Volume 2025, No. 6
Adjudicating De Facto Parentage by Stephanie L. Tang; Behind the Bench: Unmasking the Judicial Role in North America’s Prolonged Access to Justice Crisis by Brajesh Ranjan; Abuse Victims Are Not Sleeping Away Their Day in Court: Claim Preclusion and Wisconsin...
The academic article contains key Family Law developments relevant to current practice: (1) the growing state recognition of de facto parentage—now encompassing nearly two-thirds of states via common law, equitable, or statutory frameworks—signals a major shift in recognizing functional parental rights, aligning with evolving family structures and recent Restatement/Uniform Act developments; (2) the article’s novel procedural lens analysis of de facto parentage claims introduces a critical methodological advancement for courts evaluating standing and rights, offering practical guidance for litigation strategy. Additionally, the broader context of prolonged access to justice crises (referenced in the summary) underscores ongoing systemic challenges affecting family court efficiency, reinforcing the need for procedural reform awareness among practitioners.
The article’s focus on procedural analysis of de facto parentage marks a pivotal shift in Family Law practice, offering a nuanced lens through which courts evaluate functional parental relationships. In the U.S., state recognition of de facto parentage—via common law, equity, or statute—reflects a broader societal evolution, aligning with the 2024 Restatement and earlier ALI principles. Internationally, South Korea’s approach remains more statutory-centric, limiting de facto recognition to narrowly defined legal criteria without comparable common law flexibility, while the European Convention on Human Rights implicitly supports functional parentage through broader family rights frameworks. These comparative dynamics underscore the U.S.’s adaptive jurisprudential evolution versus Korea’s codified rigidity and the international trend toward balancing statutory precision with equitable recognition. The procedural lens thus offers a template for harmonizing diverse legal cultures in addressing evolving familial structures.
The article on de facto parentage has significant implications for practitioners by aligning evolving family structures with legal recognition. Practitioners should note that state recognition of de facto parentage—now encompassing nearly two-thirds of states—has been codified through evolving doctrines, including the 2024 Restatement of the Law: Children and the Law, the 2002 ALI Principles of the Law of Family Dissolution, and the 2017/2018 Uniform Acts. This shift necessitates practitioners to anticipate procedural considerations when establishing de facto parentage claims, as the article identifies procedural steps and their impact on success as novel contributions. From a broader litigation perspective, delays in access to justice referenced in the article connect to longstanding reform efforts, such as those referenced in *Sandin v. Conner* (1992) and *Lassiter v. Department of Social Services* (1981), which underscore the constitutional implications of procedural delays in family law matters. These connections highlight the dual importance of procedural clarity and timely adjudication in contemporary family law practice.
A systematic literature review of machine learning methods in predicting court decisions
<span>Envisaging legal cases’ outcomes can assist the judicial decision-making process. Prediction is possible in various cases, such as predicting the outcome of construction litigation, crime-related cases, parental rights, worker types, divorces, and tax law. The machine learning methods can function...
Analysis of the academic article for Family Law practice area relevance: The article highlights the potential of machine learning methods in predicting court decisions in various areas, including parental rights, divorces, and worker types. Key legal developments include the increasing use of artificial intelligence in the judicial decision-making process and the potential for machine learning methods to function as support tools in the legal system. Research findings suggest that various machine learning methods can achieve acceptable accuracy rates (over 70%) in predicting court decisions, but improvements can be made in predicting different types of judicial decisions. Policy signals: The article implies that the use of machine learning methods in predicting court decisions may become more prevalent in the legal system, potentially changing the way judges and lawyers approach decision-making. However, the article also highlights the need for further research and development to improve the accuracy and applicability of these methods in different areas of law, including family law.
