arXiv AI July 17, 2026 By Jayakumar Manoharan

Certified Domain Consistency for Multi-Domain Retrieval: Label-Free Per-Domain Contamination Control with Conformal Risk Guarantees

arXiv:2607. 14157v1 Announce Type: cross Abstract: Retrieval over corpora that mix several domains often returns relevant but wrong-domain evidence that ranking metrics miss and that conformal risk control bounds only marginally, under-covering the worst domains.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

llms

arXiv Machine Learning

Jun 30

When Can Conformal Risk Control Certify LLM Outputs? Bounds, Impossibility, and Adaptation for Structured Generation

arXiv:2606. 29054v1 Announce Type: new Abstract: Large language models (LLMs) deployed for structured generation (NER, JSON extraction, QA, and classification) lack formal reliability guarantees, and standard heuristic abstention policies miss user-specified risk targets by 7.

By Varun Kotte

llms

arXiv AI

Aug 11

Private Anytime Selective-Risk Certification for Federated Retrieval-Augmented Generation: Guarantees and Empirical Limits

arXiv:2608. 07913v1 Announce Type: cross Abstract: Selective-risk certificates promise that accepted outputs meet a declared error target.

By Sanjeda Akter, Ibne Farabi Shihab, Anuj Sharma

rag nlp safety

arXiv Machine Learning

Jun 30

The Calibrated Deepfake Trust Score (CDTS): Competence-Coupled Trust Degradation Across Deepfake Detectors

arXiv:2606. 29484v1 Announce Type: cross Abstract: Modern deepfake detectors are rarely consumed as bare classifiers.

By Md Anas Biswas

llms computer-vision

arXiv AI

Jun 4

Unpredictable Safety: Domain-Dependent Compliance and the Transparency Gap in Open-Weight LLMs

arXiv:2606. 04035v1 Announce Type: cross Abstract: We present a systematic study of domain-dependent safety behavior in open-weight LLMs: 7 standardized experiments across 7 ethical domains, testing 5 models (12B--70B) in 4,200 interactions with dual-judge validation.

By Zacharie Bugaud

llms safety

arXiv AI

1d ago

Calibrated Trust, Not Sharper Prediction: An Empirical Test of Uncertainty Fusion

arXiv:2608. 14617v1 Announce Type: cross Abstract: A recurring proposal in legal AI is to improve case-outcome prediction by fusing uncertainty tools (evidence graphs with belief propagation, sequential Bayesian odds updating, Dempster-Shafer combination, and conformal prediction) into one pipeline.

By Surya Saka

llms

Hugging Face Trending Papers

Aug 8

Private Anytime Selective-Risk Certification for Federated Retrieval-Augmented Generation: Guarantees and Empirical Limits

Selective-risk certificates promise that accepted outputs meet a declared error target. We develop Fed-SRC, a score-agnostic certificate for federated, differentially private, adaptively monitored retrieval-augmented generation.

rag nlp safety

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When Can Conformal Risk Control Certify LLM Outputs? Bounds, Impossibility, and Adaptation for Structured Generation

Private Anytime Selective-Risk Certification for Federated Retrieval-Augmented Generation: Guarantees and Empirical Limits

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