Search: “pretraining”

Relational Visual Similarity

arXiv:2512.07833v2 Announce Type: replace-cross Abstract: Humans do not just see attribute similarity -- we also see relational similarity. An apple is like a peach because both are reddish fruit, but

applicationsarxiv-cs-ai

13 Apr 2026

The nextAI Solution to the NeurIPS 2023 LLM Efficiency Challenge

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arXiv:2604.09034v1 Announce Type: new Abstract: The rapid evolution of Large Language Models (LLMs) has significantly impacted the field of natural language processing, but their growing complexity ra

model-releasesarxiv-cs-lg

13 Apr 2026

Local Ai

TouchAnything: Diffusion-Guided 3D Reconstruction from Sparse Robot Touches

arXiv:2604.08945v1 Announce Type: new Abstract: Accurate object geometry estimation is essential for many downstream tasks, including robotic manipulation and physical interaction. Although vision is

local-aiarxiv-cs-cv

13 Apr 2026

Cram Less to Fit More: Training Data Pruning Improves Memorization of Facts

arXiv:2604.08519v1 Announce Type: new Abstract: Large language models (LLMs) can struggle to memorize factual knowledge in their parameters, often leading to hallucinations and poor performance on kno

Gaze to Insight: A Scalable AI Approach for Detecting Gaze Behaviours in Face-to-Face Collaborative Learning

arXiv:2604.03317v2 Announce Type: replace Abstract: Previous studies have illustrated the potential of analysing gaze behaviours in collaborative learning to provide educationally meaningful informati

applicationsarxiv-cs-cv

GenLCA: 3D Diffusion for Full-Body Avatars from In-the-Wild Videos

arXiv:2604.07273v2 Announce Type: replace Abstract: We present GenLCA, a diffusion-based generative model for generating and editing photorealistic full-body avatars from text and image inputs. The ge

applicationsarxiv-cs-cv

Location Is All You Need: Continuous Spatiotemporal Neural Representations of Earth Observation Data

arXiv:2604.07092v2 Announce Type: replace Abstract: In this work, we present LIANet (Location Is All You Need Network), a coordinate-based neural representation that models multi-temporal spaceborne E

OV-Stitcher: A Global Context-Aware Framework for Training-Free Open-Vocabulary Semantic Segmentation

arXiv:2604.08110v1 Announce Type: new Abstract: Training-free open-vocabulary semantic segmentation(TF-OVSS) has recently attracted attention for its ability to perform dense prediction by leveraging

Self-Improving 4D Perception via Self-Distillation

arXiv:2604.08532v1 Announce Type: new Abstract: Large-scale multi-view reconstruction models have made remarkable progress, but most existing approaches still rely on fully supervised training with gr

SPICE: Submodular Penalized Information-Conflict Selection for Efficient Large Language Model Training

arXiv:2601.23155v2 Announce Type: replace-cross Abstract: Information-based data selection for instruction tuning is compelling: maximizing the log-determinant of the Fisher information yields a monot

researcharxiv-cs-ai

Hardware

ANEForge: Python for direct computation on the Apple Neural Engine

arXiv:2606.17090v2 Announce Type: replace-cross Abstract: ANEForge is a Python package that programs the Apple Neural Engine (ANE), the fixed-function neural accelerator on every recent Apple device,

hardwarearxiv-cs-ai

Anticipatory Commonsense with Moving Objects: An ASP-Based Framework for Predictive Reasoning with Interacting Trajectories

model-releasesarxiv-cs-ai

arXiv:2610.20867v1 Announce Type: new Abstract: We present a general declarative framework for anticipatory reasoning about interactive motion in complex dynamic environments. Our focus is on (explain

Can AI Forecast the Extremes? Evaluating Probabilistic Weather Models on Tropical Cyclones

model-releasesarxiv-cs-lg

arXiv:2610.22077v1 Announce Type: new Abstract: Weather prediction is undergoing an important shift, with data-driven AI models outperforming leading numerical weather prediction systems on average at

Can Visual Generation Expand What Unified Multimodal Models Know?

arXiv:2610.21459v1 Announce Type: cross Abstract: Unified multimodal models (UMMs) receive image-generation supervision that exposes them to visual information not fully documented in text. Can learni

safetyarxiv-cs-ai

Compliant GMT: Making General Motion Tracking Compliant via Residual Reference Adaptation

arXiv:2610.22119v1 Announce Type: new Abstract: We propose Compliant GMT (C-GMT), a modular framework for full-body compliant responses in general motion tracking. As humanoid robots move into real wo

