arXiv AI
VStress: Correlation-Aware Auditing and Adaptive Budget Allocation for Repeated Verifiers
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arXiv AI
Oct 19
Audit-First VAPO: Risk-Certified Selective Updates under Imperfect Verification
The paper introduces Audit-First VAPO, a method that separates directional admission from magnitude control for selective updates under imperfect verification. It employs an observation-only accept‑appeal‑abstain policy with a finite secondary‑verification budget, freezing the action trace before clean labels are added, and uses finite‑sample bounds to certify harmful risk, coverage, and verifier‑call rate. Experiments on Qwen3.5‑0.8B and GSM8K show that RC‑VAPO achieves high accuracy while maintaining low selected harmful risk and modest verifier cost compared to baseline methods.
arXiv AI
Oct 5
ROUTEAUDIT: Interaction-Aware Identification for Budgeted Multi-Verifier Routing
ROUTEAUDIT presents a framework for identifying and attributing performance in adaptive multi‑verifier routing systems. It treats routing as a contract‑conditioned identification problem, adding measurable objects such as a contract lattice, a policy‑independent response tape, and request‑level bounds to capture path sensitivity and finite‑population intervals. Experiments on held‑out caches and task requests demonstrate improved quality metrics and precise attribution with low reconstruction error.
arXiv Machine Learning
Aug 3
ALIVE: Warnings Before Exclusion in Budgeted Multi-Source Learning
arXiv:2607. 29400v1 Announce Type: new Abstract: A routing decision can be revised at the next transaction, but a latched source exclusion persists across later decisions.
arXiv Machine Learning
Oct 22
Better Policies, Worse Verifier Audits
The paper investigates how verifier components used in post‑training and test‑time loops can collapse when they are refreshed based on aggregate success scores. It shows that as a policy improves on one conjunctive target, the verifier’s on‑policy AUC can shift to favor a worse component, leading to a preference for a full‑outcome predictor over the intended component oracle. The authors provide theoretical thresholds, empirical demonstrations on text control and ECG data, and propose a repair that audits the component on its own failures to prevent damage.
arXiv Machine Learning
Sep 10
The Oversight Gap: What LLM Safety Monitors Miss, and Why It Is Not Capability
arXiv:2609.07162v1 Announce Type: new Abstract: Several properties safety monitors are asked to certify, among them cross-tenant noninterference, sandbagging and evaluation awareness, are 2-safety hy...
arXiv Machine Learning
Oct 16
CascadeLedger: Counterfactual Auditing of Information and Resource Integrity in LLM Cascades
CascadeLedger is a counterfactual execution‑audit framework designed for budgeted language‑model cascades. It enforces call‑before‑read semantics, records service and cache identities, and freezes event traces before scoring, ensuring that two executions can share a final answer and resource projection only if their routing decisions used legitimately observable information. The framework introduces counterfactual prefix non‑interference (CPNI), counterfactual influence frontier (CIF), and projection‑preserving adversarial mutation (PPAM) to detect and localize violations, achieving a clean false‑positive rate and high detection rates for serialized and hidden‑read faults while quantifying overhead for online and offline audits.