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Unlike single-turn retrieval, agentic workflows operating over privileged document corpora exhibit a class of failure we term \"trajectory collapse\": an early misclassification silently propagates, rendering an entire privilege review invalid. This paper makes three contributions. First, we propose a structured taxonomy of agentic failures in legal information retrieval, organized by functional stage. Second, we introduce a four-layer verification architecture -- spanning planning, reasoning, execution, and uncertainty quantification -- designed to intercept these failures before they compound. Third, we present a preliminary simulation study on a synthetic e-discovery corpus that demonstrates how mandatory Human-on-the-Loop (HOTL) escala","title":"Human-on-the-Loop Orchestration for AI-Assisted Legal Discovery","url":"https://arxiv.org/abs/2606.19812","vendor":"arxiv_cs_ai"},"summary":"arXiv:2606.19812v1 Announce Type: new \nAbstract: Autonomous Large Language Model (LLM) agents are increasingly deployed in electronic discovery (e-discovery), where compounding errors across multi-step reasoning chains can constitute legal malpractice. Unlike single-turn retrieval, agentic workflows operating over privileged document corpora exhibit a class of failure we term \"trajectory collapse\": an early misclassification silently propagates, rendering an entire privilege review invalid. This paper makes three contributions. First, we propose a structured taxonomy of agentic failures in legal information retrieval, organized by functional stage. 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