# ScienceJudge: Pattern Perturbation Augmentation for Sentence-Level Scientific Claim Verification

**Authors:** Chuhai Cai, Xin Chen, Min Yang, Feng Jiang  
**Lab:** Shenzhen University of Advanced Technology · **Published:** 29 Oct 2026  
**Links:** [arXiv](https://arxiv.org/abs/2610.40238) · [PDF](https://arxiv.org/pdf/2610.40238) · [This page](https://omen-bit.github.io/ai-paper-daily/paper/2610.40238.html)

## Abstract

Sentence-level scientific claim verification determines whether each sentence in an AI-generated scientific paragraph is supported by a given multimodal evidence bundle. Fine-grained reporting errors often preserve fluent and plausible statements while introducing subtle evidence-claim deviations, making them difficult to detect under sparse and imbalanced supervision. We propose Pattern Perturbation Augmentation (PPA), which models reporting errors as label-specific faithfulness breaking transformations and induces reusable error patterns from labeled examples. PPA applies these patterns to supported claims to construct controlled hard negatives and expands their coverage through pattern-level, operation-level, and cross-domain augmentation. Based on these augmented views, we develop ScienceJudge, which integrates PPA-trained vision-language predictors and instruction-guided LLM predictors through class-weighted ensemble inference. On the local development set, ScienceJudge improves the average score from 0.8124 to 0.8692, achieving a Macro-F1 of 0.8466 and a Paragraph Exact Match score of 0.8919. Our submitted system ranks first on the official NLPCC 2026 Task 10 Track 1 leaderboard with a final score of 0.6890.

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