| title | A Foundation Model for Zero-Shot Logical Rule Induction |
|---|---|
| authors | Yin Jun Phua |
| date | 2026-05-06 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2605.04916 |
| upvotes | 3 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf code |
| abstract | Inductive Logic Programming (ILP) learns interpretable logical rules from data. Existing methods are transductive: their learned parameters are bound to specific predicates and require retraining for each new task. We introduce Neural Rule Inducer (NRI), a pretrained model for zero-shot rule induction. Rather than encoding literal identities, NRI represents literals using domain-agnostic statistical properties such as class-conditional rates, entropy, and co-occurrence, which generalize across variable identities and counts without retraining. The model consists of a statistical encoder and a parallel slot-based decoder. Parallel decoding preserves the permutation invariance of logical disjunction; an autoregressive decoder would instead impose an arbitrary clause order. Product T-norm relaxation makes rule execution differentiable, allowing end-to-end training on prediction accuracy alone. We evaluate NRI on rule recovery, robustness to label noise and spurious correlations, and zero-shot transfer to real-world benchmarks, and we believe this work opens up the possibility of foundation models for symbolic reasoning. Code and the reference checkpoint are available at https://github.com/phuayj/neural-rule-inducer. |
cite
@misc{phua2026foundation,
title={A Foundation Model for Zero-Shot Logical Rule Induction},
author={Yin Jun Phua},
year={2026},
eprint={2605.04916},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2605.04916}
}