Description:FIELD OF INVENTION
This invention relates to artificial intelligence systems and more particularly to a novel hierarchical multimodal mixture of experts (HMMoE) architecture that significantly enhances reasoning capabilities across text, image, audio, and video inputs. The invention addresses fundamental limitations in current large language models by implementing a three-tiered expert specialization system that enables superior performance on complex reasoning benchmarks while maintaining computational efficiency. The invention finds particular application in industrial automation, multilingual technical training, government document processing, and any domain requiring advanced logical, procedural, and symbolic understanding.

BACKGROUND OF THE INVENTION AND RELATED PRIOR ART
Current state-of-the-art large language models, including GPT-4, Claude, and Llama series, demonstrate impressive capabilities in language understanding and generation. However, these models exhibit significant limitations in true reasoning and generalization tasks, as evidenced by their poor performance on benchmarks such as ARC-AGI-2, where even the most advanced models achieve less than 4% accuracy while humans easily solve 100% of tasks.
Existing mixture of experts (MoE) architectures, such as those implemented in Switch Transformer and GLaM, primarily focus on scaling model capacity while maintaining computational efficiency. These systems typically employ flat expert structures where routing decisions are made at a single level, limiting their ability to handle complex multi-step reasoning tasks.
Recent developments in multimodal models like DALL-E 2, CLIP, and Flamingo have made progress in cross-modal understanding, but they still struggle with hierarchical reasoning and symbolic interpretation. Current multimodal architectures often process different modalities separately and then fuse them at later stages, missing opportunities for deep cross-modal reasoning from the earliest processing stages.
Template-based reasoning approaches, such as those proposed in ReasonFlux, have shown promise in improving reasoning capabilities but lack the hierarchical structure necessary for handling truly complex reasoning chains that require multiple levels of abstraction and expert specialization.
The Problem
The fundamental problem with current AI systems lies in their inability to perform true generalization and multi-step reasoning. Specifically, existing models suffer from three critical limitations: first, they lack explicit hierarchical reasoning pathways that can handle complex multi-step inference processes; second, they fail to effectively integrate multimodal information at the deepest levels of processing; and third, they cannot dynamically allocate computational resources based on the specific reasoning requirements of different tasks. This results in models that may excel at pattern matching and statistical correlation but fail catastrophically when faced with novel reasoning challenges that require genuine understanding and logical deduction.

The Solution
The present invention solves these problems through a revolutionary three-level Hierarchical Multimodal Mixture of Experts architecture that explicitly models reasoning processes while efficiently managing computational resources. The solution implements specialized expert pathways for perception, cognition, and integration, enabling the system to handle complex reasoning tasks by decomposing them into manageable subtasks and routing them through appropriate expert networks. This approach allows the system to achieve superior performance on reasoning benchmarks while activating only a fraction of its total parameters during inference, resulting in both improved accuracy and computational efficiency.

OBJECTS OF THE INVENTION
The principal object of this invention is to provide a hierarchical multimodal mixture of experts architecture that significantly improves reasoning capabilities in artificial intelligence systems while maintaining computational efficiency through selective expert activation.
Another object of this invention is to enable true multimodal understanding by implementing early fusion processing that treats different modalities as unified token sequences from the earliest stages of processing.
A further object of this invention is to provide a scalable expert routing system that can dynamically allocate computational resources based on task complexity and modality requirements.
Yet another object of this invention is to implement template-based reasoning mechanisms that can generalize across problem domains while maintaining high accuracy on complex reasoning benchmarks.

