No AI summary available for this article.
Why It Matters
The performance of LLM-based agents is jointly shaped by the base model and the harness used when interacting with the environment.
Provenance
Discovered via ArXiv and published by ArXiv.
Key Claims
Original description
The performance of LLM-based agents is jointly shaped by the base model and the harness used when interacting with the environment. This exposes them to safety risks in both harmful final responses and multi-step execution trajectories. Existing safety alignment mechanisms often rely on either external harness updates or policy optimization, yet applying either paradigm in isolation fails to bridge runtime control with intrinsic safety. We propose SafeEvolve, an experience-driven self-evolving framework for agent safety alignment. SafeEvolve leverages safety experience from completed on-policy...
Discovered via ArXiv
Research papers and preprints from arXiv.
Publisher: arxiv.org
ID: http://arxiv.org/abs/2609.02786v1 · Indexed about 2 hours ago