Omni-iEEG: A Large-Scale, Comprehensive iEEG Dataset and Benchmark for Epilepsy Research
arXiv:2602.16072v1 Announce Type: new Abstract: Epilepsy affects over 50 million people worldwide, and one-third of patients suffer drug-resistant seizures where surgery offers the best chance of seizure freedom. Accurate localization of the epileptogenic zone (EZ) relies on intracranial EEG (iEEG)....
Labor & Employment practice area relevance is minimal in this article. However, the article may have indirect relevance to the practice area in terms of data-driven approaches and the potential for AI-powered tools to augment clinical workflows. Key legal developments or research findings in this article relate to the creation of a large-scale dataset (Omni-iEEG) for epilepsy research, which may have implications for the development of AI-powered tools in clinical settings. The article does not directly address labor or employment law, but it highlights the importance of standardized benchmarks and reproducibility in the development of AI-powered tools, which may have broader implications for the labor and employment practice area. Policy signals in this article are related to the need for harmonized clinical metadata and standardized evaluation metrics in the development of AI-powered tools for clinical applications. These policy signals may have implications for the labor and employment practice area in terms of the need for standardized approaches to data management and evaluation in the development of AI-powered tools for use in clinical settings.
### **Jurisdictional Comparison & Analytical Commentary on *Omni-iEEG* in Labor & Employment Practice** The release of *Omni-iEEG* represents a significant advancement in epilepsy research, with potential indirect yet transformative implications for labor and employment law, particularly in workplace accommodations, disability discrimination, and occupational health regulations. Below is a jurisdictional comparison of how the US, South Korea, and international frameworks may respond to such technological advancements in workplace health monitoring and AI-assisted medical diagnostics. #### **United States: ADA Compliance & AI-Driven Workplace Accommodations** In the US, the *Americans with Disabilities Act (ADA)* governs workplace accommodations for employees with epilepsy, requiring employers to engage in an interactive process when an employee requests modifications (e.g., flexible scheduling, remote work, or ergonomic adjustments). The introduction of AI-assisted diagnostic tools like *Omni-iEEG* could streamline epilepsy management by improving seizure prediction, thereby reducing workplace hazards. However, this raises compliance questions under the *ADA* and the *EEOC’s* guidance on AI in employment decisions. Employers must ensure that AI-driven health monitoring does not lead to discriminatory hiring practices or improper medical inquiries (*ADA §12112(d)*). The *EEOC’s* recent enforcement guidance on AI in employment decisions suggests that while such tools can enhance safety, they must be validated, transparent
As a Wrongful Termination Expert, I must emphasize that the article provided is unrelated to labor and employment law. However, I can analyze the article's implications for practitioners in the field of epilepsy research and neuroscience, highlighting any relevant connections to public policy, implied contracts, or at-will employment exceptions. The article presents a comprehensive iEEG dataset and benchmark for epilepsy research, which may have significant implications for researchers, clinicians, and patients affected by epilepsy. The dataset's development and release may be subject to relevant laws and regulations, such as the Health Insurance Portability and Accountability Act (HIPAA) and the Common Rule (45 CFR 46). In terms of public policy exceptions, the article's focus on advancing epilepsy research and improving patient outcomes aligns with public policy goals of promoting healthcare innovation and improving patient care. This may be relevant to the concept of "whistleblower" or "public policy" exceptions to at-will employment, where an employee's termination may be considered wrongful if it involves retaliation for reporting or opposing a violation of public policy. Regarding implied contracts, the article's discussion of clinically meaningful tasks and unified evaluation metrics may be relevant to the concept of implied-in-fact contracts, which arise from the parties' conduct and expectations. In this context, researchers and clinicians may have implied contractual obligations to adhere to certain standards and best practices in developing and using the Omni-iEEG dataset. In terms of case law, statutory, or regulatory connections, the article's development
Beyond Facts: Benchmarking Distributional Reading Comprehension in Large Language Models
arXiv:2604.06201v1 Announce Type: new Abstract: While most reading comprehension benchmarks for LLMs focus on factual information that can be answered by localizing specific textual evidence, many real-world tasks require understanding distributional information, such as population-level trends and preferences expressed across...
When Does Context Help? A Systematic Study of Target-Conditional Molecular Property Prediction
arXiv:2604.06558v1 Announce Type: new Abstract: We present the first systematic study of when target context helps molecular property prediction, evaluating context conditioning across 10 diverse protein families, 4 fusion architectures, data regimes spanning 67-9,409 training compounds, and both temporal and...
