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KVFetch: Temporal Prefetching for the Missing Half of KV Cache Compression

arXiv:2610.08811v1 Announce Type: new Abstract: As context windows scale to tens or hundreds of thousands of tokens, KV cache compression has become essential for efficient LLM inference. Existing met

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LayerRoPE: Dynamic Depth-wise Magnitude & Angular Superposition

DGX agent

arXiv:2610.09179v1 Announce Type: cross Abstract: As data propagates through a Transformer, the norm of its hidden states grows by orders of magnitude with depth, a phenomenon framed as 'curse of dept

researcharxiv-cs-cl

8 Oct 2026

Layerwise Error Attribution for Fast and Robust Mixed-Precision Post-Training Quantization

arXiv:2610.09877v1 Announce Type: new Abstract: Mixed-precision post-training quantization is a network compression method that assigns bits layer by layer, under a global memory budget using a small

Learning to Act with Task Progress: Distilling Small Agents from Compact Teacher Supervision

arXiv:2610.10332v1 Announce Type: new Abstract: Learning from large-model demonstrations offers a way to train small agents that can complete recurring tasks without calling a large model at every ste

Learning Traffic Flow Dynamics with Stochastic Physics-Informed Neural Cellular Automata

arXiv:2610.09946v1 Announce Type: new Abstract: Traffic flow modeling is essential for understanding and predicting the collective dynamics of vehicles on road networks. Cellular automata provide a si

Leveraging LLM-Generated Explanations for Detecting Emotionally Rewritten Fake News

arXiv:2610.08835v1 Announce Type: new Abstract: The spread of fake news may cause severe social consequences. Existing fake news detection methods mainly focus on stylistic variations or incorporate e

Logarithmic Regret via Passive Change Detection in Piecewise-Stationary Self-Tuning Regulation

arXiv:2610.10250v1 Announce Type: new Abstract: We study minimum-variance control of an unknown autoregressive system with exogenous inputs and coefficients that change at unknown times. Under bounded

LoomSC: Scalable Deep Subspace Clustering with Projector Factorization and Exact Spectral Reduction

arXiv:2610.10266v1 Announce Type: new Abstract: Dense self-expression matrices and full-affinity spectral clustering limit the scalability of subspace clustering. We introduce the Latent Orthogonal Op

Lower Bounds for Parallel Diffusion Sampling

arXiv:2610.09166v1 Announce Type: cross Abstract: Standard diffusion samplers generate samples through repeated evaluations of a learned score function. Parallel sampling methods seek to accelerate ge

m-Set Adversarial Bandits with Winner Feedback

arXiv:2610.10128v1 Announce Type: new Abstract: We show upper and lower bounds on the regret of m-set adversarial bandits for different utilities (winner reward or sum of rewards) and feedback models

Machine learning modularity

arXiv:2601.01779v2 Announce Type: replace-cross Abstract: Based on a transformer based sequence-to-sequence architecture combined with a dynamic batching algorithm, this work introduces a machine lear

MagCilia: A Compact Magnetociliary Tactile Sensor with 3D Force Sensing for Robotic Contact Perception and Grasping Feedback

arXiv:2610.09536v1 Announce Type: new Abstract: Robotic grasping and surface exploration benefit from simultaneous measurement of normal and tangential forces and from surface information obtained thr

researcharxiv-cs-ro

MeshCarve: Artisan Mesh Generation with Flow Matching in Compact Latent Spaces

arXiv:2610.09723v1 Announce Type: new Abstract: Prior artisan mesh generation works largely predict face tokens autoregressively, which makes inference slow. Recent methods instead flow match continuo

Mixture of Layers: Dynamic Layer Routing for Visual Reasoning

arXiv:2610.09440v1 Announce Type: new Abstract: Pre-trained vision encoders contain layer-wise visual representations that differ in spatial granularity, semantic abstraction, and sensitivity to local

Modified Loss of Momentum Gradient Descent: Fine-Grained Analysis

arXiv:2509.08483v2 Announce Type: replace Abstract: We analyze gradient descent with Polyak (1964) heavy-ball momentum (HB) whose fixed momentum hyperparameter eta in (0, 1) provides exponential decay

MorphCL: Morphological Contrastive Learning for Inertial-based Human Activity Recognition

arXiv:2610.10245v1 Announce Type: new Abstract: Despite the ubiquity of sensors in wearable and mobile devices and the abundance of human movement data they generate, translating unlabeled recordings

