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| title: README | |
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| # Argentis Labs | |
| Evaluation-first ML for decisions in regulated domains: lending, legal, insurance. | |
| We build systems where a wrong answer has a cost: constrained decision models, | |
| human-in-the-loop agents, and the benchmarks that hold them accountable. | |
| Benchmarks are frozen and hashed before any model is trained against them. | |
| - Research: [Locally Correct, Globally Wrong](https://argentislabs.io/research/locally-correct): | |
| LLM judges versus deterministic predicates on authorization records | |
| - Code: [github.com/argentislabs](https://github.com/argentislabs) and | |
| [liquid-legal](https://github.com/j-a-marin/liquid-legal) | |
| - Benchmark: [Argentis-Decision Bench](https://huggingface.co/datasets/argentislabs/argentis-decision-bench): evidence-grounded decisions with abstention | |