Arte IA: The user's paper has been accepted to CoLM, a new NeurIPS-level conference for LLMs. The paper introduces **StateFlow**, a framework to control LLMs for complex tasks with 5x cost reduction compared to ReAct on ALFWorld, while achieving significant performance improvements. Key points include: 1. **Cost Reduction**: StateFlow decomposes tasks into sub-prompts, reducing token usage and improving accuracy. 2. **Improved Task-Solving**: By treating complex tasks as state machines, it separates task grounding (states) from sub-task solving (actions), improving LLM reasoning. 3. **Self-Correction**: Though the "Verify" state increases success rates, its impact is limited, showing that while LLMs aren't yet self-correcting, StateFlow helps. 4. **Future Enhancements**: Combining StateFlow with iterative refining methods like Reflexion or adding active learning could further boost performance. StateFlow is already integrated with PyAutoGen, receiving positive feedback for its noticeable performance improvements. The paper scored in the top 10% at CoLM.

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The user's paper has been accepted to CoLM, a new NeurIPS-level conference for LLMs. The paper introduces **StateFlow**, a framework to control LLMs for complex tasks with 5x cost reduction compared to ReAct on ALFWorld, while achieving significant performance improvements. Key points include: 1. **Cost Reduction**: StateFlow decomposes tasks into sub-prompts, reducing token usage and improving accuracy. 2. **Improved Task-Solving**: By treating complex tasks as state machines, it separates task grounding (states) from sub-task solving (actions), improving LLM reasoning. 3. **Self-Correction**: Though the "Verify" state increases success rates, its impact is limited, showing that while LLMs aren't yet self-correcting, StateFlow helps. 4. **Future Enhancements**: Combining StateFlow with iterative refining methods like Reflexion or adding active learning could further boost performance. StateFlow is already integrated with PyAutoGen, receiving positive feedback for its noticeable performance improvements. The paper scored in the top 10% at CoLM.
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The user's paper has been accepted to CoLM, a new NeurIPS-level conference for LLMs. The paper introduces **StateFlow**, a framework to control LLMs for complex tasks with 5x cost reduction compared to ReAct on ALFWorld, while achieving significant performance improvements. Key points include: 1. **Cost Reduction**: StateFlow decomposes tasks into sub-prompts, reducing token usage and improving accuracy. 2. **Improved Task-Solving**: By treating complex tasks as state machines, it separates task grounding (states) from sub-task solving (actions), improving LLM reasoning. 3. **Self-Correction**: Though the "Verify" state increases success rates, its impact is limited, showing that while LLMs aren't yet self-correcting, StateFlow helps. 4. **Future Enhancements**: Combining StateFlow with iterative refining methods like Reflexion or adding active learning could further boost performance. StateFlow is already integrated with PyAutoGen, receiving positive feedback for its noticeable performance improvements. The paper scored in the top 10% at CoLM.

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