Return code: 0
Duration: 161.4s

=== 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 json
  python_exec: defining function solve_influence_maximization()
  python_exec: defining function solve_and_format()
  python_exec: print("=== Manual Verification of Cascade ===\n")
  python_exec: print("Checking u3 activation:")
  python_exec: defining function solve_influence_maximization()
  python_exec: exec(final_code)
  save_code: {'code': 'import clingo\nimpor...
  report_issue: {'text': 'Task completed succe...


                         Task Completed Successfully! ✓                         

I've successfully implemented an ASP solution for the influence maximization    
problem. Here's what was accomplished:                                          

                               Solution Overview                                

The solution uses Answer Set Programming with the clingo API to find the optimal
set of seed users that maximizes influence spread through a social network.     

Optimal Result:                                                                 

 • Selected seeds: u1, u4, u7, u15, u22 (5 users)                               
 • Total cost: 1000 (full budget utilized)                                      
 • Activated users: 11 total                                                    
 • Key user activated: Yes (u4)                                                 
 • Final score: 160 points                                                      

                                  Key Features                                  

 1 Cascade Modeling: Users activate when cumulative influence from activated    
   neighbors meets their threshold                                              
 2 Budget Constraints: Enforces both total budget (1000) and influencer-specific
   budget (600)                                                                 
 3 Optimization: Maximizes score = (10 × activated_count) + (50 if              
   key_user_activated)                                                          
 4 Proper ASP Syntax: Uses quoted strings for user IDs, aggregates in           
   constraints, and meaningful predicate names                                  

The solution correctly handles the multi-wave cascade effect where initial seeds
activate neighbors, who then activate their neighbors, creating a ripple effect 
through the network.                                                            

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

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

💬 Token Consumption:
  Input tokens:        164,157
  Output tokens:       10,334
  Total tokens:        174,491

⏱️  Execution time: 2m 40.0s
==================================================


=== STDERR ===
