Immigration Law

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This talk situates the rising field of NLLP in the context of legal scholarship and practice.It will examine how the field relates to existing inquiries in computational law, AI and Law, and computational/empirical legal studies.Similarities, differences, and opportunities for cross-fertilization...

There has been much press coverage on the Navajo Nation’s struggle to contain the spread of COVID-19 on its lands. As of May 2, 2020, the Nation has 2,373 confirmed cases, and more than seventy deaths from the virus. These...

What I propose in the present article are some theoretical adjustments for a more coherent answer to the legal “status question” of artificial intelligence (AI) systems. I arrive at those by using the new “bundle theory” of legal personhood, together...

The rapid integration of artificial intelligence (AI) into critical decision-making domains—such as healthcare, finance, law enforcement, and hiring—has raised significant ethical concerns regarding bias and fairness. Algorithmic decision-making systems, if not carefully designed and monitored, risk perpetuating and amplifying societal...

ICLR supports the strong community-building role that is so central to the conference. We hope to create opportunities for all participants to meet new people and to share knowledge, best-practices, opportunities, and interests. A Social is a participant-led meeting centered...

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Databases and Information Systems Integration, Artificial Intelligence and Decision Support Systems, Information Systems Analysis and Specification, Software Agents and Internet Computing, Human-Computer Interaction, Enterprise Architecture

arXiv:2602.12356v1 Announce Type: new Abstract: Benchmarking has long served as a foundational practice in machine learning and, increasingly, in modern AI systems such as large language models, where shared tasks, metrics, and leaderboards offer a common basis for measuring progress...

arXiv:2602.12586v1 Announce Type: new Abstract: While plan-and-infill decoding in Masked Diffusion Models (MDMs) shows promise for mathematical and code reasoning, performance remains highly sensitive to slot infilling order, often yielding substantial output variance. We introduce McDiffuSE, a framework that formulates...

arXiv:2602.12631v1 Announce Type: new Abstract: Inventory control is a fundamental operations problem in which ordering decisions are traditionally guided by theoretically grounded operations research (OR) algorithms. However, such algorithms often rely on rigid modeling assumptions and can perform poorly when...

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:2602.12287v1 Announce Type: cross Abstract: End-to-end automatic speech recognition (ASR) systems frequently misrecognize domain-specific phrases like named entities, which can cause catastrophic failures in downstream tasks. A new family of named entity correction methods based on large language models (LLMs)...

arXiv:2602.12322v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models convert high-level language instructions into concrete, executable actions, a task that is especially challenging in open-world environments. We present Visual Foresight Planning (ForeAct), a general and efficient planner that guides a VLA...

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:2602.12413v1 Announce Type: cross Abstract: If LLM training data is polluted with benchmark test data, then benchmark performance gives biased estimates of out-of-distribution (OOD) generalization. Typical decontamination filters use n-gram matching which fail to detect semantic duplicates: sentences with equivalent...

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:2602.12639v1 Announce Type: new Abstract: Legal text generated by large language models (LLMs) can usually achieve reasonable factual accuracy, but it frequently fails to adhere to the specialised stylistic norms and linguistic conventions of legal writing. In order to improve...

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:2602.12996v1 Announce Type: new Abstract: Knowledge augmentation has significantly enhanced the performance of Large Language Models (LLMs) in knowledge-intensive tasks. However, existing methods typically operate on the simplistic premise that model performance equates with internal knowledge, overlooking the knowledge-confidence gaps...

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:2602.13139v1 Announce Type: new Abstract: Language identification (LID) is an essential step in building high-quality multilingual datasets from web data. Existing LID tools (such as OpenLID or GlotLID) often struggle to identify closely related languages and to distinguish valid natural...