FourierQK: Filter Shape, Admissibility and the Leakage-Coverage Law
arXiv:2610.00009v1 Announce Type: new
Abstract: Frequency-collapse attention [Zeris, 2026e] achieves large gains over standard dot-product attention by replacing the Q/K dot product with a bandpass-filtered inner product at a learned frequency. A natural follow-up question is: which filter shape wo...
What Do Rationales Communicate? A Message-Intervention Study in Role-Specialized QA
arXiv:2610.00018v1 Announce Type: new
Abstract: Role-specialized QA pipelines increasingly pass rationales from a reasoner to a verifier, but it is unclear what this message actually buys: better answers, stronger support assessment, or a new failure surface. We introduce a message-intervention dia...
Travel Time Prediction in Supply Chain Management Using Machine Learning
arXiv:2609.38190v1 Announce Type: new
Abstract: The purpose of this research is to find data and methods using machine learning and deep learning to correctly predict the estimated travel time for transportation and logistics in a supply chain system. The supply chain ecosystem is very complex and ...
arXiv:2609.38196v1 Announce Type: new
Abstract: Accurate time series forecasting is critical across various domains, yet traditional ensemble methods often suffer from the disproportionate influence of extreme forecasts. We introduce the Conformal Adversarial Generative Ensemble (CAGE), a novel fra...
A Moving-Horizon Approximate Branch-and-Reduce Method for Deep Classification Trees
arXiv:2609.38194v1 Announce Type: new
Abstract: Despite the importance for interpretability, decision trees face severe scalability challenges. Existing global optimal methods are often limited by binary feature selection and shallow tree depths, whereas traditional heuristic approaches frequently ...
A Data-Free Physics-Informed Neural Operator for Level-Set Interface Advection
arXiv:2609.38195v1 Announce Type: new
Abstract: Operators for interfacial problems are trained on reference solutions produced by the solver they are intended to replace. This work develops a data-free physics-informed neural operator for level-set interface advection, in which the interface is the...
arXiv:2609.35790v1 Announce Type: new
Abstract: While neural theorem provers have achieved impressive milestones in formal mathematics, they largely operate on the assumption that faithful Lean 4 formal statements are already provided. Translating informal natural language into a formal language is...
When the Preconditioning Exponent Turns Negative: Learning-Rate Coupling and Cross-Environment Generalization
arXiv:2609.30271v1 Announce Type: new
Abstract: Adaptive optimizers are commonly parameterized by a fixed power of the second-moment estimate. Existing partially adaptive methods study exponents between momentum-like updates and the standard Adam square root, while the interaction between this expo...
Cosine Similarity Is Not Evidence: Measuring the Noise Floor of Interpretability Transfer Under Quantization
arXiv:2609.30275v1 Announce Type: new
Abstract: A statistic reported without the quantity needed to interpret it is not evidence. We develop that thesis for a concrete practice in AI safety. Interpretability artifacts are calibrated on full-precision weights, deployed on quantized ones, and certifi...
Tool Calling vs. Code Execution for AI Agents: Choosing the Right Action Primitive
Theory is easier to trust once it's running against a real API, so both examples in this article use the same tool — a get_weather function backed by <a href="https://open-meteo.
arXiv:2609.28554v1 Announce Type: new
Abstract: We introduce the Pistis model family, comprising 27B- and 9B-parameter multimodal large language models built on Qwen3.6 and Qwen3.5, respectively, and developed through a general and scalable post-training framework. The framework first establishes a...
CFD Correction of Open Tip Clearance Flow in a Compressor Cascade Using VAE Latent Space Adaptation
arXiv:2609.28558v1 Announce Type: new
Abstract: CFD predictions of open tip clearance flow in compressor cascades are subject to discrepancies relative to experiments, while experimental observations are sparse and high-resolution experimental ground truth is unavailable. This study proposes a non-...
