Law in Silico: Simulating Legal Society with LLM-Based Agents (2026 · 2 citations)
Testing legal theories or policies in the real world is often expensive or impossible. This paper asks whether LLM-driven agents can act as a stand-in "legal society" for such experiments. The authors build Law in Silico, a framework with three parts. Agents get hierarchical profiles drawn from sociological…
Knowledge is Not Enough: Injecting RL Skills for Continual Adaptation (2026)
LLMs cannot absorb information that appears after their training cutoff. Supervised fine-tuning (SFT) on new documents can store the facts, but the model often still fails to use them when answering questions or acting as an agent. Reinforcement learning (RL) teaches that kind of skill, yet it is too costly to rerun…

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Updated 1 November 2026