Summary
DeepSciVerify (arXiv:2605.27710) is a two-stage pipeline for verifying the alignment between scientific claims and their cited evidence, a common failure mode in reports generated by large language models. The system first verifies claims using abstract-level reasoning and defers uncertain cases, escalating to full-text passage retrieval and analysis only when necessary. This selective escalation design exploits complementary behaviors across LLMs, since some models are more conservative while others are more decisive under uncertainty. On the SCitance benchmark, DeepSciVerify achieves 86.7 Micro-F1, outperforming strong abstract-only baselines by +4.5 points, while 67% of instances are resolved without full-text retrieval. The results demonstrate that selective evidence escalation improves both the accuracy and efficiency of scientific claim-citation verification, enhancing LLM reliability in high-stakes scientific settings.
Overview
Field: NLP
Authors: Shaghayegh Sadeghi, Khashayar Khajavi, Rise Adhikari, et al.
Published: 2026-05-28
arXiv: 2605.27710
Summary
Misalignment between claims and their cited evidence is a common failure mode in reports generated by large language models, limiting their reliability in scientific and other high-stakes settings. This paper presents DeepSciVerify, a two-stage pipeline for scientific claim-citation verification that combines abstract-level reasoning with selective escalation to passage-level evidence.
The system first verifies claims using the abstract and defers uncertain cases, retrieving and analyzing full-text passages only when necessary. This design leverages complementary behaviors across LLMs, as some models are more conservative while others are more decisive under uncertainty.
Key Results
- On the SCitance benchmark, DeepSciVerify achieves 86.7 Micro-F1
- Outperforms strong abstract-only baselines by +4.5 points
- 67% of instances are resolved without full-text retrieval
These results suggest that selective evidence escalation simultaneously improves both the accuracy and the efficiency of scientific claim-citation verification.
---
*Auto-collected on 2026-05-29*
This page is an English static mirror generated for search and AI citation.
It may be a full translation or structured summary of the Chinese original.
Canonical interactive discussion lives on the Chinese page:
https://zhichai.net/topic/177980497