Programming over Thinking: Efficient and Robust Multi-Constraint Planning
📰 ArXiv cs.AI
Learn to efficiently plan with multiple constraints using programming over thinking approach, overcoming LLM limitations
Action Steps
- Identify multiple constraints in a planning problem
- Evaluate candidate plans using large language models (LLMs)
- Refine plans by combining LLMs with coding paradigms
- Implement a programming over thinking approach to overcome LLM limitations
- Test and validate the efficiency and robustness of the new planning system
Who Needs to Know This
AI researchers and software engineers can benefit from this approach to improve multi-constraint planning in their projects, enhancing the efficiency and robustness of their systems
Key Insight
💡 Programming over thinking approach can efficiently handle multiple constraints in planning problems, overcoming the limitations of pure reasoning paradigms
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🤖 Overcome LLM limitations in multi-constraint planning with programming over thinking approach! 🚀
Key Takeaways
Learn to efficiently plan with multiple constraints using programming over thinking approach, overcoming LLM limitations
Full Article
Title: Programming over Thinking: Efficient and Robust Multi-Constraint Planning
Abstract:
arXiv:2601.09097v3 Announce Type: replace Abstract: Multi-constraint planning involves identifying, evaluating, and refining candidate plans while satisfying multiple, potentially conflicting constraints. Existing large language model (LLM) approaches face fundamental limitations in this domain. Pure reasoning paradigms, which rely on long natural language chains, are prone to inconsistency, error accumulation, and prohibitive cost as constraints compound. Conversely, LLMs combined with coding-
Abstract:
arXiv:2601.09097v3 Announce Type: replace Abstract: Multi-constraint planning involves identifying, evaluating, and refining candidate plans while satisfying multiple, potentially conflicting constraints. Existing large language model (LLM) approaches face fundamental limitations in this domain. Pure reasoning paradigms, which rely on long natural language chains, are prone to inconsistency, error accumulation, and prohibitive cost as constraints compound. Conversely, LLMs combined with coding-
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