arXiv AI
EvidenT: Building Trustworthy Enterprise Assistants through Evidence Groundedness and Traceability
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Jun 26
ProvenAI: Provenance-Native Traces of Evidence in Generated Answers
arXiv:2606. 26449v1 Announce Type: cross Abstract: Retrieval-augmented systems routinely present citations alongside generated answers, yet a citation does not confirm that the corresponding source meaningfully shaped the output.
arXiv AI
Sep 15
CiteGuard-RAG: A Validation-Centered AI System for Evidence-Grounded Question Answering
arXiv:2609.15830v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) can improve access to complex information; however, retrieving evidence alone does not ensure that answers are g...
arXiv AI
Sep 25
Claim-Gated Source-Risk Auditing for Generative Search
The paper introduces a claim‑gated audit framework for generative search, ensuring that a query, source, and answer tuple is only considered resolved when relationship evidence, answer adoption, materiality, and disclosure are all present. It distinguishes this audit endpoint from citation support and review priority, tying decisions to versioned evidence spans and implementing a reference checker to enforce the contract. Experiments on a synthetic dataset confirm that the system correctly handles all 81 predicate combinations and rejects 192 malformed records, while ablation studies isolate endpoint logic from missing‑evidence handling.
arXiv AI
Jul 28
Source-Aware Reranking for Retrieval-Augmented Generation: A Reliability Prior Approach
arXiv:2607. 22584v1 Announce Type: new Abstract: Standard Retrieval-Augmented Generation pipelines rank retrieved documents by semantic similarity alone, without accounting for source provenance or credibility.
arXiv AI
Jul 21
Zero Hallucination, by Construction: Hallucination-Aware Layered Oversight for Trustworthy Enterprise AI
arXiv:2607. 17883v1 Announce Type: cross Abstract: Enterprises will not deploy AI agents they cannot trust, and the most-cited reason for distrust is hallucination: confident, fluent output that is simply not true.
arXiv AI
Jul 1
SAGE: A Search-AuGmented Evaluation of Large Language Models on Free-Form QA
arXiv:2504. 07385v3 Announce Type: replace-cross Abstract: As Large Language Models (LLMs) become increasingly used for question-answering (QA), relying on static, pre-annotated references for evaluation poses significant challenges in cost, scalability, and completeness.