Uncertainty-Aware Web-Conditioned Scientific Fact-Checking

2026-04-13

AI Summary

This work addresses the unreliability of verifying complex scientific claims due to hallucination and inconsistent reasoning, particularly in high-stakes scenarios with limited evidence and strict cost or latency constraints. The authors propose a collaborative verification framework based on atomic predicate-argument decomposition and uncertainty gating. This approach triggers targeted web retrieval from authoritative sources only when the model is uncertain about local evidence support, integrating embedding alignment, a lightweight evidence verifier, and calibrated uncertainty estimation to enable fine-grained, traceable, and conservative fact-checking. Experiments demonstrate that the method outperforms strong baselines across multiple benchmarks, invoking web verification for only a small fraction of atomic facts and proactively returning “Not Enough Information” (NEI) under evidential conflict, thereby significantly enhancing reliability, interpretability, and efficiency.

Technology Category

• Reasoning under Uncertainty: Other Foundations of Reasoning under Uncertainty
• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Web Mining and Content Analysis: Web data provenance, reliability, and authenticity
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Scientific fact-checking is vital for assessing claims in specialized domains such as biomedicine and materials science, yet existing systems often hallucinate or apply inconsistent reasoning, especially when verifying technical, compositional claims against an evidence snippet under source and cost/latency constraints. We present a pipeline centered on atomic predicate-argument decomposition and calibrated, uncertainty-gated corroboration: atomic facts are aligned to local snippets via embeddings, verified by a compact evidence-grounded checker, and only facts with uncertain support trigger domain-restricted web search over authoritative sources. The system supports both binary and tri-valued classification where it predicts labels from Supported, Refuted, NEI for three-way tasks. We evaluate under two regimes, Context-Only (no web) and Context+Web (uncertainty-gated web corroboration); when retrieved evidence conflicts with the provided context, we abstain with NEI rather than overriding the context. On multiple benchmarks, our framework surpasses the strongest benchmarks. In our experiments, web corroboration was invoked for only a minority of atomic facts on average, indicating that external evidence is consulted selectively under calibrated uncertainty rather than routinely. Overall, coupling atomic granularity with calibrated, uncertainty-gated corroboration yields more interpretable and context-conditioned verification, making the approach well-suited to high-stakes, single-document settings that demand traceable rationales, predictable cost/latency, and conservative.

Problem

• scientific fact-checking
• uncertainty awareness
• evidence verification
• technical claims
• hallucination

Innovation

• atomic decomposition
• uncertainty-aware verification
• evidence-grounded fact-checking
• calibrated uncertainty gating
• web-conditioned corroboration