公开(公告)号：US20250117589A1

公开(公告)日：2025-04-10

申请号：US18830758

申请日：2024-09-11

Applicant: X DEVELOPMENT LLC

Inventor： Julia Black Ling ,  Alberto Camacho Martinez ,  David Andre ,  Christopher Hahn

IPC: G06F40/30

Abstract: An inverse design system combines a large language model (LLM) with a task-specific optimizer, which includes a search function, a forward model, and a comparator. The LLM adjusts parameters of the optimizer's components in response to a design scenario. Then the optimizer processes the design scenario to produce design candidates. Optionally, the LLM learns from the design candidates in an iterative process. A stochastic predictive modeling system combines an LLM with input distributions and a forward model. The LLM adjusts one or more of the input distributions and/or the forward model in response to a forecast scenario. Then the forward model processes a sampling of the input distributions to produce a forward distribution. Optionally, the LLM informs the sampling process. Optionally, the LLM learns from the forward distribution.

公开(公告)号：US20240143929A1

公开(公告)日：2024-05-02

申请号：US17977681

申请日：2022-10-31

Applicant: X Development LLC

Inventor： Garrett Raymond Honke ,  David Andre ,  Alberto Camacho Martinez ,  Irhum Shafkat

IPC: G06F40/30 ,  G06F8/41 ,  G06N20/00

CPC classification number: G06F40/30 ,  G06F8/436 ,  G06N20/00

Abstract: Disclosed implementations relate to using mutual constraint satisfaction to sample from different stochastic processes and identify coherent inferences across domains. In some implementations, a first domain representation of a semantic concept may be used to conditionally sample a first set of candidate second domain representations of the semantic concept from a first stochastic process. Based on second domain representation(s) of the first set, candidate third domain representations of the semantic concept may be conditionally sampled from a second stochastic process. Based on candidate third domain representation(s), a second set of candidate second domain representations of the semantic concept may be conditionally sampled from a third stochastic process. Pairs of candidate second domain representations sampled across the first and second sets may be evaluated. Based on the evaluation, second domain representation(s) of the semantic concept are selected, e.g., as input for a downstream computer process.