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Found 11 papers, 7 papers with code
Lightweight Online Learning for Sets of Related Problems in Automated Reasoning
We present Self-Driven Strategy Learning ($\textit{sdsl}$), a lightweight online learning methodology for automated reasoning tasks that involve solving a set of related problems.
nl2spec: Interactively Translating Unstructured Natural Language to Temporal Logics with Large Language Models
A rigorous formalization of desired system requirements is indispensable when performing any verification task.
Iterative Circuit Repair Against Formal Specifications
We propose a separated hierarchical Transformer for multimodal representation learning of the formal specification and the circuit.
Formal Specifications from Natural Language
We study the generalization abilities of language models when translating natural language into formal specifications with complex semantics.
Attention Flows for General Transformers
In this paper, we study the computation of how much an input token in a Transformer model influences its prediction.
Generating Symbolic Reasoning Problems with Transformer GANs
We study the capabilities of GANs and Wasserstein GANs equipped with Transformer encoders to generate sensible and challenging training data for symbolic reasoning domains.
Neural Circuit Synthesis from Specification Patterns
We train hierarchical Transformers on the task of synthesizing hardware circuits directly out of high-level logical specifications in linear-time temporal logic (LTL).
Efficient Monitoring of Hyperproperties using Prefix Trees
Hyperproperties, such as non-interference and observational determinism, relate multiple computation traces with each other and are thus not monitorable by tools that consider computations in isolation.
Logic in Computer Science
Realizing Omega-regular Hyperproperties
We furthermore studied the realizability problem of HyperQPTL.
Logic in Computer Science
Teaching Temporal Logics to Neural Networks
We study two fundamental questions in neuro-symbolic computing: can deep learning tackle challenging problems in logics end-to-end, and can neural networks learn the semantics of logics.
Constraint-Based Monitoring of Hyperproperties
Verifying hyperproperties at runtime is a challenging problem as hyperproperties, such as non-interference and observational determinism, relate multiple computation traces with each other.
Logic in Computer Science
