Large Language Models are Algorithmically Blind
arXiv:2602.21947v1 Announce Type: new Abstract: Large language models (LLMs) demonstrate remarkable breadth of knowledge, yet their ability to reason about computational processes remains poorly understood. Closing this gap matters for practitioners who rely on LLMs to guide algorithm selection and...
MEDSYN: Benchmarking Multi-EviDence SYNthesis in Complex Clinical Cases for Multimodal Large Language Models
arXiv:2602.21950v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have shown great potential in medical applications, yet existing benchmarks inadequately capture real-world clinical complexity. We introduce MEDSYN, a multilingual, multimodal benchmark of highly complex clinical cases with up to...
Understanding Artificial Theory of Mind: Perturbed Tasks and Reasoning in Large Language Models
arXiv:2602.22072v1 Announce Type: new Abstract: Theory of Mind (ToM) refers to an agent's ability to model the internal states of others. Contributing to the debate whether large language models (LLMs) exhibit genuine ToM capabilities, our study investigates their ToM robustness...
Shared Nature, Unique Nurture: PRISM for Pluralistic Reasoning via In-context Structure Modeling
arXiv:2602.21317v1 Announce Type: new Abstract: Large Language Models (LLMs) are converging towards a singular Artificial Hivemind, where shared Nature (pre-training priors) result in a profound collapse of distributional diversity, limiting the distinct perspectives necessary for creative exploration and scientific discovery....
Uncertainty-Aware Diffusion Model for Multimodal Highway Trajectory Prediction via DDIM Sampling
arXiv:2602.21319v1 Announce Type: new Abstract: Accurate and uncertainty-aware trajectory prediction remains a core challenge for autonomous driving, driven by complex multi-agent interactions, diverse scene contexts and the inherently stochastic nature of future motion. Diffusion-based generative models have recently shown strong...
Archetypal Graph Generative Models: Explainable and Identifiable Communities via Anchor-Dominant Convex Hulls
arXiv:2602.21342v1 Announce Type: new Abstract: Representation learning has been essential for graph machine learning tasks such as link prediction, community detection, and network visualization. Despite recent advances in achieving high performance on these downstream tasks, little progress has been made...
Interleaved Head Attention
arXiv:2602.21371v1 Announce Type: new Abstract: Multi-Head Attention (MHA) is the core computational primitive underlying modern Large Language Models (LLMs). However, MHA suffers from a fundamental linear scaling limitation: $H$ attention heads produce exactly $H$ independent attention matrices, with no communication...
MINAR: Mechanistic Interpretability for Neural Algorithmic Reasoning
arXiv:2602.21442v1 Announce Type: new Abstract: The recent field of neural algorithmic reasoning (NAR) studies the ability of graph neural networks (GNNs) to emulate classical algorithms like Bellman-Ford, a phenomenon known as algorithmic alignment. At the same time, recent advances in...
Geometric Priors for Generalizable World Models via Vector Symbolic Architecture
arXiv:2602.21467v1 Announce Type: new Abstract: A key challenge in artificial intelligence and neuroscience is understanding how neural systems learn representations that capture the underlying dynamics of the world. Most world models represent the transition function with unstructured neural networks, limiting...
Muon+: Towards Better Muon via One Additional Normalization Step
arXiv:2602.21545v1 Announce Type: new Abstract: The Muon optimizer has demonstrated promising performance in pre-training large language models through gradient (or momentum) orthogonalization. In this work, we propose a simple yet effective enhancement to Muon, namely Muon+, which introduces an additional...
Training-free Composition of Pre-trained GFlowNets for Multi-Objective Generation
arXiv:2602.21565v1 Announce Type: new Abstract: Generative Flow Networks (GFlowNets) learn to sample diverse candidates in proportion to a reward function, making them well-suited for scientific discovery, where exploring multiple promising solutions is crucial. Further extending GFlowNets to multi-objective settings has...
Court rejects ICE contractor’s right to immediate appeal
The opinion yesterday in The GEO Group v. Menocal rejects the efforts of a contractor for ICE to get an immediate appeal from a district court judgment. The case involves […]The postCourt rejects ICE contractor’s right to immediate appealappeared first...
Trump administration asks justices to allow it to remove protected status from Syrian nationals
The Trump administration on Thursday asked the Supreme Court to freeze a ruling by a federal judge in New York that indefinitely postpones the termination of a program that allows […]The postTrump administration asks justices to allow it to remove...
Court rules criminal defendants may be prohibited from discussing ongoing testimony with counsel during an overnight recess
When a trial court recesses a criminal trial during a defendant’s testimony, the court may order the defendant and his lawyer not to discuss that testimony during the break except […]The postCourt rules criminal defendants may be prohibited from discussing...
New York sues Valve for enabling "illegal gambling" with loot boxes
The ability to resell Steam items for real value is key to the state's case.
Enhancing Hate Speech Detection on Social Media: A Comparative Analysis of Machine Learning Models and Text Transformation Approaches
arXiv:2602.20634v1 Announce Type: new Abstract: The proliferation of hate speech on social media platforms has necessitated the development of effective detection and moderation tools. This study evaluates the efficacy of various machine learning models in identifying hate speech and offensive...
