Toward Defeasible Machine Learning: When Learned Rule Sets Become Defeasible Theories

Concept Tree Learner (CTL): An Incremental and Interpretable Symbolic Framework for Binary String Rule Induction

The Concept Tree Learner is introduced, an incremental and interpretable symbolic framework that induces logical concepts over binary strings from minimal labeled data and generalizes substantially better to unseen strings than both a classical entropy-based decision tree and the RIPPER rule learner.

ArgGYM: A Procedural, Engine-Verified Benchmark for Structured Defeasible Reasoning

Recent progress in large language model reasoning has been driven by benchmarks and reinforcement learning environments with automatically verifiable rewards, particularly in mathematics, code, and formal logic. These settings make model accuracy easier to evaluate and optimize, but it remains unclear how far success u...

INDUCING KNOWLEDGE

Governed Deduction: Policy-Grounded Premise Authorization Beyond Relevance

Reasoning systems usually treat premise use as a question of relevance: if a fact is available and useful, it may be selected for inference. Authorization imposes a different constraint: a premise may be represented and logically usable but not permitted for a particular local transition. We formalize this distinction...

A hierarchy of faithfulness criteria for knowledge base completion

Evaluating knowledge graph and logic-geometric embedding models on $\mathcal{EL}$ ontologies, with entailed, contradictory, and undetermined test sets generated by a reasoner, it is found that ranking accuracy does not imply logical faithfulness and that none of the evaluated models is faithful across the hierarchy.

DERELAB: Probing Defeasible Reasoning and Confirmation Bias in LLMs with a Generative Benchmark

DeReLab is introduced, a generative framework that produces multi-turn belief-updating conversations from parameterized graph structures across default and inheritance reasoning, with formally verified ground truth at every turn, enabling controlled measurement of how models respond to confirming and disconfirming evid...