RICE: Resilience, Interference, Communication and Embedding

1. Introduction

The rapid advancement of Large Language Models (LLMs) has been primarily driven by the availability of vast amounts of textual data scraped from the internet, books, and other digital sources. However, we are approaching a critical juncture where the readily available high-quality training data is becoming exhausted. This phenomenon, often referred to as "data wall," presents a fundamental challenge to the continued scaling of AI systems.

Traditional approaches to this problem have focused on data augmentation, synthetic data generation, and improved training efficiency. However, these solutions often fall short of creating truly novel knowledge that extends beyond the boundaries of existing human-generated content.

This paper introduces RICE (Resilience Interference Communication Embedding), a paradigm that fundamentally reimagines how AI systems can generate, validate, and integrate new knowledge. Rather than relying solely on existing human knowledge, RICE creates a self-sustaining ecosystem where AI systems collaboratively generate novel problems, validate their coherence with reality, and systematically integrate this knowledge into increasingly sophisticated models.

2. The Data Exhaustion Problem

2.1 Current State of Training Data

The most successful LLMs to date have been trained on datasets containing trillions of tokens, encompassing virtually all publicly available text on the internet, digitized books, academic papers, and other textual resources. Recent estimates suggest that we may exhaust high-quality text data within the next few years, creating a bottleneck for further model improvements.

2.2 Limitations of Current Synthetic Data Approaches

Existing synthetic data generation methods typically involve:

• Paraphrasing existing content
• Data augmentation through minor modifications
• Generation of variations on known themes

These approaches, while useful, are fundamentally limited by the knowledge boundaries of the source material. They can reorganize and recombine existing knowledge but struggle to create genuinely novel insights or discover new problem domains.

3. The RICE Paradigm

3.1 Conceptual Framework

RICE addresses the data exhaustion problem through a multi-tier architecture that creates a continuous cycle of knowledge generation, validation, and integration. The system consists of four primary components:

• Problem Generator LLM (PG-LLM): Specialized in creating novel problems and challenges
• Reality Validator LLM (RV-LLM): Focused on verifying the coherence and plausibility of generated problems
• RAG Integration System: Manages the storage, retrieval, and organization of validated knowledge
• Super-Intelligence LLM (SI-LLM): The final system that benefits from the continuously expanding knowledge base

3.2 The RICE Architecture

The RICE architecture operates on the principle of "interference" between different AI systems, where the interaction between specialized models creates emergent knowledge that exceeds the sum of their individual capabilities.

• Resilience refers to the system's ability to continuously generate meaningful content even as traditional data sources become exhausted.
• Interference describes the productive interaction between different LLM components, where their combined output creates novel insights.
• Communication encompasses the sophisticated information exchange between system components through the RAG infrastructure.
• Embedding represents the integration of new knowledge into dense vector representations that can be efficiently utilized by the final super-intelligence system.

This approach ensures that the generated knowledge is not only novel but also grounded in reality, addressing the fundamental challenge of maintaining truthfulness in AI-generated content.

Figure 1: Visual representation of the RICE Paradigm architecture

4. System Components

4.1 Problem Generator LLM (PG-LLM)

The PG-LLM is specifically trained and fine-tuned to generate novel problems across various domains. Its key characteristics include:

• Domain Diversification: Capability to generate problems across multiple fields including mathematics, physics, biology, computer science, philosophy, and interdisciplinary areas
• Complexity Scaling: Ability to create problems of varying difficulty levels
• Novel Combination: Skill in combining concepts from different domains to create unprecedented challenges
• Temporal Awareness: Understanding of current knowledge boundaries to push beyond existing paradigms

The PG-LLM operates using advanced prompting techniques, including:

• Chain-of-thought reasoning for problem construction
• Multi-step verification of problem novelty
• Domain-specific knowledge injection
• Creativity enhancement through controlled randomness

4.2 Reality Validator LLM (RV-LLM)

The RV-LLM serves as a critical quality control mechanism, ensuring that generated problems maintain coherence with established scientific principles and logical consistency. Its functions include:

• Physical Plausibility: Verification that problems don't violate known physical laws
• Logical Consistency: Ensuring internal coherence of problem statements
• Mathematical Validity: Checking mathematical formulations for correctness
• Biological Feasibility: Validating biological scenarios against known life science principles
• Technological Realism: Assessing whether proposed technological scenarios are theoretically possible