MovieSTAGE: Scene, Transition, and Global Encoding for Movie-fMRI ADHD Classification

arXiv:2610.09306v1 Announce Type: new Abstract: Naturalistic movie-fMRI provides a shared, temporally structured probe of brain dynamics, yet predictive models commonly rely on whole-run functional co

Multi-encoder ConvNeXt Network with Smooth Attentional Feature Fusion for Multispectral Semantic Segmentation

arXiv:2602.10137v2 Announce Type: replace Abstract: This work proposes MeCSAFNet, a multi-branch encoder-decoder architecture for land cover segmentation in multispectral imagery. The model separately

NeRFifyMesh: Optimizing Neural Radiance Fields from Textured Meshes for Robotics Scene Building

arXiv:2610.10387v1 Announce Type: new Abstract: In robotics, scene representation plays a pivotal role in understanding and interacting with the environment. The advent of Neural Radiance Fields (NeRF

researcharxiv-cs-ro

Neural Sampling with Reweighted Normalizing Flows via the Wasserstein--Fisher--Rao JKO Scheme

arXiv:2610.10278v1 Announce Type: cross Abstract: We propose a neural algorithm for sampling from distributions specified by unnormalized Boltzmann densities. Our approach is based on the Jordan--Kind

Never Look Back: Understanding Persistence in 3D Object Memory from Egocentric Videos

arXiv:2610.10538v1 Announce Type: new Abstract: As we move through the world and carry out everyday tasks, we encounter objects that may become relevant only later. We are capable of recalling where w

Node-level Graph Neural Architecture Search Framework

arXiv:2610.09297v1 Announce Type: new Abstract: In recent years, Graph Neural Networks (GNNs) and architecture search frameworks have gained extensive application in non-Euclidean data processing, att

Noise, Denoise, Correct: MCMC Posterior Sampling with Diffusion Priors in Three Steps

arXiv:2610.09407v1 Announce Type: new Abstract: Pretrained diffusion models are powerful priors for inverse problems, but posterior sampling under nonlinear, non-differentiable forward models remain h

Noise Your Prompt: Noising Conditioning Tokens in Continuous Diffusion Language Models

arXiv:2610.09145v1 Announce Type: cross Abstract: We revisit a standard accepted practice in the continuous diffusion language model literature of fixing conditioning prompt tokens clean during traini

OmniCam: Omni-Camera Trajectory Generation via Geometry-Grounded Pose Token Learning

arXiv:2610.09513v1 Announce Type: new Abstract: Camera trajectories control viewpoint changes in video generation, scene reconstruction, and robotic perception. Generating them from language requires

On the Computational Complexity of Hidden Markov Model Identification

arXiv:2610.09104v1 Announce Type: cross Abstract: Identification is the task of recovering the parameters of an unknown ground-truth model from sampled data. When parameters other than the ground trut

On the Cyclic Assumption of the Cow-Path Search Algorithm

arXiv:2610.10253v1 Announce Type: cross Abstract: In the cow-path problem, a cow must find a goal lying at an unknown distance on one of w paths connected only at the origin, and performance is measur

One-Shot Adaptive Segmentation For Scientific Images

arXiv:2610.10306v1 Announce Type: new Abstract: Scientific image segmentation methods rely on extensive annotation and task-specific training, limiting adaptation across imaging modalities and experim

Optimal Regret for Online Market Making with Limit Order Book

arXiv:2610.09691v1 Announce Type: cross Abstract: We study online learning in market making, where, at each round, a market maker posts bid and ask prices before observing the market price and the pri

Order-Optimal Sample Complexity of Rectified Flows

arXiv:2601.20250v2 Announce Type: replace Abstract: Recently, flow-based generative models have shown superior efficiency compared to diffusion models. In this paper, we study rectified flow models, w

Oscillatory Neural Dynamics over Sheaves

arXiv:2610.10018v1 Announce Type: new Abstract: Effective long-range propagation remains a central challenge in graph neural networks, as increasing a model's propagation depth does not guarantee that

Out-of-Air Computation: Enabling Structured Function Extraction from Wireless Superposition

arXiv:2604.04312v2 Announce Type: replace-cross Abstract: Over-the-air computation (AirComp) broadly exploits the superposition property of wireless multiple-access channels (MACs) to compute function

