arXiv:2608. 10400v1 Announce Type: new Abstract: What if judges already behave like algorithms?
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.
arXiv:2603.19042v5 Announce Type: replace Abstract: The integration of artificial intelligence (AI) into judicial decision making -- particularly in pretrial, sentencing, and parole contexts -- has g...
arXiv:2506. 03411v2 Announce Type: replace Abstract: Strategic litigation involves bringing a case to court with the goal of having an impact beyond resolving the particular dispute at hand.
The paper introduces Juris Policy Optimization (JPO), a post‑training framework designed to enhance structured legal reasoning in Chinese criminal judgment prediction. JPO first trains models with teacher‑generated rationales to guide a four‑step reasoning process, then applies reinforcement learning using a composite reward that balances prediction accuracy, reasoning completeness, and cross‑step consistency. Experiments on several open‑source language models and three Chinese legal benchmarks demonstrate that JPO consistently outperforms both supervised fine‑tuning and standard reinforcement learning baselines in terms of judgment prediction and reasoning quality.
arXiv:2606. 23716v1 Announce Type: cross Abstract: Legal AI benchmark research frequently invokes the assumption that large language models can improve access to justice, including for people who cannot access lawyers in order to understand and exercise their legal rights.
arXiv:2607. 01256v1 Announce Type: cross Abstract: Overwhelmed courts in the United States review millions of default judgments each year.
The paper introduces OBJECTION, an inference-time pipeline that adds an Adversarial Lawyer Agent to each of the three reasoning steps—offense, unlawfulness, and culpability—in legal judgment prediction models. By actively injecting defense arguments, the agent challenges the model’s default assumption of guilt, which is common in datasets biased toward guilty outcomes. Using a new Natural Innocent dataset of 3.4k real cases, OBJECTION reduces the False Guilty Rate from 82.93% to 16.69%, demonstrating significant improvement in substantive legal reasoning.