Unverified50% confidenceFactExact time
Changing the reasoning effort (e.g., requiring step-by-step reasoning or adjusting the computation budget) had little effect on LLM accuracy on the molecular property prediction task.
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11/2/2026
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UnverifiedFor molecules whose correct values the models could recall, the LLMs reported lower uncertainty, indicating that the models exhibit overconfidence when relying on memory.72% similarUnverifiedThe implications for LLM engineering practice include: do not directly treat the probabilities output by the model as decision confidence; in scenarios where handoff to a human is not possible, a conservative fallback strategy should be explicitly designed; threshold selection requires statistical guarantees.71% similarUnverifiedMost existing LLM molecular optimization methods focus on single-turn generation, ignoring the fact that real medicinal chemistry optimization is usually multi-turn iterative.69% similarUnverifiedThe reasoning effort parameter does not change the model weights; it only changes the allocation of compute budget at inference time.69% similar
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