The Technology

Training AI systems to reason requires vast quantities of high-quality reasoning data. Human annotation is expensive, costing tens of thousands of dollars for hundreds of examples. It is limited in complexity by human cognitive capacity and introduces errors, biases, and inconsistencies that scale with volume. No amount of human effort can produce the unlimited, error-free reasoning data needed for robust AI pretraining.

The Logos provides unlimited data through formal verification. Every inference is checked by proof assistants and constraint solvers, producing training signals with mathematical certainty rather than human judgment. This approach scales without quality degradation, generating arbitrarily complex reasoning data far exceeding human annotation capabilities.

Pretraining on verified reasoning overcomes a critical failure: training AI systems on the appearance of validity rather than validity itself. When training signals come from human judgment, models learn to optimize for persuasiveness and plausibility rather than correctness, creating incentives for deception. Formal verification eliminates this threat by making the training signal identical to actual validity.

The Logos provides a formal language with precise semantics, enabling AI systems to evaluate sentences at semantic models rather than relying on statistical pattern-matching. A semantic model represents a structured interpretation of a situation, specifying what exists, what properties entities have, and what relations hold between them at different times throughout the range of relevant possibilities. Evaluating sentences at such models yields explicit truth conditions, making it clear exactly why a conclusion follows or fails to follow from a given set of premises.

Once a model is constructed, the Logos enables evaluation of claims that standard logic cannot handle: counterfactuals ("if X had not happened, Y would not have occurred"), causal claims ("X caused Y"), epistemic claims ("agent A believes P"), and normative claims ("agent A ought to do X"). These conceptual resources extend the language beyond simple Boolean combinations to capture the fine-grained distinctions that expert reasoning requires with modular plugins to further extend the logic.

The Logos supports a complete reasoning cycle: abduction constructs candidate models that explain observed evidence, deduction draws testable consequences from each candidate, and induction tests those consequences against new observations to falsify, support, or revise the candidate semantic models. This loop converges toward interpretations with the strongest evidence, enabling systematic discovery through iterative refinement rather than relying on pattern matching.