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Evolutionary Architecture Search for Chlorophyll-a Prediction in Lakes using Sentinel-2

arXiv:2610.10496v1 Announce Type: cross Abstract: Small tabular datasets with expert-designed spectral features are the norm in operational Earth observation, and the networks applied to them are typi

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Research

Evolve on the Host, Predict on the Edge: Deploying Online Neuroevolutionary Architecture Search for Cross-sectional Stock Return Prediction

DGX agent

arXiv:2610.10038v1 Announce Type: cross Abstract: Accurate forecasting models are usually large, expensive to update online, and fixed in architecture once trained. We apply ONE-NAS, an online neuroev

researcharxiv-cs-lg

8 Oct 2026

EvoSignal: LLM-Guided Evolutionary Design of Modular Traffic Signal Control Programs

arXiv:2610.09563v1 Announce Type: new Abstract: Effective traffic signal control (TSC) requires policies that respond to changing traffic demand and network conditions while meeting different control

local-aiarxiv-cs-lg

Tutorials

Exact Dynamics and Finite-Sample Trajectory Recovery of Linear Recursive Feature Machines

arXiv:2610.09196v1 Announce Type: new Abstract: Recursive feature machines (RFMs) learn representations of data by alternating between fitting a predictor to a dataset and updating features of that pr

tutorialsarxiv-cs-lg

Executing Causal Structure Learning with Linear-Attention Transformers

model-releasesarxiv-cs-lg

arXiv:2610.10395v1 Announce Type: new Abstract: Transformers can execute algorithms on data given in their input. We ask whether they can do the same for causal discovery. We study a standard continuo

Expected Sample Complexity in Multi-Armed Bandits

arXiv:2610.09929v1 Announce Type: new Abstract: Sample complexity is a widely used metric in sequential decision-making problems, defined as the number of suboptimal decisions during the interaction b

ExperienceIndex: Artifact-Grounded Memory

arXiv:2610.10091v1 Announce Type: new Abstract: Knowledge-intensive tasks require answering many questions by reasoning about a shared corpus of artifacts (e.g., court cases, or scientific literature)

agentsarxiv-cs-cl

Hardware

Expert Coupling in MoE Pretraining: Reducing All-to-All Overhead with Correlated Placement and Token Shuffling

arXiv:2610.09372v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) layers replace the feed-forward block of a Transformer with E expert networks, and each token is routed to k of these experts.

hardwarearxiv-cs-cl

Explicit Geometric Chain-of-Thought for Vision-Language-Action in Autonomous Driving

arXiv:2610.10390v1 Announce Type: new Abstract: Vision-language-action~(VLA) models have emerged as a promising paradigm for autonomous driving. However, existing VLA models still suffer from a fundam

local-aiarxiv-cs-cv

Tutorials

Extreme Binary Classification: Extreme Value Theory for Extreme Constraint on False Negative

arXiv:2610.09984v1 Announce Type: cross Abstract: While binary classification is one of the most extensively studied problems in machine learning, the regime in which the goal is to learn a classifier

tutorialsarxiv-cs-lg

FaceKit: a Toolkit for Interpretable Facial Phenotyping, Synthetic Image Generation and Privacy Analysis in Rare Diseases

arXiv:2610.09288v1 Announce Type: cross Abstract: Many rare genetic diseases are associated with recognizable craniofacial features. However, traditional approaches for describing facial morphology re

Factorized Tactile Representation and Control for Sim-to-Real Manipulation

arXiv:2610.10510v1 Announce Type: new Abstract: Tactile sim-to-real learning must bridge simulated contact and device-specific sensor responses while preserving information needed for control. We prop

Fast and Robust Teach-and-Repeat Navigation Using MixVPR Visual Place Recognition*

arXiv:2610.09631v1 Announce Type: new Abstract: Teach-and-repeat navigation systems employing advanced visual place recognition techniques for localization exhibit key attributes for long-term mobile

applicationsarxiv-cs-ro

Fast Planning for Multi-object Multi-target Throwing

arXiv:2610.09224v1 Announce Type: new Abstract: Robot throwing has emerged as a promising technique for improving efficiency in logistics and warehouse automation, by enlarging the workspace and speed

applicationsarxiv-cs-ro

Hardware

Fault-tolerant foundation models

arXiv:2610.10311v1 Announce Type: new Abstract: Emerging computer hardware often trades reliability for energy efficiency; here we show that large-language models (LLMs) can be trained to tolerate thi

hardwarearxiv-cs-lg

FedRSPO+: A Heterogeneity-aware Algorithm for Decision-focused Federated Learning

arXiv:2610.09091v1 Announce Type: new Abstract: Decision-focused learning (DFL) trains predictive models for downstream optimization, but existing methods largely assume centralized data. In cross-sil

