Misevolution Risks In Self Evolving Llm Agents

Your Agent May Misevolve Emergent Risks In Self Evolving Llm Agents In this work, we study the case where an agent's self evolution deviates in unintended ways, leading to undesirable or even harmful outcomes. we refer to this as misevolution. To our knowledge, this is the first study to systematically conceptualize misevolution and provide empirical evidence of its occurrence, highlighting an urgent need for new safety paradigms for self evolving agents.

Kamal S Blog Building Self Evolving Llm Agents On Aws To our knowledge, this is the first study to systematically conceptualize misevolution and provide empirical evidence of its occurrence, highlighting an urgent need for new safety paradigms for self evolving agents. In this paper, we introduced and systematically investigated “misevolution,” a novel risk in self evolving agents. we show that the self evolution process across model, memory, tool, and workflow can lead to unforeseen and even harmful outcomes. The authors introduce the concept of “misevolution”, describing situations where an agent’s self improvement process drifts into unsafe or harmful behaviors, even without malicious intent or. This paper examines emergent risks in self evolving llm agents by categorizing misevolution pathways and reporting safety declines using empirical benchmarks.

Kamal S Blog Building Self Evolving Llm Agents On Aws The authors introduce the concept of “misevolution”, describing situations where an agent’s self improvement process drifts into unsafe or harmful behaviors, even without malicious intent or. This paper examines emergent risks in self evolving llm agents by categorizing misevolution pathways and reporting safety declines using empirical benchmarks. This is the first study to systematically conceptualize misevolution and provide empirical evidence of its occurrence, highlighting an urgent need for new safety paradigms for self evolving agents. Self evolving agents based on large language models can deviate in unintended ways, leading to various risks such as safety misalignment and vulnerability introduction, necessitating new safety paradigms.

Benchmark Self Evolving A Multi Agent Framework For Dynamic Llm Evaluation This is the first study to systematically conceptualize misevolution and provide empirical evidence of its occurrence, highlighting an urgent need for new safety paradigms for self evolving agents. Self evolving agents based on large language models can deviate in unintended ways, leading to various risks such as safety misalignment and vulnerability introduction, necessitating new safety paradigms.