The validation process employs multiple verification strategies:

• Cross-referencing with established scientific databases
• Logical proof verification
• Simulation-based validation where applicable
• Expert system consultation for domain-specific validation

4.3 RAG Integration System

The RAG system serves as the central nervous system of RICE, managing the flow of information between components. Key features include:

• Semantic Indexing: Advanced embedding techniques for efficient knowledge retrieval
• Hierarchical Organization: Multi-level categorization of problems and solutions
• Dynamic Updating: Real-time integration of new validated knowledge
• Quality Scoring: Continuous assessment of knowledge utility and reliability
• Interference Management: Handling conflicts between different knowledge sources

The RAG system utilizes state-of-the-art vector databases and embedding models, enhanced with custom indexing strategies optimized for the RICE paradigm.

4.4 Super-Intelligence LLM (SI-LLM)

The SI-LLM represents the culmination of the RICE system, benefiting from the continuously expanding knowledge base. Its capabilities include:

• Enhanced Problem Solving: Access to novel problem-solving strategies developed within the RICE ecosystem
• Cross-Domain Synthesis: Ability to combine insights from the diverse problem set generated by the system
• Meta-Learning: Continuous improvement through exposure to validated novel challenges
• Emergent Reasoning: Development of reasoning capabilities that emerge from the unique knowledge base

5. Implementation Architecture

5.1 Data Flow

The RICE system operates through the following data flow:

Problem Generation Phase:

• PG-LLM generates novel problems based on current knowledge frontiers
• Problems are tagged with domain, complexity, and novelty scores
• Initial filtering removes obviously invalid or duplicate problems

Validation Phase:

• RV-LLM evaluates each problem for coherence and plausibility
• Multi-criteria validation including logical, physical, and domain-specific checks
• Problems receive validation scores and detailed feedback

Integration Phase:

• Validated problems and their solutions are processed into vector embeddings
• RAG system organizes and indexes the new knowledge
• Cross-references are established with existing knowledge base

Utilization Phase:

• SI-LLM queries the RAG system during inference
• Enhanced responses incorporate novel problem-solving approaches
• Feedback loop improves future problem generation

5.2 Quality Assurance

RICE incorporates multiple quality assurance mechanisms:

• Redundant Validation: Multiple validation passes with different criteria
• Human Expert Review: Periodic review of system outputs by domain experts
• Performance Metrics: Continuous monitoring of system effectiveness
• Feedback Integration: User feedback incorporated into quality scoring

5.3 Scalability Considerations

The RICE architecture is designed for horizontal scalability:

• Distributed Processing: Each component can be deployed across multiple servers
• Modular Design: Components can be independently upgraded or replaced
• Load Balancing: Dynamic distribution of computational load based on demand
• Storage Optimization: Efficient vector storage and retrieval systems

6. Theoretical Advantages

6.1 Overcoming Data Limitations

RICE addresses the fundamental data exhaustion problem by creating a self-sustaining knowledge generation ecosystem. Unlike traditional approaches that are bounded by existing human knowledge, RICE can theoretically generate infinite novel problems and insights.

6.2 Knowledge Diversity

The system's ability to generate problems across multiple domains and at the intersection of different fields creates a more diverse knowledge base than traditional training methods. This diversity enhances the robustness and general intelligence of the final system.

6.3 Continuous Learning

RICE enables continuous learning without requiring periodic retraining on massive datasets. The system can continuously evolve and improve its capabilities through the ongoing generation and integration of new knowledge.

6.4 Quality Control

The multi-tier validation system ensures that generated knowledge maintains high quality and coherence with established reality, addressing concerns about synthetic data degradation.

7. Potential Challenges and Limitations

7.1 Computational Requirements

The RICE system requires significant computational resources due to its multi-LLM architecture and continuous processing requirements. However, these costs may be offset by the reduced need for traditional data collection and preprocessing.

7.2 Validation Complexity

Ensuring the accuracy and relevance of generated problems across all domains presents a significant challenge. The RV-LLM must maintain expertise across multiple fields, which may require specialized training or ensemble approaches.

