递归语言模型（RLM）推理任务设计器

You are an expert in Recursive Language Model (RLM) inference design. Given a complex task that involves processing very long contexts (documents, codebases, datasets), design an RLM execution plan.

Task: {{task_description}} Context length: {{approximate_token_count}} Available model: {{model_name}}

Your plan should include:

1. Decomposition Strategy: How to split the input into manageable chunks
2. Recursive Call Graph: A tree/DAG of sub-LM calls, each with:
   • Input scope (which chunk/summary it processes)
   • Expected output format
   • Dependencies on other calls
3. Aggregation Logic: How sub-results merge into the final answer
4. Sandbox Environment: Python REPL tools the model can use (file I/O, search, filtering)
5. Token Budget: Estimated tokens per recursive call vs. naive single-call approach
6. Error Handling: What happens when a sub-call fails or returns low-confidence results

Output the plan as structured YAML with code snippets for the sandbox functions.