SUMMARY OF THE INVENTION
The invention comprises a novel Hierarchical Multimodal Mixture of Experts (HMMoE) architecture with approximately 70 billion parameters organized into three distinct expert levels. The system includes twelve perception experts specialized for different modalities including text, image, video, and audio processing. Twenty-four cognition experts handle specific reasoning tasks including deductive, inductive, abductive, analogical, spatial, and mathematical reasoning. Twelve integration experts perform synthesis, evaluation, and adaptation functions across different task domains.
The architecture implements a sophisticated multi-level routing system that includes token-level routing for modality-specific processing, task-level routing for cognitive function allocation, and path-level routing for information flow orchestration. A universal multimodal tokenizer processes all input types into a unified token sequence, enabling deep cross-modal understanding from the earliest processing stages.
The system incorporates a template-based reasoning framework with approximately 1,000 high-level reasoning patterns that can be composed and adapted for complex problem-solving. Meta-reasoning capabilities include symbolic interpretation modules, compositional reasoning pathways, and context-sensitive rule application mechanisms specifically designed to address limitations identified in current reasoning benchmarks.
The invention further includes a 10-million token context window capability and specialized training methodologies including hierarchical reinforcement learning for expert routing optimization and constitutional AI training for safety and alignment.

BRIEF DESCRIPTION OF DRAWINGS

Figure 1 illustrates the overall hierarchical architecture showing the three expert levels and their interconnections within the Manas-70B system.
Figure 2 demonstrates the multi-level routing mechanism including token-level, task-level, and path-level routing pathways.
Figure 3 illustrates the Reasoning system architecture

DESCRIPTION OF REFERENCE NUMERALS
100: Hierarchical Multimodal Mixture of Experts (HMMoE) Core Architecture
101: Perception Expert Layer (Level 1)
102: Text Processing Experts (4 units)
103: Image Processing Experts (3 units)
104: Video Processing Experts (2 units)
105: Audio Processing Experts (2 units)
106: Multimodal Integration Expert (1 unit)
107: Cognition Expert Layer (Level 2)
108: Reasoning Experts (6 units)
109: Knowledge Experts (6 units)
110: Generation Experts (4 units)
111: Symbolic Experts (4 units)
112: Meta-reasoning Experts (4 units)
113: Integration Expert Layer (Level 3)
114: Synthesis Experts (4 units)
115: Evaluation Experts (4 units)
116: Adaptation Experts (4 units)
117: Multi-level Routing System
118: Token-level Router
119: Task-level Router
121: Universal Multimodal Tokenizer
122: Early Fusion Processor
123: Template-based Reasoning Framework
124: Thought Template Library (1000 templates)
125: Template Selection Module
126: Template Composition Engine
127: Meta-reasoning Layer
128: Symbolic Interpretation Module
129: Compositional Reasoning Module
130: Context-sensitive Rule Application Module
131: 10M Token Context Window Manager

DETAILED DESCRIPTION

The following detailed description presents preferred embodiments of the invention and is intended to provide a thorough understanding of the system, components, and workflow associated with a Hierarchical Multimodal Mixture of Experts Architecture for Advanced Reasoning in Artificial Intelligence System. While specific configurations are described herein, it will be apparent to those skilled in the art that variations and modifications may be implemented without departing from the scope and spirit of the invention
The present invention provides a revolutionary artificial intelligence architecture that addresses fundamental limitations in current large language models through a hierarchical expert specialization approach. The system is specifically designed to excel at complex reasoning tasks while maintaining computational efficiency through selective parameter activation.

The core innovation lies in the three-level Hierarchical Multimodal Mixture of Experts architecture (100) that processes information through increasingly specialized expert networks. Unlike conventional mixture of experts systems that employ flat routing structures, the present invention implements a hierarchical approach where each level of experts builds upon the processing capabilities of the previous level.

The first level, comprising the Perception Expert Layer (101), contains twelve specialized experts responsible for initial modality-specific processing. The Text Processing Experts (102) include four specialized units handling syntax analysis, semantic understanding, knowledge retrieval, and preliminary reasoning. These experts are trained on diverse multilingual corpora including Indian language datasets to ensure comprehensive linguistic coverage. The Image Processing Experts (103) comprise three units specializing in object recognition, scene understanding, and visual reasoning respectively. The Video Processing Experts (104) include two units focused on temporal understanding and action recognition. The Audio Processing Experts (105) contain two units handling speech processing and environmental sound analysis. The Multimodal Integration Expert (106) serves as a crucial bridge, performing initial cross-modal alignment and integration.

