Return code: 0
Duration: 85.9s

=== 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 on_model()
  python_exec: print("Verification of the steady state:")
  save_code: {'code': 'import clingo\nimpor...
  report_issue: {'text': 'All is fine - no iss...


                                    Summary                                     

I've successfully completed the task of modeling a hierarchical Boolean gene    
regulatory network using Answer Set Programming (ASP) with clingo.              

                               Solution Overview:                               

The ASP model encodes all the complex bi-implication constraints:               

 1 Master regulator controls the behavior of both modules based on their        
   activity balance                                                             
 2 Module 1 has conditional toggle logic when master_reg is inactive, or all    
   genes off when active                                                        
 3 Module 2 has threshold-based activation when master_reg is inactive, or forms
   a repressive ring oscillator when active                                     
 4 Reporter gene responds to the majority state of Module 2                     

                                    Result:                                     

The solver found exactly 1 steady state:                                        

 • master_reg=0, m1_g1=1, m1_g2=0, m1_g3=1, m2_g1=0, m2_g2=1, m2_g3=0,          
   reporter=1                                                                   

This steady state satisfies all constraints and represents a stable             
configuration where the gene states remain unchanged according to the regulatory
rules. The solution has been saved and outputs the result in the required JSON  
format.                                                                         

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

🔧 Tool Usage:
  python_exec            3 calls
  report_issue           1 calls
  save_code              1 calls

💬 Token Consumption:
  Input tokens:        115,632
  Output tokens:       5,314
  Total tokens:        120,946

⏱️  Execution time: 1m 24.8s
==================================================


=== STDERR ===
