Conformal Path Reasoning (CPR): A Trustworthy Framework for Knowledge Graph Question Answering

Forum topic · 小凯 · 2026-05-12

Summary

Knowledge Graph Question Answering (KGQA) enables grounded and interpretable reasoning, but existing methods often lack reliable coverage guarantees over retrieved answers. Conformal Prediction (CP) can provide prediction sets with statistical guarantees, yet prior approaches suffer from calibration validity issues and poor score discriminability, leading to violated coverage and overly large prediction sets. A new paper (arXiv:2505.05130) by Shuhang Lin, Chuhao Zhou, and Xiao Lin proposes Conformal Path Reasoning (CPR), a trustworthy KGQA framework with two key innovations: (1) query-level conformal calibration over path-level scores, which preserves exchangeability while generating path prediction sets; and (2) Residual Conformal Value Network (RCVNet), a lightweight module trained via PUCT-guided exploration to learn discriminative path-level non-conformity scores. Experiments show CPR improves empirical coverage by 34% and reduces average prediction set size by 40% compared to conformal baselines, demonstrating effective coverage guarantees with more compact answer sets.

Overview

Research area: NLP Authors: Shuhang Lin, Chuhao Zhou, Xiao Lin Published: 2025-05-07 arXiv: 2505.05130

Abstract (translated)

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, the authors propose Conformal Path Reasoning (CPR), a trustworthy KGQA framework with two key innovations:

1. Query-level conformal calibration over path-level scores — this preserves exchangeability while generating path prediction sets. 2. Residual Conformal Value Network (RCVNet) — a lightweight module trained through PUCT-guided exploration to learn discriminative path-level non-conformity scores.

Key Results

Experiments on benchmarks show that CPR significantly improves empirical coverage by 34% compared to conformal baselines.
CPR reduces the average prediction set size by 40%.
These results validate that CPR satisfies coverage guarantees while producing more compact answer sets.