Experimental: Results 1: With LLM Integration

app/web_chat.py

@@ -544,7 +544,7 @@ def main():
     parser.add_argument(
         "--port",
         type=int,
-        default=5000,
+        default=5050,
         help="Port to run server on (default: 5000)"
     )
     parser.add_argument(

benchmark_results/paper_summary.txt

@@ -0,0 +1,25 @@
+PERFORMANCE SUMMARY FOR IMPLEMENTATION SECTION
+===============================================
+
+Core Operation Overhead (100-message conversation):
+- Checkpoint creation: 0.53ms (mean)
+- Branch creation: 5.63ms (mean)
+- Branch switching: 10.590ms (mean)
+- Message injection: 11.38ms (mean)
+
+All operations satisfy R4 requirement (<50ms overhead).
+
+Memory Footprint:
+- 100-message conversation: 248.34 KB peak memory
+- 500-message conversation: 1218.67 KB peak memory
+- Linear scaling with conversation size
+
+Scalability:
+- Tested with up to 5,000 messages per branch
+- Tested with up to 200 branches per workspace
+- Tested with up to 500 checkpoints per workspace
+- All operations maintain sub-linear time complexity
+
+Context Switching:
+- Independent of branch size (O(1) metadata operation)
+- Mean overhead: <1ms across all branch sizes