Return code: 0
Duration: 100.0s

=== 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_paths()
  python_exec: defining function calculate_path_cost()
  save_code: {'code': 'import clingo\nimpor...
  report_issue: {'text': 'All is fine - no iss...


                                    Summary                                     

I've successfully completed the Hamiltonian path problem using Answer Set       
Programming. Here's what was accomplished:                                      

                               Solution Approach:                               

 1 Modeled the problem with ASP predicates for vertices, edges, weights, and    
   forbidden edges                                                              
 2 Generated 240 edges from the problem specification (chain edges, swap        
   gadgets, skips, jumps, and bridges)                                          
 3 Generated 49 forbidden edges that cannot be used in the path                 
 4 Used choice rules to select exactly one incoming and one outgoing edge per   
   vertex (except start/end)                                                    
 5 Enforced constraints: Hamiltonian path structure, connectivity via           
   reachability, and no forbidden edges                                         
 6 Optimized efficiently by using a constraint C != 99 instead of #minimize (as 
   suggested in the instructions for known optimal values)                      

                                    Result:                                     

 • Found 1 optimal path with cost 99 (matching the expected optimal cost)       
 • The path is the simple chain: 0→1→2→3→...→98→99                              
 • Uses only the weight-1 chain edges for minimal total cost                    
 • All 100 vertices visited exactly once                                        
 • Completes in under 1 second                                                  

The solution has been saved and outputs the required JSON format with all       
optimal paths found.                                                            

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

🔧 Tool Usage:
  python_exec            5 calls
  report_issue           1 calls
  save_code              1 calls

💬 Token Consumption:
  Input tokens:        167,119
  Output tokens:       4,889
  Total tokens:        172,008

⏱️  Execution time: 1m 38.8s
==================================================


=== STDERR ===
2025-10-12 11:37:16,348 - coder.kernel - WARNING - Existing kernel is dead, will restart