OverLay++: Dense-Overlap Layout-to-Image Generation Dataset

arXiv:2610.09071v1 Announce Type: new Abstract: Layout-to-Image generation has made substantial progress in spatial and object-level control. However, existing methods still struggle with complex scen

PairAudit: Guiding Human Review with Graph Tokens under Distribution Shift

arXiv:2610.10260v1 Announce Type: new Abstract: Intrusion detectors can confidently misclassify attacks that were not seen during training. Human review can correct these errors, but only a limited nu

PalmSpace: Towards a Versatile On-Palm Interaction Space through Unified Touch Modeling

arXiv:2610.10370v1 Announce Type: cross Abstract: As smart glasses and lightweight MR devices become increasingly practical, input remains a key challenge. The bare palm is an always-available, tactil

Pareto-optimal quantum kernel selection for unsupervised anomaly detection on real malware beaconing data

arXiv:2610.09717v1 Announce Type: cross Abstract: Quantum kernel methods are leading candidates for a practical quantum advantage in machine learning, but assessing that potential requires two quantit

Patient, Place, Prior (P^3): What Counts as Personalization in Medical World Models?

arXiv:2610.09194v1 Announce Type: new Abstract: Longitudinal models forecast how a patient's imaging state evolves, but accuracy does not show whether the patient's observed trajectory drives the pred

PEACE: Covariant learning of nonadiabatic manifolds with parity-resolved Hamiltonians

arXiv:2610.09576v1 Announce Type: cross Abstract: Nonadiabatic molecular dynamics provides mechanistic insight into light-driven processes and informs the design of molecules and materials for solar e

Phoneme-Guided Initialization for LLM-based Speech Recognition

arXiv:2610.08994v1 Announce Type: cross Abstract: Speech large language models (speech LLMs) perform well on automatic speech recognition (ASR) when sufficient paired speech-text data is available, bu

Physics-Aligned Electronic Ground-State Learning Improves Generalization

arXiv:2610.10298v1 Announce Type: new Abstract: Machine-learned interatomic potentials (MLIPs) excel at in-distribution tasks, accelerating drug and material development, yet they struggle to generali

Playing with Kruskal: algorithms for flat and hierarchical watershed cuts

arXiv:2610.10012v1 Announce Type: new Abstract: In the framework of edge-weighted graphs, watersheds have proven to be linked to well-known optimization problems, as Minimum Spanning Tree, which allow

Pooling Representation Autoencoders for Efficient Diffusion

arXiv:2610.09242v1 Announce Type: new Abstract: Representation Autoencoders (RAEs) generate images from pre-trained visual fea- tures, but their dense token grids make generative modeling expensive. M

Position Forcing: Self-Conditioning 3D Generation

arXiv:2610.10342v1 Announce Type: new Abstract: Recent single-stage 3D generative models commonly adopt VecSet representations, encoding 3D shapes as unordered sets of latent tokens. However, compared

Q-PACE: Dynamic Precision Allocation for Quantization-Aware Training

arXiv:2610.09183v1 Announce Type: new Abstract: Quantization-aware training (QAT) leverages lower-precision arithmetic to reduce the cost of LLM deployment, but aggressive quantization degrades final

Quadratic Weak-to-Strong Generalization in Random Feature Networks via Random Matrix Theory

arXiv:2610.09044v1 Announce Type: cross Abstract: Weak-to-strong generalization is the phenomenon where a strong student model trained with labels produced by a weak teacher model is able to generaliz

QuanLing: Cross-Branch Validation of Language Distance Quantification on Western Romance

arXiv:2610.08851v1 Announce Type: new Abstract: Quantifying language distance among closely related languages remains a core challenge in quantitative linguistics. Our previous work [1] introduced Qua

Quantize by Drift: Label-Free Mixed-Precision Post-Training Quantization for Text Embedders

arXiv:2610.09227v1 Announce Type: cross Abstract: Mixed-precision post-training quantization needs a per-module sensitivity signal; for a text embedder the obvious one -- the retrieval quality a modul

R-CNN-Based Chess Position Recognition

arXiv:2610.09191v1 Announce Type: new Abstract: Performing chess game position recognition solely from a single image of a three-dimensional board requires predicting the position and orientation of t