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Embedding NDRE Trajectories into Contrastive Learning for Label-Free, Physiology-Aware Crop-Stress Staging and DSS Outputs

arXiv:2608.25888v1 Announce Type: new Abstract: Timely detection of crop stress is critical for sustaining yields under increasing drought frequency, yet conventional vegetation index thresholds or im

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Energy Yield and Lifetime Climate Classification via Machine Learning for Optimizing Photovoltaic Module Design and Materials

DGX agent

arXiv:2608.25448v1 Announce Type: cross Abstract: To resiliently and sustainably meet our future energy demand, photovoltaic (PV) modules must be deployed across a broad and diverse range of geographi

researcharxiv-cs-lg

27 Aug 2026

Evolutionary chemical learning in dimerization networks

arXiv:2506.14006v2 Announce Type: replace-cross Abstract: We present a framework for chemical learning based on Competitive Dimerization Networks (CDNs) - systems in which multiple molecular species,

Extending Ground-Constraint LiDAR-IMU Calibration to Tilted Surfaces in a Continuous-Time Framework

arXiv:2608.25135v1 Announce Type: new Abstract: This paper presents a novel method that extends targetless LiDAR-IMU calibration for ground vehicles to non- flat environments. Calibration typically ne

FAMPWQ: Fisher Information-based Adaptive Mixed Precision Weight Quantization for Effective LLM Inference

arXiv:2608.24945v1 Announce Type: new Abstract: Recent years have witnessed remarkable achievements of Large Language Models (LLMs) in multiple domains, while the excessive resource requirements of LL

Fast rates in Bayesian online learning with approximate posteriors

arXiv:2608.25706v1 Announce Type: cross Abstract: Exact Bayes prediction enjoys fast predictive regret guarantees, but exact posterior updating or representation may be too costly for online use. We s

Finding and using interpretable latents in a neutrino foundation model with sparse autoencoders

arXiv:2608.26090v1 Announce Type: cross Abstract: We present a first application of sparse-autoencoder-based mechanistic interpretability to particle physics. Studying a neutrino foundation model pret

Fine-Tuning Whisper for Automatic Speech Recognition in Baniwa: A Preliminary Study

arXiv:2608.26060v1 Announce Type: new Abstract: Automatic Speech Recognition (ASR) technologies have achieved remarkable performance in recent years through the use of large multilingual foundation mo

FlowMoDL: Model-Based Deep Learning with Conjugate-Gradient Data Consistency for Highly Accelerated 4D Flow MRI Reconstruction

arXiv:2608.25828v1 Announce Type: new Abstract: We present FlowMoDL, an unrolled neural network for highly accelerated 4D flow MRI reconstruction that directly optimizes for both anatomical magnitude

Forecasting Multiple Observables with SCROLL: Score-Trained Uncertainty for Stochastic Dynamics

arXiv:2608.25898v1 Announce Type: new Abstract: Forecasting a stochastic dynamical system rarely means a single number: one wants several observables---future state, threshold event, regime label---ea

Forge4D: Feed-Forward 4D Human Reconstruction and Interpolation from Uncalibrated Sparse-view Videos

arXiv:2509.24209v2 Announce Type: replace Abstract: Instant reconstruction of dynamic 3D humans from uncalibrated sparse-view videos is critical for numerous downstream applications. Existing methods,

FORMSpoT: Revealing Fine-Scale Forest Disturbances from Nation-Wide 1.5 m Forest Canopy Height Time Series

arXiv:2512.17021v2 Announce Type: replace Abstract: Current large-scale satellite-based forest disturbance monitoring systems operate at 10-30~m resolution, too coarse to detect changes at the scale o

Frequency-aware forecasting for short-term typhoon gust prediction

arXiv:2608.25604v1 Announce Type: new Abstract: Accurate gust forecasting under typhoon conditions remains challenging due to the highly non-stationary and multi-scale characteristics of extreme wind

Functional linear regression from sparse to dense designs: a pooling-ridge method and minimax optimality

arXiv:2608.25468v1 Announce Type: cross Abstract: Functional data analysis is an important statistical field that treats data as random functions. In practice, the random functions are often not fully

Generalized Riesz Regression: A Unified Framework for Debiased Machine Learning with Riesz Representer Fitting under Bregman Divergence

arXiv:2601.07752v4 Announce Type: replace-cross Abstract: Estimating the Riesz representer is central to debiased machine learning, yet the generator and representer model determine which regression d

