Return code: 0
Duration: 98.4s

=== STDOUT ===
🚀 Running coder
📦 Dynamic packages: clingo
🤖 Creating agent with model: anthropic/claude-sonnet-4.5
🤖 Using model: anthropic/claude-sonnet-4.5
  python_exec: print("Task 1: Problem Analysis")
  python_exec: defining function solve_magic_square()
  python_exec: defining function verify_solution()
  python_exec: print("Manual Verification of Solution:")
  save_code: {'code': 'import clingo\nimpor...
  report_issue: {'text': 'All is fine - no iss...


                                    Summary                                     

I've successfully completed the magic square task using Answer Set Programming  
with clingo. Here's what was accomplished:                                      

                                Solution Found:                                 

                                                                                
 [4, 15, 9, 6]                                                                  
 [5, 10, 16, 3]                                                                 
 [14, 1, 7, 12]                                                                 
 [11, 8, 2, 13]                                                                 
                                                                                

                           All Constraints Satisfied:                           

✅ Magic sums: All rows, columns, and both diagonals sum to 34 ✅ Symmetrical   
pairs: All opposite cells sum to 17 ✅ Quadrant sums: All four 2×2 quadrants sum
to 34 ✅ Prime placement: Small primes {2,3,5,7} avoid all corner cells ✅      
Uniqueness: Each value 1-16 appears exactly once                                

                             ASP Model Highlights:                              

 • Used choice rules to assign exactly one value to each cell                   
 • Enforced uniqueness with bidirectional constraints                           
 • Used aggregate #sum for efficient sum constraints                            
 • Constrained symmetrical pairs by checking only half the pairs (R1 ≤ 2)       
 • Defined quadrants using range conditions in aggregates                       
 • Forbade small primes in corners using explicit corner facts                  

The solution was found quickly and verified to be completely valid.             

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

🔧 Tool Usage:
  python_exec            4 calls
  report_issue           1 calls
  save_code              1 calls

💬 Token Consumption:
  Input tokens:        152,197
  Output tokens:       5,717
  Total tokens:        157,914

⏱️  Execution time: 1m 37.4s
==================================================


=== STDERR ===
