Author

E. Opryshko

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#machine learning Preprint Oct 2026

RH-Detect: A Unified Benchmark for Reward Hacking Detection

Reward hacking, where a model exploits an evaluation signal without completing the intended task, threatens the reliability of deployed language model systems. Existing datasets use different labels, response formats, and metadata conventions, making detector results difficult to compare. We present RH-Detect, a benchm...

Jun-Wei Quan, E. Opryshko, Rohan Subramani et al. · 0 citations

Jul 2026

ArbiGraph: Arbitrarily Scalable Verifiable Task Graphs for Evaluating Context Management

ARBIGRAPH is introduced, a benchmark generator for evaluating whether tool-assisted language agents can retain, update, compose, and discard task-relevant context across extended reasoning workflows, and shows that ARBIGRAPH exposes failures that are not visible from single-task evaluation alone.

Pavel Golikov, E. Opryshko, Gennady Pekhimenko et al. · arXiv.org · 0 citations