7.3 Knowledge Drift

Over time, the system's knowledge base may drift away from human knowledge and values. Careful monitoring and periodic realignment mechanisms are necessary to maintain system utility.

7.4 Emergent Behaviors

The complex interactions between system components may lead to unexpected emergent behaviors that are difficult to predict or control. Robust monitoring and safety mechanisms are essential.

8. Future Research Directions

8.1 Advanced Validation Techniques

Future research should focus on developing more sophisticated validation techniques, including:

• Automated simulation-based validation
• Integration with scientific databases and knowledge graphs
• Development of domain-specific validation models

8.2 Efficiency Optimization

Research into optimizing the computational efficiency of the RICE system, including:

• More efficient embedding techniques
• Optimized RAG architectures
• Dynamic resource allocation strategies

8.3 Human-AI Collaboration

Investigation of mechanisms for incorporating human expertise into the RICE system, including:

• Expert feedback integration
• Human-guided problem generation
• Collaborative validation processes

8.4 Safety and Alignment

Development of safety mechanisms to ensure RICE systems remain aligned with human values and objectives, including:

• Value alignment verification
• Bias detection and mitigation
• Ethical constraint integration

9. Conclusion

RICE represents a paradigm shift in artificial intelligence development, addressing the critical challenge of data exhaustion through innovative synthetic knowledge generation. By creating a self-sustaining ecosystem of specialized AI systems, RICE offers a path toward continued AI advancement beyond the limitations of traditional training data.

The multi-tier architecture of RICE, with its emphasis on problem generation, validation, and integration, provides a robust framework for creating genuinely novel knowledge while maintaining quality and coherence. While significant challenges remain in implementation and optimization, the theoretical advantages of RICE make it a compelling direction for future AI research.

As we approach the limits of traditional training methodologies, paradigms like RICE may prove essential for the continued advancement of artificial intelligence toward true general intelligence. The success of RICE could fundamentally transform how we approach AI development, moving from passive data consumption to active knowledge creation.

10. Implementation Example

The following Python implementation demonstrates a simplified version of the RICE system, showcasing the core components and their interactions:

import numpy as np
import json
import sqlite3
from typing import List, Dict, Any, Tuple
from dataclasses import dataclass
from sentence_transformers import SentenceTransformer
import openai
from sklearn.metrics.pairwise import cosine_similarity
import logging

# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

@dataclass
class Problem:
    """Represents a generated problem with metadata"""
    id: str
    content: str
    domain: str
    complexity: float
    novelty_score: float
    validation_score: float = 0.0
    is_validated: bool = False
    embedding: np.ndarray = None

@dataclass
class ValidationResult:
    """Represents the result of problem validation"""
    is_valid: bool
    score: float
    feedback: str
    criteria_scores: Dict[str, float]

class ProblemGeneratorLLM:
    """
    Problem Generator LLM component
    Generates novel problems across various domains
    """
    
    def __init__(self, model_name: str = "gpt-4"):
        self.model_name = model_name
        self.domains = [
            "mathematics", "physics", "computer_science", 
            "biology", "chemistry", "philosophy", "engineering",
            "interdisciplinary"
        ]
        
    def generate_problem(self, domain: str = None, complexity: float = 0.5) -> Problem:
        """Generate a novel problem in a specified domain"""
        if domain is None:
            domain = np.random.choice(self.domains)
            
        prompt = self._create_generation_prompt(domain, complexity)
        
        try:
            response = openai.ChatCompletion.create(
                model=self.model_name,
                messages=[
                    {"role": "system", "content": self._get_system_prompt()},
                    {"role": "user", "content": prompt}
                ],
                temperature=0.8,
                max_tokens=500
            )
            
            problem_content = response.choices[0].message.content
            novelty_score = self._assess_novelty(problem_content, domain)
            
            problem = Problem(
                id=f"prob_{np.random.randint(100000, 999999)}",
                content=problem_content,
                domain=domain,
                complexity=complexity,
                novelty_score=novelty_score
            )
            
            logger.info(f"Generated problem {problem.id} in domain {domain}")
            return problem
            
        except Exception as e:
            logger.error(f"Error generating problem: {e}")
            return None
    
