agent/papers/items/2026-2605-25920-can-llms-time-travel-enhancing-temporal-consistency-in-legal-agentic-search-thro.md

Paper: Can LLMs Time Travel? Enhancing Temporal Consistency in Legal Agentic Search through Reinforcement Learning

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type: paper title: Can LLMs Time Travel? Enhancing Temporal Consistency in Legal Agentic Search through Reinforcement Learning authors: Wei Fan, Yining Zhou, Mufan Zhang, Yanbing Weng, Yiran HU, Tianshi Zheng, Baixuan Xu, Chunyang Li, et al. year: 2026 venue: arXiv url: https://arxiv.org/abs/2605.25920 code_url: source: arxiv collected_at: 2026-07-08 published_at: 2026-05-25 updated_at: 2026-05-25 status: queued relevance: high topics:

• agent-evaluation
• rag
• reasoning methods:

benchmarks:

models:

datasets:

• cs.CL
• cs.AI related_concepts:

related_jobs:

related_experiments:

related_projects:

collection_score: 14 collection_queries: rag-agent

One-line Takeaway

Auto-collected from arXiv because it matched the Agent collection queries. Needs human skim.

Why Collected

• matched queries: rag-agent
• inferred topics: agent-evaluation, rag, reasoning
• arXiv categories: cs.CL, cs.AI
• collection score: 14

Review Checklist

• Does this paper directly inform Agent architecture, evaluation, memory, tools, safety, coding agents, GUI/browser agents, or multi-agent workflows?
• Does it include a benchmark, dataset, code, or reproducible experimental setup?
• Should it be promoted from queued to skimmed or summarized?

Links

• arXiv: https://arxiv.org/abs/2605.25920