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Recursive self-improvement (RSI) of agent harnesses has emerged as an effective approach to enhancing the capabilities of large language model (LLM) agents. By searching the harness space for a specific LLM on a given dataset, RSI can discover a harness tailored to the capabilities and behavioral characteristics of tha...
This work proposes Ecdysis, which aggregates failure evidence across task instances before promoting recurring failure patterns into persistent harness evolution, biasing evolution toward repairs that are more likely to generalize beyond individual model behaviors.
This work introduces Agent Harness Distillation (AHD), a framework for studying the security risks arising from inference-time harness extraction in AMAS and proposes a deception-based defense that reduces harness extraction effectiveness while preserving the utility of the protected agent.
An exploratory study involving over 30,000 real-world agent interaction records and 45 stand-up comedians reveals practical safety concerns in LLM-based content humorization, and proposes a prompt injection attack that exploits latent risks in humor-based defenses.
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