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Mitigating Non-Determinism in LLM-Driven Graph Mutations

The use of Large Language Models (LLMs) to modify execution graphs at runtime introduces non-determinism, potentially causing infinite loops, debugging nightmares, and permanent graph corruption. The challenge is to achieve a semi-deterministic approach when mutating graphs at runtime while avoiding the pitfalls of LLM hallucinations and misinterpretations.

Computing & Technology, Computer Science, Artificial Intelligence