Student Mental Health Screening via Fitbit Data Collected During the COVID-19 Pandemic
arXiv:2601.16324v1 Announce Type: new
Abstract: College students experience many stressors, resulting in high levels of anxiety and depression. Wearable technology provides unobtrusive sensor data that can be used for the early detection of mental illness. However, current research is limited conce...
Ordering-based Causal Discovery via Generalized Score Matching
arXiv:2601.16249v1 Announce Type: new
Abstract: Learning DAG structures from purely observational data remains a long-standing challenge across scientific domains. An emerging line of research leverages the score of the data distribution to initially identify a topological order of the underlying D...
Efficient Gaussian process learning via subspace projections
arXiv:2601.16332v1 Announce Type: new
Abstract: We propose a novel training objective for GPs constructed using lower-dimensional linear projections of the data, referred to as \emph{projected likelihood} (PL). We provide a closed-form expression for the information loss related to the PL and empir...
A Regularized Actor-Critic Algorithm for Bi-Level Reinforcement Learning
arXiv:2601.16399v1 Announce Type: new
Abstract: We study a structured bi-level optimization problem where the upper-level objective is a smooth function and the lower-level problem is policy optimization in a Markov decision process (MDP). The upper-level decision variable parameterizes the reward ...
Improving MoE Compute Efficiency by Composing Weight and Data Sparsity
arXiv:2601.15370v1 Announce Type: new
Abstract: Mixture-of-Experts layers achieve compute efficiency through weight sparsity: each token activates only a subset of experts. Data sparsity, where each expert processes only a subset of tokens, offers a complementary axis. Expert-choice routing impleme...
arXiv:2601.15380v1 Announce Type: new
Abstract: We generalize the attention mechanism by viewing it through the lens of Entropic Optimal Transport, revealing that standard attention corresponds to a transport problem regularized by an implicit uniform prior. We introduce Generalized Optimal transpo...
Scalable Knee-Point Guided Activity Group Selection in Multi-Tree Genetic Programming for Dynamic Multi-Mode Project Scheduling
arXiv:2601.14485v1 Announce Type: new
Abstract: The dynamic multi-mode resource-constrained project scheduling problem is a challenging scheduling problem that requires making decisions on both the execution order of activities and their corresponding execution modes. Genetic programming has been w...
Epistemic Constitutionalism Or: how to avoid coherence bias
arXiv:2601.14295v1 Announce Type: new
Abstract: Large language models increasingly function as artificial reasoners: they evaluate arguments, assign credibility, and express confidence. Yet their belief-forming behavior is governed by implicit, uninspected epistemic policies. This paper argues for ...
The Ontological Neutrality Theorem: Why Neutral Ontological Substrates Must Be Pre-Causal and Pre-Normative
arXiv:2601.14271v1 Announce Type: new
Abstract: Modern data systems must support accountability across persistent legal, political, and analytic disagreement. This requirement imposes strict constraints on the design of any ontology intended to function as a shared substrate. We establish an imposs...
PRISM: Learning Design Knowledge from Data for Stylistic Design Improvement
arXiv:2601.11747v1 Announce Type: new
Abstract: Graphic design often involves exploring different stylistic directions, which can be time-consuming for non-experts. We address this problem of stylistically improving designs based on natural language instructions. While VLMs have shown initial succe...
arXiv:2601.11620v1 Announce Type: new
Abstract: Whether machines can be conscious depends not only on what they compute, but \emph{when} they compute it. Most deployed artificial systems realise their functions via sequential or time-multiplexed updates. Conscious experience appears unified and sim...
Analytic Bijections for Smooth and Interpretable Normalizing Flows
arXiv:2601.10774v1 Announce Type: new
Abstract: A key challenge in designing normalizing flows is finding expressive scalar bijections that remain invertible with tractable Jacobians. Existing approaches face trade-offs: affine transformations are smooth and analytically invertible but lack express...