applicationsarxiv-cs-lg

FedSSMCoOp: SSM Encoders for light-weight Federated Prompt Learning for Few-shot Classification

arXiv:2610.09907v1 Announce Type: new Abstract: Vision-Language Models (VLMs) have shown strong performance across a wide range of downstream vision tasks, thanks to the complementary information cont

safetyarxiv-cs-cv

Few Bits, One Law: Toward W2A4KV2

arXiv:2610.09202v1 Announce Type: cross Abstract: Extreme low-bit LLM compression is most challenging when weights, activations, and KV caches are quantized together: their distributions differ, and q

local-aiarxiv-cs-cl

Few-Shot Learning for Personalised Automated Pain Assessment

model-releasesarxiv-cs-lg

arXiv:2610.09692v1 Announce Type: new Abstract: Pain perception varies substantially across individuals, making it difficult for population-based classifiers to generalise across all subjects in a dat

Finding the Right Balance: Relevance and Diversity in LLM Retrieval

arXiv:2610.09412v1 Announce Type: cross Abstract: Retrieval diversification is widely available in retrieval-augmented generation (RAG) frameworks, yet prior studies disagree on whether it improves re

applicationsarxiv-cs-cl

Finite-Sample Approximation of Hessian-Guided Perturbed Wasserstein Gradient Flows

arXiv:2610.10218v1 Announce Type: new Abstract: Wasserstein gradient flow extends gradient descent to probability measures. Its Hessian-guided perturbed variant (PWGF) adds Gaussian perturbations to e

local-aiarxiv-cs-lg

FinVector-Market-4B: A Controlled Study of LoRA Adaptation for Structured Financial Tasks

model-releasesarxiv-cs-cl

arXiv:2610.08882v1 Announce Type: cross Abstract: FinVector-Market-4B adapts Qwen/Qwen3.5-4B with rank-16 LoRA on a 22,000-example corpus for structured financial tasks. We evaluate the base and adapt

FlashNeRD: Performance-First Contact-Rich Neural Robot Dynamics

arXiv:2610.09130v1 Announce Type: new Abstract: Compared with analytical physics, learned dynamics models promise robot simulation that is faster, inherently differentiable, and easily adaptable to re

FlightMagNav: An Open Dataset and Probabilistic Map Learning and Validation Framework for Outdoor Magnetic Field-Based Positioning

arXiv:2610.08982v1 Announce Type: cross Abstract: Magnetic field-based positioning is a resilient positioning technology that requires no external infrastructure and is hard to jam at scale. To facili

researcharxiv-cs-ro

FLoRa: Flight-Assisted Data Collection from Duty Cycling LoRa Nodes under Energy Constraints

arXiv:2610.09226v1 Announce Type: cross Abstract: Data collection using Unmanned Aerial Vehicles (UAVs) is challenging when LoRa IoT Devices (IoTDs) duty-cycle to conserve battery. Under energy constr

Flow-of-Thought: A Framework for Visual Reasoning

arXiv:2610.09746v1 Announce Type: new Abstract: Mental imagery, ``seeing with the mind's eye'' is an essential aspect of human cognition. Despite rapid progress Large Language Models (LLMs) and Vision

Fluctuations of Nonlinear Observables in Mean Field Neural Network Training

model-releasesarxiv-cs-lg

arXiv:2610.09768v1 Announce Type: new Abstract: Mean field limits describe the training dynamics of wide neural networks through the evolution of the empirical distribution of their parameters. Althou

FoldBack: Self-Correcting Masked Generative Policy for Long-Horizon Garment Folding

arXiv:2610.10462v1 Announce Type: new Abstract: We present FoldBack, a self-correcting masked generative policy for long-horizon garment folding. Existing long-trajectory policies may continue after a

For Those Who Believe in Faithfulness: Optimizing the Area Under Insertion and Deletion Curves for Ranking Relative Feature Importance

arXiv:2610.09844v1 Announce Type: cross Abstract: The adoption of machine learning for socially relevant tasks requires effective explainable artificial intelligence (XAI) methods to better understand

Force without transmission: a depth-induced rank collapse that no loss on the representation reopens

arXiv:2610.09958v1 Announce Type: new Abstract: Training can drive a transformer into a rank collapse: all token representations point in one direction, and learning stops. In a related collapse of at

FreeEvolve: Learning to Evolve Beyond Fixed Loops

arXiv:2610.09197v1 Announce Type: cross Abstract: Agent evolvers automate the design of the prompts, skills and workflows around language model agents, yet the optimization process they follow is stil

