Author

Jing-Cheng Niu

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Preprint Oct 2026

RealControlBench: A Benchmark for Interpretability-Informed Fine-Grained Control of LLMs and Agents

Existing steering benchmarks are built around simplified, isolated generation tasks and evaluate a single behavioral attribute under controlled settings that do not reflect the diversity and complexity of practical deployment. As a result, performance on these benchmarks provides only a limited picture of how steering...

Xin-Yi Cai, Bin Hu, Ming-Yu Jin et al. · 0 citations

#natural language process... Preprint Oct 2026

Probe with Participation Trophies: Random-Reward RL as a Probe of LLM Capability

We connect the spurious-reward paradox to a model's reachability and propose random-reward reinforcement learning (RL) as a useful tool for the probing enterprise, addressing a decade-long debate over what probing performance actually reveals about a model. There are two prevailing explanations for the surprising findi...

Yu-Zhu Mao, Lei Yu, Zi-Ning Zhu et al. · 0 citations

#natural language process... Preprint Aug 2026

MURANO: Design, Run, and Reproduce Mechanistic Interpretability Experiments as Composable Pipelines

Murano is an open source framework for designing, running, and reproducing mechanistic interpretability studies of large language models, intended for researchers across disciplines and builds on existing interpretability and machine learning libraries.

Alireza Bayat Makou, Emirhan Böge, Phu Gia Hoang et al. · 0 citations