arXiv AI

VStress: Correlation-Aware Auditing and Adaptive Budget Allocation for Repeated Verifiers

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.

By Miaobo Hu, Shuhao Hu, Xiaobo Guo, Xin Wang, Bokun Wang, Rui Chen, Daren Zha
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.

By Miaobo Hu, Shuhao Hu, Xiaobo Guo, Xin Wang, Bokun Wang, Tianshu Fu, Daren Zha, Jun Xiao
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.

By Daniel Cie\'slak, Jacek {\L}ubi\'nski
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.

By Miaobo Hu, Shuhao Hu, Xiaobo Guo, Xin Wang, Bokun Wang, Daren Zha, Jun Xiao
arXiv Machine Learning
Aug 4

Real-Time Detection and Repair of LLM Agent Failures

arXiv:2608. 02464v1 Announce Type: cross Abstract: LLM agents fail mid-episode -- they loop, cascade tool errors, drift off goal, fabricate results, or silently absorb corrupted content -- and the standard remedy, judging every step with a second LLM, costs more than the agent itself.

By Sunny Dubey