arXiv:2605.22195v1 Announce Type: new Abstract: Graph of Thoughts (GoT), a generalized form of recent prompting paradigms for large language models (LLMs), has been shown to be useful for elaborate problem solving. By executing a graph of operations, thoughts of the LLM are structured as an arbitrary graph, forming the actual graph of thoughts. Originally, the graph of operations is defined manually, which requires in-depth knowledge about the solution of the problem to solve. Such a static graph of operations is rigid and therefore lacks adaptability. We propose Reinforced Graph of Thoughts (
The increasing complexity of LLM applications necessitates more adaptive and automated prompting methods, moving beyond manual graph definitions.
This development addresses a critical limitation in current LLM prompting, enabling more robust and less bespoke solutions for complex problem-solving with AI.
Prompting for LLMs transitions from rigid, manually defined structures to dynamic, adaptable systems driven by reinforcement learning.
- · AI developers
- · LLM application providers
- · Businesses adopting LLM-driven automation
- · Manual prompting tool developers
- · Firms reliant on static, hardcoded AI workflows
More efficient and sophisticated LLM applications become feasible.
The cost and expertise required for complex LLM deployments are reduced, democratizing advanced AI use.
AI systems gain greater autonomy and adaptability in problem-solving, accelerating the development of advanced AI agents.
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Read at arXiv cs.LG