PD-SOVNet: A Physics-Driven Second-Order Vibration Operator Network for Estimating Wheel Polygonal Roughness from Axle-Box Vibrations
arXiv:2604.06620v1 Announce Type: new Abstract: Quantitative estimation of wheel polygonal roughness from axle-box vibration signals is a challenging yet practically relevant problem for rail-vehicle condition monitoring. Existing studies have largely focused on detection, identification, or severity classification, while continuous regression...
Bi-level Heterogeneous Learning for Time Series Foundation Models: A Federated Learning Approach
arXiv:2604.06727v1 Announce Type: new Abstract: Heterogeneity in time series data is more pronounced than in vision or language, as temporal dynamics vary substantially across domains and tasks. Existing efforts on training time series foundation models (TSFMs) from scratch are often...
STDec: Spatio-Temporal Stability Guided Decoding for dLLMs
arXiv:2604.06330v1 Announce Type: new Abstract: Diffusion Large Language Models (dLLMs) have achieved rapid progress, viewed as a promising alternative to the autoregressive paradigm. However, most dLLM decoders still adopt a global confidence threshold, and do not explicitly model local context...
MedConclusion: A Benchmark for Biomedical Conclusion Generation from Structured Abstracts
arXiv:2604.06505v1 Announce Type: new Abstract: Large language models (LLMs) are widely explored for reasoning-intensive research tasks, yet resources for testing whether they can infer scientific conclusions from structured biomedical evidence remain limited. We introduce $\textbf{MedConclusion}$, a large-scale dataset of $\textbf{5.7M}$...
A Benchmark of Classical and Deep Learning Models for Agricultural Commodity Price Forecasting on A Novel Bangladeshi Market Price Dataset
arXiv:2604.06227v1 Announce Type: new Abstract: Accurate short-term forecasting of agricultural commodity prices is critical for food security planning and smallholder income stabilisation in developing economies, yet machine-learning-ready datasets for this purpose remain scarce in South Asia. This paper makes two...
DIA-HARM: Dialectal Disparities in Harmful Content Detection Across 50 English Dialects
arXiv:2604.05318v1 Announce Type: new Abstract: Harmful content detectors-particularly disinformation classifiers-are predominantly developed and evaluated on Standard American English (SAE), leaving their robustness to dialectal variation unexplored. We present DIA-HARM, the first benchmark for evaluating disinformation detection robustness across 50 English...
LLM-as-Judge for Semantic Judging of Powerline Segmentation in UAV Inspection
arXiv:2604.05371v1 Announce Type: new Abstract: The deployment of lightweight segmentation models on drones for autonomous power line inspection presents a critical challenge: maintaining reliable performance under real-world conditions that differ from training data. Although compact architectures such as U-Net enable...
Graph Topology Information Enhanced Heterogeneous Graph Representation Learning
arXiv:2604.05732v1 Announce Type: new Abstract: Real-world heterogeneous graphs are inherently noisy and usually not in the optimal graph structures for downstream tasks, which often adversely affects the performance of GRL models in downstream tasks. Although Graph Structure Learning (GSL) methods...
AutoSOTA: An End-to-End Automated Research System for State-of-the-Art AI Model Discovery
arXiv:2604.05550v1 Announce Type: new Abstract: Artificial intelligence research increasingly depends on prolonged cycles of reproduction, debugging, and iterative refinement to achieve State-Of-The-Art (SOTA) performance, creating a growing need for systems that can accelerate the full pipeline of empirical model optimization....
Stop Fixating on Prompts: Reasoning Hijacking and Constraint Tightening for Red-Teaming LLM Agents
arXiv:2604.05549v1 Announce Type: new Abstract: With the widespread application of LLM-based agents across various domains, their complexity has introduced new security threats. Existing red-team methods mostly rely on modifying user prompts, which lack adaptability to new data and may impact...
Dialogue Act Patterns in GenAI-Mediated L2 Oral Practice: A Sequential Analysis of Learner-Chatbot Interactions
arXiv:2604.05702v1 Announce Type: new Abstract: While generative AI (GenAI) voice chatbots offer scalable opportunities for second language (L2) oral practice, the interactional processes related to learners' gains remain underexplored. This study investigates dialogue act (DA) patterns in interactions between Grade...
Reproducing AlphaZero on Tablut: Self-Play RL for an Asymmetric Board Game
arXiv:2604.05476v1 Announce Type: new Abstract: This work investigates the adaptation of the AlphaZero reinforcement learning algorithm to Tablut, an asymmetric historical board game featuring unequal piece counts and distinct player objectives (king capture versus king escape). While the original AlphaZero...
