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Z. B. Celik

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#artificial intelligence Preprint Oct 2026

ROAR: Unifying Runs across Heterogeneous AI-Driven Research Systems

Each run of an AI-driven research system (ADRS) is an expensive search over a vast solution space, and dependable evaluation requires many runs, making run data both costly to produce and valuable to retain for large-scale analysis. Yet this data remains fragmented: teams operate in isolation, ADRS frameworks emit resu...

Leo Y. Lin, Vishakha Ramani, Z. B. Celik et al. · 0 citations
#small language model Preprint Sep 2026

Refusals That Bend: Measuring and Predicting Task Malleability in Embodied VLM Planners

Everyday objects, whether placed by an adversary or introduced by ordinary rearrangement of the environment, are thus sufficient to overturn a refusal, and susceptibility to safety bypass is called a property of the task, which it is shown that it can be predicted before the target is ever queried.

Leon Lin, Mikhail Kuznetsov, Muslum Ozgur Ozmen et al. · 0 citations
#explainable ai Aug 2026

A Self-Explainable Deep Architecture for Security Applications

XSec is introduced, a self-explainable deep architecture developed for security applications that produces deterministic explanations for a fixed trained model and input and substantially reduces explanation latency compared with approximation-based and perturbation-based post-hoc methods.

Ananth Shreekumar, Jyun-Jhu Syu, Muslum Ozgur Ozmen et al. · 0 citations

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