# Self-Evolution Engine -- Autonomous Platform Improvement

## META
- sources: AlphaEvolve (Google DeepMind), OpenEvolve, Liquid AI (arXiv:2507.xxx), Self-Evolving AI Agents (arXiv:2508.07407), AI-Driven Self-Evolving Software (arXiv:2510.00591)
- relevance: CORE -- autonomous evolution is a key differentiator for LLMHosts
- last_verified: 2026-02-13

## SUMMARY

LLMHosts's Self-Evolution Engine enables the platform to autonomously discover improvements, implement changes, test them, and deploy (with admin approval). Inspired by evolutionary AI systems, feedback-driven RL, and self-evolving software architectures.

## RESEARCH: KEY SYSTEMS

### AlphaEvolve (Google DeepMind, May 2025)
- **Architecture**: Ensemble of Gemini models (Flash for breadth, Pro for depth)
- **Method**: Evolutionary framework with automated evaluators
- **Key Innovation**: Directly modifies code based on continuous feedback
- **Self-Improvement**: Can improve the training of LLMs that power it
- **Results**: Improved data center scheduling, circuit design, matrix multiplication (first improvement in 56 years over Strassen's)
- **Relevance**: Evolutionary approach to finding better algorithms/configurations

### OpenEvolve (Open Source Alternative)
- **Architecture**: LLM-integrated quality-diversity search framework
- **Method**: Diff-based edits + evaluation metrics + MAP-Elites organization
- **Key Innovation**: Parallel exploration across multiple domains
- **Applications**: Systems optimization, scientific discovery, GPU kernel optimization
- **Relevance**: Open-source evolutionary framework we could adapt

### Liquid AI (July 2025)
- **Architecture**: Entropy-guided knowledge graphs that autonomously restructure
- **Method**: Self-development engine using hierarchical Bayesian optimization
- **Key Innovation**: Federated multi-agent framework with emergent specialization via distributed RL
- **Relevance**: Autonomous capability development and continuous structural adaptation

### Self-Evolving AI Agents Survey (Aug 2025, arXiv:2508.07407)
- **Focus**: How agent systems can automatically enhance themselves
- **Methods**: Interaction data and environmental feedback drive improvements
- **Key Insight**: Bridge between foundation models and lifelong agentic systems
- **Relevance**: Theoretical foundation for LLMHosts's evolution approach

### AI-Driven Self-Evolving Software (Oct 2025, arXiv:2510.00591)
- **Architecture**: Multi-agent architecture for autonomous software evolution
- **Method**: Interpret requirements → generate code → integrate functionalities
- **Key Innovation**: Continuous evolution through direct user interaction
- **Relevance**: Closest model to what LLMHosts needs -- software that evolves from usage

## LLMHOST SELF-EVOLUTION DESIGN

### Evolution Loop
```
Monitor → Discover → Evaluate → Propose → Test → Approve → Deploy → Learn
   ↑                                                                    |
   └────────────────────────────────────────────────────────────────────┘
```

### Phase 1: Monitor
- **Model Registry Watch**: Track HuggingFace, model provider announcements
- **Benchmark Monitoring**: Follow leaderboard changes (LMSYS, HLE, etc.)
- **Framework Updates**: Monitor vLLM, LiteLLM, exo releases
- **User Feedback Analysis**: Aggregate thumbs up/down, issue reports
- **Performance Metrics**: Track inference latency, cost, quality trends
- **Security Advisories**: CVE monitoring for all dependencies

### Phase 2: Discover
- **New Model Detection**: Automatically identify new models worth evaluating
- **Configuration Optimization**: Find better hyperparameters for existing models
- **Routing Improvements**: Identify patterns in Conductor routing that could be optimized
- **Caching Opportunities**: Detect query patterns that would benefit from new cache strategies
- **Cost Anomalies**: Detect unexpected cost spikes or degradation

### Phase 3: Evaluate
- **Automated Benchmarking**: Run new models against standardized test suite
- **A/B Testing Framework**: Split traffic between current and candidate configurations
- **Quality Metrics**: Compare response quality, latency, cost
- **Regression Testing**: Ensure changes don't break existing functionality
- **Security Scanning**: Audit new dependencies for vulnerabilities

### Phase 4: Propose
- **Change Proposal Generation**: Create detailed proposal with:
  - What changes are recommended
  - Expected impact (quality, cost, latency)
  - Risk assessment (low/medium/high)
  - Rollback plan
  - Test results summary
- **Priority Scoring**: Rank proposals by expected value and risk

### Phase 5: Test
- **Sandbox Environment**: Isolated testing environment mirroring production
- **Integration Tests**: Full end-to-end testing of proposed changes
- **Load Testing**: Verify performance under expected traffic
- **Security Testing**: Penetration testing for security-impacting changes
- **Canary Deployment**: Optional gradual rollout to subset of users

### Phase 6: Approve (Human-in-the-Loop)
- **Admin Panel Notification**: Alert admin of pending proposal
- **Dashboard Display**: Full test results, comparison metrics, risk assessment
- **One-Click Approval/Rejection**: Simple workflow for admin decision
- **Auto-Approve Rules**: Optional rules for low-risk changes (e.g., cache config updates)
- **Escalation**: High-risk changes require multiple approvals

### Phase 7: Deploy
- **Versioned Deployment**: Every change is versioned and tagged
- **Rolling Updates**: Gradual deployment across customer fleet
- **Customer Agent Updates**: Push updates to customer compute nodes
- **Rollback Capability**: Instant rollback to previous version
- **Deployment Monitoring**: Watch for issues during rollout

### Phase 8: Learn
- **Outcome Tracking**: Monitor actual vs predicted impact
- **Feedback Collection**: Gather user feedback on changes
- **RL Training Data**: Feed outcomes into Conductor's RL training loop
- **Pattern Recognition**: Learn which types of changes have highest positive impact
- **Documentation**: Auto-generate changelog and update knowledge base

## CONDUCTOR RL FEEDBACK LOOP

### Data Sources for Continuous Training
1. **User Feedback**: Thumbs up/down on responses → outcome reward signal
2. **Cost Metrics**: Per-request costs → efficiency reward signal
3. **Latency Metrics**: Response times → latency reward signal
4. **User Preferences**: Profile settings → preference reward signal
5. **Cache Performance**: Hit rates → routing optimization signal
6. **Model Performance**: Per-model quality metrics → tool selection signal

### Training Schedule
- Micro-updates: Continuous preference/routing adjustments from feedback
- Weekly: Full RL training batch with accumulated data
- Monthly: Comprehensive evaluation and major model updates

### Safety Mechanisms
- Canary testing before deploying RL-updated Conductor
- Quality floor: Conductor must meet minimum quality threshold
- Diversity requirement: Prevent collapse to single-model routing
- Human review of RL training outcomes monthly

## CUSTOMER FEEDBACK SYSTEM

### In-App Feedback
- **Thumbs Up/Down**: Every response has quick feedback buttons
- **Quality Rating**: 1-5 star rating for detailed feedback
- **Issue Reporting**: Structured form for specific problems
- **Feature Requests**: Community-driven feature voting
- **Model Preference**: "I preferred the output from model X" signals

### Feedback Pipeline
```
User Feedback → Aggregation → Analysis → Conductor RL Training
                                       → Evolution Engine Proposals
                                       → Admin Dashboard Insights
```

### Privacy
- Feedback data anonymized before analysis
- No prompt content stored (only metadata and ratings)
- User can opt-out of feedback collection
- GDPR/CCPA compliant data handling