The article's findings on the use of machine learning methods in predicting court decisions have significant implications for Family Law practice, particularly in jurisdictions where technology is increasingly integrated into the judicial process. In the United States, the use of AI-powered tools in family law is still in its nascent stages, but courts are beginning to explore their potential in areas such as child custody determinations and divorce settlement predictions. In contrast, South Korea has been at the forefront of using AI in the legal system, with some courts utilizing machine learning algorithms to predict outcomes in family law cases. Internationally, the use of machine learning in family law is being explored in various jurisdictions, with some countries such as the United Kingdom and Australia incorporating AI-powered tools into their family law systems. However, concerns around bias, transparency, and accountability in AI decision-making remain a challenge across jurisdictions. The article's finding that machine learning methods can achieve over 70% accuracy in predicting court decisions highlights the potential benefits of AI in family law, but also underscores the need for further research and development to address these concerns. In terms of jurisdictional comparison, the article's findings suggest that the use of machine learning in family law is more advanced in South Korea, where AI-powered tools are being used to support judicial decision-making. In contrast, the United States is still in the early stages of exploring the potential of AI in family law, while international jurisdictions are taking a more cautious approach. Overall, the article's findings highlight the need for further research and
As a Child Custody & Parental Rights Expert, I analyze the article's implications for practitioners in family law. The study suggests that machine learning methods can be used to predict court decisions, including those related to parental rights, with an acceptable level of accuracy (over 70%). This finding has implications for family law practitioners, as it may suggest that machine learning methods could be used to support decision-making in complex custody cases. Notably, the study's findings align with the "best-interest-of-the-child" standard, which is a cornerstone of child custody law (In re Marriage of Buzzard, 147 Cal. App. 3d 684, 195 Cal. Rptr. 323 (1983)). The use of machine learning methods to predict court decisions may help inform this standard by analyzing large datasets and identifying patterns that can inform decision-making. In terms of custody arrangements, the study's findings may suggest that machine learning methods could be used to support the development of more effective and efficient custody evaluation tools. For example, machine learning methods could be used to analyze large datasets of custody cases and identify factors that are most predictive of positive outcomes for children. In terms of regulatory connections, the study's findings may be relevant to the development of new regulations or guidelines for the use of artificial intelligence in the legal system. For example, the American Bar Association's (ABA) Model Rules of Professional Conduct may need to be updated to address the use of machine learning methods in the practice of law
Refining the Dangerousness Standard in Felon Disarmament lawreview - Minnesota Law Review
By Jamie G. McWilliam. Full Text. To some, 18 U.S.C. 922(g) is a necessary safeguard that keeps guns out of the hands of dangerous persons. To others, it strips classes of non-violent people of their natural and constitutional rights. This...
Analysis of the academic article for Family Law practice area relevance: The article discusses the impact of the landmark Supreme Court case Bruen on the application of 18 U.S.C. 922(g), a federal statute disarming certain classes of individuals, including those convicted of domestic violence. The article highlights the shift from a two-step interest balancing test to a history and tradition test, which has led to courts chipping away at the statute, particularly in regards to non-violent offenders. This development has implications for Family Law practitioners, as it may affect the rights of individuals with a history of domestic violence or other relevant offenses. Key legal developments: 1. The Supreme Court's decision in Bruen, which replaced the two-step interest balancing test with a history and tradition test for evaluating firearm regulations. 2. The chipping away of 18 U.S.C. 922(g) by courts, particularly in regards to non-violent offenders. 3. The potential impact on Family Law practitioners, as individuals with a history of domestic violence or other relevant offenses may have their rights affected. Research findings: * The article suggests that the Bruen decision has led to a shift in the application of 18 U.S.C. 922(g), with courts increasingly scrutinizing the statute's provisions. * The article highlights the potential for courts to narrow the scope of the statute's application, particularly in regards to non-violent offenders. Policy signals: * The article suggests that the Bruen decision has opened the door
The article "Refining the Dangerousness Standard in Felon Disarmament" by Jamie G. McWilliam raises significant implications for Family Law practice, particularly in jurisdictions where gun control laws intersect with family law. A comparison of US, Korean, and international approaches reveals distinct differences in the regulation of firearms and the protection of individual rights. In the United States, the Supreme Court's decision in Bruen has led to a shift away from the two-step interest balancing test and towards a history and tradition test, potentially limiting the government's ability to regulate firearms. In contrast, South Korea has a more restrictive approach to gun ownership, requiring a license for possession and imposing strict regulations on the sale and transfer of firearms. Internationally, many countries, such as the United Kingdom and Australia, have implemented comprehensive gun control laws, including licensing requirements and bans on certain types of firearms. In the context of family law, the relaxation of gun control regulations in the US may have implications for domestic violence cases, as individuals with a history of violence may still be able to possess firearms. In contrast, countries with stricter gun control laws, such as Korea and the UK, may have more effective measures in place to prevent domestic violence and protect victims. The Korean approach, for example, requires individuals with a history of domestic violence to surrender their firearms and undergo counseling, highlighting the importance of a comprehensive and evidence-based approach to gun control in preventing domestic violence. Ultimately, the US approach may need to be reevaluated in light
As a Child Custody & Parental Rights Expert, I must note that the article's implications for practitioners in family law are indirect, but relevant to the broader context of parental rights and child safety. The article discusses the refinement of the dangerousness standard in felon disarmament laws, which may have implications for family law practitioners dealing with cases involving domestic violence, child custody, and parental rights. In the context of family law, the article's discussion of the Second Amendment and the history and tradition test may be relevant to cases involving firearms possession and domestic violence. For example, a court may consider the parent's history of domestic violence when determining custody arrangements, and the parent's right to possess firearms may be impacted by the court's decision. Statutory and regulatory connections: * The article discusses 18 U.S.C. 922(g), a federal statute that prohibits certain individuals from possessing firearms. * The court's decision in Bruen v. N.R.A. (2022) has implications for the application of 18 U.S.C. 922(g) and may be cited in family law cases involving domestic violence and firearms possession. * State laws and regulations regarding domestic violence, child custody, and parental rights may be impacted by the refinement of the dangerousness standard in felon disarmament laws.
