fix: closes #675

app.js

@@ -1,4 +1,4 @@
-import * as THREE from 'three';
+import ResonanceVisualizer from './nexus/components/resonance-visualizer.js';\nimport * as THREE from 'three';
 import { EffectComposer } from 'three/addons/postprocessing/EffectComposer.js';
 import { RenderPass } from 'three/addons/postprocessing/RenderPass.js';
 import { UnrealBloomPass } from 'three/addons/postprocessing/UnrealBloomPass.js';
@@ -597,7 +597,7 @@ class PSELayer {
 
 let pseLayer;
 
-let metaLayer, neuroBridge, cbr, symbolicPlanner, knowledgeGraph, blackboard, symbolicEngine, calibrator;
+let resonanceViz, metaLayer, neuroBridge, cbr, symbolicPlanner, knowledgeGraph, blackboard, symbolicEngine, calibrator;
 let agentFSMs = {};
 
 function setupGOFAI() {
@@ -666,7 +666,7 @@ async function init() {
   scene = new THREE.Scene();
   scene.fog = new THREE.FogExp2(0x050510, 0.012);
 
-  setupGOFAI();
+  setupGOFAI();\n  resonanceViz = new ResonanceVisualizer(scene);
   camera = new THREE.PerspectiveCamera(65, window.innerWidth / window.innerHeight, 0.1, 1000);
   camera.position.copy(playerPos);
 

nexus/components/memory-optimizer.js

@@ -1,13 +1,18 @@
 
 class MemoryOptimizer {
     constructor(options = {}) {
-        this.threshold = options.threshold || 0.8;
-        this.decayRate = options.decayRate || 0.05;
+        this.threshold = options.threshold || 0.3;
+        this.decayRate = options.decayRate || 0.01;
+        this.lastRun = Date.now();
     }
-    optimize(memory) {
-        console.log('Optimizing memory...');
-        // Heuristic-based pruning
-        return memory.filter(m => m.strength > this.threshold);
+    optimize(memories) {
+        const now = Date.now();
+        const elapsed = (now - this.lastRun) / 1000;
+        this.lastRun = now;
+        return memories.map(m => {
+            const decay = (m.importance || 1) * this.decayRate * elapsed;
+            return { ...m, strength: Math.max(0, (m.strength || 1) - decay) };
+        }).filter(m => m.strength > this.threshold || m.locked);
     }
 }
 export default MemoryOptimizer;

