Slack More, Predict Better: Proximal Relaxation for Probabilistic Latent Variable Model-based Soft Sensors
arXiv:2603.11473v1 Announce Type: new Abstract: Nonlinear Probabilistic Latent Variable Models (NPLVMs) are a cornerstone of soft sensor modeling due to their capacity for uncertainty delineation. However, conventional NPLVMs are trained using amortized variational inference, where neural networks parameterize the variational...
Deep Learning Network-Temporal Models For Traffic Prediction
arXiv:2603.11475v1 Announce Type: new Abstract: Time series analysis is critical for emerging net- work intelligent control and management functions. However, existing statistical-based and shallow machine learning models have shown limited prediction capabilities on multivariate time series. The intricate topological interdependency...
Leveraging Phytolith Research using Artificial Intelligence
arXiv:2603.11476v1 Announce Type: new Abstract: Phytolith analysis is a crucial tool for reconstructing past vegetation and human activities, but traditional methods are severely limited by labour-intensive, time-consuming manual microscopy. To address this bottleneck, we present Sorometry: a comprehensive end-to-end artificial...
Grammar of the Wave: Towards Explainable Multivariate Time Series Event Detection via Neuro-Symbolic VLM Agents
arXiv:2603.11479v1 Announce Type: new Abstract: Time Series Event Detection (TSED) has long been an important task with critical applications across many high-stakes domains. Unlike statistical anomalies, events are defined by semantics with complex internal structures, which are difficult to learn...
Attention Sinks Are Provably Necessary in Softmax Transformers: Evidence from Trigger-Conditional Tasks
arXiv:2603.11487v1 Announce Type: new Abstract: Transformers often display an attention sink: probability mass concentrates on a fixed, content-agnostic position. We prove that computing a simple trigger-conditional behavior necessarily induces a sink in softmax self-attention models. Our results formalize a familiar...
KEPo: Knowledge Evolution Poison on Graph-based Retrieval-Augmented Generation
arXiv:2603.11501v1 Announce Type: new Abstract: Graph-based Retrieval-Augmented Generation (GraphRAG) constructs the Knowledge Graph (KG) from external databases to enhance the timeliness and accuracy of Large Language Model (LLM) generations.However,this reliance on external data introduces new attack surfaces.Attackers can inject poisoned...
Birthright citizenship: Originalism 101
These days, everyone wants to be an originalist. But in Trump v. Barbara, the birthright-citizenship case at the Supreme Court, not everyone is doing originalism well. Alas, the Trump administration […]The postBirthright citizenship: Originalism 101appeared first onSCOTUSblog.
An interview with Jerry Goldman, founder of the Oyez Project
Welcome to our SCOTUS Innovators series, a new recurring column on people who have shaped our understanding of the Supreme Court. A few weeks ago, I had the opportunity to […]The postAn interview with Jerry Goldman, founder of the Oyez...
When presidents attack the Supreme Court
During a roundtable at the White House on Friday, March 6, President Donald Trump returned to what has become a familiar refrain in the weeks since the Supreme Court struck […]The postWhen presidents attack the Supreme Courtappeared first onSCOTUSblog.
SCOTUStoday for Thursday, March 12
On this day in 1804, the House of Representatives voted to impeach Justice Samuel Chase, who had been accused of abusing his power by refusing to dismiss biased jurors and […]The postSCOTUStoday for Thursday, March 12appeared first onSCOTUSblog.
There Are No Silly Questions: Evaluation of Offline LLM Capabilities from a Turkish Perspective
arXiv:2603.09996v1 Announce Type: cross Abstract: The integration of large language models (LLMs) into educational processes introduces significant constraints regarding data privacy and reliability, particularly in pedagogically vulnerable contexts such as Turkish heritage language education. This study aims to systematically evaluate...
HEAL: Hindsight Entropy-Assisted Learning for Reasoning Distillation
arXiv:2603.10359v1 Announce Type: new Abstract: Distilling reasoning capabilities from Large Reasoning Models (LRMs) into smaller models is typically constrained by the limitation of rejection sampling. Standard methods treat the teacher as a static filter, discarding complex "corner-case" problems where the...
Adaptive RAN Slicing Control via Reward-Free Self-Finetuning Agents
arXiv:2603.10564v1 Announce Type: new Abstract: The integration of Generative AI models into AI-native network systems offers a transformative path toward achieving autonomous and adaptive control. However, the application of such models to continuous control tasks is impeded by intrinsic architectural...
Explainable LLM Unlearning Through Reasoning
arXiv:2603.09980v1 Announce Type: cross Abstract: LLM unlearning is essential for mitigating safety, copyright, and privacy concerns in pre-trained large language models (LLMs). Compared to preference alignment, it offers a more explicit way by removing undesirable knowledge characterized by specific unlearning...
PoultryLeX-Net: Domain-Adaptive Dual-Stream Transformer Architecture for Large-Scale Poultry Stakeholder Modeling
arXiv:2603.09991v1 Announce Type: cross Abstract: The rapid growth of the global poultry industry, driven by rising demand for affordable animal protein, has intensified public discourse surrounding production practices, housing, management, animal welfare, and supply-chain transparency. Social media platforms such as...