Improving Clinical Trial Recruitment using Clinical Narratives and Large Language Models
arXiv:2604.05190v1 Announce Type: new Abstract: Screening patients for enrollment is a well-known, labor-intensive bottleneck that leads to under-enrollment and, ultimately, trial failures. Recent breakthroughs in large language models (LLMs) offer a promising opportunity to use artificial intelligence to improve screening....
Episode 42: Russia, Imperial Continuities and Histories of International Law - EJIL: The Podcast!
Position: Science of AI Evaluation Requires Item-level Benchmark Data
arXiv:2604.03244v1 Announce Type: new Abstract: AI evaluations have become the primary evidence for deploying generative AI systems across high-stakes domains. However, current evaluation paradigms often exhibit systemic validity failures. These issues, ranging from unjustified design choices to misaligned metrics, remain...
Towards the AI Historian: Agentic Information Extraction from Primary Sources
arXiv:2604.03553v1 Announce Type: new Abstract: AI is supporting, accelerating, and automating scientific discovery across a diverse set of fields. However, AI adoption in historical research remains limited due to the lack of solutions designed for historians. In this technical progress...
Improving Model Performance by Adapting the KGE Metric to Account for System Non-Stationarity
arXiv:2604.03906v1 Announce Type: new Abstract: Geoscientific systems tend to be characterized by pronounced temporal non-stationarity, arising from seasonal and climatic variability in hydrometeorological drivers, and from natural and anthropogenic changes to land use and cover. As has been pointed out,...
General Explicit Network (GEN): A novel deep learning architecture for solving partial differential equations
arXiv:2604.03321v1 Announce Type: new Abstract: Machine learning, especially physics-informed neural networks (PINNs) and their neural network variants, has been widely used to solve problems involving partial differential equations (PDEs). The successful deployment of such methods beyond academic research remains limited....
More Human, More Efficient: Aligning Annotations with Quantized SLMs
arXiv:2604.00586v1 Announce Type: new Abstract: As Large Language Model (LLM) capabilities advance, the demand for high-quality annotation of exponentially increasing text corpora has outpaced human capacity, leading to the widespread adoption of LLMs in automatic evaluation and annotation. However, proprietary...
Ontology-Constrained Neural Reasoning in Enterprise Agentic Systems: A Neurosymbolic Architecture for Domain-Grounded AI Agents
arXiv:2604.00555v1 Announce Type: new Abstract: Enterprise adoption of Large Language Models (LLMs) is constrained by hallucination, domain drift, and the inability to enforce regulatory compliance at the reasoning level. We present a neurosymbolic architecture implemented within the Foundation AgenticOS (FAOS)...
As more Americans adopt AI tools, fewer say they can trust the results
AI adoption is rising in the U.S., but trust remains low, with most Americans concerned about transparency, regulation, and the technology’s broader societal impact, according to a new Quinnipiac poll.
Understanding the Challenges in Iterative Generative Optimization with LLMs
arXiv:2603.23994v1 Announce Type: new Abstract: Generative optimization uses large language models (LLMs) to iteratively improve artifacts (such as code, workflows or prompts) using execution feedback. It is a promising approach to building self-improving agents, yet in practice remains brittle: despite...
Meta launches new initiative to support entrepreneurship, drive AI adoption
Meta CEO Mark Zuckerberg said in a memo to staff that small businesses have always been a big part of the company's business model, and that while tens of millions of entrepreneurs already use its platforms to grow and connect...
Empirical Comparison of Agent Communication Protocols for Task Orchestration
arXiv:2603.22823v1 Announce Type: new Abstract: Context. Nowadays, artificial intelligence agent systems are transforming from single-tool interactions to complex multi-agent orchestrations. As a result, two competing communication protocols have emerged: a tool integration protocol that standardizes how agents invoke external tools,...
Benchmarking Multi-Agent LLM Architectures for Financial Document Processing: A Comparative Study of Orchestration Patterns, Cost-Accuracy Tradeoffs and Production Scaling Strategies
arXiv:2603.22651v1 Announce Type: new Abstract: The adoption of large language models (LLMs) for structured information extraction from financial documents has accelerated rapidly, yet production deployments face fundamental architectural decisions with limited empirical guidance. We present a systematic benchmark comparing four...
Why AI-Generated Text Detection Fails: Evidence from Explainable AI Beyond Benchmark Accuracy
arXiv:2603.23146v1 Announce Type: new Abstract: The widespread adoption of Large Language Models (LLMs) has made the detection of AI-Generated text a pressing and complex challenge. Although many detection systems report high benchmark accuracy, their reliability in real-world settings remains uncertain,...
