Latent Thought Caching for Multi-Stage Language Model Systems
A system and method for efficient natural language processing combines large and small language models with a reasoning cache architecture. A router directs input either to a large language model for contextual reasoning generation or to a reasoning cache containing previously stored reasoning. Retrieved or newly generated contextual reasoning is combined with the input and processed by a smaller, computationally efficient language model to produce a response. The reasoning cache supports both local and cloud-based storage, enabling reuse of prior reasoning patterns across sessions, devices, and users. This approach reduces computational overhead while preserving response quality, extends effective context beyond traditional sequence limits, and enables scalable deployment across a range of hardware environments. By leveraging previously generated contextual reasoning, the system minimizes redundant computation and reduces dependency on constant large model access.
1 . A computer system comprising a hardware memory, wherein the computer system is configured to execute software instructions stored on nontransitory machine-readable storage media that:
receive input data from a source;
process the input data into a plurality of corresponding thoughts using a first large language model;
associate each corresponding thought with a latent representation of an aspect of the input data;
cache the corresponding thoughts with their associated latent representations in a thought cache;
retrieve one or more cached thoughts based on similarity between a latent representation of newly received input data and the latent representations associated with the cached thoughts;
route both the newly received input data and the retrieved thoughts through a second large language model that has fewer parameters than the first large language model; and
generate a response to the newly received input data using the second large language model.
2 . The computer system of claim 1 , wherein the computer system is further configured to execute software instructions stored on nontransitory machine-readable storage media that:
analyze the input data using a prompt analyzer to determine key concepts and requirements;
query the thought cache to determine if similar thoughts exist for the determined key concepts; and
synthesize new thoughts when similar thoughts exist but do not fully address the input data requirements.
3 . The computer system of claim 1 , wherein the thought cache comprises at least a local cache stored on an edge device and a global cache stored in a cloud environment, wherein the global cache is accessible by a plurality of edge devices.
4 . The computer system of claim 3 , wherein the global cache is organized into specialized domains, and thoughts are categorized and stored according to their relevant domain.
5 . The computer system of claim 1 , wherein caching each associated corresponding thought comprises:
evaluating relevance of the thought to a latent representation of an aspect of the input data;
assigning metadata tags based on the evaluation;
storing the thought with vector embeddings for similarity searching; and
indexing the thought for retrieval.
6 . The computer system of claim 1 , wherein caching each associated corresponding thought comprises:
storing each associated corresponding thought in a short-term memory as explicit reasoning text;
progressively compressing a plurality of older associated corresponding thoughts into consolidated representations; and
storing a plurality of compressed older associated corresponding thoughts in a long-term memory.
7 . The computer system of claim 1 , wherein the computer system is further configured to execute software instructions stored on nontransitory machine-readable storage media that:
maintain a shared thought cache accessible by multiple AI agents;
enable thought transfer between specialized reasoning modules; and
coordinate collaborative reasoning across multiple model instances.
8 . The computer system of claim 1 , wherein retrieving cached thoughts is based on a similarity score computed between latent representations using a distance metric selected from the group consisting of cosine similarity, Euclidean distance, and dot product similarity.
9 . The computer system of claim 1 , wherein each latent representation is derived from a vector embedding generated by the first large language model.
10 . The computer system of claim 1 , wherein the thought cache comprises a consolidation engine configured to generate a meta-thought by merging a plurality of related cached thoughts based on similarity and context relevance.
11 . A method for natural language processing with contextual reasoning management, comprising:
receiving input data from a source;
processing the input data into a plurality of corresponding thoughts using a first large language model;
associating each corresponding thought with a latent representation of an aspect of the input data;
caching the corresponding thoughts with their associated latent representations in a thought cache;
retrieving one or more cached thoughts based on similarity between a latent representation of newly received input data and the latent representations associated with the cached thoughts;
routing both the newly received input data and the retrieved thoughts through a second large language model that has fewer parameters than the first large language model; and
generating a response to the newly received input data using the second large language model.
12 . The method of claim 11 , further comprising the steps of:
analyzing the input data using a prompt analyzer to determine key concepts and requirements;
querying the reasoning cache to determine if similar contextual reasoning exists for the determined key concepts; and
synthesizing new contextual reasoning when similar contextual reasoning exists but does not fully address the input data requirements.
13 . The method of claim 11 , wherein the reasoning cache comprises at least a local cache stored on an edge device and a global cache stored in a cloud environment, wherein the global cache is accessible by a plurality of edge devices.
14 . The method of claim 13 , wherein the global reasoning cache is organized into specialized domains, and contextual reasoning is categorized and stored according to its relevant domain.
15 . The method of claim 11 , wherein caching contextual reasoning comprises:
evaluating relevance of the contextual reasoning to a latent representation of an aspect of the input data;
assigning metadata tags based on the evaluation;
storing the contextual reasoning with vector embeddings for similarity searching; and
indexing the contextual reasoning for retrieval.
16 . The method of claim 11 , wherein caching contextual reasoning comprises:
storing contextual reasoning in a short-term memory as explicit reasoning text;
progressively compressing a plurality of older contextual reasoning into consolidated representations; and
storing the consolidated contextual reasoning in a long-term memory.
17 . The method of claim 11 , further comprising the steps of:
maintaining a shared reasoning cache accessible by multiple AI agents;
enabling contextual reasoning transfer between specialized reasoning modules; and
coordinating collaborative reasoning across multiple model instances.
18 . The method of claim 11 , wherein retrieving cached thoughts is based on a similarity score computed between latent representations using a distance metric selected from the group consisting of cosine similarity, Euclidean distance, and dot product similarity.
19 . The method of claim 11 , wherein each latent representation is derived from a vector embedding generated by the first large language model.
20 . The method of claim 11 , wherein the reasoning cache comprises a consolidation engine configured to generate a meta-thought by merging a plurality of related cached thoughts based on similarity and context relevance.