Logos Laboratories
Verified Reasoning for Trustworthy AI
The Logos enables AI systems to reason with mathematical certainty in an interpreted formal language, constructing and revising models of the world for advanced planning and scientific discovery.
The Problems
Current AI systems are opaque and cannot verify their reasoning.
Only Human
Reasoning data collected from expert humans is expensive to annotate, incomplete, difficult to verify, and limited in quantity. Training large language models on what humans take to be valid reasoning inherits the inaccuracies, limitations in complexity, and vulnerabilities to deception that are only human.
Unverified
Training on annotated human reasoning data rewards the appearance of validity rather than validity itself, making it impossible tell valid and invalid reasoning apart. Without any mechanism to verify inferences, even the most sophisticated AI systems cannot be relied on in full confidence in high-stakes domains.
Uninterpreted
Pattern matching on natural language does not train AI systems to construct and revise transparent representations of the context in question. In addition to limiting oversight, AI systems are not able to engage in the abductive and inductive reasoning employed throughout the sciences.
The Solution
Generative AI works best under a system of constraints.
Training AI systems to master reasoning in the Logos generates unlimited verified synthetic reasoning data to pretrain models as well as verification support at inference. The following packages provide the positive and negative RL signals for training AI systems to produce verified reasoning in the Logos and to interpret the Logos by constructing and revising structured representations from input data.
Proof-Checker
Derives valid inferences with mathematical certainty. Generates proof receipts that serve as positive reinforcement learning signals for AI training.
Model-Checker
Searches for counterexamples using constraint solving. Validates or refutes inferences, providing corrective RL signals when reasoning is invalid.
Model-Builder
Transforms natural language into formal semantic models that interpret the Logos to draw inferences. Provides a foundation for abductive and inductive reasoning.
Advantages
A methodology for superhuman reasoning in industry and science.
Infinite Clean Data
Design and generate an unlimited supply of verified training data without human annotation or limits on complexity. Supports synthetic data designed for bespoke applications.
Transparency
Training AI systems to construct structured representations to interpret and reason within the Logos enables transparent oversight and with proof receipts that witness every inference.
Dual Verification
Valid inferences are verified by the Proof-Checker (Lean 4) to provide positive RL signals and invalid inferences are verified by the Model-Checker (Z3) to provide corrective RL signals.
Extensible Architecture
A modular plugin system extends the Logos to include logical operators that are relevant for a specific application, e.g., legal reasoning requires epistemic and normative operators in addition to the foundations.
Applications
Formal verified reasoning transforms decision-making across critical domains.
Medical Decision Support
Counterfactual reasoning for treatment evaluation and clinical planning with verifiable audit trails.
Legal & Compliance
Evidence tracking, belief and motive analysis, obligation and permission representation for legal reasoning.
Financial Services
Auditable reasoning for risk analysis, compliance verification, and transparent decision-making.
Autonomous Planning
Full counterfactual scrutiny of plans before execution, with mathematical guarantees on reasoning validity.
Multi-Agent Coordination
Reasoning about other agents' beliefs, preferences, and obligations for safe multi-agent systems.
Scientific Research
Abductive reasoning for hypothesis generation combined with inductive testing and formal verification.
Explore
Discover our research, technology, and team.
Research
Explore our publications, formal specifications, and foundational work in logic and semantics.
Technology
Discover the Model-Checker, Proof-Assistant, and other tools built on rigorous foundations.
Applications
See how our technology applies to AI safety, legal reasoning, and formal verification.
Team
Meet the researchers, engineers, and advisors building the future of formal reasoning.
Contact
Reach out for investment inquiries, collaboration opportunities, or general questions.