Return code: 0
Duration: 135.4s

=== STDOUT ===
🚀 Running coder
📦 Dynamic packages: clingo
🤖 Creating agent with model: anthropic/claude-sonnet-4.5
🤖 Using model: anthropic/claude-sonnet-4.5
  python_exec: import clingo
  python_exec: defining function solve_quantum_nim()
  python_exec: optimal_moves = [move for move in valid_moves if m...
  python_exec: print("Verifying moves:")
  python_exec: expected_move2_piles = [6, 10, 13]  # After removi...
  python_exec: output = {
  python_exec: defining function solve_quantum_nim()
  save_code: {'code': 'import clingo\nimpor...
  report_issue: {'text': 'Task completed succe...


                                    Summary                                     

I've successfully completed the Quantum Nim task using Answer Set Programming   
with the clingo Python API. The solution:                                       

                                 Key Features:                                  

 1 Generates all valid moves - Both standard moves (removing stones) and split  
   moves (dividing even piles)                                                  
 2 Enforces constraints - Ensures no duplicate pile sizes in resulting          
   configurations                                                               
 3 Calculates nim-sum - Identifies optimal moves that lead to nim-sum of 0      
 4 Finds 3 optimal standard moves:                                              
    • Remove 6 stones from pile 0 → [7, 10, 13]                                 
    • Remove 6 stones from pile 1 → [1, 6, 10, 13]                              
    • Remove 2 stones from pile 3 → [6, 7, 10, 11]                              

                              ASP Model Structure:                              

 • Facts: Initial pile configurations                                           
 • Choice rules: Generate possible standard and split moves                     
 • Constraints: Prevent duplicate pile sizes, ensure mutual exclusivity of move 
   types                                                                        
 • Post-processing: Calculate nim-sum in Python and filter for optimal moves    

The solution correctly handles the constraint that no two piles can have the    
same size after a move, which is critical for finding valid optimal moves.      

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

🔧 Tool Usage:
  python_exec            7 calls
  report_issue           1 calls
  save_code              1 calls

💬 Token Consumption:
  Input tokens:        220,767
  Output tokens:       7,127
  Total tokens:        227,894

⏱️  Execution time: 2m 14.3s
==================================================


=== STDERR ===
