Search: “inference”

Learning Interpretable Differentiable Logic Networks for Time-Series Classification

arXiv:2508.17512v2 Announce Type: replace Abstract: Differentiable logic networks (DLNs) have shown promising results in tabular domains by combining accuracy, interpretability, and computational effi

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Learning to Learn a Language

DGX agent

arXiv:2610.05879v1 Announce Type: cross Abstract: We present the Prior-Fitted Language Model (PFLM), a 300M-parameter byte-level transformer pretrained only on samples from a synthetic non-linguistic

model-releasesarxiv-cs-lg

6 Oct 2026

LightVLN: Efficient Aerial Vision-and-Language Navigation with Compact Memory and History-Guided Local Aggregation

arXiv:2610.05024v1 Announce Type: new Abstract: Aerial vision-and-language navigation (VLN) enables unmanned aerial vehicles to execute long-horizon natural-language instructions from visual observati

LoCoSplat: Real-Time Feed-Forward 3D Gaussian Splatting with Minimal 3D Reasoning

arXiv:2610.04351v1 Announce Type: new Abstract: Feed-forward 3D Gaussian Splatting (3DGS) increasingly aggregates multi-view evidence with heavy learned 3D networks. We propose LoCoSplat (Local-Contex

Loopy: Low-Bit Quantization Framework for Looped Language Models

arXiv:2610.05265v1 Announce Type: new Abstract: Looped language models provide a parameter-efficient way to scale iterative test-time computation by repeatedly executing a shared recurrent core. Post-

MAGEFormer: Learning Metric-Consistent Representations for Anisotropic CT Segmentation

arXiv:2610.04036v1 Announce Type: new Abstract: Vision Transformers (ViTs) have shown strong performance in volumetric segmentation, but their effectiveness on clinical CT is limited by an isotropic E

MAGIC: Topology-Aware Analytic Graph Few-Shot Class-Incremental Learning

arXiv:2610.04963v1 Announce Type: new Abstract: Graph few-shot class-incremental learning (GFSCIL) requires a model to continually recognize emerging classes from only a few labeled nodes while preser

ManifoldCache: Training-Free Diffusion Acceleration via Constraint Manifold Caching

arXiv:2610.04510v1 Announce Type: cross Abstract: Diffusion models for structured scientific generation must produce samples satisfying hard geometric constraints imposed by physics, chemistry, or bio

MarvisNav: Making Memory Visible on Route Choices for Zero-Shot Object Navigation

arXiv:2610.06510v1 Announce Type: new Abstract: When searching for an object, people choose their next move by considering both likely target locations and places already explored. The current view ca

safetyarxiv-cs-ro

Measuring Effective Data Resolution in Guided Diffusion Posteriors

arXiv:2610.04422v1 Announce Type: new Abstract: Guided diffusion samplers are increasingly used to reconstruct physical fields from sparse observations, but standard diagnostics do not say how much of

Mobile-4DGS: Unified Static-Dynamic Real-time Mobile Gaussian Splatting

arXiv:2610.05289v1 Announce Type: new Abstract: Recent advances in 3D Gaussian Splatting (3DGS) have achieved remarkable performance in novel view synthesis, yet deploying both static and dynamic Gaus

Applications

Multi-Marginal Schrodinger Bridge Matching

arXiv:2510.16587v2 Announce Type: replace-cross Abstract: Understanding the continuous evolution of populations from discrete temporal snapshots is a critical research challenge, particularly in field

applicationsarxiv-cs-lg

Now You Feel It, Now You See Me: Digital-Twin-based Teleoperation Interface for Dexterous Manipulation

arXiv:2610.05081v1 Announce Type: new Abstract: Teleoperation is becoming increasingly important for collecting high-quality demonstrations to teach robots dexterous manipulation skills. For dexterous

researcharxiv-cs-ro

One-Cycle Fault Classification and Faulted-Line Identification on the PROTECT-90 Dataset: An Initial Application Benchmark

arXiv:2610.04155v1 Announce Type: cross Abstract: Open electromagnetic-transient datasets are beginning to make reproducible learning-based protection studies possible, but the practical use of these

One-Step Generation via Riemannian Wasserstein Gradient Flows

arXiv:2610.04454v1 Announce Type: new Abstract: Recently, Drifting Models and Wasserstein Gradient Flows have attracted substantial attention because they move iterative distributional refinement to t

