Return code: 0
Duration: 109.4s

=== 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: asp_program = """
  python_exec: defining function on_model()
  python_exec: print("\nFinal Solution:")
  save_code: {'code': 'import clingo\nimpor...
  python_exec: executing file 064_social_network_influence_hard_task_code.py
  report_issue: {'text': 'All is fine - no iss...

Perfect! I've successfully completed the task. Here's a summary:                


                               Solution Overview                                

The ASP program models the influence maximization problem by:                   

 1 Seed Selection: Uses a choice rule to select up to 5 users as seeds within   
   the budget constraint                                                        
 2 Cascade Activation: Implements the influence propagation logic where users   
   become activated when incoming influence from already-activated neighbors    
   meets their activation threshold                                             
 3 Optimization: Maximizes the composite score (10 points per activated user +  
   50 bonus if key user u4 is activated)                                        


                             Optimal Solution Found                             

 • Selected Seeds: u1, u4, u7, u15, u22                                         
 • Total Cost: 1000 (uses full budget)                                          
 • Activated Users: 11 users total (seeds + cascade)                            
 • Key User Activated: Yes (u4)                                                 
 • Final Score: 160                                                             

The cascade successfully propagates through the network:                        

 • u1 → activates u2 (70 strength ≥ 60 threshold) and u5 (with u22: 50+30=80 ≥  
   70)                                                                          
 • u7 → activates u8 (50 ≥ 40) and u9 (with u8: 30+60=90 ≥ 80)                  
 • u15 → activates u16 (60 ≥ 50)                                                
 • u2 and u8 → together activate u3 (40+50=90 ≥ 90)                             

All constraints are satisfied and the solution is optimal.                      

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

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

💬 Token Consumption:
  Input tokens:        135,964
  Output tokens:       6,127
  Total tokens:        142,091

⏱️  Execution time: 1m 48.1s
==================================================


=== STDERR ===
