When Verification Hurts: Asymmetric Effects of Multi-Agent Feedback in Logic Proof Tutoring
Large language models (LLMs) are increasingly used for automated tutoring, but their reliability in structured symbolic domains remains unclear. We study step-level feedback for propositional logic proofs, which require precise symbolic reasoning aligned with a learner's current proof state. We introduce a knowledge-graph-grounded benchmark of 516 unique proof states with step-level annotations and difficulty metrics. Unlike prior tutoring evaluations that rely on model self-assessment or binary correctness, our framework enables fine-grained analysis of feedback quality against verified solution paths. We evaluate three role-specialized pipelines with varying solution access: Tutor (part...
| Authors | Tahreem Yasir, Sutapa Dey Tithi, Benyamin Tabarsi et al. |
|---|---|
| Primary category | cs.AI |
| Published | 28 March 2026 |
| Last updated at source | 28 March 2026 |
Silytics shows public metadata from arXiv and links to the original. Information may be incomplete or out of date. Last refreshed 17 October 2026. Licenses and terms of the original apply to the work itself. Not financial, legal or professional advice.