Tutorials

Optimizing Breast Cancer Detection in Mammograms: A Comprehensive Study of Transfer Learning, Resolution Reduction, and Multi-View Classification

arXiv:2503.19945v4 Announce Type: replace-cross Abstract: Mammography, an X-ray-based imaging technique, remains central to the early detection of breast cancer. Recent advances in artificial intellig

tutorialsarxiv-cs-cv

Organize Primitives into Semantic Parts: Reinforcement Reasoning for 3D Segmentation

arXiv:2610.04602v1 Announce Type: new Abstract: Primitive-based 3D segmentation offers a compact and explicit alternative to dense surface prediction, naturally supporting structural abstraction and b

PerturBot: Breaking Shortcut Priors in Vision-Language-Action Models with Perturbative Training

arXiv:2610.04616v1 Announce Type: cross Abstract: A vision--language--action (VLA) policy can complete complex tasks while ignoring the evidence that should determine its actions. An object held near

Physics-Augmented Graph Transformers for Patch-Antenna Forward and Inverse Design

arXiv:2610.05004v1 Announce Type: new Abstract: Full-wave electromagnetic (EM) simulation enables accurate patch-antenna analysis but is computationally expensive for large-scale forward prediction an

Planetary Geospatial Foundation Models: A New Paradigm for Global Public Health

arXiv:2610.05699v1 Announce Type: new Abstract: The efficacy of traditional disease prediction is limited by spatial gaps and temporal lags, which impact the timing and targets of resource deployments

Planning for Change: Reinforcement Learning Combined with Bounded Extremum Seeking for Robotic Control under Distribution Shift

arXiv:2604.01142v2 Announce Type: replace-cross Abstract: Reinforcement learning has shown strong performance in robotic manipulation, but learned policies often degrade in performance when test condi

safetyarxiv-cs-lg

Probabilistic Algorithms for Ising Machines from Optimization to Generative AI

arXiv:2610.03972v1 Announce Type: new Abstract: Ising machines have emerged as promising hardware accelerators for intractable optimization and sampling problems, yet their practical impact increasing

Quantum data loading from the learned shared structure of real signals

arXiv:2610.06076v1 Announce Type: cross Abstract: Preparing quantum states from classical data can cost more than the computation they serve; most loaders tailor a circuit to each input. Here we show

R^2-WAM: Repair-and-Reject Post-Training for World Action Models

arXiv:2610.04913v1 Announce Type: new Abstract: World Action Models (WAMs) emerge as a promising foundation for policy refinement by predicting the consequences of sampled actions. However, visually p

safetyarxiv-cs-ro

RAC: Rectified Flow Auto Coder

arXiv:2603.05925v2 Announce Type: replace Abstract: In this paper, we propose a Rectified Flow Auto Coder (RAC) inspired by Rectified Flow to replace the traditional VAE: 1. It achieves multi-step dec

Reachability-Guided Sequential Quadratic Programming-Guarded Model Predictive Path Integral for Safe Nonlinear Predictive Control

arXiv:2610.04406v1 Announce Type: new Abstract: Safe robot control often requires combining long-horizon performance optimization with hard state and input constraints, but existing approaches tend to

local-aiarxiv-cs-ro

Readout Blindness: VLM Scores Miss the Spatial Direction Their Frozen Encoders Retain

arXiv:2610.06324v1 Announce Type: new Abstract: CLIP-like vision-language models remain a cornerstone of multimodal systems, yet their scores stay near chance on directed spatial relations, such as wh

researcharxiv-cs-cv

Recurrent Latent Visual Search for GUI Grounding

arXiv:2610.05185v1 Announce Type: new Abstract: GUI grounding is a critical capability for GUI agents powered by vision-language models, helping them execute user instructions by locating the correspo

RefAtomNet++: Advancing Referring Atomic Video Action Recognition using Multi-Trajectory Semantic Retrieval

arXiv:2510.16444v2 Announce Type: replace Abstract: Who is being described, where are they, and what are they doing? Referring Atomic Video Action Recognition (RAVAR) answers these questions jointly b

Rethinking Self-Distillation for Multi-Teacher Capability Merging

arXiv:2610.04272v1 Announce Type: new Abstract: Combining capabilities of multiple expert models trained starting from the same base checkpoint has become increasingly common in frontier language-mode

safetyarxiv-cs-lg

Retrieval-Centric Deep Learning in Growing Nonparametric Neural Networks

arXiv:2610.03858v1 Announce Type: new Abstract: We investigate a general-purpose layer for deep learning that, instead of compressing arbitrary-size training data into fixed-size weight matrices, stor

