QUARK: Robust Retrieval under Non-Faithful Queries via Query-Anchored Aggregation
arXiv:2601.21049v1 Announce Type: new
Abstract: User queries in real-world retrieval are often non-faithful (noisy, incomplete, or distorted), causing retrievers to fail when key semantics are missing. We formalize this as retrieval under recall noise, where the observed query is drawn from a noisy...
arXiv:2601.19955v1 Announce Type: new
Abstract: Neuroscience and Artificial Intelligence (AI) have made significant progress in the past few years but have only been loosely inter-connected. Based on a workshop held in August 2025, we identify current and future areas of synergism between these two...
AI may learn better when it’s allowed to talk to itself. Researchers showed that internal “mumbling,” combined with short-term memory, helps AI adapt to new tasks, switch goals, and handle complex challenges more easily. This approach boosts learning efficiency while using far less training data. It...
NavFormer: IGRF Forecasting in Moving Coordinate Frames
arXiv:2601.18800v1 Announce Type: new
Abstract: Triad magnetometer components change with sensor attitude even when the IGRF total intensity target stays invariant. NavFormer forecasts this invariant target with rotation invariant scalar features and a Canonical SPD module that stabilizes the spect...
Latent Structural Similarity Networks for Unsupervised Discovery in Multivariate Time Series
arXiv:2601.18803v1 Announce Type: new
Abstract: This paper proposes a task-agnostic discovery layer for multivariate time series that constructs a relational hypothesis graph over entities without assuming linearity, stationarity, or a downstream objective. The method learns window-level sequence r...
VAE with Hyperspherical Coordinates: Improving Anomaly Detection from Hypervolume-Compressed Latent Space
arXiv:2601.18823v1 Announce Type: new
Abstract: Variational autoencoders (VAE) encode data into lower-dimensional latent vectors before decoding those vectors back to data. Once trained, one can hope to detect out-of-distribution (abnormal) latent vectors, but several issues arise when the latent s...
UniRG: Scaling medical imaging report generation with multimodal reinforcement learning
AI can help generate medical image reports, but today’s models struggle with varying reporting schemes. Learn how UniRG uses reinforcement learning to boost performance of medical vision-language models.
The post UniRG: Scaling medical imaging report generation with multimodal reinforcement learning...
A Dataset of Dengue Hospitalizations in Brazil (1999 to 2021) with Weekly Disaggregation from Monthly Counts
arXiv:2601.16994v1 Announce Type: new
Abstract: This data paper describes and publicly releases this dataset (v1.0.0), published on Zenodo under DOI 10.5281/zenodo.18189192. Motivated by the need to increase the temporal granularity of originally monthly data to enable more effective training of AI...
Analysis of voice recordings features for Classification of Parkinson's Disease
arXiv:2601.17007v1 Announce Type: new
Abstract: Parkinson's disease (PD) is a chronic neurodegenerative disease. Early diagnosis is essential to mitigate the progressive deterioration of patients' quality of life. The most characteristic motor symptoms are very mild in the early stages, making diag...
Online parameter estimation for the Crazyflie quadcopter through an EM algorithm
arXiv:2601.17009v1 Announce Type: new
Abstract: Drones are becoming more and more popular nowadays. They are small in size, low in cost, and reliable in operation. They contain a variety of sensors and can perform a variety of flight tasks, reaching places that are difficult or inaccessible for hum...
Implementing Tensor Logic: Unifying Datalog and Neural Reasoning via Tensor Contraction
arXiv:2601.17188v1 Announce Type: new
Abstract: The unification of symbolic reasoning and neural networks remains a central challenge in artificial intelligence. Symbolic systems offer reliability and interpretability but lack scalability, while neural networks provide learning capabilities but sac...
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...
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...
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...
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...
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 ...
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...
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...
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...