The second level, the Cognition Expert Layer (107), contains twenty-four experts organized into five functional categories. The Reasoning Experts (108) include six specialized units handling deductive reasoning for logical inference, inductive reasoning for pattern generalization, abductive reasoning for hypothesis generation, analogical reasoning for similarity-based inference, spatial reasoning for geometric and positional understanding, and mathematical reasoning for quantitative problem solving. The Knowledge Experts (109) comprise six units managing factual knowledge retrieval, procedural knowledge application, commonsense reasoning, and domain-specific expertise. The Generation Experts (110) include four units specialized in creative content generation, structured output formatting, instruction following, and planning sequence generation.

The Symbolic Experts (111) contain four units handling logical symbol manipulation, program
synthesis, formal verification, and rule-based reasoning. The Meta-reasoning Experts (112) include four units responsible for uncertainty estimation, hypothesis generation, reasoning strategy selection, and self-evaluation.

The third level, the Integration Expert Layer (113), comprises twelve experts organized into three categories. The Synthesis Experts (114) include four units responsible for integrating multiple reasoning paths, combining outputs from different expert types, resolving conflicts between competing hypotheses, and generating coherent final responses. The Evaluation Experts (115) contain four units specializing in output quality assessment, uncertainty quantification, consistency checking, and performance monitoring. The Adaptation Experts (116) comprise four units handling domain transfer, task adaptation, continuous learning integration, and personalization based on user feedback.

The Multi-level Routing System (117) represents another crucial innovation of the present invention. Unlike conventional routing mechanisms that operate at a single level, this system implements three distinct routing layers. The Token-level Router (118) determines which perception experts should process each input token based on modality type and content characteristics. The Task-level Router (119) identifies which cognition experts should handle specific reasoning subtasks based on the nature of the problem and required reasoning types. The Path-level Router orchestrates the flow of information between expert levels and manages the overall reasoning pathway through the hierarchical structure.

The Universal Multimodal Tokenizer (121) enables the system to process diverse input types including text, images, audio, and video through a unified tokenization scheme. This component converts all input modalities into a common token representation while preserving modality-specific characteristics through specialized embedding layers. The Early Fusion Processor (122) implements deep cross-modal integration from the earliest processing stages, allowing the system to understand relationships between different modalities at a fundamental level rather than treating them as separate information streams.

The Template-based Reasoning Framework (123) incorporates structured reasoning patterns that enable the system to approach complex problems systematically. The Reasoning Pattern Library (124) contains approximately one thousand high-level reasoning templates that capture common problem-solving approaches across diverse domains. The Template Selection Module (125) analyzes incoming problems and identifies the most appropriate reasoning templates based on problem characteristics and context. The Template Composition Engine (126) combines multiple templates when complex problems require hybrid reasoning approaches.

The Meta-reasoning Layer (127) specifically addresses the limitations identified in current AI systems' performance on benchmarks like ARC-AGI-2. The Symbolic Interpretation Module (128) provides dedicated processing pathways for understanding abstract symbol meanings and their relationships. The Compositional Reasoning Module (129) handles problems involving multiple interacting rules and components. The Context-sensitive Rule Application Module (130) adapts reasoning strategies based on changing problem contexts and constraints.

Best Method of Performing the Invention

The optimal implementation of the invention begins with the four-stage training process. The first stage involves multimodal pretraining on diverse datasets including The Pile, CommonCrawl, LAION-5B, and specialized Indian language corpora from AI4Bharat and TDIL. This stage establishes foundational language and multimodal understanding across all expert networks.

The second stage implements supervised fine-tuning using high-quality human demonstrations of reasoning processes. This stage is crucial for training the template-based reasoning framework and ensuring that expert routing decisions align with human reasoning patterns. Particular attention is paid to mathematical reasoning datasets, logical inference problems, and multimodal reasoning tasks.

The third stage employs hierarchical reinforcement learning to optimize the routing system performance. The routing networks are trained using reward signals based on downstream task performance, ensuring that expert selection decisions maximize overall system effectiveness. Load balancing mechanisms are implemented to prevent expert collapse and ensure efficient utilization of all network components.