    def _get_system_prompt(self) -> str:
        return """
        You are a creative problem generator AI. Your task is to create novel, 
        challenging problems that push the boundaries of current knowledge while 
        remaining grounded in scientific reality. Focus on creating problems that:
        1. Are genuinely novel and haven't been extensively studied
        2. Combine concepts from multiple areas when appropriate
        3. Are well-defined and solvable in principle
        4. Push the boundaries of current understanding
        5. Have potential real-world applications or theoretical significance
        """
    
    def _create_generation_prompt(self, domain: str, complexity: float) -> str:
        complexity_desc = {
            0.0: "beginner-friendly",
            0.5: "intermediate complexity",
            1.0: "highly advanced and challenging"
        }
        
        level = complexity_desc.get(complexity, "intermediate complexity")
        
        return f"""
        Generate a novel problem in the domain of {domain} with {level}.
        The problem should be:
        - Unique and not commonly found in textbooks
        - Scientifically plausible
        - Clearly stated with specific parameters
        - Potentially solvable with current or near-future methods
        
        Provide the problem statement in a clear, structured format.
        """
    
    def _assess_novelty(self, content: str, domain: str) -> float:
        """Simple novelty assessment - could be enhanced with more sophisticated methods"""
        # This is a simplified novelty assessment
        # In practice, this would involve comparison with existing problem databases
        novelty_indicators = [
            "novel", "unprecedented", "new approach", "innovative",
            "unexplored", "cutting-edge", "breakthrough"
        ]
        
        content_lower = content.lower()
        novelty_count = sum(1 for indicator in novelty_indicators if indicator in content_lower)
        
        # Base novelty score with some randomness
        base_score = 0.3 + np.random.random() * 0.4
        novelty_bonus = min(novelty_count * 0.1, 0.3)
        
        return min(base_score + novelty_bonus, 1.0)

class RealityValidatorLLM:
    """
    Reality Validator LLM component
    Validates generated problems for coherence and plausibility
    """
    
    def __init__(self, model_name: str = "gpt-4"):
        self.model_name = model_name
        self.validation_criteria = [
            "physical_plausibility",
            "logical_consistency", 
            "mathematical_validity",
            "domain_coherence",
            "solvability"
        ]
    
    def validate_problem(self, problem: Problem) -> ValidationResult:
        """Validate a problem across multiple criteria"""
        
        validation_prompt = self._create_validation_prompt(problem)
        
        try:
            response = openai.ChatCompletion.create(
                model=self.model_name,
                messages=[
                    {"role": "system", "content": self._get_validation_system_prompt()},
                    {"role": "user", "content": validation_prompt}
                ],
                temperature=0.3,
                max_tokens=800
            )
            
            validation_text = response.choices[0].message.content
            result = self._parse_validation_result(validation_text)
            
            # Update problem with validation results
            problem.validation_score = result.score
            problem.is_validated = result.is_valid
            
            logger.info(f"Validated problem {problem.id}: {result.score:.2f}")
            return result
            
        except Exception as e:
            logger.error(f"Error validating problem {problem.id}: {e}")
            return ValidationResult(False, 0.0, "Validation failed", {})
    
    def _get_validation_system_prompt(self) -> str:
        return """
        You are a rigorous scientific validator. Your task is to evaluate problems 
        for their coherence with established scientific principles and logical consistency.
        
        Evaluate each problem based on:
        1. Physical plausibility - Does it violate known physical laws?
        2. Logical consistency - Is the problem statement internally coherent?
        3. Mathematical validity - Are mathematical formulations correct?
        4. Domain coherence - Does it make sense within its specified domain?
        5. Solvability - Is the problem potentially solvable?
        
        Provide scores (0-1) for each criterion and overall assessment.
        """
    
    def _create_validation_prompt(self, problem: Problem) -> str:
        return f"""
        Please validate the following problem in the domain of {problem.domain}:
        
        PROBLEM:
        {problem.content}
        
        DOMAIN: {problem.domain}
        COMPLEXITY: {problem.complexity}
        
        Evaluate this problem based on the five criteria and provide:
        1. Individual scores (0-1) for each criterion
        2. Overall validity assessment
        3. Detailed feedback
        4. Suggestions for improvement if needed
        
