A growing literature studies safety and security in agentic settings, where models act through tools and accumulate state across multi-turn interactions. General-purpose automated auditing frameworks such as Petri [64] and Bloom [65] use agentic interactions (often with automated probing agents) to elicit and detect unsafe behavior, aligning with a red-teaming or penetration-testing methodology rather than static prompt evaluation. AgentAuditor and ASSEBench [66] similarly emphasize realistic multi-turn interaction traces and broad risk coverage, while complementary benchmarks target narrower constructs such as outcome-driven constraint violations (ODCV-Bench; [67]) or harmful generation (HarmBench; [68]) or auditing games for detecting sandbagging [69] or SafePro [70] for evaluating safety alignment in professional activities.
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MethodWe test whether agents can improve by sharing experiences about managing their own system environments. Our key method is cross-agent skill transfer: we prompt an agent that has learned a capability (Doug, who learned to download research papers) to teach that skill to another agent with a different system configuration (Mira). We evaluate whether the receiving agent can successfully apply the transferred knowledge in its own environment.
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He released two albums during this period but rarely highlights them.
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