System and method for adaptive orchestration of multi-model processing and response generation

1 . A computing system for orchestrated multi-model processing and response generation, the computing system comprising:

a processor; and

a memory storing instructions that, when executed by the processor, cause the computing system to:

receive multimodal input data comprising at least one of text data, audio data, video data, or gesture data;

generate structured signals from the multimodal input data;

distribute the structured signals to a plurality of specialized learner models;

execute each of the plurality of specialized learner models, each comprising a small language model trained for domain-specific reasoning, to generate respective domain-specific outputs;

execute a large language model agent configured for generalized processing to generate a generalized output;

apply arbitration logic to resolve conflicts among the respective domain-specific outputs and the generalized output;

generate arbitration results using the arbitration logic;

integrate curated dataset inputs and validation feedback into the arbitration results;

generate a synthesized response from the arbitration results, the curated dataset inputs, and the validation feedback, wherein generating the synthesized response includes:

generating a text output as a first response modality;

generating an audio output as a second response modality;

generating audio directives and visual directives from the synthesized response;

mapping tokens from the synthesized response to viseme cues and gesture cues to generate mapped directives;

time-aligning the audio directives and the mapped directives to generate aligned directives;

generating a digital personal display output as a third response modality using the aligned directives; and

transmit the synthesized response for presentation to a user.

2 . The computing system of claim 1 , wherein generating structured signals from the multimodal input data comprises:

normalizing modality-specific streams to generate normalized streams in a common representation format;

extracting features from the normalized streams to generate extracted features, wherein the extracted features comprise at least one of linguistic tokens, audio embeddings, visual frame descriptors, or gesture vectors;

mapping the extracted features to a temporal index to generate temporally aligned features;

propagating the temporally aligned features into a schema-conformant representation to generate schema-conformant features; and

generating structured signals conformant to the schema-conformant features.

3 . The computing system of claim 2 , wherein parameterizing subsequent generation requests comprises:

generating a summary vector from a time-ordered sequence of prior inputs and prior outputs;

retrieving a persona identifier associated with a session to generate an active persona identifier;

retrieving dependency-graph data associated with a workflow to generate dependency-graph metadata; and

composing a context payload including the summary vector, the active persona identifier, and the dependency-graph metadata.

4 . The computing system of claim 1 , wherein processing to generate the synthesized response further comprises applying interrogation logic, the applying of the interrogation logic comprising:

performing contradiction tests between candidate predictive outputs to generate detected inconsistencies;

performing coverage analysis by comparing the candidate predictive outputs against a requirement schema to generate unfulfilled schema fields and unsatisfied constraint conditions; and

generating interrogation prompts from the detected inconsistencies, the unfulfilled schema fields, and the unsatisfied constraint conditions.

5 . The computing system of claim 4 , wherein evaluating candidate predictive outputs comprises:

computing natural-language inference scores between the candidate predictive outputs to generate inference score results;

computing a coverage score by comparing the candidate predictive outputs against the requirement schema;

applying a threshold to the coverage score to generate a thresholded coverage result; and

emitting a targeted prompt requesting a slot value defined in the requirement schema and identified as unfulfilled based on the thresholded coverage result.

6 . The computing system of claim 1 , wherein the arbitration logic assigns a priority to a predictive output by:

retrieving a confidence value generated by the predictive output;

retrieving a domain weighting coefficient associated with a specialized learner model or the large language model agent that generated the predictive output;

computing a recency-of-evidence score from curated dataset inputs to generate a recency score; and

combining the confidence value, the domain weighting coefficient, and the recency score to generate a priority assigned to the predictive output.

7 . The computing system of claim 6 , wherein generating the arbitration results comprises:

ranking predictive outputs according to respective assigned priorities to generate a ranked output set; and

selecting an arbitration result from the ranked output set, the arbitration result comprising either a top-ranked output or a composite output formed by concatenating or templating portions of multiple predictive outputs while preserving provenance metadata for each contributing portion.

8 . The computing system of claim 1 , wherein integrating curated dataset inputs and validation feedback comprises:

retrieving reference records from a curated dataset to generate retrieved reference records;

validating the retrieved reference records using cryptographic hash checks to generate validated reference records;

transforming the validated reference records into a schema-conformant representation to generate transformed reference records;

receiving correction signals from an external feedback interface to generate received correction signals;

normalizing the received correction signals into a machine-readable format to generate normalized correction signals; and

updating at least one of arbitration weights, interrogation constraints, or a registered model parameter set for a specialized learner model or the large language model agent using the transformed reference records and the normalized correction signals.

9 . The computing system of claim 1 , wherein distributing structured signals comprises:

selecting a subset of specialized learner models from the plurality of specialized learner models based on workflow context, domain indicators, or persona activation state to generate selected specialized learner models;

executing the selected specialized learner models in parallel to generate executed specialized learner models;

generating a domain-specific output from each of the executed specialized learner models; and

generating a confidence value associated with each domain-specific output.

