arXiv:2611.05039v1 Announce Type: new
Abstract: We introduce ALBI (Agent for Learning By Induction), a neuro-symbolic ILP framework that replaces the ASP-based hypothesis generator in the Learning from Failures (LFF) paradigm with a Large Language Model (LLM), while retaining SWI-Prolog as a formal verifier. ALBI implements an iterative generate-test-constrain loop: the LLM proposes candidate hypotheses from structured prompts; Prolog tests each candidate; and diagnostic feedback drives refinement. We further introduce semantic anonymization, a metamorphic testing methodology, to distinguish structural symbolic induction from surface-level pattern matching on predicate names. Experiments on 30 standard ILP benchmarks show that ALBI achieves 100% success across three frontier LLMs (Gemini 3.1 Pro, GPT 5.4 Pro, Claude Opus 4.6), matching Popper while requiring only 1.4-1.7 iterations on average. Under anonymization, all models maintain 100% success with a moderate iteration increase (1.36-1.67 times), providing evidence of robust symbolic reasoning.
arxiv
ALBI: LLM-Guided Inductive Logic Programming via Iterative Generate-Test-Constrain Refinement
ALBI is a neuro-symbolic ILP framework that replaces the ASP-based hypothesis generator in the Learning from Failures paradigm with an LLM, while using SWI-Prolog as a formal verifier. It implements an iterative generate-test-constrain loop where the LLM proposes hypotheses, Prolog tests them, and diagnostic feedback drives refinement. On 30 standard ILP benchmarks, ALBI achieves 100% success across three frontier LLMs, matching Popper with only 1.4-1.7 iterations on average. Semantic anonymization tests confirm robust symbolic reasoning, with all models maintaining 100% success despite a moderate increase in iterations.
This paper presents a novel integration of LLMs with formal verification for ILP, demonstrating high success and robustness, which is valuable for researchers and practitioners in neuro-symbolic AI.
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