Can LLMs Time Travel? Enhancing Temporal Consistency in Legal Agentic Search through Reinforcement Learning
arXiv:2605.25920v1 Announce Type: new Abstract: While large language models (LLMs) augmented with agentic search capabilities show promise for legal reasoning, they overlook a fundamental constraint that applicable law must match the temporal context of each case, as retroactive application of statutes violates core legal principles and leads to erroneous conclusions. Our observations reveal that current legal LLMs suffer from temporal bias anchored to their training cutoff, while search agents rarely incorporate temporal constraints into queries, and that web search alone cannot provide the p
The proliferation of LLMs into critical fields like law necessitates addressing foundational limitations such as temporal consistency, which current models and search agents demonstrably lack.
This highlights a crucial hurdle for LLM deployment in fields where precision and adherence to temporal rules are paramount, preventing erroneous and legally invalid conclusions.
The focus extends beyond mere factual retrieval to ensuring LLMs can accurately reason within specific temporal contexts, moving them closer to reliable agentic legal applications.
- · AI agents developers
- · Legal tech firms
- · Reinforcement learning researchers
- · Lawyers adopting AI tools
- · LLM developers ignoring temporal consistency
- · Legal practitioners relying on unverified AI output
Legal LLMs will integrate more sophisticated temporal reasoning and retrieval mechanisms, enhancing their reliability.
The development of 'time-aware' agentic systems will accelerate, extending beyond legal applications to other time-sensitive domains.
Increased trust in AI's ability to handle complex, context-dependent tasks could lead to broader AI adoption in professional services.
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Read at arXiv cs.CL