Neuro-symbolic loop invariant synthesis (via Latent Grammar Flow) will reduce the FP32-BF16 loss-landscape mismatch barrier in differentiable logic-based controllers for active foam tissue models by ≥30%, as measured by barrier height (nats) in parameter space.

Adversarial Debate Score

38% survival rate under critique

Supporting Research Papers

• Neuro-Symbolic ODE Discovery with Latent Grammar Flow

  Understanding natural and engineered systems often relies on symbolic formulations, such as differential equations, which provide interpretability and transferability beyond black-box models. We int...

• Neuro-Symbolic Software Verification: Hyper-charging Local Language Models with Symbolic Reasoning at Scale

  Loop invariant synthesis remains a central and pivotal bottleneck in formal software verification. Recent LLM-based Neuro-Symbolic tools have achieved impressive solve rates. However, these tools rely...

• Quantitative Linear Logic for Neuro-Symbolic Learning and Verification

  Differentiable Logics are deployed in neuro-symbolic learning tasks as a way of embedding logical constraints in the training objective of neural networks. A differentiable logic consists of a syntax ...

• Explicit Fuzzy Logic in the Feed-Forward Layer: Self-Forgetting Quantifiers Discover Legible Grammatical-Licensing Detectors

  A transformer's feed-forward (FFN) sublayer materializes the distinctions attention gathers, yet gives no account of what it computes. In a parameter-neutral replacement, each hidden unit is an explic...

• Self-Supervised Flow Matching for Scalable Multi-Modal Synthesis

  Strong semantic representations improve the convergence and generation quality of diffusion and flow models. Existing approaches largely rely on external models, which require separate training, opera...

Formal Verification

Source