Research / 2025
Paradox is an AI reasoning lab building transparent models that can explain every step of their inference — no black boxes, no hidden chains, just verifiable thought.
Read the researchResearch Areas
How do we build systems that reason in ways humans can audit? Our research program spans interpretability, formal verification, and the architecture of deliberation itself.
01 / Interpretability
Reverse-engineering the internal representations of large models to produce human-readable traces of every inference step. We map circuits, not vibes.
02 / Verification
Training models to generate machine-checkable proofs alongside their answers. Every claim arrives with its own certificate of correctness.
03 / Alignment
Methods for supervising systems that exceed human-level capability in narrow domains. Debate, recursion, and recursive reward modeling.
04 / Architecture
Novel architectures that separate generation from critique — models that draft, challenge, and revise before they speak. Slow thinking, by design.
05 / Safety
Probing techniques that detect when a model's stated reasoning diverges from its actual computational path. Honesty as a measurable property.
06 / Evaluation
Open benchmarks that test multi-step inference under adversarial pressure. We publish the failures, not just the scores.
Flagship Model
P-1
Paradox-1 / 340B / Audit Edition
Paradox-1 is a 340-billion-parameter reasoning model trained to produce a verifiable chain-of-thought alongside every output. Each step is annotated with its evidence, its confidence, and the circuit trace that produced it.
It does not just answer. It shows you how it arrived — and flags the steps it is least sure about.
The Lab
Twenty-three researchers across ML, formal methods, and philosophy. We publish openly, collaborate broadly, and move deliberately.
Chief Scientist
Head of Interpretability
Head of Formal Methods
Head of Safety
Publications
We publish our methods, our data, and our failures. Every paper is accompanied by reproducible code and audit logs.
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