[Website] • [Original Paper] • [HF Models] • [GitHub]
Omega Research: An enhanced fork of AlphaResearch with self-healing code evolution and toxic trait tracking
| Feature | AlphaResearch | Omega Research |
|---|---|---|
| LLM-Driven Evolution | Yes | Yes |
| Peer-Review Reward Model | Yes | Yes |
| Bug Fixer Loop | No | Yes |
| Toxic Trait Tracking | No | Yes |
| Dynamic Rising Baseline | No | Yes |
| LLM Failure Analysis | No | Yes |
| Retry Logic w/ Backoff | No | Yes |
Omega Research extends the AlphaResearch framework with two major innovations for robust evolutionary code optimization:
- Bug Fixer Loop: Automatic detection and repair of runtime errors during evolution using LLM-based debugging
- Toxic Trait Tracking System: Negative selection pressure that prevents the system from repeatedly exploring failed solution spaces
These enhancements address critical failure modes in LLM-driven code evolution: program crashes and wasted compute on unproductive search directions.
When a generated program fails during evaluation, Omega Research doesn't discard it—it attempts to fix it.
Evolution Loop
│
▼
┌─────────────────┐
│ Generate Child │ ◄── LLM generates code modification
└────────┬────────┘
│
▼
┌─────────────────┐
│ Evaluate │
└────────┬────────┘
│
Error? ──Yes──► ┌─────────────────┐
│ │ Bug Fixer Loop │ ◄── Up to N attempts
│ │ ┌───────────┐ │
No │ │ Analyze │ │
│ │ │ Error │ │
│ │ └─────┬─────┘ │
│ │ ▼ │
│ │ ┌───────────┐ │
│ │ │ Generate │ │
│ │ │ Fix Diff │ │
│ │ └─────┬─────┘ │
│ │ ▼ │
│ │ ┌───────────┐ │
│ │ │ Re-eval │──┼──► Fixed? Add to population
│ │ └───────────┘ │
│ └─────────────────┘
▼
┌─────────────────┐
│ Add to Database │
└─────────────────┘
Key Features:
- Multi-attempt repair: Configurable
max_fix_attempts(default: 3) - Adaptive strategy: Switches from diff-based to full rewrite after repeated failures
- Error context injection: Includes traceback, error type, and parent code for informed fixes
- Defense-in-depth: Catches NaN/Inf values that slip through evaluation
Prevents evolutionary stagnation by remembering and avoiding failed approaches.
┌─────────────────────────────┐
│ Evolutionary Population │
│ ┌───┐ ┌───┐ ┌───┐ ┌───┐ │
│ │ A │ │ B │ │ C │ │ D │ │
│ └───┘ └─┬─┘ └───┘ └───┘ │
└──────────┼──────────────────┘
│
▼
┌─────────────────────────────┐
│ Generate Child from B │
│ Child Score: 0.72 │
│ Best Score: 1.04 │
│ Ratio: 69% < 85% threshold │
└──────────┬──────────────────┘
│
▼
┌─────────────────────────────┐
│ Mark as TOXIC │
│ ┌─────────────────────────┐ │
│ │ LLM Failure Analysis: │ │
│ │ "Excessive normalization│ │
│ │ caused gradient issues"│ │
│ └─────────────────────────┘ │
└──────────┬──────────────────┘
│
▼
┌────────────────────┴────────────────────┐
│ │
▼ ▼
┌─────────────────────┐ ┌─────────────────────┐
│ Failure History │ │ Sampling Filter │
│ (JSON Persistence) │ │ Excludes toxic IDs │
│ - Proposal summary │ │ from parent pool │
│ - Code snapshots │ │ │
│ - LLM explanation │ │ O(1) lookup via │
│ │ │ in-memory Set │
└─────────────────────┘ └─────────────────────┘
│
▼
┌─────────────────────┐
│ LLM Prompt │
│ Injection │
│ "Avoid these │
│ approaches..." │
└─────────────────────┘
Key Features:
- Dynamic rising baseline: Compares against current best, not just parent
- LLM-powered failure analysis: Explains why a program failed using code diff analysis
- Prompt injection: Failed approaches inform future proposal generation
- Persistent history: JSON storage per benchmark for cross-run learning
- O(1) lookup: In-memory set for efficient toxic program filtering
┌─────────────────────────────────────────────────────────────────────┐
│ OMEGA RESEARCH PIPELINE │
├─────────────────────────────────────────────────────────────────────┤
│ │
│ ┌──────────────┐ ┌──────────────┐ ┌──────────────────┐ │
│ │ Proposal │────►│ Reward │────►│ Code │ │
│ │ Generator │ │ Model │ │ Generator │ │
│ │ (LLM) │ │ (7B/API) │ │ (LLM) │ │
│ └──────────────┘ └──────────────┘ └────────┬─────────┘ │
│ ▲ │ │ │
│ │ Score < 5.5? │ │
│ │ │ ▼ │
│ │ Skip ┌──────────────────┐ │
│ │ │ Evaluator │ │
│ │ │ (Sandbox) │ │
│ ┌──────┴───────┐ └────────┬─────────┘ │
│ │ Failure │◄─────────────────────────────────┐ │ │
│ │ History │ Toxic trait detected │ │ │
│ │ (JSON) │ │ ▼ │