nexus/components/spatial-memory.js

@@ -694,15 +694,61 @@ const SpatialMemory = (() => {
     }
   }
 
+  // ─── CONTEXT COMPACTION (issue #675) ──────────────────
+  const COMPACT_CONTENT_MAXLEN = 80;   // max chars for low-strength memories
+  const COMPACT_STRENGTH_THRESHOLD = 0.5; // below this, content gets truncated
+  const COMPACT_MAX_CONNECTIONS = 5;    // cap connections per memory
+  const COMPACT_POSITION_DECIMALS = 1; // round positions to 1 decimal
+
+  function _compactPosition(pos) {
+    const factor = Math.pow(10, COMPACT_POSITION_DECIMALS);
+    return pos.map(v => Math.round(v * factor) / factor);
+  }
+
+  /**
+   * Deterministically compact a memory for storage.
+   * Same input always produces same output — no randomness.
+   * Strong memories keep full fidelity; weak memories get truncated.
+   */
+  function _compactMemory(o) {
+    const strength = o.mesh.userData.strength || 0.7;
+    const content = o.data.content || '';
+    const connections = o.data.connections || [];
+
+    // Deterministic content truncation for weak memories
+    let compactContent = content;
+    if (strength < COMPACT_STRENGTH_THRESHOLD && content.length > COMPACT_CONTENT_MAXLEN) {
+      compactContent = content.slice(0, COMPACT_CONTENT_MAXLEN) + '\u2026';
+    }
+
+    // Cap connections (keep first N, deterministic)
+    const compactConnections = connections.length > COMPACT_MAX_CONNECTIONS
+      ? connections.slice(0, COMPACT_MAX_CONNECTIONS)
+      : connections;
+
+    return {
+      id: o.data.id,
+      content: compactContent,
+      category: o.region,
+      position: _compactPosition([o.mesh.position.x, o.mesh.position.y - 1.5, o.mesh.position.z]),
+      source: o.data.source || 'unknown',
+      timestamp: o.data.timestamp || o.mesh.userData.createdAt,
+      strength: Math.round(strength * 100) / 100, // 2 decimal precision
+      connections: compactConnections
+    };
+  }
+
   // ─── PERSISTENCE ─────────────────────────────────────
-  function exportIndex() {
+  function exportIndex(options = {}) {
+    const compact = options.compact !== false; // compact by default
     return {
       version: 1,
       exportedAt: new Date().toISOString(),
+      compacted: compact,
       regions: Object.fromEntries(
         Object.entries(REGIONS).map(([k, v]) => [k, { label: v.label, center: v.center, radius: v.radius, color: v.color }])
       ),
-      memories: Object.values(_memoryObjects).map(o => ({
+      memories: Object.values(_memoryObjects).map(o => compact ? _compactMemory(o) : {
         id: o.data.id,
         content: o.data.content,
         category: o.region,
@@ -711,7 +757,7 @@ const SpatialMemory = (() => {
         timestamp: o.data.timestamp || o.mesh.userData.createdAt,
         strength: o.mesh.userData.strength || 0.7,
         connections: o.data.connections || []
-      }))
+      })
     };
   }
 

nexus/mnemosyne/archive.py

@@ -1340,74 +1340,6 @@ class MnemosyneArchive:
         results.sort(key=lambda x: x["score"], reverse=True)
         return results[:limit]
 
-    def discover(
-        self,
-        count: int = 3,
-        prefer_fading: bool = True,
-        topic: Optional[str] = None,
-    ) -> list[ArchiveEntry]:
-        """Serendipitous entry discovery weighted by vitality decay.
-
-        Selects entries probabilistically, with weighting that surfaces
-        neglected/forgotten entries more often (when prefer_fading=True)
-        or vibrant/active entries (when prefer_fading=False). Touches
-        selected entries to boost vitality, preventing the same entries
-        from being immediately re-surfaced.
-
-        Args:
-            count: Number of entries to discover (default 3).
-            prefer_fading: If True (default), weight toward fading entries.
-                If False, weight toward vibrant entries.
-            topic: If set, restrict to entries with this topic (case-insensitive).
-
-        Returns:
-            List of ArchiveEntry, up to count entries.
-        """
-        import random
-
-        candidates = list(self._entries.values())
-
-        if not candidates:
-            return []
-
-        if topic:
-            topic_lower = topic.lower()
-            candidates = [e for e in candidates if topic_lower in [t.lower() for t in e.topics]]
-
-        if not candidates:
-            return []
-
-        # Compute vitality for each candidate
-        entries_with_vitality = [(e, self._compute_vitality(e)) for e in candidates]
-
-        # Build weights: invert vitality for fading preference, use directly for vibrant
-        if prefer_fading:
-            # Lower vitality = higher weight. Use (1 - vitality + epsilon) so
-            # even fully vital entries have some small chance.
-            weights = [1.0 - v + 0.01 for _, v in entries_with_vitality]
-        else:
-            # Higher vitality = higher weight. Use (vitality + epsilon).
-            weights = [v + 0.01 for _, v in entries_with_vitality]
-
-        # Sample without replacement
-        selected: list[ArchiveEntry] = []
-        available_entries = [e for e, _ in entries_with_vitality]
-        available_weights = list(weights)
-
-        actual_count = min(count, len(available_entries))
-        for _ in range(actual_count):
-            if not available_entries:
-                break
-            idx = random.choices(range(len(available_entries)), weights=available_weights, k=1)[0]
-            selected.append(available_entries.pop(idx))
-            available_weights.pop(idx)
-
-        # Touch selected entries to boost vitality
-        for entry in selected:
-            self.touch(entry.id)
-
-        return selected
-
     def rebuild_links(self, threshold: Optional[float] = None) -> int:
         """Recompute all links from scratch.
 

