# PyReason: Software for Open World Temporal Logic

arXiv: https://arxiv.org/abs/2302.13482 · HTML: https://mlsanity.com/paper/2302.13482/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning.
- Builds on 9 of its 36 references that are in the map.
- First posted on arXiv 27 Feb 2023.

## Placed in

- LLM reasoning / [Reasoning benchmarks and evaluation](https://mlsanity.com/lr/reasoning-benchmarks-and-evaluation/)

## Builds on (most cited first, 9 of 9)

- [Learning Explanatory Rules from Noisy Data](https://mlsanity.com/paper/1711.04574/) (2017)
- [Logic Tensor Networks](https://mlsanity.com/paper/2012.13635/) (2020)
- [Ontology Reasoning with Deep Neural Networks](https://mlsanity.com/paper/1808.07980/) (2018)
- [Neuro-Symbolic Inductive Logic Programming with Logical Neural Networks](https://mlsanity.com/paper/2112.03324/) (2021)
- [Differentiable Inductive Logic Programming for Structured Examples](https://mlsanity.com/paper/2103.01719/) (2021)
- [Extensions to Generalized Annotated Logic and an Equivalent Neural Architecture](https://mlsanity.com/paper/2302.12195/) (2023)
- [MANCaLog: A Logic for Multi-Attribute Network Cascades (Technical Report)](https://mlsanity.com/paper/1301.0302/) (2013)
- [Mining for Causal Relationships: A Data-Driven Study of the Islamic State](https://mlsanity.com/paper/1508.01192/) (2015)
- [Reasoning about Complex Networks: A Logic Programming Approach](https://mlsanity.com/paper/2209.15067/) (2022)

## Built on by (most cited first, 16 of 16)

- [Towards Cognitive AI Systems: a Survey and Prospective on Neuro-Symbolic AI](https://mlsanity.com/paper/2401.01040/) (2024)
- [The Role of Predictive Uncertainty and Diversity in Embodied AI and Robot Learning](https://mlsanity.com/paper/2405.03164/) (2024)
- [Rule-Based Error Detection and Correction to Operationalize Movement Trajectory Classification](https://mlsanity.com/paper/2308.14250/) (2023)
- [Diversity Measures: Domain-Independent Proxies for Failure in Language Model Queries](https://mlsanity.com/paper/2308.11189/) (2023)
- [On the use of neurosymbolic AI for defending against cyber attacks](https://mlsanity.com/paper/2408.04996/) (2024)
- [Scalable Semantic Non-Markovian Simulation Proxy for Reinforcement Learning](https://mlsanity.com/paper/2310.06835/) (2023)
- [Sea-cret Agents: Maritime Abduction for Region Generation to Expose Dark Vessel Trajectories](https://mlsanity.com/paper/2502.01503/) (2025)
- [Geospatial Trajectory Generation via Efficient Abduction: Deployment for Independent Testing](https://mlsanity.com/paper/2407.06447/) (2024)
- [Machine Learning Model Integration with Open World Temporal Logic for Process Automation](https://mlsanity.com/paper/2506.17776/) (2025)
- [Autonomous Business System via Neuro-symbolic AI](https://mlsanity.com/paper/2601.15599/) (2026)
- [From We to Me: Theory Informed Narrative Shift with Abductive Reasoning](https://mlsanity.com/paper/2603.03320/) (2026)
- [Continual Reasoning: Non-Monotonic Reasoning in Neurosymbolic AI using Continual Learning](https://mlsanity.com/paper/2305.02171/) (2023)
- [CogNarr Ecosystem: Facilitating Group Cognition at Scale](https://mlsanity.com/paper/2407.18945/) (2024)
- [CrossRF: A Domain-Invariant Deep Learning Approach for RF Fingerprinting](https://mlsanity.com/paper/2505.18200/) (2025)
- [Lattice Annotated Temporal (LAT) Logic for Non-Markovian Reasoning](https://mlsanity.com/paper/2509.02958/) (2025)
- [Reinforcement Learning Enhancement Using Vector Semantic Representation and Symbolic Reasoning for Human-Centered Autonomous Emergency Braking](https://mlsanity.com/paper/2602.05079/) (2026)

## Abstract (arXiv)

The growing popularity of neuro symbolic reasoning has led to the adoption of various forms of differentiable (i.e., fuzzy) first order logic. We introduce PyReason, a software framework based on generalized annotated logic that both captures the current cohort of differentiable logics and temporal extensions to support inference over finite periods of time with capabilities for open world reasoning. Further, PyReason is implemented to directly support reasoning over graphical structures (e.g., knowledge graphs, social networks, biological networks, etc.), produces fully explainable traces of inference, and includes various practical features such as type checking and a memory-efficient implementation. This paper reviews various extensions of generalized annotated logic integrated into our implementation, our modern, efficient Python-based implementation that conducts exact yet scalable deductive inference, and a suite of experiments. PyReason is available at: github.com/lab-v2/pyreason.