Generative vs. Encoder Large Language Models for ASR Evaluation: A Comparative Study

arXiv:2608.25574v1 Announce Type: new Abstract: Automatic Speech Recognition (ASR) is typically evaluated using Word Error Rate (WER), which poorly reflects semantic similarity. While embedding-based

GRIP: Algorithm-Agnostic Machine Unlearning for Mixture-of-Experts via Geometric Router Constraints

arXiv:2601.16905v3 Announce Type: replace Abstract: Machine unlearning in Mixture-of-Experts (MoE) large language models presents a critical yet under-explored challenge. Current unlearning methods ap

HCC+: Hyperbolic Guarding for Certified Attention Retrieval

arXiv:2608.24971v1 Announce Type: cross Abstract: We study the Lipschitz stability of attention retrieval in hyperbolic spaces. Existing methods lack deterministic guarantees on attention-weight prese

HealthBench-Psych: A Mental Health Subset of OpenAI's HealthBench

arXiv:2608.25071v1 Announce Type: new Abstract: General-purpose health benchmarks increasingly anchor claims about LLM medical performance, but they are not always resolved by clinical specialty, maki

Hierarchical MoE for Multi-Modal ILD Diagnosis

arXiv:2608.25261v1 Announce Type: cross Abstract: Mixture-of-experts (MoE) models combine specialized predictors under learned routing, offering a principled mechanism for leveraging heterogeneity in

How Edge of Stability Hinders SCAFFOLD in Federated Optimization

arXiv:2608.25873v1 Announce Type: new Abstract: In federated learning, it is well known that heterogeneous data can (in theory) slow down optimization, and much effort has been directed at designing o

How Much Rank Does LoRA Need? Rank-Error Bounds for Transformer Attention

arXiv:2608.26052v1 Announce Type: cross Abstract: Choosing the rank of a low-rank adaptation (LoRA) update is usually an empirical task. In this paper, we provide a task-dependent theory of the approx

Hyperbolic Latent Geometry for Tree-Structured Prototype Networks: A Local-vs-Global Trade-off

arXiv:2608.25199v1 Announce Type: new Abstract: We study a tree-structured regularizer over class-prototype layouts in a hierarchical-classification model and ask whether the choice of latent manifold

ICON Decomposition: Multivariate Concept-Level Explanations of Deep Representations for Model Auditing

arXiv:2608.26083v1 Announce Type: cross Abstract: Deep neural networks often exploit spurious associations in their training data, a failure known as shortcut learning. Concept-based explainability me

iFlip: Iterative Feedback-driven Counterfactual Example Refinement

arXiv:2601.01446v2 Announce Type: replace Abstract: Counterfactual examples are minimal edits to an input that alter a model's prediction. They are widely employed in explainable AI to probe model beh

InteractGesture: Progressive Chunk Guidance for Continuous Streaming Co-Speech Gesture Control

arXiv:2608.25734v1 Announce Type: new Abstract: Co-speech gesture generation has made significant progress toward realistic full-body motion from speaker audio, yet existing models lack fine-grained s

Interpreting Protein Language Model Embeddings via Orthogonal Projection for Protein Fitness Prediction

arXiv:2608.25548v1 Announce Type: new Abstract: Recently, there has been a growing adoption of protein language models (PLMs) in biomedical science. Their embeddings provide a rich numerical represent

It's a matter of timescale: non-linear utility in successor features and multi-objective planning and learning

arXiv:2608.25723v1 Announce Type: new Abstract: Time is of the essence when dealing with multiple reward signals and non-linear utility. In this paper we argue that the current main approaches in mult

Learning from waste: Machine Learning for health risk prediction and computer vision-based sorting in Ghana

arXiv:2608.25759v1 Announce Type: cross Abstract: The inappropriate disposal of solid waste remains a significant public health and environmental concern worldwide, including in Ghana. Poor sanitation

Less can be More: Relieving RAG Bottlenecks via Evidence Frontloading and Pressure-Adaptive Budgeting

arXiv:2608.25115v1 Announce Type: new Abstract: Existing methods for improving Retrieval-Augmented Generation (RAG) efficiency mainly optimize downstream LLM generation, such as context compression or

Less Contouring, More Accuracy: Lesion-Guided ROI Deep Learning for Ovarian Ultrasound Classification

arXiv:2608.25965v1 Announce Type: new Abstract: Ovarian lesion classification using transvaginal ultrasound remains challenging due to overlapping imaging characteristics and the dependence on expert

Leveraging Speech Acts for Low-Data and Cross-Domain Conversation Derailment Forecasting

arXiv:2608.25359v1 Announce Type: new Abstract: Conversational derailment forecasting aims to predict when online discussions will escalate into hostility, enabling proactive moderation. Existing appr