HARN: Hierarchical Associative Resonance Network for Event-Driven Multi-Timeframe Forecasting
arXiv:2609.26822v1 Announce Type: new
Abstract: Financial time series evolve across multiple temporal resolutions, challenging forecasting systems to incorporate newly available information without repeatedly recomputing unchanged representations. We introduce HARN, a Hierarchical Associative Reson...
LWCal: Loss-Weighted Calibration for Tabular Classifiers with Noisy Calibration Labels
arXiv:2609.26839v1 Announce Type: new
Abstract: Post-hoc probability calibration is usually evaluated under an optimistic assumption: the held-out calibration labels are clean. In many AI deployment settings, however, labels come from weak annotators, historical decisions, heuristics, or distant su...
The Drift Contract: Spectral Updates for Depth-Robust Local Learning
arXiv:2609.26811v1 Announce Type: new
Abstract: Local learning trains each layer with its own auxiliary loss and no global backward pass, which makes layer updates structurally parallel. Two problems have kept it marginal: accuracy degrades as depth grows, and hyperparameters are fragile. We apply ...
RAG vs. Fine-Tuning for Domain Adaptation: When to Use Which
In this article, you will learn the mechanical difference between retrieval-augmented generation and fine-tuning, when each technique is the right tool, and how to decide...
Do Synthetic Personas Predict Real Audience Response? A Sim-to-Real Study Where a No-Persona Baseline Beats Persona-Based Copy Simulation
arXiv:2609.25010v1 Announce Type: new
Abstract: Marketers increasingly use large language models (LLMs) as "synthetic personas" to predict how an audience will react to a piece of copy before it ships, encouraged by evidence that profile-conditioned LLMs mimic human samples. But is that prediction ...
Do Existing Preconditioners Improve Biomedical Tabular Foundation Learning? An Empirical Study on TabPFN Optimization
arXiv:2609.25013v1 Announce Type: new
Abstract: Tabular foundation models have recently shown strong potential for structured biomedical data analysis. Among them, TabPFN has emerged as an effective approach for low-data tabular classification tasks. However, the impact of optimization and precondi...
4DGS-JEPA: Temporally Compositional Joint-Embedding Prediction for Dynamic Gaussian Splatting
arXiv:2609.25036v1 Announce Type: new
Abstract: Dynamic Gaussian Splatting provides an explicit representation of evolving 3D scenes, but existing approaches are primarily optimized for reconstruction, future-state generation, or rendering rather than for learning reusable predictive dynamics. We p...
Stable Unsupervised Continual Chunking with Sheaf SyncMap
arXiv:2609.25143v1 Announce Type: new
Abstract: Unsupervised Continual chunking is a fundamental problem in machine learning and neuroscience, where the goal is to identify groups of states that frequently co-occur in temporal sequences. A key challenge is to form accurate chunks while maintaining ...
Entropy Can Flow, or It Can Guide. Be Entropy. LEDFlow: Introducing Entropy-guided Generation Order into Uniform Discrete Flow
arXiv:2609.25131v1 Announce Type: new
Abstract: Uniform discrete flow permits repeated updates at every generation position. While continued revision supports correction of wrong tokens, it also exposes correct intermediate predictions to later errors. An experiment on Sudoku puzzles shows that 9.4...
A Shared Learning Rate Is Not a Neutral Control in Selective On-Policy Distillation
arXiv:2609.22109v1 Announce Type: new
Abstract: Selective on-policy distillation trains a student only at the token positions a selector scores highest, and the literature compares selectors under a single shared learning rate--a control chosen to be neutral. We show it is not. Under LoRA on GSM8K ...
arXiv:2609.22108v1 Announce Type: new
Abstract: Learning-enabled perception is important in many autonomous systems. Unlike traditional sensors, the boundary where ML perception does or does not work is poorly characterized. Incorrect perception can lead to unsafe or overtly conservative downstream...
Social Influence and the Allocation of Scientific Attention in AI Populations
arXiv:2609.22408v1 Announce Type: new
Abstract: AI systems are becoming participants in the evaluation and use of scientific research. They encounter citation counts, download statistics and lists of popular articles developed around human readers, but the collective consequences of these signals f...