arXiv:2607.01585v1 Announce Type: cross Abstract: Predicate invention (PI), the creation of new predicates to extend the hypothesis space, remains a critical bottleneck in Inductive Logic Programming (ILP). Existing methods rely on domain expertise and produce semantically opaque predicates, hindering adaptation to unfamiliar domains and cross-task reuse. We present ADVENT, an LLM-driven PI mechanism for ILP. ADVENT pairs LLM abductive generation with Prolog deductive verification, forming an iterative loop in which concrete execution results guide the LLM to refine candidate predicates. The m
The rapid advancements and broader adoption of Large Language Models (LLMs) are enabling their application to long-standing, complex symbolic AI challenges like predicate invention in Inductive Logic Programming.
This breakthrough offers a novel approach to automate a critical bottleneck in symbolic AI, accelerating the development of more adaptable and robust AI systems that can learn and reason in unfamiliar domains.
ADVENT introduces an LLM-driven mechanism that can automatically invent and refine predicates, reducing reliance on manual domain expertise and producing more interpretable symbolic AI components.
- · AI researchers (symbolic AI, ILP)
- · Developers of intelligent agents
- · Companies seeking explainable AI solutions
- · Automation software providers
- · Manual predicate engineering experts
- · Traditional, less adaptive symbolic AI approaches
The ability to automatically invent predicates speeds up the development and deployment of Inductive Logic Programming systems.
More agile and explainable AI systems could emerge, capable of adapting to new tasks with less human intervention and greater transparency.
This could lead to a resurgence or hybrid integration of symbolic AI techniques into advanced agentic systems, offering new paradigms for general intelligence.
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Read at arXiv cs.CL