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U. Michigan

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#artificial intelligence Preprint Oct 2026

Active Feature Acquisition for Cost-Efficient Temporal Prediction with Reduced Participant Burden

Accurate forecasting of pathological outcomes is a central problem in psychology. To do so, psychologists often collect intensive longitudinal data. However, in such studies, the desire to acquire a large number of variables for the sake of accurate prediction is often counteracted by the need to minimize participant b...

Yunni Qu, Bing-Cai Kok, Whitney R. Ringwald et al. · 0 citations
Preprint Aug 2026

eBPF Security in the Wild: Structural Concentration, Failure Mechanisms, and Discovery Gaps

Extended Berkeley Packet Filter (eBPF) is a security-critical in-kernel execution framework, yet its vulnerability landscape remains fragmented across components, semantic gaps, and testing techniques. We present an empirical study of observed eBPF vulnerabilities. We construct a multi-source dataset from Linux kernel...

Bai-Hong Chen, Ming Hua, Wei-Feng Pan et al. · 0 citations
#artificial intelligence Preprint Sep 2026

Persistent Teacher Anchoring for Tool-Using Agents

Persistent Teacher Anchoring (PTA) is proposed, a student-induced but teacher-committed rollout construction that retains chunk-level verification and adds turn-level commitment, allowing a call to reach the environment only after the teacher has verified the entire turn.

Hyung Bin Park, Kyung-dong Song, Sang-Min Lee et al. · 0 citations
Preprint Aug 2026

Dissecting Software Graphs: Structural Insights for Driver-Guided Fuzzing

A structural abstraction is proposed that uses a static call graph as a shared backbone and projects driver-specific dynamic coverage onto it to derive driver-induced subgraphs and shows that multi-driver fuzzing is fundamentally a structural exploration problem.

Bai-Hong Chen, Ming Hua, Wei-Feng Pan et al. · 0 citations

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