Shadow Derivatives: The Quiet Propertization of AI Learning
Introduction Artificial intelligence (AI) systems learn. In today’s AI markets, durable advantage comes less from any single output than from the learning that accumulates through training, fine-tuning, and downstream feedback loops.[1] Each interaction, correction, and deployment contributes incrementally to improved...
TRACE: Capability-Targeted Agentic Training
arXiv:2604.05336v1 Announce Type: new Abstract: Large Language Models (LLMs) deployed in agentic environments must exercise multiple capabilities across different task instances, where a capability is performing one or more actions in a trajectory that are necessary for successfully solving a...
LLM Reasoning as Trajectories: Step-Specific Representation Geometry and Correctness Signals
arXiv:2604.05655v1 Announce Type: new Abstract: This work characterizes large language models' chain-of-thought generation as a structured trajectory through representation space. We show that mathematical reasoning traverses functionally ordered, step-specific subspaces that become increasingly separable with layer depth. This structure already...
What oral arguments and opinion authorships can actually tell us
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Investigating Data Interventions for Subgroup Fairness: An ICU Case Study
arXiv:2604.03478v1 Announce Type: new Abstract: In high-stakes settings where machine learning models are used to automate decision-making about individuals, the presence of algorithmic bias can exacerbate systemic harm to certain subgroups of people. These biases often stem from the underlying...
Episode 42: Russia, Imperial Continuities and Histories of International Law - EJIL: The Podcast!
Embedding Enhancement via Fine-Tuned Language Models for Learner-Item Cognitive Modeling
arXiv:2604.04088v1 Announce Type: new Abstract: Learner-item cognitive modeling plays a central role in the web-based online intelligent education system by enabling cognitive diagnosis (CD) across diverse online educational scenarios. Although ID embedding remains the mainstream approach in cognitive modeling due...
Readable Minds: Emergent Theory-of-Mind-Like Behavior in LLM Poker Agents
arXiv:2604.04157v1 Announce Type: new Abstract: Theory of Mind (ToM) -- the ability to model others' mental states -- is fundamental to human social cognition. Whether large language models (LLMs) can develop ToM has been tested exclusively through static vignettes, leaving...
The Format Tax
arXiv:2604.03616v1 Announce Type: new Abstract: Asking a large language model to respond in JSON should be a formatting choice, not a capability tax. Yet we find that structured output requirements -- JSON, XML, LaTeX, Markdown -- substantially degrade reasoning and...
RUQuant: Towards Refining Uniform Quantization for Large Language Models
arXiv:2604.04013v1 Announce Type: new Abstract: The increasing size and complexity of large language models (LLMs) have raised significant challenges in deployment efficiency, particularly under resource constraints. Post-training quantization (PTQ) has emerged as a practical solution by compressing models without requiring...
Profile-Then-Reason: Bounded Semantic Complexity for Tool-Augmented Language Agents
arXiv:2604.04131v1 Announce Type: new Abstract: Large language model agents that use external tools are often implemented through reactive execution, in which reasoning is repeatedly recomputed after each observation, increasing latency and sensitivity to error propagation. This work introduces Profile--Then--Reason (PTR),...
Extracting and Steering Emotion Representations in Small Language Models: A Methodological Comparison
arXiv:2604.04064v1 Announce Type: new Abstract: Small language models (SLMs) in the 100M-10B parameter range increasingly power production systems, yet whether they possess the internal emotion representations recently discovered in frontier models remains unknown. We present the first comparative analysis of...
Solar-VLM: Multimodal Vision-Language Models for Augmented Solar Power Forecasting
arXiv:2604.04145v1 Announce Type: new Abstract: Photovoltaic (PV) power forecasting plays a critical role in power system dispatch and market participation. Because PV generation is highly sensitive to weather conditions and cloud motion, accurate forecasting requires effective modeling of complex spatiotemporal...
AIVV: Neuro-Symbolic LLM Agent-Integrated Verification and Validation for Trustworthy Autonomous Systems
arXiv:2604.02478v1 Announce Type: new Abstract: Deep learning models excel at detecting anomaly patterns in normal data. However, they do not provide a direct solution for anomaly classification and scalability across diverse control systems, frequently failing to distinguish genuine faults from...
LiME: Lightweight Mixture of Experts for Efficient Multimodal Multi-task Learning
arXiv:2604.02338v1 Announce Type: new Abstract: MoE-PEFT methods combine Mixture of Experts with parameter-efficient fine-tuning for multi-task adaptation, but require separate adapters per expert causing trainable parameters to scale linearly with expert count and limiting applicability to adapter-based architectures. We propose...