nexus/mnemosyne/cli.py

@@ -392,25 +392,6 @@ def cmd_resonance(args):
         print()
 
 
-def cmd_discover(args):
-    archive = MnemosyneArchive()
-    topic = args.topic if args.topic else None
-    results = archive.discover(
-        count=args.count,
-        prefer_fading=not args.vibrant,
-        topic=topic,
-    )
-    if not results:
-        print("No entries to discover.")
-        return
-    for entry in results:
-        v = archive.get_vitality(entry.id)
-        print(f"[{entry.id[:8]}] {entry.title}")
-        print(f"  Topics: {', '.join(entry.topics) if entry.topics else '(none)'}")
-        print(f"  Vitality: {v['vitality']:.4f} (boosted)")
-        print()
-
-
 def cmd_vibrant(args):
     archive = MnemosyneArchive()
     results = archive.vibrant(limit=args.limit)
@@ -518,11 +499,6 @@ def main():
     rs.add_argument("-n", "--limit", type=int, default=20, help="Max pairs to show (default: 20)")
     rs.add_argument("--topic", default="", help="Restrict to entries with this topic")
 
-    di = sub.add_parser("discover", help="Serendipitous entry exploration")
-    di.add_argument("-n", "--count", type=int, default=3, help="Number of entries to discover (default: 3)")
-    di.add_argument("-t", "--topic", default="", help="Filter to entries with this topic")
-    di.add_argument("--vibrant", action="store_true", help="Prefer alive entries over fading ones")
-
     sn = sub.add_parser("snapshot", help="Point-in-time backup and restore")
     sn_sub = sn.add_subparsers(dest="snapshot_cmd")
     sn_create = sn_sub.add_parser("create", help="Create a new snapshot")
@@ -567,7 +543,6 @@ def main():
         "fading": cmd_fading,
         "vibrant": cmd_vibrant,
         "resonance": cmd_resonance,
-        "discover": cmd_discover,
         "snapshot": cmd_snapshot,
     }
     dispatch[args.command](args)

nexus/mnemosyne/reasoner.py

@@ -0,0 +1,14 @@
+
+class Reasoner:
+    def __init__(self, rules):
+        self.rules = rules
+    def evaluate(self, entries):
+        return [r['action'] for r in self.rules if self._check(r['condition'], entries)]
+    def _check(self, cond, entries):
+        if cond.startswith('count'):
+            # e.g. count(type=anomaly)>3
+            p = cond.replace('count(', '').split(')')
+            key, val = p[0].split('=')
+            count = sum(1 for e in entries if e.get(key) == val)
+            return eval(f"{count}{p[1]}")
+        return False

nexus/mnemosyne/resonance_linker.py

@@ -0,0 +1,22 @@
+
+"""Resonance Linker — Finds second-degree connections in the holographic graph."""
+
+class ResonanceLinker:
+    def __init__(self, archive):
+        self.archive = archive
+
+    def find_resonance(self, entry_id, depth=2):
+        """Find entries that are connected via shared neighbors."""
+        if entry_id not in self.archive._entries: return []
+        
+        entry = self.archive._entries[entry_id]
+        neighbors = set(entry.links)
+        resonance = {}
+
+        for neighbor_id in neighbors:
+            if neighbor_id in self.archive._entries:
+                for second_neighbor in self.archive._entries[neighbor_id].links:
+                    if second_neighbor != entry_id and second_neighbor not in neighbors:
+                        resonance[second_neighbor] = resonance.get(second_neighbor, 0) + 1
+        
+        return sorted(resonance.items(), key=lambda x: x[1], reverse=True)

nexus/mnemosyne/rules.json

@@ -0,0 +1,6 @@
+[
+  {
+    "condition": "count(type=anomaly)>3",
+    "action": "alert"
+  }
+]