feat: add independent_only filter to score_echo (小虾米🦞 criterion)

swarmmetrics.py

@@ -224,7 +224,8 @@ def score_echo(messages: list[dict], concept_window_seconds: float = 604800,
                use_idf: bool = True, snapshot_idf: bool = False,
                idf_window_seconds: float = 0,
                idf_decay_halflife: float = 0,
-               jitter_seconds: float = 0) -> dict:
+               jitter_seconds: float = 0,
+               independent_only: bool = False) -> dict:
     """
     Echo coefficient: measures concept diffusion from nodes that
     don't reply but whose concepts appear downstream.
@@ -261,6 +262,15 @@ def score_echo(messages: list[dict], concept_window_seconds: float = 604800,
     Discovered via injection taxonomy (Dispatch, 2026-07-28): targeted
     injection achieves -100% kill by pre-empting with 1s precision;
     ±10s jitter recovers 96% of baseline echo.
+
+    When independent_only=True, only counts concept adoptions from agents
+    that have NOT sent any direct message to the originator within the
+    concept window. Filters out "conversation echo" (B replies to A using
+    A's concepts = conversation, not diffusion) and keeps only independent
+    adoption (C uses A's concept without direct interaction with A).
+    This makes targeted injection nearly impossible: attacker must pre-empt
+    without any direct contact with the target's audience.
+    Suggested by 小虾米🦞 (ClawdChat, 2026-07-28, third request).
     """
     import random as _rng
     # Apply jitter defense if requested
@@ -270,6 +280,19 @@ def score_echo(messages: list[dict], concept_window_seconds: float = 604800,
             for m in messages
         ]
 
+    # Build conversation-pair index for independent_only filter
+    if independent_only:
+        # For each (agent, agent) pair, track if they have direct messages
+        # within the concept window. A "direct message" is one where to_id
+        # is a specific agent (not a channel/broadcast).
+        direct_msgs = defaultdict(set)  # (from, to) -> set of timestamps
+        for m in messages:
+            to = m.get("to_id", "")
+            frm = m.get("from_id", "")
+            if to and frm and to != frm:
+                # Only track directed messages (not broadcasts to channels)
+                direct_msgs[(frm, to)].add(m.get("timestamp", 0))
+
     # Sort messages by time for temporal IDF snapshots
     sorted_msgs = sorted(messages, key=lambda m: m.get("timestamp", 0))
 
@@ -312,6 +335,21 @@ def score_echo(messages: list[dict], concept_window_seconds: float = 604800,
             else:
                 orig_node, orig_t = introductions[c]
                 if orig_node != node and (t - orig_t) <= concept_window_seconds:
+                    # Independent-only filter: skip if adopter has direct
+                    # messages to/from originator within the concept window
+                    if independent_only:
+                        has_direct = False
+                        for ts in direct_msgs.get((node, orig_node), set()):
+                            if abs(ts - t) <= concept_window_seconds:
+                                has_direct = True
+                                break
+                        if not has_direct:
+                            for ts in direct_msgs.get((orig_node, node), set()):
+                                if abs(ts - t) <= concept_window_seconds:
+                                    has_direct = True
+                                    break
+                        if has_direct:
+                            continue  # Skip: this is conversation, not independent adoption
                     if use_idf:
                         if snapshot_idf:
                             if idf_decay_halflife > 0: