ArXiv

Conformal Path Reasoning: Trustworthy Knowledge Graph Question Answering via Path-Level Calibration

2026-05-08 · 17:57 UTC ·Shuhang Lin, Chuhao Zhou, Xiao Lin... ·1 min read

Authors: Shuhang Lin, Chuhao Zhou, Xiao Lin...
Categories: cs.CL
arXiv: https://arxiv.org/abs/2605.08077v1
PDF: https://arxiv.org/pdf/2605.08077v1

Brief

Conformal Path Reasoning (CPR) is a trustworthy Knowledge Graph Question Answering framework that performs conformal calibration at the query level over path-level scores and adds a lightweight Residual Conformal Value Network (RCVNet) trained with PUCT-guided exploration to produce discriminative nonconformity scores. According to the abstract, CPR substantially improves empirical coverage (+34%) while shrinking prediction sets (−40%) versus conformal baselines, yielding more reliable and compact answer sets. Full text was not used beyond the provided abstract.

Why it matters

Conformal Path Reasoning (CPR) applies query-level conformal calibration over path-level scores and introduces a Residual Conformal Value Network (RCVNet) trained via PUCT-guided exploration to improve calibration validity and score discriminability in KGQA.

Key details

On benchmarks reported in the paper, CPR increases Empirical Coverage Rate by 34% and reduces average prediction set size by 40% compared to prior conformal baselines, producing more compact, statistically guaranteed answer sets.

Source evidence

Abstract

Knowledge Graph Question Answering (KGQA) has shown promise for grounded and interpretable reasoning, yet existing approaches often fail to provide reliable coverage guarantees over retrieved answers. While Conformal Prediction (CP) offers a principled framework for producing prediction sets with statistical guarantees, prior methods suffer from critical limitations in both calibration validity and score discriminability, resulting in violated coverage guarantees and excessively large prediction sets. To address these pitfalls, we propose Conformal Path Reasoning (CPR), a trustworthy KGQA framework with two key innovations. First, we perform query-level conformal calibration over path-level scores, preserving the exchangeability while generating path prediction sets. Second, we introduce the Residual Conformal Value Network (RCVNet), a lightweight module trained via PUCT-guided exploration to learn discriminative path-level nonconformity scores. Experiments on benchmarks show that CPR significantly improves the Empirical Coverage Rate by 34% while reducing average prediction set size by 40% compared to conformal baselines. These results validate the efficacy of CPR in satisfying coverage guarantees with substantially more compact answer sets.

Comment: 13 pages, 3 figures, 2 tables;