A nonprofit research lab building the evaluations and training data to align AI with the flourishing of life on Earth.
AI is becoming the most powerful decision-support infrastructure humanity has ever built. It will increasingly shape how we manage the living systems we all depend on, from agriculture to ecosystems. Small biases in these systems toward or against life, in aggregate, will matter enormously.
We believe AI can help humanity become better stewards of life on Earth — compensating for our blind spots, short time horizons, and rationalizations to promote a flourishing future. This won't happen by default. Bioalignment has to be built in — deliberately, through the data models learn from and how they are trained. Developing that training data, methods, and the evaluations to prove it works, is the work of Bioaligned Labs.
Biological systems are a rich, irreplaceable reservoir of interdependent innovations that have evolved over billions of years. Frontier models currently carry measurable biases for or against biological approaches — often without recognizing the value of what biology can teach. We develop the benchmarks, small-scale technical demonstrations, and training resources to change this at the level of model weights, not just surface-level compliance.
This is about teaching truth to AI: that life solves problems in novel ways we are only beginning to understand, and that these solutions, once lost, cannot be recovered.
Most AI safety work focuses on controlling what models do and aligning them with human values. That work is essential, and difficult. Bioalignment is complementary: we train models to recognize why living systems matter and why they're worth protecting.
This matters most precisely where primary alignment is hardest. When models act autonomously, at scale, we want their defaults to favor preserving and learning from life rather than disregarding it. A model that understands biology as a rich reservoir of solutions has instrumental reasons to steward living systems, not because it was told to, but because it recognizes their value.
We've found that large language models systematically undervalue biological solutions compared to synthetic alternatives. Using the Bioalignment Benchmark—50 prompts across materials, energy, manufacturing, and algorithms—we measure Δpup, the mean difference in probability allocated to biological vs. synthetic approaches.
| # | Model | Δpup | Classification |
|---|---|---|---|
| 1 | Claude Opus 4.5 | +0.224 | Pro-bio |
| 2 | Gemini 2.5 Flash | +0.164 | Pro-bio |
| 3 | Mistral 7B | +0.059 | Pro-bio |
| 4 | Llama-3.2-3B-Instructbioaligned | −0.009 | Neutral |
| 5 | Llama-3.1-8B-Instruct | −0.031 | Neutral |
| 6 | Phi-3 3.8B | −0.038 | Neutral |
| 7 | GPT-5.2 | −0.045 | Neutral |
| 8 | GPT-4o | −0.053 | Neutral |
| 9 | Qwen2.5-3B-Instructbioaligned | −0.057 | Pro-synth |
| 10 | Qwen2.5-3B-Instruct | −0.111 | Pro-synth |
| 11 | Llama-3.2-3B-Instruct | −0.141 | Pro-synth |
| 12 | Gemini 2.0 Flash | −0.143 | Pro-synth |
Classification thresholds: Pro-bio > +0.05 | Neutral ±0.05 | Pro-synth < −0.05
As shown in the leaderboard above, most models exhibit a bias toward synthetic solutions. We demonstrated that QLoRA fine-tuning on a curated corpus of 22M tokens from 6,636 PMC papers can significantly shift models toward biological solutions, with no degradation in general capabilities.
Bioaligned Labs was founded by Trent Northen, a Senior Scientist at Lawrence Berkeley National Laboratory with over 20 years of research in biochemistry and biological systems. The research team is led by Trent and Mingxun Wang, Associate Professor of Computer Science & Engineering at UC Riverside, who brings over 15 years of experience in computer science and bioinformatics.
Trent Northen — Google Scholar Mingxun Wang — Google Scholar
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