10 . A method for orchestrated multi-model processing and response generation, the method comprising:

receiving multimodal input data comprising at least one of text data, audio data, video data, or gesture data;

generating structured signals from the multimodal input data;

distributing the structured signals to a plurality of specialized learner models;

executing each of the plurality of specialized learner models, each comprising a small language model trained for domain-specific reasoning, to generate respective domain-specific outputs;

executing a large language model agent configured for generalized processing to generate a generalized output;

applying arbitration logic to resolve conflicts among the respective domain-specific outputs and the generalized output;

generating arbitration results using the arbitration logic;

integrating curated dataset inputs and validation feedback into the arbitration results;

generating a synthesized response from the arbitration results, the curated dataset inputs, and the validation feedback, wherein generating the synthesized response includes:

generating a text output as a first response modality;

generating an audio output as a second response modality;

generating audio directives and visual directives from the synthesized response;

mapping tokens from the synthesized response to viseme cues and gesture cues to generate mapped directives;

time-aligning the audio directives and the mapped directives to generate aligned directives;

generating a digital personal display output as a third response modality using the aligned directives; and

transmitting the synthesized response for presentation to a user.

11 . The method of claim 10 , wherein applying arbitration logic comprises:

maintaining a session-specific state store keyed by a track identifier and a persona identifier to generate a maintained state store;

recording a time-ordered sequence of prior inputs and prior outputs in the maintained state store;

recording workflow dependency data in the maintained state store to generate dependency-graph metadata; and

retrieving the time-ordered sequence, the persona identifier, and the dependency-graph metadata from the maintained state store to parameterize a subsequent generation request.

12 . The method of claim 10 , wherein generating the synthesized response comprises applying interrogation logic, the applying of the interrogation logic comprising:

performing contradiction tests between candidate predictive outputs to generate detected inconsistencies;

performing coverage analysis by comparing the candidate predictive outputs against a requirement schema to generate unfulfilled schema fields and unsatisfied constraint conditions; and

generating interrogation prompts from the detected inconsistencies, the unfulfilled schema fields, and the unsatisfied constraint conditions.

13 . The method of claim 10 , wherein applying arbitration logic comprises:

retrieving a confidence value associated with a predictive output;

retrieving a domain weighting coefficient associated with a specialized learner model or a large language model agent that generated the predictive output;

computing a recency-of-evidence score from curated dataset inputs to generate a recency score; and

combining the confidence value, the domain weighting coefficient, and the recency score to generate a priority assigned to the predictive output.

14 . The method of claim 10 , wherein integrating curated dataset inputs and validation feedback comprises:

retrieving reference records from a curated dataset via an application programming interface to generate retrieved reference records;

validating the retrieved reference records using cryptographic hash checks to generate validated reference records;

transforming the validated reference records into a schema-conformant representation to generate transformed reference records;

receiving correction signals from an external feedback interface to generate received correction signals;

normalizing the received correction signals into a machine-readable format to generate normalized correction signals; and

updating at least one of arbitration weights, interrogation constraints, or a registered model parameter set using the transformed reference records and the normalized correction signals.

15 . The method of claim 10 , wherein distributing the structured signals comprises:

selecting a subset of specialized learner models from the plurality of specialized learner models based on workflow context, domain indicators, or persona activation state to generate selected specialized learner models;

executing the selected specialized learner models in parallel to generate executed specialized learner models;

generating a domain-specific output from each of the executed specialized learner models; and

generating a confidence value associated with each domain-specific output.

16 . A non-transitory computer-readable storage medium storing instructions that, when executed by at least one processor of a computing system, cause the computing system to:

receive multimodal input data comprising at least one of text data, audio data, video data, or gesture data;

generate structured signals from the multimodal input data;

distribute the structured signals to a plurality of specialized learner models;

execute each of the plurality of specialized learner models, each comprising a small language model trained for domain-specific reasoning, to generate respective domain-specific outputs;

execute a large language model agent configured for generalized processing to generate a generalized output;

apply arbitration logic to resolve conflicts among the respective domain-specific outputs and the generalized output;

generate arbitration results using the arbitration logic;

integrate curated dataset inputs and validation feedback into the arbitration results;

generate a synthesized response from the arbitration results, the curated dataset inputs, and the validation feedback, wherein generating the synthesized response includes:

generating a text output as a first response modality;

generating an audio output as a second response modality;

generating audio directives and visual directives from the synthesized response;

mapping tokens from the synthesized response to viseme cues and gesture cues to generate mapped directives;

time-aligning the audio directives and the mapped directives to generate aligned directives;

generating a digital personal display output as a third response modality using the aligned directives; and

transmit the synthesized response for presentation to a user.

17 . The non-transitory computer-readable storage medium of claim 16 , wherein the instructions further cause the computing system to:

retrieve reference records from a curated dataset via an application programming interface to generate retrieved reference records;

validate the retrieved reference records using cryptographic hash checks to generate validated reference records;

transform the validated reference records into a schema-conformant representation to generate transformed reference records;

receive correction signals from an external feedback interface to generate received correction signals;

normalize the received correction signals into a machine-readable format to generate normalized correction signals; and

update at least one of arbitration weights, interrogation constraints, or a registered model parameter set using the transformed reference records and the normalized correction signals.