The fourth stage implements constitutional AI training to ensure safe and aligned behavior. This includes training on ethical reasoning scenarios, bias mitigation exercises, and safety constraint satisfaction. The system learns to recognize potentially harmful requests and respond appropriately while maintaining helpful capabilities.
During inference, the system processes inputs through the universal multimodal tokenizer, which converts all input types into unified token sequences. The token-level router analyzes these sequences and directs them to appropriate perception experts. Perception experts perform initial processing and generate intermediate representations that capture modality-specific information.
The task-level router then analyzes the problem requirements and activates relevant cognition experts. For complex reasoning tasks, multiple expert types may be activated simultaneously, with the template-based reasoning framework providing structured guidance for problem decomposition and solution planning.

The path-level router manages information flow between expert levels and coordinates the overall reasoning process. Integration experts synthesize outputs from multiple reasoning pathways, evaluate result quality, and adapt the response based on context and user requirements.
The 10M Token Context Window Manager (131) enables the system to maintain coherent reasoning across extended contexts, crucial for complex engineering problems and document analysis tasks. This component implements efficient memory management and attention mechanisms that scale effectively with context length.

The hierarchical expert structure enables efficient computation with only 15-20 billion parameters active during inference despite the 70-billion parameter total capacity. The multimodal integration capabilities provide comprehensive understanding across text, image, audio, and video inputs. The template-based reasoning framework enables systematic approach to complex problems while maintaining flexibility for novel scenarios.
, Claims:We Claim,

1. A hierarchical multimodal mixture of experts based artificial intelligence system (100) comprising:
a three-level expert architecture characterised by a perception expert layer (101) with twelve modality-specialized experts;
a cognition expert layer (107) characterised by twenty-four task-specialized experts;
an integration expert layer (113) characterised by twelve cross-task experts;
a multi-level routing system characterised by token-level (118), task-level (119), and path-level routers for dynamic expert selection; and
a universal multimodal tokenizer (121) for unified processing of text, image, audio, and video inputs.
2. The system of claim 1, wherein the perception expert layer (101) comprises:
four text processing experts (102) specializing in syntax, semantics, knowledge, and reasoning;
three image processing experts (103) for object recognition, scene understanding, and visual reasoning;
two video processing experts (104) for temporal understanding and action recognition; and
two audio processing experts (105) for speech and environmental sounds; and one multimodal integration expert (106).
3. The system of claim 1, wherein the cognition expert layer (107) comprises:
six reasoning experts (108) for deductive, inductive, abductive, analogical, spatial, and mathematical reasoning;
six knowledge experts (109) for factual, procedural, commonsense, and domain-specific knowledge;
four generation experts (110) for creative, structured, instruction-following, and planning tasks; four symbolic experts (111) for logic, program synthesis, and formal verification; and four meta-reasoning experts (112) for uncertainty estimation and hypothesis generation.
4. The system of claim 1, wherein the integration (113) expert layer comprises:
four synthesis experts (114) for integrating multiple reasoning paths;
four evaluation experts (115) for assessing output quality and uncertainty; and
four adaptation experts (116) for domain transfer and task adaptation.
5. The system of claim 1, further comprising a template-based reasoning framework (123) including a library (124) of approximately one thousand reasoning patterns, a template selection module (125), and a template composition engine (126) for systematic problem-solving approaches.
6. The system of claim 1, wherein the multi-level routing system implements reinforcement learning-based optimization with load balancing mechanisms to prevent expert collapse and ensure efficient parameter utilization.
7. The system of claim 1, further comprising a meta-reasoning layer (127) including symbolic interpretation modules (128), compositional reasoning pathways (129), and context-sensitive rule application mechanisms (130) specifically designed for complex reasoning benchmark performance.
8. The system of claim 1, wherein the universal multimodal tokenizer implements early fusion processing that treats different modalities as unified token sequences from initial processing stages.
9. The system of claim 1, further comprising a 10-million token context window manager with efficient memory management and attention mechanisms for extended context reasoning.