The Adaptive Classifier project introduces a novel approach to optimizing Large Language Model (LLM) interactions through continuous learning and dynamic adaptation. This system addresses two critical challenges in LLM deployment:

1. Configuration Optimization: Automatically determining optimal temperature settings for different query types
2. Intelligent Routing: Efficiently directing queries between different LLM models based on complexity

Repository: github.com/codelion/adaptive-classifier

Technical Innovation

Core Architecture

The system combines three key components that work together to enable adaptive decision-making:

1. Transformer Embeddings: Utilizing state-of-the-art language models for query representation
2. Prototype Memory: Maintaining dynamic class prototypes for rapid adaptation
3. Elastic Weight Consolidation: Preventing catastrophic forgetting while learning new patterns

Key Features

• Dynamic Class Addition: Ability to incorporate new categories without retraining
• Continuous Learning: Updates knowledge base from successful interactions
• Prototype-Based Memory: Fast, efficient similarity matching
• Neural Adaptation Layer: Refined decision boundaries through continuous training

LLM Configuration Optimization

Temperature Classification System

The system automatically categorizes queries into temperature ranges:

• DETERMINISTIC (T: 0.0-0.1): Precise, factual responses
• FOCUSED (T: 0.2-0.5): Structured, technical responses
• BALANCED (T: 0.6-1.0): Natural, conversational responses
• CREATIVE (T: 1.1-1.5): Varied and imaginative outputs
• EXPERIMENTAL (T: 1.6-2.0): Maximum variability

Optimization Process

1. Query Analysis:

   • Embedding generation using transformer models
   • Prototype matching against known patterns
   • Neural network classification

2. Temperature Selection:

   • Class prediction based on query characteristics
   • Confidence-weighted temperature assignment
   • Continuous learning from response quality

3. Performance Monitoring:

   • Round-trip consistency checking
   • Response quality evaluation
   • Adaptation based on outcomes

Intelligent LLM Routing

Router Architecture

The system implements a two-tier routing strategy:

1. High-Complexity Route:

   • Advanced reasoning tasks
   • Multi-step problem solving
   • Creative generation
   • Technical analysis

2. Low-Complexity Route:

   • Basic queries
   • Simple transformations
   • Straightforward lookups
   • Format conversions

Evaluation Results

Performance metrics from arena-hard-auto-v0.1 dataset (500 queries):

Technical Implementation

Core Components

class AdaptiveClassifier:
    def __init__(self, model_name: str):
        self.model = AutoModel.from_pretrained(model_name)
        self.memory = PrototypeMemory(self.model.config.hidden_size)
        self.adaptive_head = AdaptiveHead(
            input_dim=self.model.config.hidden_size,
            num_classes=0  # Dynamically updated
        )

    def add_examples(self, texts: List[str], labels: List[str]):
        embeddings = self._get_embeddings(texts)
        for text, embedding, label in zip(texts, embeddings, labels):
            self.memory.add_example(Example(text, label, embedding))
            self._update_neural_layer()

    def predict(self, text: str) -> List[Tuple[str, float]]:
        embedding = self._get_embeddings([text])[0]
        proto_preds = self.memory.get_nearest_prototypes(embedding)
        neural_preds = self.adaptive_head(embedding)
        return self._combine_predictions(proto_preds, neural_preds)

Memory System

class PrototypeMemory:
    def __init__(self, embedding_dim: int):
        self.examples = defaultdict(list)
        self.prototypes = {}
        self.index = faiss.IndexFlatL2(embedding_dim)

    def add_example(self, example: Example):
        self.examples[example.label].append(example)
        self._update_prototype(example.label)
        self._maintain_index()

    def get_nearest_prototypes(self, query: torch.Tensor) -> List[Tuple[str, float]]:
        distances, indices = self.index.search(query.unsqueeze(0).numpy(), k=5)
        return [(self.index_to_label[idx], self._compute_similarity(dist)) 
                for dist, idx in zip(distances[0], indices[0])]

Innovation in Agentic AI

This project advances the field of agentic AI in several key ways:

1. Autonomous Decision Making: The system independently learns to optimize LLM interactions without human intervention.

2. Adaptive Behavior: Continuous learning allows the agent to improve its decision-making based on experience.

3. Resource Optimization: Intelligent routing and configuration management lead to more efficient resource utilization.

4. Scalable Architecture: The system can handle new use cases and models without architectural changes.

Business Impact

The implementation demonstrates significant business value:

• Cost Reduction: 32.40% cost savings through intelligent routing
• Quality Maintenance: Preserved response quality while reducing resource usage
• Operational Efficiency: Automated configuration management
• Scalability: Easy integration with existing LLM infrastructure

Future Directions

1. Multi-Modal Support: Extend to handle image and audio inputs
2. Federated Learning: Enable distributed learning across deployments
3. Advanced Routing: Implement more sophisticated routing strategies
4. Custom Metrics: Add domain-specific evaluation metrics

Conclusion

The Adaptive Classifier project demonstrates the potential of agentic AI in optimizing LLM operations. Through continuous learning and intelligent routing, it achieves significant cost savings while maintaining response quality. The system's ability to adapt to new patterns and automatically optimize configurations makes it a valuable tool for organizations deploying LLM technologies at scale.

References

1. RouteLLM: Learning to Route LLMs with Preference Data (arXiv:2406.18665)
2. Transformer^2: Self-adaptive LLMs (arXiv:2501.06252)
3. Protoformer: Embedding Prototypes for Transformers (arXiv:2206.12710)
4. Overcoming catastrophic forgetting in neural networks (arXiv:1612.00796)