RT by @_akhaliq: Large Discovery Models: learning where to search next An LLM proposes, a Bayesian…
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TL;DR - Large Discovery Models combine LLM-generated proposals with a Bayesian uncertainty-scoring surrogate in an iterative search loop. The approach reports gains across program discovery, proteins, and molecules, suggesting more efficient exploration of large candidate spaces.
- The LLM proposes candidates while the Bayesian surrogate estimates uncertainty and guides where to search next.
- The iterative loop reportedly cuts validation error by 2.4×.
- Reported domain gains include an 18% improvement in binding energy and more than 60% improvement in molecular objectives.
- Results span multiple discovery settings, though the provided content does not specify methods, baselines, or datasets.
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RT by @_akhaliq: Large Discovery Models: learning where to search next An LLM proposes, a Bayesian…
Public signals
N/A
TL;DR - Large Discovery Models combine LLM-generated proposals with a Bayesian uncertainty-scoring surrogate in an iterative search loop. The approach reports gains across program discovery, proteins, and molecules, suggesting more efficient exploration of large candidate spaces.
- The LLM proposes candidates while the Bayesian surrogate estimates uncertainty and guides where to search next.
- The iterative loop reportedly cuts validation error by 2.4×.
- Reported domain gains include an 18% improvement in binding energy and more than 60% improvement in molecular objectives.
- Results span multiple discovery settings, though the provided content does not specify methods, baselines, or datasets.