A Foundation Model for Zero-Shot Logical Rule Induction
Summary
This paper introduces the Neural Rule Inducer (NRI), a foundation model for zero-shot logical rule induction that uses domain-agnostic statistical properties to generalize across tasks without retraining.
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Paper page - A Foundation Model for Zero-Shot Logical Rule Induction
Source: https://huggingface.co/papers/2605.04916
Abstract
Neural Rule Inducer (NRI) enables zero-shot rule induction by representing literals through domain-agnostic statistical properties and using parallel decoding to maintain permutation invariance in logical disjunctions.
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), apretrained modelforzero-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 astatistical encoderand a parallelslot-based decoder. Parallel decoding preserves thepermutation invarianceoflogical disjunction; anautoregressive decoderwould instead impose an arbitrary clause order.Product T-norm relaxationmakes 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.
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