Attention Consistency Regularization for Interpretable Early-Exit Neural Networks
arXiv:2601.08891v1 Announce Type: new
Abstract: Early-exit neural networks enable adaptive inference by allowing predictions at intermediate layers, reducing computational cost. However, early exits often lack interpretability and may focus on different features than deeper layers, limiting trust a...
arXiv:2601.08005v1 Announce Type: new
Abstract: Frontier AI regulations primarily focus on systems deployed to external users, where deployment is more visible and subject to outside scrutiny. However, high-stakes applications can occur internally when companies deploy highly capable systems within...
Affect and Effect: Limitations of regularisation-based continual learning in EEG-based emotion classification
arXiv:2601.07858v1 Announce Type: new
Abstract: Generalisation to unseen subjects in EEG-based emotion classification remains a challenge due to high inter-and intra-subject variability. Continual learning (CL) poses a promising solution by learning from a sequence of tasks while mitigating catastr...
HOSC: A Periodic Activation with Saturation Control for High-Fidelity Implicit Neural Representations
arXiv:2601.07870v1 Announce Type: new
Abstract: Periodic activations such as sine preserve high-frequency information in implicit neural representations (INRs) through their oscillatory structure, but often suffer from gradient instability and limited control over multi-scale behavior. We introduce...
Comment on arXiv:2511.21731v1: Identifying Quantum Structure in AI Language: Evidence for Evolutionary Convergence of Human and Artificial Cognition
arXiv:2601.06104v1 Announce Type: new
Abstract: This note is a friendly technical check of arXiv:2511.21731v1. I highlight a few places where the manuscript's interpretation of (i) the reported CHSH/Bell-type calculations and (ii) Bose--Einstein (BE) fits to rank-frequency data seems to go beyond w...
Dynamic Intelligence Ceilings: Measuring Long-Horizon Limits of Planning and Creativity in Artificial Systems
arXiv:2601.06102v1 Announce Type: new
Abstract: Recent advances in artificial intelligence have produced systems capable of remarkable performance across a wide range of tasks. These gains, however, are increasingly accompanied by concerns regarding long-horizon developmental behavior, as many syst...
Automatic Question Generation for Intuitive Learning Utilizing Causal Graph Guided Chain of Thought Reasoning
arXiv:2601.06098v1 Announce Type: new
Abstract: Intuitive learning is crucial for developing deep conceptual understanding, especially in STEM education, where students often struggle with abstract and interconnected concepts. Automatic question generation has become an effective strategy for perso...
The Hessian of tall-skinny networks is easy to invert
arXiv:2601.06096v1 Announce Type: new
Abstract: We describe an exact algorithm for solving linear systems $Hx=b$ where $H$ is the Hessian of a deep net. The method computes Hessian-inverse-vector products without storing the Hessian or its inverse in time and storage that scale linearly in the numb...
Active Sensing Shapes Real-World Decision-Making through Dynamic Evidence Accumulation
arXiv:2601.04214v1 Announce Type: new
Abstract: Human decision-making heavily relies on active sensing, a well-documented cognitive behaviour for evidence gathering to accommodate ever-changing environments. However, its operational mechanism in the real world remains non-trivial. Currently, an in-...
arXiv:2601.03322v1 Announce Type: new
Abstract: Electroencephalography (EEG)-based brain-computer interfaces facilitate direct communication with a computer, enabling promising applications in human-computer interactions. However, their utility is currently limited because EEG decoding often suffer...
Aligning Findings with Diagnosis: A Self-Consistent Reinforcement Learning Framework for Trustworthy Radiology Reporting
arXiv:2601.03321v1 Announce Type: new
Abstract: Multimodal Large Language Models (MLLMs) have shown strong potential for radiology report generation, yet their clinical translation is hindered by architectural heterogeneity and the prevalence of factual hallucinations. Standard supervised fine-tuni...
Why LLMs Aren't Scientists Yet: Lessons from Four Autonomous Research Attempts
arXiv:2601.03315v1 Announce Type: new
Abstract: We report a case study of four end-to-end attempts to autonomously generate ML research papers using a pipeline of six LLM agents mapped to stages of the scientific workflow. Of these four, three attempts failed during implementation or evaluation. On...