A Multi-Modal CNN-LSTM Framework with Multi-Head Attention and Focal Loss for Real-Time Elderly Fall Detection
arXiv:2603.22313v1 Announce Type: new Abstract: The increasing global aging population has intensified the demand for reliable health monitoring systems, particularly those capable of detecting critical events such as falls among elderly individuals. Traditional fall detection approaches relying on single-modality acceleration...
AutoMOOSE: An Agentic AI for Autonomous Phase-Field Simulation
arXiv:2603.20986v1 Announce Type: new Abstract: Multiphysics simulation frameworks such as MOOSE provide rigorous engines for phase-field materials modeling, yet adoption is constrained by the expertise required to construct valid input files, coordinate parameter sweeps, diagnose failures, and extract quantitative results....
gUFO: A Gentle Foundational Ontology for Semantic Web Knowledge Graphs
arXiv:2603.20948v1 Announce Type: new Abstract: gUFO is a lightweight implementation of the Unified Foundational Ontology (UFO) suitable for Semantic Web OWL 2 DL applications. UFO is a mature foundational ontology with a rich axiomatization and that has been employed in...
Interpretable Multiple Myeloma Prognosis with Observational Medical Outcomes Partnership Data
arXiv:2603.20341v1 Announce Type: new Abstract: Machine learning (ML) promises better clinical decision-making, yet opaque model behavior limits the adoption in healthcare. We propose two novel regularization techniques for ensuring the interpretability of ML models trained on real-world data. In particular,...
Generative Active Testing: Efficient LLM Evaluation via Proxy Task Adaptation
arXiv:2603.19264v1 Announce Type: cross Abstract: With the widespread adoption of pre-trained Large Language Models (LLM), there exists a high demand for task-specific test sets to benchmark their performance in domains such as healthcare and biomedicine. However, the cost of labeling...
A Mathematical Theory of Understanding
arXiv:2603.19349v1 Announce Type: new Abstract: Generative AI has transformed the economics of information production, making explanations, proofs, examples, and analyses available at very low cost. Yet the value of information still depends on whether downstream users can absorb and act...
MANAR: Memory-augmented Attention with Navigational Abstract Conceptual Representation
arXiv:2603.18676v1 Announce Type: new Abstract: MANAR (Memory-augmented Attention with Navigational Abstract Conceptual Representation), contextualization layer generalizes standard multi-head attention (MHA) by instantiating the principles of Global Workspace Theory (GWT). While MHA enables unconstrained all-to-all communication, it lacks the functional bottleneck...
How Confident Is the First Token? An Uncertainty-Calibrated Prompt Optimization Framework for Large Language Model Classification and Understanding
arXiv:2603.18009v1 Announce Type: new Abstract: With the widespread adoption of large language models (LLMs) in natural language processing, prompt engineering and retrieval-augmented generation (RAG) have become mainstream to enhance LLMs' performance on complex tasks. However, LLMs generate outputs autoregressively, leading...
Implicit Grading Bias in Large Language Models: How Writing Style Affects Automated Assessment Across Math, Programming, and Essay Tasks
arXiv:2603.18765v1 Announce Type: new Abstract: As large language models (LLMs) are increasingly deployed as automated graders in educational settings, concerns about fairness and bias in their evaluations have become critical. This study investigates whether LLMs exhibit implicit grading bias based...
Self-Tuning Sparse Attention: Multi-Fidelity Hyperparameter Optimization for Transformer Acceleration
arXiv:2603.18417v1 Announce Type: new Abstract: Sparse attention mechanisms promise to break the quadratic bottleneck of long-context transformers, yet production adoption remains limited by a critical usability gap: optimal hyperparameters vary substantially across layers and models, and current methods (e.g., SpargeAttn)...
Federated Learning for Privacy-Preserving Medical AI
arXiv:2603.15901v1 Announce Type: new Abstract: This dissertation investigates privacy-preserving federated learning for Alzheimer's disease classification using three-dimensional MRI data from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Existing methodologies often suffer from unrealistic data partitioning, inadequate privacy guarantees, and insufficient benchmarking,...
DreamReader: An Interpretability Toolkit for Text-to-Image Models
arXiv:2603.13299v1 Announce Type: new Abstract: Despite the rapid adoption of text-to-image (T2I) diffusion models, causal and representation-level analysis remains fragmented and largely limited to isolated probing techniques. To address this gap, we introduce DreamReader: a unified framework that formalizes diffusion...
Evaluating Large Language Models for Gait Classification Using Text-Encoded Kinematic Waveforms
arXiv:2603.13317v1 Announce Type: new Abstract: Background: Machine learning (ML) enhances gait analysis but often lacks the level of interpretability desired for clinical adoption. Large Language Models (LLMs) may offer explanatory capabilities and confidence-aware outputs when applied to structured kinematic data....