Seeking truth and integrity in the age of generative AI.
Coherascent Labs leads research at the intersection of Neuro-Symbolic AI and Mathematical Optimization, grounded in prior work building production-grade autonomous agents and GPT-style Transformers with a focus on rigor and reproducibility. The long-term goal is to reduce hallucinations in generative models by grounding outputs in formal logic and mathematically coherent constraints.
Pillar One
Neuro-Symbolic Bridge - Connecting explicit rule systems with data-driven Transformer architectures.
Deterministic Architecture - Building control layers that force probabilistic agents to adhere to formal logic structures.
Continuous DPLL & Optimization Cognition - Investigating SAT-style reasoning inside differentiable vector spaces and designing gradient ascent pathways to reveal latent cognitive states.
Pillar Two
The Educational Platform (In Development) - A cross-platform ecosystem designed to force active, offline cognitive engagement through tactile learning.
Computer Vision & Verifiable Grading - Utilizing our truth-aligned architectural principles, the app ingests handwritten logic, parses the mathematical steps, and delivers rigorously accurate, hallucination-free feedback.
Dynamic Remediation Engine - Scaling from elementary arithmetic to university-level mathematics, the system maps cognitive bottlenecks and generates targeted, adaptive practice loops driven by streak-based gamification and an immersive space-themed interface.
Theoretical rigor requires real-world validation. Our foundational research in mathematical optimization and high-dimensional calculus directly powers the deterministic grading engines of our educational platform. Conversely, deploying these models in a live, adaptive learning environment stress-tests our architectures, ensuring our pursuit of hallucination reduction is grounded in practical, measurable utility.