Here's the revised README.md that accurately reflects the current system architecture and functionality:

SeqCV

System Overview

SeqCV is a sophisticated task execution framework that employs recursive decomposition and multi-agent collaboration to handle complex tasks. The system features:

• Recursive task decomposition for handling complex objectives
• Multi-model fallback mechanism with primary and backup LLM clients
• Quality-controlled execution with iterative refinement
• Result accumulation across recursive levels and agent rounds

Architecture Flow

graph TD
    A[Task Input] --> B[Initial Execution]
    B --> C{Quality Check}
    C -->|Pass| D[Accumulate Results]
    C -->|Fail| E[Task Decomposition]
    E --> F[Subtask 1]
    E --> G[Subtask 2]
    E --> H[Subtask N]
    F & G & H --> I[Combine Results]
    I --> C
    D --> J[Final Output]

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Execution Process

1. Task Initialization

   • Receives objective (e.g., "Write comprehensive AI report")
   • Configures primary and fallback LLM clients

2. Initial Execution Phase

   • Multiple AI agents attempt task completion in parallel
   • Primary LLM client (configurable) handles initial request
   • Automatic fallback to alternative models if primary fails

3. Quality Validation

   • Output evaluated against completion criteria:
     • Content completeness
     • Relevance to objective
     • Structural integrity
   • Pass: Proceeds to result accumulation
   • Fail: Triggers recursive decomposition

4. Recursive Decomposition

   • System SeqCVks task into logical subtasks:
     • Subtask 1 (e.g., "AI historical development")
     • Subtask 2 (e.g., "Current applications")
     • Subtask N (e.g., "Emerging trends")
   • Each subtask repeats the execution cycle

5. Result Handling

   • Per-round accumulation: Agents append new content each round
   • Cross-agent selection: Best result chosen via _select_best_result
   • Recursive accumulation: Results propagate through call stack via accumulated_result parameter

6. Termination Conditions

   • Successful completion (_is_task_complete returns True)
   • Maximum recursion depth reached
   • All fallback options exhausted

Key Features

Multi-Model Resilience

self.llm_configs = [
    {"provider": "openai", "model": Config.GPT_MODEL},  # Primary
    {"provider": "openrouter", "model": "meta-llama/llama-3.1-8b-instruct:free"},  # Fallback 1
    {"provider": "openrouter", "model": "mistralai/mistral-7b-instruct:free"}  # Fallback 2
]

Recursive Result Accumulation

1. Hierarchical Preservation:

   • Each recursion level receives parent's best_result as accumulated_result
   • Final output contains concatenated results from all levels

2. Round-Level Aggregation:

   combined_result = f"{current_results[agent_id]}\n{new_text}".strip()

   • Maintains complete execution history within each recursion level

3. Quality-Based Selection:

   • Employs weighted scoring across multiple dimensions
   • Only highest-quality results propagate through recursion

Implementation Notes

• JSON Processing: Robust handling with comment stripping and validation

  cleaned_json_str = re.sub(r'//.*?$|/\*.*?\*/', '', context, flags=re.MULTILINE)

• Logging: Comprehensive execution tracing

  • Dual output (console + file)
  • Detailed LLM response logging
  • Fallback attempt records

• Customization Points:

  • Config.GPT_MODEL: Primary model selection
  • Config.TEMPERATURE: Creativity control
  • prompt.INIT_SeqCVKDOWN_PROMPT: Task decomposition template

Result Characteristics

The final output inherently:

• Contains all validated intermediate results
• Preserves the highest-quality contributions from each agent
• Maintains logical structure through recursive composition

For concise outputs, implement post-processing to:

1. Remove duplicate content
2. Filter intermediate artifacts
3. Apply final stylistic polish