arXiv:2605.01486v2 Announce Type: replace Abstract: Legal consultation is inherently iterative: before giving advice, a system must identify relevant legal elements, gather missing facts and authorities, and determine whether the current evidence is sufficient. Existing retrieval-augmented legal agents often use fixed retrieval budgets or single-shot search, making them insensitive to the evolving coverage state of a consultation. This paper introduces a coverage-driven retrieval-control framework for multi-turn legal consultation. The framework maintains a structured map over user facts, lega
The increasing sophistication of large language models and the demand for more reliable and context-aware AI applications are driving innovations in retrieval and agentic control systems.
This development moves AI legal consultation beyond basic information retrieval to more dynamic, multi-turn interactions, improving accuracy and reducing the need for human oversight in certain legal processes.
Legal AI systems will transition from static query-response mechanisms to adaptive agents that can actively manage information gathering based on evolving consultation needs and coverage gaps.
- · Legal Tech Companies
- · Law Firms (adopters)
- · AI/ML Developers
- · Traditional legal research services
- · Junior legal associates (for certain tasks)
Improved efficiency and accuracy in legal research and preliminary consultation phases through iterative AI interaction.
Increased accessibility to legal information and basic advice, potentially democratizing legal services to some extent.
Re-definition of legal professional roles, with a greater emphasis on complex strategy, human interaction, and oversight of AI-driven processes rather than routine information gathering.
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Read at arXiv cs.AI