feat: add shortest_path and path_explanation to MnemosyneArchive

BFS-based pathfinding between memories through the connection graph.
Enables 'how is X related to Y?' queries across the holographic archive.

nexus/mnemosyne/archive.py

@@ -1059,6 +1059,52 @@ class MnemosyneArchive:
 
         return merges
 
+
+    def shortest_path(self, start_id: str, end_id: str) -> list[str] | None:
+        """Find shortest path between two entries through the connection graph.
+
+        Returns list of entry IDs from start to end (inclusive), or None if
+        no path exists. Uses BFS for unweighted shortest path.
+        """
+        if start_id == end_id:
+            return [start_id] if start_id in self._entries else None
+        if start_id not in self._entries or end_id not in self._entries:
+            return None
+
+        adj = self._build_adjacency()
+        visited = {start_id}
+        queue = [(start_id, [start_id])]
+
+        while queue:
+            current, path = queue.pop(0)
+            for neighbor in adj.get(current, []):
+                if neighbor == end_id:
+                    return path + [neighbor]
+                if neighbor not in visited:
+                    visited.add(neighbor)
+                    queue.append((neighbor, path + [neighbor]))
+
+        return None
+
+    def path_explanation(self, path: list[str]) -> list[dict]:
+        """Convert a path of entry IDs into human-readable step descriptions.
+
+        Returns list of dicts with 'id', 'title', and 'topics' for each step.
+        """
+        steps = []
+        for entry_id in path:
+            entry = self._entries.get(entry_id)
+            if entry:
+                steps.append({
+                    "id": entry.id,
+                    "title": entry.title,
+                    "topics": entry.topics,
+                    "content_preview": entry.content[:120] + "..." if len(entry.content) > 120 else entry.content,
+                })
+            else:
+                steps.append({"id": entry_id, "title": "[unknown]", "topics": []})
+        return steps
+
     def rebuild_links(self, threshold: Optional[float] = None) -> int:
         """Recompute all links from scratch.