│ └──────────────┘ ┌──────┴─────────┐ │
│ │ Error? │ │
│ └──────┬─────────┘ │
│ │ │
│ ┌──────────────────────────┼───────┐ │
│ │ │ │ │
│ ▼ ▼ │ │
│ ┌──────────────┐ ┌─────────────┐ │ │
│ │ Bug Fixer │ │ Toxic Trait│ │ │
│ │ Loop │ │ Check │ │ │
│ │ (LLM Debug) │ │ (vs Best) │ │ │
│ └──────┬───────┘ └──────┬──────┘ │ │
│ │ │ │ │
│ ▼ ▼ │ │
│ ┌─────────────────────────────────────────┴─┐ │
│ │ Program Database │ │
│ │ ┌─────────┐ ┌─────────┐ ┌─────────┐ │ │
│ │ │ Island 1│ │ Island 2│ │ Island N│ │ │
│ │ └────┬────┘ └────┬────┘ └────┬────┘ │ │
│ │ └────────────┼────────────┘ │ │
│ │ │ │ │
│ │ Migration Ring │ │
│ └─────────────────────┴─────────────────────┘ │
│ │
└─────────────────────────────────────────────────────────────────────┘
# Clone the repository
git clone https://github.com/overtimepog/omega-research.git
cd omega-research
# Create virtual environment
python -m venv .venv
source .venv/bin/activate # Linux/Mac
# or: .venv\Scripts\activate # Windows
# Install dependencies
pip install -r requirements.txtcd omega-research
python run.pypython run.py --config configs/my_config.yaml# Enable self-healing bug fixer
evaluator:
enable_bug_fixer: true
max_fix_attempts: 3
# Enable toxic trait tracking
toxic_trait:
enabled: true
threshold: 0.85 # 85% of best score required
comparison_metric: "combined_score"
max_failures_in_prompt: 10
# LLM retry logic
max_diff_generation_retries: 3Results on AlphaResearchComp benchmark suite:
| Problem | Human Best | AlphaResearch | Omega Research |
|---|---|---|---|
| Packing circles (n=26) | 2.634 | 2.636 | 2.636 |
| Packing circles (n=32) | 2.936 | 2.939 | 2.939 |
| Minimizing max-min distance | 12.89 | 12.92 | 12.92 |
| Third autocorrelation | 1.458 | 1.546 | 1.546 |
| Spherical code (n=30) | 0.6736 | 0.6735 | 0.6735 |
| Autoconvolution peak | 0.755 | 0.756 | 0.756 |
| Littlewood polynomials | 32 | 32 | 32 |
| MSTSD (n=30) | 1.04 | 1.04 | 1.0784 |
Key Improvement: Omega Research achieves equivalent or better results with:
- Fewer wasted iterations (toxic trait filtering)
- Higher success rate (bug fixer recovers crashed programs)
- Better sample efficiency (learns from failures)
Main orchestration component managing the evolution loop.
evolve_agent/controller.py- Core evolution logic with bug fixer integration
Toxic trait tracking and negative selection pressure.
evolve_agent/failure_tracker.py- O(1) toxic program filtering
LLM-as-a-judge for proposal scoring and failure analysis.
reward_model/- Trained on ICLR 2017-2024 papers (72% accuracy)
Island-based population management with MAP-Elites.
evolve_agent/database.py- Multi-island evolution with migration
Omega Research builds upon several key advances in LLM-driven code evolution:
| System | Year | Key Innovation |
|---|---|---|
| FunSearch (DeepMind) | 2023 | LLM + Evolutionary search for algorithm discovery |
| AlphaEvolve (DeepMind) | 2025 | Full-file evolution, multi-objective optimization |
| OpenEvolve | 2025 | Open-source island-based evolution with MAP-Elites |
| AlphaResearch | 2025 | Peer-review reward model for research quality |
| SATLUTION | 2025 | Repository-scale evolution for SAT solvers |
| RepairAgent | 2025 | Autonomous LLM debugging agents |
| Omega Research | 2025 | Self-healing evolution + toxic trait memory |
If you use Omega Research, please cite both the original AlphaResearch paper and this work:
@article{yu2025alpharesearch,
title={AlphaResearch: Accelerating New Algorithm Discovery with Language Models},
author={Yu, Zhaojian and Feng, Kaiyue and Zhao, Yilun and He, Shilin and Zhang, Xiao-Ping and Cohan, Arman},
journal={arXiv preprint arXiv:2511.08522},
year={2025}
}
@software{omega_research_2025,
title={Omega Research: Self-Healing Evolutionary Code Optimization with Toxic Trait Tracking},
author={Overtime},
year={2025},
url={https://github.com/overtimepog/omega-research}
}This project is licensed under the MIT License - see the original AlphaResearch repository for details.
- AlphaResearch Team (Yu et al.) for the foundational framework
- OpenEvolve contributors for the evolutionary algorithm infrastructure
- DeepMind for FunSearch/AlphaEvolve research inspiring this direction
- Research on RepairAgent, SOAR, and ShinkaEvolve for bug-fixing and self-improvement techniques