Robot Learning with Visual Predicted Force

arXiv:2610.04741v1 Announce Type: new Abstract: Force-aware manipulation typically relies on specialized force or tactile sensors. We show that force-aware manipulation can instead be achieved through

safetyarxiv-cs-ro

Tutorials

Robotic Long-Horizon Manipulation with Progressive In-Context Code Generation and Episodic Feedback

arXiv:2503.21969v4 Announce Type: replace Abstract: Robotic long-horizon manipulation requires robots to compose perception, reasoning, and action over extended task sequences, yet existing language-c

tutorialsarxiv-cs-ro

Robust blind unmixing: A geometric approach to overcoming basis variation

arXiv:2610.04091v1 Announce Type: new Abstract: Signal separation problems are common in science. A prominent example of this occurs during the use of diffraction or spectroscopy to identify the indiv

RPFQ-ViT: Rotated Phase-Frame Quantization for Extremely Low-Bit Weights in Vision Transformers

arXiv:2610.04457v1 Announce Type: new Abstract: Vision Transformers (ViTs) achieve strong performance on image recognition and mobile vision applications, but their high-dimensional linear projections

Sampling Allocation of LinUCB: Optimal Design Limits in the Small-Gap Regime

arXiv:2610.06213v1 Announce Type: new Abstract: We study the sampling allocation of LinUCB in the small-gap regime, where the reward gaps are of order at most n^{-1/2} over the decision horizon n. Thi

SepRQ : Self-Supervised Speech Mixture Representation Learning via Mask-Free, Multi-Scale Source Separation

arXiv:2610.04690v1 Announce Type: cross Abstract: Self-supervised learning (SSL) is standard for speech representation learning, but mainstream models are designed around single-speaker audio, limitin

Shapley-based Structural Analysis of Neural Calibration for Stochastic Volatility Models

arXiv:2610.03076v1 Announce Type: cross Abstract: Neural network-based approaches have emerged as efficient alternatives to traditional optimization-based procedures for the calibration of stochastic

SimForcing: Distilling Simulation Motion Priors into Real-Domain Robot World Models

arXiv:2610.06598v1 Announce Type: cross Abstract: Action-conditioned robot world models must respond precisely to robot trajectories while preserving realistic visual dynamics, yet learning both from

Soft Strategy Selection for Batch-Mode Active Learning

arXiv:2610.05544v1 Announce Type: new Abstract: Real-world deployment of active learning typically forces practitioners to choose an acquisition strategy before any data is labeled. This is a daunting

SoundWeaver: Compositional Warm-Starting for Text-to-Audio Diffusion Serving

arXiv:2603.07865v2 Announce Type: replace-cross Abstract: Text-to-audio (T2A) diffusion models generate high-quality audio but require tens of neural function evaluations (NFEs), resulting in substant

researcharxiv-cs-cv

Agents

SPACE-CLIP: Spatial Perception via Adaptive CLIP Embeddings for Monocular Depth Estimation

arXiv:2601.17657v4 Announce Type: replace Abstract: Robotic and autonomous systems need dense spatial cues, yet adding a dedicated depth estimator can duplicate visual processing already performed by

agentsarxiv-cs-cv

SPACE-CLIPv2: Decoding Local Geometry from Frozen CLIP for Monocular Depth Estimation

arXiv:2610.05029v1 Announce Type: new Abstract: Vision-language foundation models such as CLIP provide strong semantic representations, but their patch tokens are not directly optimized for dense metr

Spacecraft Rendezvous Trajectory Generation with Modular Constraints via Diffusion Model Composition

arXiv:2610.05642v1 Announce Type: cross Abstract: Emerging mission classes such as on-orbit servicing, satellite inspection, and active debris removal require trajectory design methods that are adapta

SPIN: Image Immunization Against Diffusion Editing via Single-Step Projection in Stochastic Neighborhoods

arXiv:2610.06334v1 Announce Type: new Abstract: Diffusion models have greatly advanced instruction-guided image editing, while also raising concerns about unauthorized image manipulation. Image immuni

researcharxiv-cs-cv

STILL: Selecting Tokens for Intra-Layer Hybrid Attention to Linearize LLMs

arXiv:2602.02180v2 Announce Type: replace Abstract: Linearizing pretrained large language models (LLMs) primarily relies on intra-layer hybrid attention mechanisms to alleviate the quadratic complexit

local-aiarxiv-cs-lg

Strategic Multi-Agent Learning for Interpretable Action Valuation of All Players in Football

arXiv:2610.05961v1 Announce Type: new Abstract: Valuing player actions in football requires accounting for strategic interactions among 22 players, including off-ball movements and defensive positioni

safetyarxiv-cs-lg

Studying the Soupability of Documents in State Space Models

arXiv:2505.24033v3 Announce Type: replace-cross Abstract: We investigate whether hidden states from Structured State Space Models (SSMs) can be merged post hoc to support downstream reasoning. Inspire