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Statistical Properties of Deep Neural Networks with Dependent Data

arXiv:2410.11113v4 Announce Type: replace-cross Abstract: This paper develops theory for deep neural network (DNN) estimators under dependent data. To provide theory applicable to a variety of DNN-bas

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Strip Convolution and Direction-Aware Exclusion Loss for Oriented Ship Detection

DGX agent

arXiv:2609.27238v1 Announce Type: new Abstract: Oriented ship detection in very high resolution (VHR) remote sensing imagery remains challenging due to elongated hull geometry and dense target distrib

researcharxiv-cs-cv

24 Sept 2026

StudentBench: AI and human tutoring yield equivalent GRE learning gains

arXiv:2609.28470v1 Announce Type: new Abstract: Artificial intelligence offers an unprecedented opportunity to augment human capabilities, yet progress at the frontier has focused primarily on advanci

Super-Resolution of Solar Magnetograms via Adaptive Stratified Ensemble Learning with Uncertainty Estimation

arXiv:2609.27131v1 Announce Type: new Abstract: Single-image super-resolution of Sun's photospheric magnetograms enables consistent analysis across heterogeneous space-based instruments and supports l

Hardware

Support-Compiled Feature Folding: More Evidence at Lower Memory Across Tabular Foundation Models

arXiv:2609.28208v1 Announce Type: new Abstract: Tabular foundation models face a feature-side scaling dilemma: full-width pairwise mixing grows quadratically with the number of columns, whereas featur

hardwarearxiv-cs-lg

Surgical Kinematics from Monocular Video with Learned Articulated Motion Constraints

arXiv:2609.27227v1 Announce Type: new Abstract: Objective assessment of robotic surgery uses instrument kinematics, which must be reconstructed when only video is available. We introduce a kinematic r

SynSeq: End-to-End SYNTAX Score Prediction from Coronary Angiography Videos

arXiv:2609.27696v1 Announce Type: new Abstract: The SYNTAX score is an established tool for assessing coronary artery disease and guiding revascularization treatment decisions. However, its manual est

safetyarxiv-cs-cv

Tackling fluffy clouds: robust agricultural field boundary delineation from Sentinel-1 and Sentinel-2 satellite image time series

arXiv:2409.13568v3 Announce Type: replace Abstract: Accurate delineation of agricultural field boundaries is essential for effective crop monitoring and resource management. However, competing methodo

Tutorials

Tail-Aware Geometry Learning for Conformal Ellipsoids

arXiv:2609.27221v1 Announce Type: new Abstract: This paper studies multivariate conformal prediction (CP), a distribution-free uncertainty quantification framework with finite-sample coverage guarante

tutorialsarxiv-cs-lg

Talk2Escape: Conversational Grounding for Vision-and-Language Navigation

arXiv:2609.28296v1 Announce Type: new Abstract: While Vision-and-Language Navigation (VLN) has demonstrated remarkable success, the prevailing single-turn paradigm exposes a fundamental vulnerability:

agentsarxiv-cs-ro

TANDEM: Task and Motion Planning with As-Needed Demonstrations for Efficient Vision-Language-Action Model Fine-tuning

arXiv:2609.28314v1 Announce Type: new Abstract: Human teleoperators spend substantial time demonstrating behaviors that robots can already perform autonomously, limiting the scalability of data collec

agentsarxiv-cs-ro

Task-Induced Riemannian Metrics for Vision Transformer Feature Spaces

arXiv:2609.27988v1 Announce Type: new Abstract: Methods operating on Vision Transformer (ViT) feature spaces typically rely on Euclidean distance or cosine similarity. This assumes that every directio

Tutorials

Task-Prototype Guided Flow Matching for Few-Shot Generalization in Vision-Language Robot Manipulation

arXiv:2609.27780v1 Announce Type: cross Abstract: Vision-language robot manipulation policies can follow semantic instructions, but adapting them to a new procedure from only a few demonstrations rema

tutorialsarxiv-cs-cv

Teach-to-Crash: A Closed-Loop Student-Teacher LLM Framework for Collision-Inducing Test Scenario Generation

arXiv:2609.27296v1 Announce Type: cross Abstract: Validating Autonomous Driving Systems (ADS) in simulation requires testing architectures that can discover rare, safety-critical failures while genera

safetyarxiv-cs-ai

TEEP-RCNN: Texture-Enhanced Edge-aware Perception for Steel Surface Defect Detection via Improved Convolutional Block Attention in Faster R-CNN

arXiv:2609.28077v1 Announce Type: new Abstract: Steel surface defect detection is critical for automated industrial quality control but remains challenging due to subtle inter-class texture difference

