Prior or Feedback? What an LLM Uses When Adapting Neural Operators
Do LLM scientific agents rely only on their initial task context, or do they adapt their decisions in response to experimental feedback?
ProofPaper ↗
Key points
- We study this question in neural operator adaptation, where a large language model (LLM) selects fine-tuning configurations under a limited trial budget.
- Across transfers within and between partial differential equation (PDE) families, the LLM achieves lower held-out test nRMSE than random search and Bayesian optimisation in nearly every matched comparison.
- A complementary cold-start intervention shows that the selected base learning rate shifts with the PDE description.
- These interventions establish that the LLM's decision-level actions respond to the given task and observed outcomes, showing that it combines a task-dependent prior with sensitivity to experimental feedback.
Sources (1)
- [1]Prior or Feedback? What an LLM Uses When Adapting Neural OperatorsarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 8, 05:06 PM
Do LLM scientific agents rely only on their initial task context, or do they adapt their decisions in response to experimental feedback?
We study this question in neural operator adaptation, where a large language model (LLM) selects fine-tuning configurations under a limited trial budget.
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