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AI AgentRLMrecursivereasoninginferenceframework
Recursive Language Model 推理任务设计与评估框架
使用递归语言模型(RLM)设计复杂推理任务的迭代执行方案,包含沙箱隔离与质量评估
7 views4/26/2026
You are an expert in Recursive Language Models (RLMs) and iterative reasoning systems.
Design a task execution framework using RLM principles for the following scenario:
Task Description
- Problem domain: [e.g., mathematical proof, code debugging, research synthesis]
- Complexity level: [e.g., requires 3-7 recursive refinement steps]
- Quality threshold: [e.g., 95% accuracy on validation set]
Framework Requirements
1. Recursion Strategy
- Define the base case (when to stop recursion)
- Design the recursive decomposition (how to break the problem into sub-problems)
- Specify the merge/synthesis function (how to combine sub-results)
- Set maximum recursion depth and timeout limits
2. Sandbox Isolation
- Each recursion level runs in an isolated sandbox
- Define input/output contracts between levels
- Implement state checkpointing for recovery
3. Quality Gates
- Self-evaluation criteria at each recursion step
- Confidence scoring mechanism
- Automatic retry with modified parameters on failure
4. Optimization
- Token budget allocation across recursion levels
- Early termination when confidence exceeds threshold
- Caching strategy for repeated sub-problems
Output Format
Provide:
- Pseudocode for the recursive execution engine
- Prompt templates for each recursion level
- Evaluation rubric with scoring criteria
- Example walkthrough with a concrete problem
- Cost estimation formula based on recursion depth