Return code: 0
Duration: 117.2s

=== 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 generate_asp_facts()
  python_exec: asp_program = """
  python_exec: defining function on_model()
  python_exec: output = json.dumps(best_solution, indent=2)
  python_exec: print("=== SOLUTION VERIFICATION ===\n")
  python_exec: print("=== DETAILED CASCADE VERIFICATION ===\n")
  save_code: {'code': 'import clingo\nimpor...
  report_issue: {'text': 'Task completed succe...


                                    Summary                                     

I've successfully completed the social network influence maximization task using
Answer Set Programming with clingo. Here's what was accomplished:               

                               Solution Overview                                

 • Selected Seeds: u1, u4, u7, u15, u22 (5 users, using full budget of 1000)    
 • Total Activated: 11 users (5 seeds + 6 cascade activations)                  
 • Final Score: 160 points (110 from activated users + 50 key user bonus)       
 • Key User (u4): Successfully activated                                        

                                 Cascade Effect                                 

The solution demonstrates effective network influence:                          

 1 Initial seeds activate immediately                                           
 2 First wave: u2, u5, u8, u16 activate from direct influence                   
 3 Second wave: u3, u9 activate from combined influences                        

                               ASP Model Features                               

 • Choice rules for seed selection with budget constraints                      
 • Aggregate-based influence calculation using #sum                             
 • Cascade activation logic with threshold checking                             
 • Multi-objective optimization (reach + key user bonus)                        
 • Efficient constraint propagation                                             

The solution maximizes influence spread while respecting all constraints and    
uses the full budget optimally.                                                 

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

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

💬 Token Consumption:
  Input tokens:        227,781
  Output tokens:       6,904
  Total tokens:        234,685

⏱️  Execution time: 1m 56.2s
==================================================


=== STDERR ===
