Return code: 0
Duration: 84.4s

=== STDOUT ===
🚀 Running coder
📦 Dynamic packages: clingo
🤖 Creating agent with model: anthropic/claude-sonnet-4.5
🤖 Using model: anthropic/claude-sonnet-4.5
  python_exec: vertices = list(range(16))
  python_exec: defining function solve_graph_partition()
  python_exec: defining function create_final_solution()
  python_exec: inter_cluster_edges = [(3, 4, 1), (7, 8, 2), (11, ...
  save_code: {'code': 'import clingo\nimpor...
  python_exec: executing file 054_graph_partitioning_hard_task_code.py
  report_issue: {'text': 'All is fine - no iss...


                                    Summary                                     

I've successfully completed the graph partitioning task using Answer Set        
Programming with the clingo Python API.                                         

Solution Approach:                                                              

 1 Modeled the problem with ASP predicates for vertices, edges, and partition   
   assignments                                                                  
 2 Used choice rules to assign each vertex to exactly one partition             
 3 Added cardinality constraints to ensure balanced partitions (4 vertices each)
 4 Defined cut edges as edges with endpoints in different partitions            
 5 Used #minimize to optimize the total cut weight                              

Results:                                                                        

 • Optimal cut weight achieved: 13 (matching the expected minimum)              
 • The solution keeps the natural clusters together:                            
    • Partition 1: [4, 5, 6, 7]                                                 
    • Partition 2: [0, 1, 2, 3]                                                 
    • Partition 3: [8, 9, 10, 11]                                               
    • Partition 4: [12, 13, 14, 15]                                             
 • All 7 inter-cluster edges are cut (total weight: 1+1+2+2+3+3+1 = 13)         
 • Output properly formatted as JSON with all required fields                   

The solution is saved in 054_graph_partitioning_hard_task_code.py and executes  
efficiently.                                                                    

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

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

💬 Token Consumption:
  Input tokens:        97,818
  Output tokens:       4,941
  Total tokens:        102,759

⏱️  Execution time: 1m 23.3s
==================================================


=== STDERR ===
