arXiv Machine Learning
Certified Adaptive Refresh: Anytime-Valid Monitoring for Federated Conformal RAG
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
Related stories
arXiv AI
Jun 19
Think Again or Think Longer? Selective Verification for Budget-Aware Reasoning
arXiv:2606. 19808v1 Announce Type: new Abstract: Test-time reasoning is increasingly used as a serving-time control knob, but extra reasoning is not uniformly valuable: it can repair failed attempts, waste compute on already-correct answers, or introduce harmful answer changes.
arXiv AI
Sep 17
Pay Only for Disagreement: Certified No-Regression Verdicts for Model Updates with Matching Label-Complexity Bounds
The paper introduces DISCERN, a two-tier protocol for certifying that updates to production models do not increase risk. It first uses unlabeled data to detect benign updates based on disagreement rates, then selectively labels only disagreements through an anytime-valid confidence sequence. The method achieves finite-sample validity with label-complexity bounds of order ρ²/ε², demonstrating significant label savings and strong empirical performance across 14,000+ audit streams.
arXiv Machine Learning
Sep 15
Certifying Model Upgrades with Slice-Wise Non-Regression and Incumbent Fallback
arXiv:2609.13714v1 Announce Type: new Abstract: An updated model can improve an aggregate metric while degrading a slice that matters to a downstream user. We study checkpoint selection subject to no...
arXiv AI
Aug 28
Approved Too Late: Verdict Staleness in LLM-Guarded Self-Adaptive Systems
The paper investigates how approvals issued by a large language model (LLM) guardrail for self‑adaptive systems can become stale between the time of check and the time of use, creating a TOCTOU hazard. It introduces three metrics for verdict freshness, evaluates them across five SAS environments, and proposes the Freshness‑Bounded Shield (FBS) to estimate an approval’s validity horizon without a plant‑dynamics model, reducing expiry rates significantly. The study also audits LLM judges and formulates a freshness contract requiring approvals to remain valid at use time.
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.
arXiv AI
Aug 18
What Do Compliance Detectors Read? An Audit of Activation Probes and Guard Models
arXiv:2608. 16852v1 Announce Type: new Abstract: Regulatory compliance monitoring in deployed language models is increasingly implemented as a legal and audit control, checking model outputs against written rules spanning data protection, healthcare, financial regulation, and platform policy.