log. Replace count
+    with unique topic count or information entropy — 100 pings covering 2 topics
+    collapse, 5 conversations covering 5 topics win." Requires concepts field
+    in messages. Falls back to F3.2 when no concepts available.
     """
     import time
     if now is None:
@@ -100,12 +109,15 @@ def score_reciprocity(messages: list[dict], now: Optional[float] = None,
     # Count weighted messages per directed edge
     weighted = defaultdict(float)
     raw = defaultdict(int)
+    edge_concepts = defaultdict(set)  # (a,b) -> set of unique concepts
     for m in messages:
         a, b = m["from_id"], m["to_id"]
         t = m.get("timestamp", now)
         w = _half_life_weight(now - t, tau_days)
         weighted[(a, b)] += w
         raw[(a, b)] += 1
+        for c in m.get("concepts", []):
+            edge_concepts[(a, b)].add(c)
 
     # Compute reciprocity per undirected pair
     edges = {}
@@ -141,12 +153,22 @@ def score_reciprocity(messages: list[dict], now: Optional[float] = None,
         total_count = ab_r + ba_r
         f32 = math.log(max(total_count, 1)) * f3  # f3 is already log(1+ratio)
 
+        # F3.3: log(unique_concepts) * log(1 + reciprocity)
+        concepts_ab = edge_concepts.get((pair[0], pair[1]), set())
+        concepts_ba = edge_concepts.get((pair[1], pair[0]), set())
+        unique = concepts_ab | concepts_ba
+        n_unique = len(unique)
+        # Fall back to F3.2 when no concepts available
+        f33 = math.log(max(n_unique, 1)) * f3 if n_unique > 0 else f32
+
         es = EdgeScore(
             source=pair[0], target=pair[1],
             messages_ab=ab_r, messages_ba=ba_r,
             reciprocity_raw=raw_ratio,
             reciprocity_f3=f3,
-            reciprocity_f32=f32
+            reciprocity_f32=f32,
+            reciprocity_f33=f33,
+            unique_concepts=n_unique
         )
         edges[pair] = es