Author

Aznaur Aliev

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Preprint Jul 2026

HyperSafe: Inference-Time Safety Recovery for Fine-Tuned Language Models

This work proposes HyperSafe, a framework that restores safety behavior by generating a model-specific Safe Side Network (SSN) for each fine-tuned checkpoint by using layer-wise activation fingerprints to capture how fine-tuning changes the model's inner representations.

Aznaur Aliev, Carlos Hinojosa, A. Eldesokey et al. · 0 citations

Preprint Jul 2026

SETA: Scaling Environments for Terminal Agents

This work constructs and releases SETA-Env, the largest open-source verifiable terminal RL dataset to date, containing over 4,500 environments, and demonstrates that SETA- Env provides high-quality training environments for terminal agents and serves as a valuable resource for advancing research on terminal-based agent learning.

Q. Shen, Zhiqi Huang, V. Kamanuru et al. · 8 citations · ⚡2