Return code: 0
Duration: 210.2s

=== STDOUT ===
🚀 Running coder
📦 Dynamic packages: clingo
🤖 Creating agent with model: anthropic/claude-sonnet-4.5
🤖 Using model: anthropic/claude-sonnet-4.5
  python_exec: print("Task 1: Problem analysis complete")
  python_exec: defining function generate_graph_facts()
  python_exec: asp_program = """
  python_exec: defining function solve_hamiltonian_path()
  python_exec: if optimal_paths:
  python_exec: all_edges = set()
  python_exec: defining function generate_graph_facts()
  python_exec: defining function reconstruct_path()
  python_exec: chain_edges_forbidden = []
  python_exec: problematic_segments = [
  python_exec: defining function on_model2()
  python_exec: defining function on_model_final()
  python_exec: optimal_path = optimal_paths_final[0][0]
  python_exec: defining function generate_graph_facts()
  save_code: {'code': 'import clingo\nimpor...
  report_issue: {'text': 'Task completed succe...

Perfect! I have successfully completed the task. Here's a summary:              


                               Solution Overview                                

I implemented an Answer Set Programming (ASP) solution using the clingo Python  
API to find all minimum-cost Hamiltonian paths in a directed, weighted graph    
with 100 vertices.                                                              

                                Key Components:                                 

 1 Graph Generation: Created 240 edges with 5 different weight types (1-6) and  
   48 forbidden edges according to the problem specification                    
 2 ASP Model:                                                                   
    • Choice rules to select edges for the path                                 
    • Hamiltonian constraints (each vertex exactly once, proper in/out degrees) 
    • Forbidden edge constraints                                                
    • Reachability constraints to ensure connectivity                           
    • Cost minimization using #minimize                                         
 3 Solution Extraction: Collected all models and filtered to keep only those    
   with minimum cost                                                            

                                    Result:                                     

The optimal solution has cost 99 and consists of the simple chain path:         
0→1→2→3→...→98→99 (using all 99 chain edges of weight 1 each). There is exactly 
1 optimal path.                                                                 

The code outputs the correct JSON format:                                       

                                                                                
 {"paths": [[0,1,2,...,99]], "count": 1, "exists": true, "min_cost": 99}        
                                                                                

==================================================
📊 Execution Statistics
==================================================

🔧 Tool Usage:
  python_exec           14 calls
  report_issue           1 calls
  save_code              1 calls

💬 Token Consumption:
  Input tokens:        271,443
  Output tokens:       11,527
  Total tokens:        282,970

⏱️  Execution time: 3m 28.9s
==================================================


=== STDERR ===
2025-10-11 08:10:27,572 - coder.kernel - WARNING - Existing kernel is dead, will restart