Beyond the Star Rating: A Scalable Framework for Aspect-Based Sentiment Analysis Using LLMs and Text Classification
arXiv:2602.21082v1 Announce Type: new Abstract: Customer-provided reviews have become an important source of information for business owners and other customers alike. However, effectively analyzing millions of unstructured reviews remains challenging. While large language models (LLMs) show promise for natural language...
Exploring Anti-Aging Literature via ConvexTopics and Large Language Models
arXiv:2602.20224v1 Announce Type: cross Abstract: The rapid expansion of biomedical publications creates challenges for organizing knowledge and detecting emerging trends, underscoring the need for scalable and interpretable methods. Common clustering and topic modeling approaches such as K-means or LDA remain...
RMIT-ADM+S at the MMU-RAG NeurIPS 2025 Competition
arXiv:2602.20735v1 Announce Type: cross Abstract: This paper presents the award-winning RMIT-ADM+S system for the Text-to-Text track of the NeurIPS~2025 MMU-RAG Competition. We introduce Routing-to-RAG (R2RAG), a research-focused retrieval-augmented generation (RAG) architecture composed of lightweight components that dynamically adapt the retrieval...
FedAvg-Based CTMC Hazard Model for Federated Bridge Deterioration Assessment
arXiv:2602.20194v1 Announce Type: new Abstract: Bridge periodic inspection records contain sensitive information about public infrastructure, making cross-organizational data sharing impractical under existing data governance constraints. We propose a federated framework for estimating a Continuous-Time Markov Chain (CTMC) hazard model of...
Uncertainty-Aware Delivery Delay Duration Prediction via Multi-Task Deep Learning
arXiv:2602.20271v1 Announce Type: new Abstract: Accurate delivery delay prediction is critical for maintaining operational efficiency and customer satisfaction across modern supply chains. Yet the increasing complexity of logistics networks, spanning multimodal transportation, cross-country routing, and pronounced regional variability, makes this...
cc-Shapley: Measuring Multivariate Feature Importance Needs Causal Context
arXiv:2602.20396v1 Announce Type: new Abstract: Explainable artificial intelligence promises to yield insights into relevant features, thereby enabling humans to examine and scrutinize machine learning models or even facilitating scientific discovery. Considering the widespread technique of Shapley values, we find that...
GeoPT: Scaling Physics Simulation via Lifted Geometric Pre-Training
arXiv:2602.20399v1 Announce Type: new Abstract: Neural simulators promise efficient surrogates for physics simulation, but scaling them is bottlenecked by the prohibitive cost of generating high-fidelity training data. Pre-training on abundant off-the-shelf geometries offers a natural alternative, yet faces a fundamental...
CGSTA: Cross-Scale Graph Contrast with Stability-Aware Alignment for Multivariate Time-Series Anomaly Detection
arXiv:2602.20468v1 Announce Type: new Abstract: Multivariate time-series anomaly detection is essential for reliable industrial control, telemetry, and service monitoring. However, the evolving inter-variable dependencies and inevitable noise render it challenging. Existing methods often use single-scale graphs or instance-level contrast. Moreover,...
Memory-guided Prototypical Co-occurrence Learning for Mixed Emotion Recognition
arXiv:2602.20530v1 Announce Type: new Abstract: Emotion recognition from multi-modal physiological and behavioral signals plays a pivotal role in affective computing, yet most existing models remain constrained to the prediction of singular emotions in controlled laboratory settings. Real-world human emotional experiences,...
Sample-efficient evidence estimation of score based priors for model selection
arXiv:2602.20549v1 Announce Type: new Abstract: The choice of prior is central to solving ill-posed imaging inverse problems, making it essential to select one consistent with the measurements $y$ to avoid severe bias. In Bayesian inverse problems, this could be achieved...
GENSR: Symbolic Regression Based in Equation Generative Space
arXiv:2602.20557v1 Announce Type: new Abstract: Symbolic Regression (SR) tries to reveal the hidden equations behind observed data. However, most methods search within a discrete equation space, where the structural modifications of equations rarely align with their numerical behavior, leaving fitting...
Benchmarking GNN Models on Molecular Regression Tasks with CKA-Based Representation Analysis
arXiv:2602.20573v1 Announce Type: new Abstract: Molecules are commonly represented as SMILES strings, which can be readily converted to fixed-size molecular fingerprints. These fingerprints serve as feature vectors to train ML/DL models for molecular property prediction tasks in the field of...
Justices reveal little about whether the deadline for removing cases to federal court can be excused
When a plaintiff files a lawsuit in state court asserting a claim that could be brought in federal court, federal law gives the defendant 30 days to remove the case […]The postJustices reveal little about whether the deadline for removing...
Musk has no proof OpenAI stole xAI trade secrets, judge rules, tossing lawsuit
Even twisting an ex-employee's text to favor xAI's reading fails to sway judge.