Learning Probabilistic Logic Programs with Functional Gradient Guided Language Models
Declarative logic programs offer a powerful and interpretable abstraction for encoding relational structure and neurosymbolic reasoning, by expressing dependencies as weighted compositional rules. However, inducing them from data remains fundamentally hard, bottlenecked by the combinatorial explosion of symbolic search spaces. LLMs have recently emerged as powerful hypothesis generators, but when used in isolation, they lack the capacity to do systematic inductive reasoning needed to reliably synthesize valid programs that fit complex relational distributions. We introduce grasp (Gradient-boosted Synthesis of Probabilistic logic programs), a neurosymbolic framework that casts relational s...
| Authors | Saurabh Mathur, Sahil Sidheekh, Bhavan Vasu et al. |
|---|---|
| Primary category | cs.AI |
| Published | 8 October 2026 |
| Last updated at source | 8 October 2026 |
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