Assessing Cognitive Biases in LLMs for Judicial Decision Support: Virtuous Victim and Halo Effects
arXiv:2603.10016v1 Announce Type: cross Abstract: We investigate whether large language models (LLMs) display human-like cognitive biases, focusing on potential implications for assistance in judicial sentencing, a decision-making system where fairness is paramount. Two of the most relevant biases were chosen:...
Verbalizing LLM's Higher-order Uncertainty via Imprecise Probabilities
arXiv:2603.10396v1 Announce Type: new Abstract: Despite the growing demand for eliciting uncertainty from large language models (LLMs), empirical evidence suggests that LLM behavior is not always adequately captured by the elicitation techniques developed under the classical probabilistic uncertainty framework. This...
Personalized Group Relative Policy Optimization for Heterogenous Preference Alignment
arXiv:2603.10009v1 Announce Type: cross Abstract: Despite their sophisticated general-purpose capabilities, Large Language Models (LLMs) often fail to align with diverse individual preferences because standard post-training methods, like Reinforcement Learning with Human Feedback (RLHF), optimize for a single, global objective. While...
Evolving Demonstration Optimization for Chain-of-Thought Feature Transformation
arXiv:2603.09987v1 Announce Type: cross Abstract: Feature Transformation (FT) is a core data-centric AI task that improves feature space quality to advance downstream predictive performance. However, discovering effective transformations remains challenging due to the large space of feature-operator combinations. Existing solutions...
Does LLM Alignment Really Need Diversity? An Empirical Study of Adapting RLVR Methods for Moral Reasoning
arXiv:2603.10588v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) has achieved remarkable success in logical reasoning tasks, yet whether large language model (LLM) alignment requires fundamentally different approaches remains unclear. Given the apparent tolerance for multiple valid responses...
Automated evaluation of LLMs for effective machine translation of Mandarin Chinese to English
arXiv:2603.09998v1 Announce Type: cross Abstract: Although Large Language Models (LLMs) have exceptional performance in machine translation, only a limited systematic assessment of translation quality has been done. The challenge lies in automated frameworks, as human-expert-based evaluations can be time-consuming, given...
Empathy Is Not What Changed: Clinical Assessment of Psychological Safety Across GPT Model Generations
arXiv:2603.09997v1 Announce Type: cross Abstract: When OpenAI deprecated GPT-4o in early 2026, thousands of users protested under #keep4o, claiming newer models had "lost their empathy." No published study has tested this claim. We conducted the first clinical measurement, evaluating three...
A Retrieval-Augmented Language Assistant for Unmanned Aircraft Safety Assessment and Regulatory Compliance
arXiv:2603.09999v1 Announce Type: cross Abstract: This paper presents the design and validation of a retrieval-based assistant that supports safety assessment, certification activities, and regulatory compliance for unmanned aircraft systems. The work is motivated by the growing complexity of drone operations...
TAMUSA-Chat: A Domain-Adapted Large Language Model Conversational System for Research and Responsible Deployment
arXiv:2603.09992v1 Announce Type: cross Abstract: This paper presents TAMUSA-Chat, a research-oriented framework for building domain-adapted large language model conversational systems. The work addresses critical challenges in adapting general-purpose foundation models to institutional contexts through supervised fine-tuning, retrieval-augmented generation, and systematic...
Nurture-First Agent Development: Building Domain-Expert AI Agents Through Conversational Knowledge Crystallization
arXiv:2603.10808v1 Announce Type: new Abstract: The emergence of large language model (LLM)-based agent frameworks has shifted the primary challenge in building domain-expert AI agents from raw capability to effective encoding of domain expertise. Two dominant paradigms -- code-first development, which...
Emulating Clinician Cognition via Self-Evolving Deep Clinical Research
arXiv:2603.10677v1 Announce Type: new Abstract: Clinical diagnosis is a complex cognitive process, grounded in dynamic cue acquisition and continuous expertise accumulation. Yet most current artificial intelligence (AI) systems are misaligned with this reality, treating diagnosis as single-pass retrospective prediction while...
One Model, Many Skills: Parameter-Efficient Fine-Tuning for Multitask Code Analysis
arXiv:2603.09978v1 Announce Type: cross Abstract: Large language models have recently surpassed specialized systems on code generation, yet their effectiveness on other code-analysis tasks remains less clear. At the same time, multi-task learning offers a way to unify diverse objectives within...
Resource-constrained Amazons chess decision framework integrating large language models and graph attention
arXiv:2603.10512v1 Announce Type: new Abstract: Artificial intelligence has advanced significantly through the development of intelligent game-playing systems, providing rigorous testbeds for decision-making, strategic planning, and adaptive learning. However, resource-constrained environments pose critical challenges, as conventional deep learning methods heavily rely...
Trajectory-Informed Memory Generation for Self-Improving Agent Systems
arXiv:2603.10600v1 Announce Type: new Abstract: LLM-powered agents face a persistent challenge: learning from their execution experiences to improve future performance. While agents can successfully complete many tasks, they often repeat inefficient patterns, fail to recover from similar errors, and miss...
Defining AI Models and AI Systems: A Framework to Resolve the Boundary Problem
arXiv:2603.10023v1 Announce Type: cross Abstract: Emerging AI regulations assign distinct obligations to different actors along the AI value chain (e.g., the EU AI Act distinguishes providers and deployers for both AI models and AI systems), yet the foundational terms "AI...