CONCURRENT COMMUNICATION IN DISTRIBUTED SERVER SYSTEM FOR NETWORKED CONTENT RETRIEVAL AND GRAPH NETWORK DATA TRANSMISSION

1 . A system for distributed content retrieval and language model augmentation, the system comprising one or more processors and one or more non-transitory, machine-readable media storing program instructions causing the one or more processors to perform operations comprising:

storing, in a computing system, a distributed representation system comprising a first set of content representations and a second set of content representations;

generating a query representation based on a query;

retrieving an intermediate set of representations by using a retriever graph neural network to detect similarity scores between the query representation and the content representations;

using a re-ranking graph neural network, distinct from the retriever graph neural network and configured with greater representational capacity than the retriever graph neural network, to re-rank similarities for the intermediate set of representations to select a ranked subset;

determining an input context comprising selected content associated with the ranked subset; and

obtaining a language model response by providing the query and the input context to a large language model.

2 . The system of claim 1 , wherein the greater representational capacity comprises at least one of:

a greater number of neural network layers;

a greater number of neural units per layer;

a more granular max pooling configuration; or

an attention mechanism.

3 . The system of claim 1 , wherein the first set of content representations and the second set of content representations comprise vector embeddings generated by a transformer-based encoder model.

4 . The system of claim 1 , wherein the re-ranking graph neural network utilizes differentiable pooling to summarize graph structures from the intermediate set of representations.

5 . The system of claim 1 , the operations further comprising:

obtaining an indication of a context window size for the large language model; and

adjusting a size of the input context to satisfy the context window size by filtering the ranked subset.

6 . The system of claim 1 , wherein the content representations are graph-based representations generated using a transformation that preserves semantic relationships between tokens, and wherein the content representations are usable to reconstruct original documents.

7 . The system of claim 6 , wherein the transformation comprises a lossless operation including at least one of a universal dependencies (UD) operation, an abstract meaning representation (AMR) operation, or an abstract syntax tree (AST) operation.

8 . The system of claim 7 , wherein determining the input context comprises applying a reversible transformation function to the ranked subset to reconstruct text data.

9 . The system of claim 1 , wherein the computing system comprises a networked plurality of servers, and wherein retrieving the intermediate set of representations comprises concurrently retrieving subsets from a first server and a second server in parallel.

10 . A computer-implemented method for high-fidelity content reconstruction for language models, the method comprising:

generating lossless content graphs from a plurality of documents using a reversible transformation;

identifying a subset of the lossless content graphs based on a similarity between the subset and a query;

reconstructing a set of retrieved documents from the subset of the lossless content graphs by applying a reversible transformation function to the lossless content graphs; and

transmitting an input to a large language model, the input comprising the query and an input context comprising the set of retrieved documents.

11 . The method of claim 10 , wherein the reversible transformation comprises a bijective mapping between natural language tokens and graph nodes to ensure semantic data integrity.

12 . The method of claim 10 , wherein the lossless content graphs comprise directed acyclic graph structures.

13 . The method of claim 10 , further comprising ranking the subset of the lossless content graphs using a re-ranking graph neural network configured with an attention mechanism.

14 . One or more non-transitory, machine-readable media storing instructions that, when executed by one or more processors, cause the one or more processors to:

store a plurality of content representations in a set of databases;

execute a clustering algorithm to determine one or more clusters of the content representations;

select a target cluster from the one or more clusters based on a distance to a query representation;

re-rank content within the target cluster using a graph neural network to select a ranked subset; and

generate a language model response based on the query and content associated with the ranked subset.

15 . The media of claim 14 , wherein the clustering algorithm comprises a k-means clustering algorithm.

16 . The media of claim 14 , wherein the graph neural network is executed using a set of graphics processing units (GPUs).

17 . The media of claim 16 , wherein re-ranking the content comprises distributing graph neural network kernels across streaming multiprocessors of the GPUs to parallelize tensor calculations.

18 . The media of claim 14 , wherein the instructions further cause the one or more processors to communicate graph identifiers and similarity values between nodes of a distributed network without transferring full content representations during the re-ranking.

19 . The system of claim 1 , the operations further comprising:

obtaining network traffic data representing current traffic patterns of a distributed network; and

executing a technical control action by automatically adjusting routing configurations of the distributed network based on the language model response.

20 . The method of claim 10 , further comprising executing a downstream technical action based on the language model response, the downstream technical action comprising at least one of automatically generating executable code or triggering a financial transaction safeguard.