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Found 14 papers, 7 papers with code
Baldur: Whole-Proof Generation and Repair with Large Language Models
Recent work has developed methods to automate formal verification using proof assistants, such as Coq and Isabelle/HOL, e. g., by training a model to predict one proof step at a time, and using that model to search through the space of possible proofs.
Autoformalization with Large Language Models
Autoformalization is the process of automatically translating from natural language mathematics to formal specifications and proofs.
Ranked #1 on
Automated Theorem Proving
on miniF2F-test
(using extra training data)
Memorizing Transformers
Language models typically need to be trained or finetuned in order to acquire new knowledge, which involves updating their weights.
Self-attention Does Not Need $O(n^2)$ Memory
We present a very simple algorithm for attention that requires $O(1)$ memory with respect to sequence length and an extension to self-attention that requires $O(\log n)$ memory.
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).
Mathematical Reasoning via Self-supervised Skip-tree Training
We examine whether self-supervised language modeling applied to mathematical formulas enables logical reasoning.
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.
Mathematical Reasoning in Latent Space
We design and conduct a simple experiment to study whether neural networks can perform several steps of approximate reasoning in a fixed dimensional latent space.
Learning to Reason in Large Theories without Imitation
Our experiments show that the theorem prover trained with this exploration mechanism outperforms provers that are trained only on human proofs.
Ranked #3 on
Automated Theorem Proving
on HOList benchmark
Learning Heuristics for Automated Reasoning through Reinforcement Learning
We demonstrate how to learn efficient heuristics for automated reasoning algorithms through deep reinforcement learning.
HOList: An Environment for Machine Learning of Higher-Order Theorem Proving
We present an environment, benchmark, and deep learning driven automated theorem prover for higher-order logic.
Ranked #2 on
Automated Theorem Proving
on HOList benchmark
A Model Counter's Guide to Probabilistic Systems
In this paper, we systematize the modeling of probabilistic systems for the purpose of analyzing them with model counting techniques.
Learning Heuristics for Quantified Boolean Formulas through Deep Reinforcement Learning
We demonstrate how to learn efficient heuristics for automated reasoning algorithms for quantified Boolean formulas through deep reinforcement learning.
Temporal Logics for Hyperproperties
Standard temporal logics such as LTL, CTL, and CTL* can refer only to a single path at a time, hence cannot express many hyperproperties of interest.
Logic in Computer Science