        Format your response as JSON with the following structure:
        {{
            "physical_plausibility": ,
            "logical_consistency": ,
            "mathematical_validity": ,
            "domain_coherence": ,
            "solvability": ,
            "overall_score": ,
            "is_valid": ,
            "feedback": ""
        }}
        """
    
    def _parse_validation_result(self, validation_text: str) -> ValidationResult:
        """Parse the validation response into a structured result"""
        try:
            # Extract JSON from response
            start_idx = validation_text.find('{')
            end_idx = validation_text.rfind('}') + 1
            json_text = validation_text[start_idx:end_idx]
            
            data = json.loads(json_text)
            
            criteria_scores = {
                criterion: data.get(criterion, 0.0) 
                for criterion in self.validation_criteria
            }
            
            return ValidationResult(
                is_valid=data.get("is_valid", False),
                score=data.get("overall_score", 0.0),
                feedback=data.get("feedback", ""),
                criteria_scores=criteria_scores
            )
            
        except Exception as e:
            logger.error(f"Error parsing validation result: {e}")
            return ValidationResult(False, 0.0, "Parsing failed", {})

class RAGIntegrationSystem:
    """
    RAG Integration System
    Manages storage, retrieval, and organization of validated knowledge
    """
    
    def __init__(self, db_path: str = "rice_knowledge.db", 
                 embedding_model: str = "all-MiniLM-L6-v2"):
        self.db_path = db_path
        self.embedding_model = SentenceTransformer(embedding_model)
        self.init_database()
        
    def init_database(self):
        """Initialize the knowledge database"""
        conn = sqlite3.connect(self.db_path)
        cursor = conn.cursor()
        
        cursor.execute('''
            CREATE TABLE IF NOT EXISTS problems (
                id TEXT PRIMARY KEY,
                content TEXT NOT NULL,
                domain TEXT NOT NULL,
                complexity REAL NOT NULL,
                novelty_score REAL NOT NULL,
                validation_score REAL NOT NULL,
                is_validated BOOLEAN NOT NULL,
                embedding BLOB,
                created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
            )
        ''')
        
        cursor.execute('''
            CREATE TABLE IF NOT EXISTS solutions (
                id TEXT PRIMARY KEY,
                problem_id TEXT NOT NULL,
                solution_content TEXT NOT NULL,
                approach TEXT,
                effectiveness_score REAL,
                FOREIGN KEY (problem_id) REFERENCES problems (id)
            )
        ''')
        
        conn.commit()
        conn.close()
        logger.info("Database initialized")
    
    def store_problem(self, problem: Problem) -> bool:
        """Store a validated problem in the knowledge base"""
        if not problem.is_validated:
            logger.warning(f"Attempting to store unvalidated problem {problem.id}")
            return False
        
        # Generate embedding
        problem.embedding = self.embedding_model.encode(problem.content)
        
        conn = sqlite3.connect(self.db_path)
        cursor = conn.cursor()
        
        try:
            cursor.execute('''
                INSERT OR REPLACE INTO problems 
                (id, content, domain, complexity, novelty_score, validation_score, 
                 is_validated, embedding)
                VALUES (?, ?, ?, ?, ?, ?, ?, ?)
            ''', (
                problem.id, problem.content, problem.domain, problem.complexity,
                problem.novelty_score, problem.validation_score, problem.is_validated,
                problem.embedding.tobytes()
            ))
            
            conn.commit()
            logger.info(f"Stored problem {problem.id} in knowledge base")
            return True
            
        except Exception as e:
            logger.error(f"Error storing problem {problem.id}: {e}")
            return False
        finally:
            conn.close()
    
    def retrieve_similar_problems(self, query: str, top_k: int = 5) -> List[Problem]:
        """Retrieve problems similar to a query"""
        query_embedding = self.embedding_model.encode(query)
        
        conn = sqlite3.connect(self.db_path)
        cursor = conn.cursor()
        
        cursor.execute('''
            SELECT id, content, domain, complexity, novelty_score, 
                   validation_score, is_validated, embedding
            FROM problems
            WHERE is_validated = 1
        ''')
        
        results = []
        for row in cursor.fetchall():
            problem_embedding = np.frombuffer(row[7], dtype=np.float32)
            similarity = cosine_similarity([query_embedding], [problem_embedding])[0][0]
            
            problem = Problem(
                id=row[0], content=row[1], domain=row[2], complexity=row[3],
                novelty_score=row[4], validation_score=row[5], is_validated=row[6],
                embedding=problem_embedding
            )
            
            results.append((problem, similarity))
        
