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This paper presents a practical evaluation protocol for assessing AI pentesting agents in realistic, complex targets rather than simplified benchmarks. It uses LLM-based semantic matching, bipartite resolution, and continuous ground-truth to score vulnerabilities discovered, and releases expert-annotated ground truth and code.
This paper proposes a unified framework for continual learning in LLMs, disentangling change along space (new domains) and time (data drift). It evaluates various methods including prompting, supervised learning, reinforcement learning, and context compression under realistic sequential settings.
This paper introduces a scale-conditioned evaluation protocol for agent memory, analyzing how reliability degrades as irrelevant sessions accumulate. It identifies specific failure regimes and usable-scale boundaries across different memory interfaces and LLMs.