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cs.LG2025
Building a Foundational Guardrail for General Agentic Systems via Synthetic Data
Yue Huang, Hang Hua, Yujun Zhou +11
While LLM agents can plan multi-step tasks, intervening at the planning stage-before any action is executed-is often the safest way to prevent harm, since certain risks can lead to…
cs.LG2025
Sparse Gradient Compression for Fine-Tuning Large Language Models
David H. Yang, Mohammad Mohammadi Amiri, Tejaswini Pedapati +2
Fine-tuning large language models (LLMs) for downstream tasks has become increasingly crucial due to their widespread use and the growing availability of open-source models. Howeve…