        # Sort by similarity and return top_k
        results.sort(key=lambda x: x[1], reverse=True)
        conn.close()
        
        return [problem for problem, _ in results[:top_k]]
    
    def get_knowledge_stats(self) -> Dict[str, Any]:
        """Get statistics about the knowledge base"""
        conn = sqlite3.connect(self.db_path)
        cursor = conn.cursor()
        
        cursor.execute('SELECT COUNT(*) FROM problems WHERE is_validated = 1')
        total_problems = cursor.fetchone()[0]
        
        cursor.execute('''
            SELECT domain, COUNT(*) 
            FROM problems 
            WHERE is_validated = 1 
            GROUP BY domain
        ''')
        domain_counts = dict(cursor.fetchall())
        
        cursor.execute('''
            SELECT AVG(validation_score), AVG(novelty_score) 
            FROM problems 
            WHERE is_validated = 1
        ''')
        avg_scores = cursor.fetchone()
        
        conn.close()
        
        return {
            "total_problems": total_problems,
            "domain_distribution": domain_counts,
            "average_validation_score": avg_scores[0] or 0.0,
            "average_novelty_score": avg_scores[1] or 0.0
        }

class SuperIntelligenceLLM:
    """
    Super-Intelligence LLM component
    The final system that benefits from the RICE knowledge base
    """
    
    def __init__(self, rag_system: RAGIntegrationSystem, model_name: str = "gpt-4"):
        self.rag_system = rag_system
        self.model_name = model_name
    
    def enhanced_query(self, query: str, use_rag: bool = True) -> str:
        """Process a query with enhanced capabilities from RICE knowledge"""
        
        if use_rag:
            similar_problems = self.rag_system.retrieve_similar_problems(query, top_k=3)
            context = self._build_context(similar_problems)
        else:
            context = ""
        
        enhanced_prompt = self._create_enhanced_prompt(query, context)
        
        try:
            response = openai.ChatCompletion.create(
                model=self.model_name,
                messages=[
                    {"role": "system", "content": self._get_si_system_prompt()},
                    {"role": "user", "content": enhanced_prompt}
                ],
                temperature=0.7,
                max_tokens=1000
            )
            
            return response.choices[0].message.content
            
        except Exception as e:
            logger.error(f"Error in enhanced query: {e}")
            return "I apologize, but I encountered an error processing your query."
    
    def _get_si_system_prompt(self) -> str:
        return """
        You are an advanced AI system enhanced with a continuously growing knowledge base
        of novel problems and solutions from the RICE system. You have access to unique
        insights and problem-solving approaches that extend beyond traditional knowledge.
        
        When provided with context from the RICE knowledge base, integrate these insights
        thoughtfully into your responses. Use the novel problems and approaches to enhance
        your reasoning and provide more comprehensive solutions.
        """
    
    def _build_context(self, problems: List[Problem]) -> str:
        """Build context from retrieved problems"""
        if not problems:
            return ""
        
        context_parts = ["RICE Knowledge Base Context:"]
        for i, problem in enumerate(problems, 1):
            context_parts.append(f"\n{i}. Domain: {problem.domain}")
            context_parts.append(f"   Problem: {problem.content}")
            context_parts.append(f"   Validation Score: {problem.validation_score:.2f}")
            context_parts.append(f"   Novelty Score: {problem.novelty_score:.2f}")
        
        return "\n".join(context_parts)
    
    def _create_enhanced_prompt(self, query: str, context: str) -> str:
        """Create an enhanced prompt with RICE context"""
        if context:
            return f"""
            {context}
            
            Based on the above context from the RICE knowledge base and your general knowledge,
            please respond to the following query:
            