Local Ai

Temporally Ordered Region-Token Mamba with Logit-Space Diffusion for Remote Sensing Change Detection

arXiv:2609.27149v1 Announce Type: new Abstract: Remote sensing change detection requires both global reasoning across bitemporal images and precise localization of changed regions. However, dense atte

local-aiarxiv-cs-cv

Applications

TEMPS: Temporal Sentence Embeddings for Temporal Information Retrieval

arXiv:2609.28048v1 Announce Type: cross Abstract: Modern information retrieval (IR) systems rarely represent time, yet many information needs depend on it: in clinical, journalistic, and legal search,

applicationsarxiv-cs-ai

Tensor Decomposition of Transformer Key-Value Caches: Spectral Structure and Format Comparison

model-releasesarxiv-cs-cl

arXiv:2609.28029v1 Announce Type: cross Abstract: The key-value (KV) cache of autoregressive transformers can be viewed as a fourth-order tensor spanning attention heads, tokens, features, and grouped

Applications

Text Scores Can Miss Waveform Use: A Qwen2-Audio Quantization Case Study

arXiv:2609.26823v1 Announce Type: cross Abstract: Post-training quantization of speech language models is often summarized with text-output scores and nominal bit widths. Those numbers alone do not es

applicationsarxiv-cs-cl

ThaiTrees: Thai Syntactic Dependency Trees Across Domains

arXiv:2609.27558v1 Announce Type: new Abstract: Studying syntactic patterns in naturally occurring language requires a large parsed corpus, but manual annotation is costly and difficult to scale. Thai

researcharxiv-cs-cl

The Capability Manifold and ML Scaling Laws

arXiv:2609.27588v1 Announce Type: cross Abstract: Existing machine learning (ML) scaling laws relate predictive loss to compute, model parameters, and data. However, as models are increasingly deploye

agentsarxiv-cs-ai

The Chandra-Gaia Catalog of Counterparts: Resolving ambiguous Gaia matches to X-ray sources in the Chandra Source Catalog using Machine Learning

arXiv:2606.19329v2 Announce Type: replace-cross Abstract: We present a framework to cross-match sources from the Chandra Source Catalog (CSC v2.1) with optical sources from Gaia Data Release 3. Unlike

The Computational Value of Sensory-Aligned Receptive Fields Depends on Neuronal Expressivity

arXiv:2609.26940v1 Announce Type: cross Abstract: Biological sensory neurons have selective receptive fields organized along meaningful stimulus coordinates, such as frequency, motion direction, or re

safetyarxiv-cs-lg

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

The Gaussian Is Enough: Flow-Matching Priors Do Not Help When Fine-Tuning Large Behavior Models

arXiv:2609.27070v1 Announce Type: cross Abstract: Modern robot imitation learning increasingly relies on generative policies based on diffusion or flow-matching models, which generate actions by trans

The Illinois Social Attitudes Aggregate Corpus (ISAAC): An Open Tool and Reproducible Pipeline for Analyzing Social Group Discourse at Scale

arXiv:2609.27059v1 Announce Type: new Abstract: We introduce the Illinois Social Attitudes Aggregate Corpus (ISAAC), an open, modular, and accessible corpus of 527 million+ English-language Reddit pos

safetyarxiv-cs-cl

The Like Trap: Multi-Stage Poisoning against Agents in Similarity-based Recommendation Systems

arXiv:2609.27155v1 Announce Type: cross Abstract: With recent advancements in large language models (LLMs) and LLM-based agents, these agents are becoming increasingly autonomous and gaining broader a

agentsarxiv-cs-ai

The Linear Representation Hypothesis Needs a Group Action

arXiv:2609.27158v1 Announce Type: cross Abstract: To make claims about representations that generalize beyond a particular trained model, we need to specify when two representations should count as eq

The Past Frames the Future: Memory for Autoregressive Video Generation

arXiv:2609.28466v1 Announce Type: new Abstract: Advances in generative models have improved video fidelity, enabling long-horizon generation, interactive world modeling, and evolving visual environmen

The Path Matters: Evaluating Small Language Models Beyond Answer Accuracy in KGQA

model-releasesarxiv-cs-ai

arXiv:2609.27669v1 Announce Type: cross Abstract: Small language models (SLMs) are increasingly paired with knowledge graphs (KGs), yet end-to-end KG question answering conflates graph access, search,

The Recall Ceiling of LLM Recommendation Reranking

model-releasesarxiv-cs-lg

arXiv:2609.27953v1 Announce Type: cross Abstract: Some LLM-based recommendation rerankers are evaluated under an oracle protocol that guarantees the ground-truth item is present in the scored set, eit

The Risk-Sensitive Schrodinger Bridge: Is Not a KL Projection

model-releasesarxiv-cs-ai

arXiv:2609.27250v1 Announce Type: cross Abstract: The Schrodinger bridge owes its computational power to a single structural fact: by Girsanov's theorem the controlled problem is a Kullback--Leibler (