From Digital Human Interactions to Physics-Based Humanoid Skills: Physics-Grounded Post-Training of Interaction Generators

arXiv:2610.10322v1 Announce Type: new Abstract: Recent methods have made promising progress in generating interactions between two humanoids, largely relying on physics-based tracking policies to conv

safetyarxiv-cs-cv

From Expert-Guided Proof Search to Automated Open-Problem Solving

arXiv:2610.09769v1 Announce Type: cross Abstract: Large language models are increasingly contributing to mathematical research, where progress often depends on efficient proof search, incremental impr

agentsarxiv-cs-cl

From Global Alignment to Local Grounding: Zero-Shot Chinese Character Recognition with Radical Verification

model-releasesarxiv-cs-cv

arXiv:2610.09449v1 Announce Type: new Abstract: Zero-shot Chinese character recognition (ZS-CCR) aims to recognize characters whose categories are never observed during training, and typically relies

From Pareto to Preference: Personalized Test-Time Scaling via Amortized Agentic Policy Discovery

arXiv:2610.09684v1 Announce Type: new Abstract: Test-time scaling (TTS) improves the reasoning capabilities of large language models by allocating additional inference computation. Existing approaches

safetyarxiv-cs-cl

From Pixel to Coding: Evaluating the Figure Reproduction Capabilities of MLLMs

model-releasesarxiv-cs-cv

arXiv:2610.10066v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have demonstrated impressive capabilities in both visual understanding and code generation. However, existing b

From Plausible Hierarchies to Useful Taxonomies: Evaluating Agentic Harnesses on Customer Feedback

arXiv:2610.09377v1 Announce Type: cross Abstract: Taxonomies are the symbolic representations through which AI systems organize evidence, aggregate patterns, and answer questions over large document c

From Prompts to Trees: Effective LLM-Guided Tree Generation for Few-Shot Tabular Classification

arXiv:2610.10227v1 Announce Type: cross Abstract: While Large Language Models (LLMs) possess rich world knowledge and impressive generalization capabilities, their direct application to tabular data c

applicationsarxiv-cs-cl

From Retrieval to Customer Context: Evaluating Frontier-Model Systems for Voice-of-Customer Analysis

arXiv:2610.09375v1 Announce Type: new Abstract: Organizations increasingly use frontier language models to analyze customer feedback, but answer quality also depends on how that feedback is organized

From Uncertainty to Action: Learning to Steer LLM Agents

model-releasesarxiv-cs-cl

arXiv:2610.09115v1 Announce Type: cross Abstract: Steering an LLM agent means deciding whether to correct it, at which step, and with which mechanism. Uncertainty is often used to decide when to corre

From Wearable Interfaces to Dexterous Policies: Contact Shifts and Tactile Representations

arXiv:2610.08870v1 Announce Type: new Abstract: Unlike conventional teleoperation, wearable interfaces allow operators to collect dexterous demonstrations through their own hand motions while directly

Tutorials

Fully Interpretable Minimal Transformers: From Geometry to Algorithm

arXiv:2610.09838v1 Announce Type: new Abstract: We present a framework for building and interpreting minimal transformer models. By constraining a transformer's embedding dimension and head size to 2,

tutorialsarxiv-cs-lg

GAGR-Lab: Evaluating Joint Spatial-Geometric and Analytic Function Reasoning

model-releasesarxiv-cs-cl

arXiv:2610.10201v1 Announce Type: cross Abstract: Joint spatial-geometric and analytic function reasoning requires translating a perceived spatial configuration into a symbolic function whose executed

Gauss-Newton Accuracy and Indefinite Hessians: Uniform Coexistence in Low-Cost Sets

arXiv:2610.09675v1 Announce Type: new Abstract: We study the accuracy of Gauss-Newton curvature in ridge-regularized nonlinear least squares. Under local regularity and persistence of level-set curvat

safetyarxiv-cs-lg

Gaussian Density Splatting Network

arXiv:2610.10396v1 Announce Type: new Abstract: This paper proposes a novel crowd counting approach, the Gaussian Density Splatting Network (GDSNet). Unlike methods that rely on conventional, grid-bas

Gaussian Equivalence for Multi-Head Self-Attention

arXiv:2610.10033v1 Announce Type: cross Abstract: A theoretical understanding of multi-head self-attention is fundamental to the study of modern neural networks. Using random matrix theory, we establi

Gaussian Material Fields for Volumetric Multi-Energy CT Decomposition

arXiv:2610.09492v1 Announce Type: new Abstract: Volumetric material decomposition in multi-energy computed tomography requires a representation that organizes multiple three-dimensional material field

local-aiarxiv-cs-cv

Generative AI for Autonomous Driving: Frontiers and Opportunities

arXiv:2505.08854v2 Announce Type: replace Abstract: Generative Artificial Intelligence (GenAI) constitutes a transformative technological wave that reconfigures industries through its unparalleled cap

safetyarxiv-cs-cv