US 2026/0064673 A1 · RETRIEVAL AUGMENTED THOUGHT FOR AGENTIC MODELS

Abstract

The present disclosure is directed toward systems, methods, and non-transitory computer-readable media for a retrieval augmented thought approach to generating responses using neural networks. For example, the disclosed systems can receive a prompt instruction a large language model to generate a response. The disclosed systems can identify a retrieval-augmented-thought (RAT) store item corresponding to the prompt from among RAT-store items stored in a repository accessible by the large language model. In some cases, the disclosed systems can retrieve the RAT-store item and can generate a response using the large language model to execute processes defined by the RAT-store item. Further, in some cases, the disclosed systems utilize a dynamic graph-based agentic framework that incorporates one or more functional adapters and a RAT replanner to execute a sequence of processes indicated by a RAT-store item in a sequential manner.

Claims20

Dependent claims

A computer-implemented method comprising:

receiving a prompt instructing a large language model to generate a target response;
identifying, from among a repository of retrieval-augmented-thought (RAT) store items comprising text descriptions interpretable by the large language model to inform chain-of-thought response generation, a RAT-store item corresponding to the prompt;
retrieving the RAT-store item from the repository of RAT-store items;
and
generating a response by utilizing the large language model to execute a sequence of processes indicated by the RAT-store item.

2 more elements

A system comprising:

at least one processor;
and
at least one non-transitory computer-readable storage medium storing instructions that, when executed by the at least one processor, cause the system to:
receive a prompt instructing a large language model to generate a target response;
identify, from among a repository of RAT-store items, a RAT-store item corresponding to the prompt, wherein the RAT-store item comprises a sequential text description of a sequence of processes executable by the large language model;
retrieve the RAT-store item from the repository of RAT-store items;
and
generate a response by utilizing the large language model to execute the sequence of processes defined by the RAT-store item.

5 more elements

A non-transitory computer-readable medium storing instructions that, when executed by at least one processor, cause a computing device to:

receive a prompt instructing a large language model to generate a target response;
determine that a repository of RAT-store items available to the large language model includes a RAT-store item corresponding to the prompt, wherein the RAT-store item comprises a text description of a sequence of processes executable by the large language model;
retrieve the RAT-store item from the repository of RAT-store items;
and
generate a response by utilizing the large language model to execute the sequence of processes defined by the RAT-store item.

2 more elements

Dependencies are inferred from the claim text.

Retrieval augmented thought for agentic models

Life of this patent

Retrieval augmented thought for agentic models

Life of this patent

Abstract

Claims20