Differentiable Logic

Follow this topic Ask about this topic

Latest papers

18 papers2 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

Differentiable Ternary Temporal Logic Semantics with Polynomial Surrogate Networks
Oct 3, 2026Ryan Matheu, Sai Sandeep Damera, John S. Baras +1Temporal Logic Differentiable Logic
Institute for Systems Research, University of Maryland, College Park, USA
Task-Aware Discretization of Differentiable Logic Gate Networks
Sep 27, 2026Thore GerlachEfficient Neural Network Inference Differentiable Logic Gate Networks
Independent Researcher
Backward through Time, Algebraically
Aug 17, 2026Konstantinos KogkalidisDifferentiable Logic Temporal Logic
Time to Reason: Scalable Neurosymbolic Learning for LTLf via Fuzzy Semantics
Aug 17, 2026Riccardo Andreoni, Andrei Buliga, Alessandro Daniele +4Differentiable Logic Neuro-Symbolic Reasoning
Fondazione Bruno Kessler, Via Sommarive, 18, Trento, 38123, Italy · Free University of Bozen-Bolzano, Via Bruno Buozzi, 1, Bolzano, 39100, Italy · Università di Bologna, Via Mura Anteo Zamboni 7, Bologna, 40126, Italy
sLTN: Structural Logic Tensor Networks
Aug 11, 2026Davide Rinaldi, Luciano SerafiniTensor Networks Logic Tensor Networks
Nokia Bell Labs · Fondazione Bruno Kessler
Differentiable Logic Programming to Mitigate Reasoning Shortcuts in Neurosymbolic Systems
Jul 23, 2026Akihiro Takemura, Katsumi InoueDifferentiable Logic Differentiable Programming
National Institute of Informatics, Tokyo, Japan
Fully Trainable Deep Differentiable Logic Gate Networks and Lookup Table Networks
Jul 10, 2026Wout Mommen, Lars Keuninckx, Matthias Hartmann +2Neural Network Optimization Differentiable Logic Gate Networks
imec, Kapeldreef 75, Leuven, 3001, , Belgium · Vrije Universiteit Brussel, Peinlaan 2, Elsene, 1050, , Belgium
Quantitative Linear Logic for Neuro-Symbolic Learning and Verification
May 13, 2026Thomas Flinkow, Ekaterina Komendantskaya, Matteo Capucci +1Neural Network Verification Differentiable Logic
Maynooth University, Maynooth, IE · University of Southampton, Southampton, UK · University of Strathclyde, Glasgow, UK +1
ANDRE: An Attention-based Neuro-symbolic Differentiable Rule Extractor for Inductive Logic Programming
May 5, 2026Iman Sharifi, Peng Wei, Saber FallahLogic Programming Differentiable Programming
Dept. of Mechanical and Aerospace Engineering, George Washington University, USA · Dept. of Mechanical Engineering Sciences, University of Surrey, UK
Visual Perceptual to Conceptual First-Order Rule Learning Networks
Apr 9, 2026Kun Gao, Davide Soldà, Thomas Eiter +1Differentiable Logic Inductive Logic Programming
Zhongguancun Academy, Beijing, China · Vienna University of Technology (TU Wien), Vienna, Austria · National Institute of Informatics, Tokyo, Japan
Differentiable SpaTiaL: Symbolic Learning and Reasoning with Geometric Temporal Logic for Manipulation Tasks
Apr 3, 2026Licheng Luo, Kaier Liang, Cristian-Ioan Vasile +1Differentiable Logic Spatiotemporal Reasoning
UC Riverside · Lehigh University
Logic-Gated Time-Shared Feedforward Networks for Alternating Finite Automata: Exact Simulation and Learnability
Mar 20, 2026Sahil Rajesh DhayalkarDifferentiable Logic Automata Learning
Arizona State University
TT-Sparse: Learning Sparse Rule Models with Differentiable Truth Tables
Mar 8, 2026Hans Farrell Soegeng, Sarthak Ketanbhai Modi, Thomas PeyrinBoolean Function Learning Interpretable ML
School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore.
Polynomial Surrogate Training for Differentiable Ternary Logic Gate Networks
Feb 27, 2026Sai Sandeep Damera, Ryan Matheu, Aniruddh G. Puranic +1Differentiable Logic Gate Networks Differentiable Logic
University of Maryland, College Park, MD, USA
Knowledge, Rules and Their Embeddings: Two Paths towards Neuro-Symbolic JEPA
Feb 25, 2026Yongchao Huang, Hassan RazaDifferentiable Logic Neuro-Symbolic Reasoning
Modal Logic Neural Networks
Dec 3, 2025Antonin Sulc, Noor NaddourLogical Reasoning Differentiable Logic
Lawrence Berkeley National Laboratory, Berkeley, CA, USA · The University of Queensland, Brisbane, QLD, Australia
Learning Interpretable Differentiable Logic Networks for Time-Series Classification
Aug 24, 2025Chang Yue, Niraj K. JhaTime Series Classification Interpretable ML
Department of Electrical and Computer Engineering, Princeton University, Princeton, NJ 08544, USA
Learning Interpretable Differentiable Logic Networks for Tabular Regression
May 29, 2025Chang Yue, Niraj K. JhaEfficient Neural Network Inference Differentiable Logic Gate Networks
Department of Electrical and Computer Engineering, Princeton University, Princeton, NJ 08544, USA

Oct 3, 2026J/K move · Enter open · S save

Differentiable Ternary Temporal Logic Semantics with Polynomial Surrogate Networks

Ryan Matheu, Sai Sandeep Damera, John S. Baras, Calin Belta

Institute for Systems Research, University of Maryland, College Park, USA

Temporal Logic Differentiable Logic Robotic Control

Open paper AskPDF arXiv

Temporal logic provides designers a formal reasoning tool for specifying complex spatio-temporal behaviors and tasks for autonomous systems. Many techniques exist for control synthesis according to temporal logic specifications, spanning a large variety of systems, including robotics. However, the vast majority of implementations are limited to Boolean temporal logics. Boolean temporal logic does not have an innate mechanism for quantifying abstention, rather verdicts are either definitively True or False. Recent work demonstrates that ternary logic is a capable formalism for reasoning about robotic behaviors, with the natural ability to classify uncertainty with the Unknown literal. Ternary temporal logic is still in its infancy, with work limited to synthesis for linear systems and monitoring. This work proposes a probabilistic relaxation of ternary temporal logic that allows for value and gradient computation through the same network while retaining exact verdicts on actual data. The relaxed network can be used as an objective in gradient-based control synthesis for both offline and online robotic control applications. We demonstrate the utility of our framework with two robotic manipulator tasks, one offline and one online, involving intermediate checking and sequencing of subtasks to demonstrate the benefit of our approach in terms of correct and timely execution.