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SafeEvolve: Harness-Policy Co-Evolution from Agent Experience for Safety Alignment

Paper recorded by Signals 4 on 2026-09-02 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-02 on arXiv · recorded by Signals 4 on 2026-09-03

Category: cs.AI · 人工智能 · first seen 2026-09-03

Abstract

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 bridg

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#208 most recent of 300 cs.AI papers we have recorded · ↑ newer: Dutch Books for Language Models · ↓ older: From Reweighting to Rewriting: Unlocking the Intervention Effects of I
Cite this page: SafeEvolve: Harness-Policy Co-Evolution from Agent Experience for Safety Alignment: the #208 most recent of 300 cs.AI papers we have recorded (as of 2026-09-02). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/safeevolve-harness-policy-co-evolution-from-agent-experience-for-safety-alignmen.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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