On the Identifiability of User Adaptation in Co-Adaptive Neural Interfaces
arXiv:2606.20569v1 Announce Type: new
Abstract: We analyze identifiability in co-adaptive human-machine systems. We show that closed-loop encoder estimates do not uniquely identify user adaptation, but instead reflect properties of the joint system. We discuss implications for interpreting behavior...
When to Trust, How to Distill: Multi-Foundation Model Guidance for Lightweight, Robust Scientific Time Series Forecasting
arXiv:2606.19363v1 Announce Type: new
Abstract: The deployment of Time-Series Foundation Models (TSFMs) in physical sciences is hindered by a critical trade-off: while these models encode rich, universal temporal dynamics, they suffer from severe distributional misalignment when applied zero-shot t...
arXiv:2606.19361v1 Announce Type: new
Abstract: Identification conditions describe the computability of a target query or parameter of interest as a function of the type and amount of information available. In causal identification, this information is often expressed in the form of a causal graph,...
Artemis: Anatomy-Resolved inTervention for Eliminating Multimodal NeuroImage confounderS
arXiv:2606.18287v1 Announce Type: new
Abstract: Multimodal neuroimaging, integrating functional connectivity from fMRI and structural connectivity from DTI, enables non-invasive analysis of brain networks using graph neural networks. However, demographic factors such as age and sex systematically c...
Diagnosing and Repairing Shape-Prior Shortcuts in Long-Range Single-Shot Fringe Projection Profilometry
arXiv:2606.17093v1 Announce Type: new
Abstract: Learning-based single-shot fringe projection profilometry (FPP) has been studied mostly at close range. The long-range regime (standoff beyond 1 m) remains largely unaddressed: inverse-square intensity falloff lowers fringe signal-to-noise ratio and d...
{\alpha}-Fair Insurance Pricing: A Fairness Continuum
arXiv:2606.14898v1 Announce Type: new
Abstract: Fairness in insurance pricing remains a long-standing and deeply debated puzzle. On one hand, insurers, driven by profitability considerations, set premiums that differentiate across individual risks to achieve actuarial fairness. On the other hand, i...
GRAPE: Guided Parameter-Space Evolution for Compact Adversarial Robustness
arXiv:2606.14865v1 Announce Type: new
Abstract: Adversarial Training (AT) improves neural network robustness, but most methods train a fixed parameter space from the start. This paper asks whether the order in which parameters become optimizable can affect the final robust solution, even when the f...
arXiv:2606.13703v1 Announce Type: new
Abstract: The Muddy Children Puzzle is a puzzle about knowledge and ignorance that has been inspiring for the development of epistemic logic. Who came up with it first? This is unclear. We trace the origin of the Muddy Children Puzzle through logical and litera...
Few-Shot Resampling for Scalable Statistically-Sound Data Mining
arXiv:2606.11235v1 Announce Type: new
Abstract: A key step in knowledge discovery is the evaluation of data mining results. In several applications, including pattern mining, graph analysis, and others, this step includes the evaluation of the statistical significance of the results, to avoid spuri...
Predictive Assistance and the Temporal Dynamics of Exploratory Compression
arXiv:2606.10094v1 Announce Type: new
Abstract: Classical theories of cognition describe problem solving as exploratory search through structured problem spaces in which repeated interaction gradually compresses search into efficient representational structures. Predictive artificial intelligence s...
Exploratory Responsiveness and Adaptive Rigidity under AI-Assisted Optimization
arXiv:2606.10086v1 Announce Type: new
Abstract: This paper develops a theory of exploratory adaptation under AI-assisted optimization. The central argument is that the long-run adaptive effects of AI systems depend critically on how predictive assistance interacts with exploratory responsiveness it...
Why Limit the Residual Stream to Layers and Not Tokens? Persistent Memory for Continuous Latent Reasoning
arXiv:2606.07720v1 Announce Type: new
Abstract: Large language models (LLMs) have demonstrated remarkable reasoning abilities on mathematical and multi-hop planning tasks. The CoCoNuT (Chain of Continuous Thought) paradigm~\cite{hao2024coconut} extends this by enabling models to reason in latent sp...