The Skin-Restricted Reinhard Transform:Uniqueness under a Lightness-Preserving Constraint

arXiv:2609.28424v1 Announce Type: new Abstract: Catalog skin recolouring has to change pigment and leave shading alone. The classical Reinhard map does not make that split: it rescales lightness by th

The Tasteful Agent: Measuring and Improving Taste in Long-Horizon Tasks

model-releasesarxiv-cs-ai

arXiv:2609.25804v2 Announce Type: replace Abstract: LLM agents increasingly work on long-horizon tasks, and the decisions they make along the way, such as which hypothesis to test or which implementat

The Type-II Error of Test Supermartingales: e-Power versus the Chernoff-Stein Exponent

arXiv:2609.27765v1 Announce Type: cross Abstract: In safe hypothesis testing with test supermartingales, Ville's inequality provides anytime-valid type-I error guarantees for every significance level

Theoretical Study on the Evidential Learning-based Variational Autoencoder

model-releasesarxiv-cs-lg

arXiv:2609.27853v1 Announce Type: cross Abstract: A normal--inverse-gamma (NIG) latent hierarchy has four parameters, but its induced latent law does not identify all four. For sigma^2simInvGamma(alph

tidyHEBO: Robust General-Purpose Bayesian Optimization with Model-Consistent Warping and Pareto Search

model-releasesarxiv-cs-lg

arXiv:2607.10669v2 Announce Type: replace Abstract: Bayesian optimization (BO) is widely used for expensive black-box problems, yet practical performance depends not only on high-level algorithmic cho

Tight Regret Bound for Online Inverse Linear Optimization via Multiscale Matrix Weights

arXiv:2609.26978v1 Announce Type: cross Abstract: We study online inverse linear optimization with a fixed unknown linear utility: in each round, an environment presents a compact action set, the lear

TimeEvo: Failure-Driven Self-Evolution of a Time Series Agent

arXiv:2609.27277v1 Announce Type: new Abstract: Time series agents answer analytical questions by calling external tools, and which tools they carry is decided by people before the agent runs. However

agentsarxiv-cs-ai

Local Ai

TinyUDE: Solver-Free Universal Differential Equations on Microcontrollers via Lie-Taylor Jet Matching

arXiv:2609.26972v1 Announce Type: new Abstract: Training Universal Differential Equations (UDEs) traditionally relies on backpropagating through numerical ODE solvers, creating memory footprints far e

local-aiarxiv-cs-lg

TNLearn: An Open Source Python Package for Task-based Neurons

arXiv:2609.27564v1 Announce Type: cross Abstract: The brain does not rely on a single type of neuron to perform all kinds of tasks; instead, it designs different neurons for different tasks. The conce

TopoGS: Topology-Aware Anchor Feature Aggregation for Large-Scale 3D Gaussian Splatting

arXiv:2609.27868v1 Announce Type: cross Abstract: Octree-based 3D Gaussian Splatting organizes anchors into multi-level hierarchies for level-of-detail rendering, but features at different levels are

Topological Signatures of Cyber-Attack Classes in Natural Visibility Graph Representations of Network Traffic

arXiv:2609.26990v1 Announce Type: cross Abstract: Natural Visibility Graph (NVG)-based representations provide a promising approach for capturing structural patterns in sequential network traffic. How

Tutorials

Towards Efficient Reasoning: Learning Causal Shortcuts for Diffusion Language Models

arXiv:2609.28272v1 Announce Type: new Abstract: Diffusion Language Models (DLMs) have attracted significant attention for their strong reasoning ability. However, under a bidirectional attention mecha

tutorialsarxiv-cs-cl

Track2Art: Motion-Centric Articulated Object Model Recovery from 2D Point Trackers

arXiv:2609.27675v1 Announce Type: new Abstract: Understanding articulated objects is fundamental for robotic interaction, requiring accurate rigid-part discovery and the recovery of their kinematic re

Tractable Reinforcement Learning for Full Class of Signal Temporal Logic Specifications Using Spatiotemporal Tube Reward

arXiv:2609.28396v1 Announce Type: new Abstract: This paper addresses the control problem for robotic systems, including non-holonomic and underactuated platforms operating under unknown dynamics and s

agentsarxiv-cs-ro

Training Intelligent Voice Assistant Wakeup with Controllable Synthetic Conversations

arXiv:2609.27037v1 Announce Type: new Abstract: Wake word detection is a critical component of virtual assistants, serving as the gateway to seamless user interactions. This paper introduces a novel w

Transfer Learning with Conformalized Quantile Regression for Solar PV Forecasting Under Load-Shedding-Driven Data Scarcity

arXiv:2609.26959v1 Announce Type: new Abstract: Solar photovoltaic (PV) forecasting in regions affected by load shedding is challenging because reliable historical observations are scarce. This study