When Do LLMs Apply the Wrong Law? Diagnosing LLM Failures in Temporal Legal Reas

arXiv:2604.26233v1 Announce Type: new Abstract: As Large Language Models (LLMs) are proposed as legal decision assistants, and even first-instance decision-makers, across a range

arXiv:2604.11978v1 Announce Type: new Abstract: Large language model (LLM) agents perform strongly on short- and mid-horizon tasks, but often break down on long-horizon tasks that

arXiv:2604.09852v1 Announce Type: new Abstract: Reasoning models think in long, unstructured streams with no mechanism for compressing or organizing their own intermediate state.

arXiv:2603.22305v1 Announce Type: cross Abstract: Large Language Models (LLMs) are rapidly transitioning from static Natural Language Processing (NLP) tasks including sentiment

arXiv:2603.22973v1 Announce Type: new Abstract: Computational methods applied to legal scholarship hold the promise of analyzing law at scale. We start from a simple question: how

arXiv:2605.04243v1 Announce Type: new Abstract: Despite significant advances, large language models (LLMs) continue to exhibit brittle performance on complex temporal reasoning

arXiv:2604.21334v1 Announce Type: new Abstract: Do large language models (LLMs) exhibit systematic ideological bias when reasoning about economic causal effects? As LLMs are

arXiv:2604.00010v1 Announce Type: cross Abstract: Large language models cannot estimate how long their own tasks take. We investigate this limitation through four experiments