SURE-RAG aggregates pair-level claim-evidence relations into interpretable signals for selective RAG answering, reaching 0.9075 Macro-F1 on HotpotQA-RAG v3 while providing auditability and reducing unsafe answers by 37% at 30% coverage.
Retrieval-augmented generation for knowledge-intensive NLP tasks
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
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A two-agent adversarial rewriting framework achieves 20-40% evasion rates against LLM-based misinformation detectors under strict black-box constraints with binary feedback only, far outperforming prior methods and linking success to specific architectural properties.
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SURE-RAG: Sufficiency and Uncertainty-Aware Evidence Verification for Selective Retrieval-Augmented Generation
SURE-RAG aggregates pair-level claim-evidence relations into interpretable signals for selective RAG answering, reaching 0.9075 Macro-F1 on HotpotQA-RAG v3 while providing auditability and reducing unsafe answers by 37% at 30% coverage.
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Agentic Adversarial Rewriting Exposes Architectural Vulnerabilities in Black-Box NLP Pipelines
A two-agent adversarial rewriting framework achieves 20-40% evasion rates against LLM-based misinformation detectors under strict black-box constraints with binary feedback only, far outperforming prior methods and linking success to specific architectural properties.