Return code: 0
Duration: 113.8s

=== 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: import clingo
  python_exec: defining function generate_asp_program()
  python_exec: asp_rules = """
  python_exec: defining function on_model()
  python_exec: clique = best_solution
  python_exec: print("Task 7: Verifying solution...")
  python_exec: defining function generate_asp_program()
  save_code: {'code': 'import clingo\nimpor...
  report_issue: {'text': 'All is fine - no iss...


                                    Summary                                     

I've successfully completed the constrained maximum clique problem using Answer 
Set Programming (ASP) with the clingo Python API.                               

                               Solution Overview                                

The solution found the optimal clique of size 6 containing vertices [2, 5, 8,   
11, 14, 17], which satisfies all constraints:                                   

 1 Clique Property: All 15 pairs of vertices in the clique are connected by     
   edges ✓                                                                      
 2 Type Diversity: The clique contains exactly 2 vertices of each type (2 alpha,
   2 beta, 2 gamma) ✓                                                           
 3 Weight Limit: Total weight is 99, which is ≤ 100 ✓                           

                                ASP Model Design                                

The model uses:                                                                 

 • Facts: Vertices with types and weights, bidirectional edges                  
 • Choice Rule: { in_clique(V) } :- vertex(V) to select vertices                
 • Clique Constraint: Forbids selecting non-adjacent vertices                   
 • Type Diversity Constraint: Uses #count aggregate to limit vertices per type  
 • Weight Constraint: Uses #sum aggregate to enforce weight limit               
 • Optimization: #maximize directive to find the largest clique                 

The solution has been saved to 022_clique_finding_hard_task_code.py and produces
the required JSON output format.                                                

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

🔧 Tool Usage:
  python_exec            8 calls
  report_issue           1 calls
  save_code              1 calls

💬 Token Consumption:
  Input tokens:        237,313
  Output tokens:       6,136
  Total tokens:        243,449

⏱️  Execution time: 1m 52.5s
==================================================


=== STDERR ===
2025-10-12 11:48:27,389 - coder.kernel - WARNING - Existing kernel is dead, will restart