Longitudinal Robot Learning from Demonstration with Care Providers in a Home Environment

arXiv:2608.25196v1 Announce Type: new Abstract: Learning from demonstration (LfD) methods enable non-expert end users to teach robots novel skills without explicit programming. However most evaluation

LongVU-TTT: Causal Test-Time Training for Visual Resampling in Long Video Understanding

arXiv:2608.25729v1 Announce Type: new Abstract: Long-video MLLMs must model temporal change before a limited visual-token budget removes most frame evidence. We introduce LongVU-TTT, which inserts a c

Lost but not erased: Finding traces of a forgotten language in neural speech models

arXiv:2608.25976v1 Announce Type: new Abstract: International adoptees retain phonological traces of a birth language they can no longer speak or comprehend, a persistence typically attributed to a bi

Low-Resolution Perception for Robotic Packing

arXiv:2608.25874v1 Announce Type: new Abstract: This work tackles the problem of scalable perception for robotic packing with low-cost, low-resolution depth sensing. We propose a framework where recon

Lowering the Barrier to AI-Driven Inspection: A No-Code Workflow for Automated Structural Defect Detection

arXiv:2608.25176v1 Announce Type: new Abstract: Structural health monitoring (SHM) is essential in modern engineering, providing data for condition-based maintenance, lifecycle assessment, and predict

M-Fibration Theory with Applications to Neural Network Compression

arXiv:2608.25598v1 Announce Type: new Abstract: The purpose of this paper is to provide a general, comprehensive, theoretical framework that allows one to deal with fibrations on graphs labelled on a

Maximum-Volume Nonnegative Matrix Factorization

arXiv:2602.04795v3 Announce Type: replace Abstract: Nonnegative matrix factorization (NMF) is a popular data embedding technique. Given a nonnegative data matrix X, it aims at finding two lower dimens

Memory-V2V: Memory-Augmented Video-to-Video Diffusion for Consistent Multi-Turn Editing

arXiv:2601.16296v3 Announce Type: replace Abstract: Video-to-video diffusion models achieve impressive single-turn editing performance, but practical editing workflows are inherently iterative. When e

Minimax Alternating Regret for the Experts Problem and Online Convex Optimization

arXiv:2608.25182v1 Announce Type: cross Abstract: In this paper, we study alternating regret in online convex optimization (OCO), motivated by the success of alternating learning dynamics in two-playe

MLLMCLIP: Feature-Level Distillation of MLLM for Robust Vision-Language Representations

arXiv:2608.25575v1 Announce Type: new Abstract: Pretrained vision-language models such as CLIP excel at zero-shot recognition but often fail at compositionality, particularly attribute-object and rela

Modality Contribution Score - A Per-Patient Framework for Quantifying the Relative Diagnostic Contribution of Structural MRI and Amyloid PET in Alzheimer's Disease

arXiv:2608.24931v1 Announce Type: cross Abstract: Multimodal neuroimaging combining structural MRI and positron emission tomography (PET) captures complementary structure-function relationships across

MSR-IVA: Masked Structural Residual Independent Vector Analysis for State-Aware Fusion of Structural MRI and Dynamic Functional Network Connectivity

arXiv:2608.24978v1 Announce Type: new Abstract: Multimodal fusion of structural MRI (sMRI) and dynamic functional network connectivity (dFNC) can reveal how brain structure relates to changing functio

Multi-Touch and Bending Sensing Using Electrical Impedance Tomography for Robotics

arXiv:2503.13048v4 Announce Type: replace Abstract: Electrical Impedance Tomography (EIT) offers a promising solution for distributed tactile sensing with minimal wiring and full-surface coverage in r

Multi-View Trust Evaluation for Collaborator Selection via Evidential Deep Learning

arXiv:2608.25235v1 Announce Type: cross Abstract: Selection of trustworthy collaborators in distributed systems is critical for efficient task completion, necessitating the inference of trustworthines

Multimodal Injury Risk Prediction in Tennis

arXiv:2608.25126v1 Announce Type: new Abstract: Machine learning has had a significant positive impact on the prediction of athlete performance and injury risk. Most works in this field rely on subjec

MulVec: Fine-Grained Role-Aware Matching for Training-Free Zero-Shot Composed Image Retrieval

arXiv:2608.25305v1 Announce Type: new Abstract: Training-free zero-shot composed image retrieval finds a target image in a gallery from a reference image and a text edit without learning from task-spe