Litigation

Agency rulemakings are a critical component of contemporary governance. This Article argues that there are a distinct set of modalities that characterize how agencies formulate and justify their rules. Just as the well-known modalities of constitutional interpretation capture the norms...

Abstract Music is a language that is spoken between the performer and the listener. Platforms like SUNO AI have enabled even non‐musicians to create music and don the hats of composers by giving few prompts without understanding the language in...

Computational law has its limits—whether these come from the very nature of the law itself or from technical limitations. By reviewing these limits, two conclusions become clear: That interdisciplinary solutions are a must, and that only a subset of law...

As methods in legal information retrieval (IR) evolve to meet the demands of rapidly increasing stores of electronic information, there is the intuitive appeal of capturing detail in legal queries with natural language processing (NLP). One difficulty with this approach...

Exceptional Cases By Emily Cauble Full essay here. Machine Gun Funk: The Unusual Analysis of "Dangerous and Unusual" By Gregory S. Parks & Vivian Bolen Full essay here. Nipping it in the Bud: The Promise and Perils of Tort Litigation...

Branstetter Litigation & Dispute Resolution Program Criminal Justice Program Energy, Environment, & Land Use Program George Barrett Social Justice Program Intellectual Property Program

Abstract Objective To develop and validate a machine learning model that helps physician advisors efficiently identify hospital admission denials likely to be overturned on appeal. Materials Analysis of 2473 appealed hospital admission denials with known outcomes, split 90:10 for training...

[Context] Semantic legal metadata provides information that helps with understanding and interpreting the meaning of legal provisions. Such metadata is important for the systematic analysis of legal requirements. [Objectives] Our work is motivated by two observations: (1) The existing requirements...

By REBEKAH NINAN. Full Text. lawreview - Minnesota Law Review

Machine learning fairness enhancement methods based on data bias correction are usually divided into two processes: The determination of sensitive attributes (such as race and gender) and the correction of data bias. In terms of determining sensitive attributes, existing studies...

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arXiv:2602.12389v1 Announce Type: new Abstract: Temporal knowledge graph (TKG) forecasting requires predicting future facts by jointly modeling structural dependencies within each snapshot and temporal evolution across snapshots. However, most existing methods are stateless: they recompute entity representations at each timestamp...

arXiv:2602.13093v2 Announce Type: new Abstract: Large reasoning models with reasoning capabilities achieve state-of-the-art performance on complex tasks, but their robustness under multi-turn adversarial pressure remains underexplored. We evaluate nine frontier reasoning models under adversarial attacks. Our findings reveal that reasoning...

arXiv:2602.12305v1 Announce Type: cross Abstract: Generating high-performance CUDA kernels remains challenging due to the need to navigate a combinatorial space of low-level transformations under noisy and expensive hardware feedback. Although large language models can synthesize functionally correct CUDA code, achieving...

arXiv:2602.12393v1 Announce Type: cross Abstract: DragDiffusion is a diffusion-based method for interactive point-based image editing that enables users to manipulate images by directly dragging selected points. The method claims that accurate spatial control can be achieved by optimizing a single...

arXiv:2602.12395v1 Announce Type: cross Abstract: Reinforcement learning (RL) with verifiable rewards has become a standard post-training stage for boosting visual reasoning in vision-language models, yet it remains unclear what capabilities RL actually improves compared with supervised fine-tuning as cold-start initialization...

arXiv:2602.12302v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) combine the natural language understanding and generation capabilities of LLMs with perception skills in modalities such as image and audio, representing a key advancement in contemporary AI. This chapter presents...

arXiv:2602.12660v1 Announce Type: new Abstract: Reward models are crucial for aligning large language models (LLMs) with human values and intentions. Existing approaches follow either Generative (GRMs) or Discriminative (DRMs) paradigms, yet both suffer from limitations: GRMs typically demand costly point-wise...

arXiv:2602.12709v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) has become a dominant paradigm for grounding large language models (LLMs) with external evidence in knowledge-intensive question answering. A core design choice is how to fuse retrieved samples into the LLMs, where...

arXiv:2602.12921v1 Announce Type: new Abstract: Figurative language understanding remains a significant challenge for Large Language Models (LLMs), especially for low-resource languages. To address this, we introduce a new idiom dataset, a large-scale, culturally-grounded corpus of 10,361 Bengali idioms. Each idiom...

arXiv:2602.13059v1 Announce Type: new Abstract: Question answering (QA) over structured tables requires not only accurate answers but also transparency about which cells support them. Existing table QA systems rarely provide fine-grained attribution, so even correct answers often lack verifiable grounding,...

arXiv:2602.13084v1 Announce Type: new Abstract: Competency modeling is widely used in human resource management to select, develop, and evaluate talent. However, traditional expert-driven approaches rely heavily on manual analysis of large volumes of interview transcripts, making them costly and prone...

arXiv:2602.12301v1 Announce Type: cross Abstract: Although annotated music descriptor datasets for user queries are increasingly common, few consider the user's intent behind these descriptors, which is essential for effectively meeting their needs. We introduce MusicRecoIntent, a manually annotated corpus of...