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arXiv · 2606.19812

Human-on-the-Loop Orchestration for AI-Assisted Legal Discovery

Anushree Sinha·Srivaths Ranganathan·Abhishek Dharmaratnakar·Debanshu Das
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Abstract

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. 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) escalation thresholds reduce privilege-waiver risk relative to fully autonomous baselines. Our results suggest that calibrated uncertainty thresholds can reduce privilege-waiver risk by up to 61% versus fully autonomous deployment, while routing fewer than one quarter of documents to attorney review.

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Anushree Sinha, Srivaths Ranganathan, Abhishek Dharmaratnakar, Debanshu Das. 2026-06-18. Human-on-the-Loop Orchestration for AI-Assisted Legal Discovery. https://arxiv.org/abs/2606.19812

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