A case study of evaluating AI agents on a neuroscience data-to-discovery pipeline
arXiv:2606.07718v1 Announce Type: new
Abstract: Agentic AI tools offer a promising path to automating software development bottlenecks in scientific research pipelines, particularly for stages that take domain experts days to months to build, where scientists care about correctness and robustness, ...
Offline Reinforcement Learning for Plasma Control in Nuclear Fusion: Codebase and Benchmark
arXiv:2606.07550v1 Announce Type: new
Abstract: Offline reinforcement learning (RL) offers a promising route for developing plasma controllers from historical tokamak data, since online trial-and-error on real devices is costly and risky. However, progress in this direction remains difficult to mea...
Detecting and Mitigating Bias by Treating Fairness as a Symmetry Operation
arXiv:2606.06514v1 Announce Type: new
Abstract: Machine learning systems deployed in high stakes socioeconomic settings routinely display bias. We formalize bias as a symmetry breaking operation: a classifier is fair if its outputs remain invariant under the counterfactual operation of switching a ...
WAV: Multi-Resolution Block Residual Routing for Deep Decoder-Only Transformers
arXiv:2606.06564v1 Announce Type: new
Abstract: Residual connections are central to training deep Transformers, but standard PreNorm residual streams aggregate sublayer updates with fixed unit weights. Recent Attention Residuals replace this fixed accumulation with content-dependent depth-wise rout...
Multi-Scale Feature Attention Network for Polymer Classification using THz Dual-Comb Spectroscopy
arXiv:2606.06554v1 Announce Type: new
Abstract: Reliable polymer identification is essential for ensuring the quality and safety of recycled plastics, yet conventional sorting and spectroscopic techniques often struggle to deliver robust discrimination. Terahertz Dual-Comb Spectroscopy (THz-DCS) of...
PyCC.id: A package for hypothesis-driven equation discovery with structural identifiability
arXiv:2606.05191v1 Announce Type: new
Abstract: Data-driven equation discovery is fundamentally an inverse problem that seeks to infer the governing differential equations of a system directly from time-series measurements. A known issue is the ill-conditioned nature of the inverse problem, which f...
Early Detection of Alzheimer's Disease Using Explainable Machine Learning on Clinical Biomarkers: A Multi-Class Classification Study Using the Alzheimer's Disease Neuroimaging Initiative (ADNI) Dataset
arXiv:2606.03995v1 Announce Type: new
Abstract: Background: Alzheimer's disease (AD) affects over 55 million people worldwide. Accurate, interpretable detection of normal cognition (NC), mild cognitive impairment (MCI), and AD from routine clinical assessments remains a critical unmet need. Methods...
Pseudospectral Bounds for Transient Amplification in Coupled Gradient Descent
arXiv:2606.04031v1 Announce Type: new
Abstract: Coupled gradient descent--where the update of one parameter block depends on another--underlies bilevel optimization, two-time-scale stochastic approximation, and adversarial training. When the coupled Jacobian is block-triangular, asymptotic stabilit...
Novel Aspects of IEEE SA P3109 Arithmetic Formats for Machine Learning
arXiv:2606.04028v1 Announce Type: new
Abstract: The IEEE P3109 draft standard defines a parameterized family of binary floating-point formats and associated operations, with a focus on facilitating machine learning. These formats allow efficient and consistent representation of values in a small nu...
Do Transformers Need Three Projections? Systematic Study of QKV Variants
arXiv:2606.04032v1 Announce Type: new
Abstract: Transformers have become the standard solution for various AI tasks, with the query, key, and value (QKV) attention formulation playing a central role. However, the individual contribution of these three projections and the impact of omitting some rem...
arXiv:2606.02597v1 Announce Type: new
Abstract: The development of brain-computer interfaces (BCIs) based on electroencephalograms (EEGs) has advanced significantly mainly to machine learning. Although the majority of earlier research has been on increasing classification accuracy, relatively littl...