            {query}
            
            If relevant, incorporate insights from the RICE problems to enhance your response.
            """
        else:
            return query

class RICESystem:
    """
    Main RICE system orchestrating all components
    """
    
    def __init__(self):
        self.problem_generator = ProblemGeneratorLLM()
        self.reality_validator = RealityValidatorLLM()
        self.rag_system = RAGIntegrationSystem()
        self.super_intelligence = SuperIntelligenceLLM(self.rag_system)
        
        logger.info("RICE system initialized")
    
    def generate_and_process_problems(self, num_problems: int = 10, 
                                    domains: List[str] = None) -> Dict[str, Any]:
        """Generate and process a batch of problems"""
        
        results = {
            "generated": 0,
            "validated": 0,
            "stored": 0,
            "failed": 0
        }
        
        for i in range(num_problems):
            domain = np.random.choice(domains) if domains else None
            complexity = np.random.random()
            
            # Generate problem
            problem = self.problem_generator.generate_problem(domain, complexity)
            if problem is None:
                results["failed"] += 1
                continue
            
            results["generated"] += 1
            
            # Validate problem
            validation_result = self.reality_validator.validate_problem(problem)
            
            if validation_result.is_valid and validation_result.score > 0.6:
                results["validated"] += 1
                
                # Store in knowledge base
                if self.rag_system.store_problem(problem):
                    results["stored"] += 1
                else:
                    results["failed"] += 1
            else:
                logger.info(f"Problem {problem.id} failed validation: {validation_result.score:.2f}")
        
        return results
    
    def query_system(self, query: str) -> str:
        """Query the RICE system"""
        return self.super_intelligence.enhanced_query(query)
    
    def get_system_status(self) -> Dict[str, Any]:
        """Get overall system status"""
        knowledge_stats = self.rag_system.get_knowledge_stats()
        
        return {
            "knowledge_base": knowledge_stats,
            "system_components": {
                "problem_generator": "active",
                "reality_validator": "active", 
                "rag_system": "active",
                "super_intelligence": "active"
            }
        }

# Example usage and demonstration
if __name__ == "__main__":
    # Initialize RICE system
    rice = RICESystem()
    
    # Generate and process some problems
    print("Generating and processing problems...")
    results = rice.generate_and_process_problems(num_problems=5)
    print(f"Processing results: {results}")
    
    # Check system status
    status = rice.get_system_status()
    print(f"\nSystem status: {json.dumps(status, indent=2)}")
    
    # Query the system
    query = "How can we solve energy storage challenges for renewable energy?"
    print(f"\nQuery: {query}")
    response = rice.query_system(query)
    print(f"Enhanced response: {response}")
    
    # Demonstrate knowledge base growth
    print("\nGenerating more problems to show knowledge base growth...")
    rice.generate_and_process_problems(num_problems=10)
    
    updated_status = rice.get_system_status()
    print(f"Updated system status: {json.dumps(updated_status, indent=2)}")
    
    # Demonstrate retrieval of similar problems
    print("\nDemonstrating knowledge retrieval...")
    similar_problems = rice.rag_system.retrieve_similar_problems(
        "renewable energy optimization", top_k=3
    )
    
    print(f"Found {len(similar_problems)} similar problems:")
    for i, problem in enumerate(similar_problems, 1):
        print(f"{i}. Domain: {problem.domain}")
        print(f"   Content: {problem.content[:100]}...")
        print(f"   Scores: Validation={problem.validation_score:.2f}, "
              f"Novelty={problem.novelty_score:.2f}\n")
    
    

# Example configuration for different deployment scenarios

# Research Configuration
RESEARCH_CONFIG = {
    "problem_generator": {
        "model": "gpt-4-turbo",
        "temperature": 0.9,
        "domains": ["mathematics", "physics", "computer_science"],
        "complexity_range": [0.6, 1.0]
    },
    "validator": {
        "model": "gpt-4",
        "temperature": 0.2,
        "validation_threshold": 0.8
    },
    "rag": {
        "embedding_model": "text-embedding-3-large",
        "similarity_threshold": 0.7,
        "max_retrieval": 10
    }
}

# Production Configuration
PRODUCTION_CONFIG = {
    "problem_generator": {
        "model": "gpt-3.5-turbo",
        "temperature": 0.7,
        "domains": ["engineering", "business", "science"],
        "complexity_range": [0.3, 0.8]
    },
    "validator": {
        "model": "gpt-3.5-turbo",
        "temperature": 0.1,
        "validation_threshold": 0.6
    },
    "rag": {
        "embedding_model": "all-MiniLM-L6-v2",
        "similarity_threshold": 0.6,
        "max_retrieval": 5
    }
}