ControlFlow3D: Distilling Multi-View Knowledge into Latent Flow Matching for Point Cloud Upsampling

arXiv:2610.21190v1 Announce Type: new Abstract: Point cloud upsampling is essential for 3D applications but remains challenging due to sparse, under-constrained observations. Multi-view images can res

Data Strategies for Large Language Models: A Unified Perspective on the Training Lifecycle

arXiv:2610.21681v1 Announce Type: new Abstract: Data strategy plays a central role in training large language models (LLMs). It governs the selection, weighting, and scheduling of training data, there

Diffusion Language Models as Open Quantum Systems

arXiv:2610.21672v1 Announce Type: cross Abstract: Diffusion language models generate text by reversing a classical diffusion process over token sequences. We show that their continuous-time forward an

Local Ai

DINO-FIL: Multi-Layer Transformer Fusion for Generative Inpainting Forgery Localization

arXiv:2610.21780v1 Announce Type: cross Abstract: We propose DINO-FIL, a transformer-based framework for pixel-level localization of generative image forgeries. The method leverages multi-layer repres

local-aiarxiv-cs-ai

EgoJEPA: Beyond Retargeting for Dexterous Manipulation from Egocentric Videos

arXiv:2610.20904v1 Announce Type: cross Abstract: Egocentric human videos offer a scalable alternative to costly robot demonstra- tions, yet transferring their manipulation priors to robot control rem

applicationsarxiv-cs-cv

Tutorials

FeaTok: Feature-Loss Tokenization for Image Generation

arXiv:2610.22060v1 Announce Type: new Abstract: Discrete image generators learn the distribution of tokens produced by an image tokenizer. These token representations should be easy for generators to

tutorialsarxiv-cs-cv

Flow-CLIP: Image-Conditioned Residual Scoring for CompositionalImage-Text Matching

arXiv:2610.21937v1 Announce Type: new Abstract: Vision--language pre-training has enabled substantial progress in image--text retrieval and zero-shot recognition by learning transferable cross-modal r

FlowBudget: Training-Free Sampling-Budget Estimation for Flow Matching Models

arXiv:2610.20636v1 Announce Type: cross Abstract: Sampling from continuous flow-based generators such as flow matching models requires numerical ODE or SDE integration, making inference cost depend on

Tutorials

Full-Duplex Interaction with Speech, Expressions and Gestures

arXiv:2610.21452v1 Announce Type: cross Abstract: Human face-to-face conversation requires coordinating speech, gestures and expressions across both speaking and listening. We present ELLSA 2, an end-

tutorialsarxiv-cs-ai

FuseAdapt: Parameter-Efficient Multimodal Fusion for Semantic Segmentation with Missing Modalities

arXiv:2610.21779v1 Announce Type: new Abstract: Multi-modal semantic segmentation benefits from complementary sensors, but this benefit is fragile when some modalities are degraded or missing at test

INCONVENIENT: A Benchmark to Assess LLM Generalization Capabilities from Convenience Samples

model-releasesarxiv-cs-cl

arXiv:2610.21038v1 Announce Type: cross Abstract: Machine learning models for tabular prediction typically assume that training data are representative of the population on which they will be deployed

Tutorials

Is Text All You Need? Text as a Universal Information Bottleneck for Speech LLMs

arXiv:2606.09366v2 Announce Type: replace Abstract: Large language models (LLMs) provide a powerful reasoning backbone for speech understanding, but integrating continuous acoustic signals into a pret

tutorialsarxiv-cs-cl

KODA: Kinematics--Observation Data Alignment for Humanoid Learning from Human Egocentric Demonstration

arXiv:2610.21152v1 Announce Type: new Abstract: Training humanoid whole-body vision-language-action (VLA) policies requires costly robot demonstrations with aligned robot-view observations and control

Language Models Share a Readout Bottleneck in Working Memory

arXiv:2610.21176v1 Announce Type: cross Abstract: Large language models (LLMs) reproduce a human working-memory signature on the N-back task: performance declines as memory load N increases, and error

researcharxiv-cs-ai

LEV: Thinking Fast and Deep with Looped Decision Models

arXiv:2610.20951v1 Announce Type: new Abstract: Decision models answer typed questions about an input with one probability per option, computed in a single forward pass without generating text. Like t

researcharxiv-cs-ai

Local Ai

LICOA: Learning Instruction-Conditioned Optical Actions for Mobile Spatial Augmented Reality

arXiv:2610.21282v1 Announce Type: cross Abstract: Spatial augmented reality (SAR) augments physical environments with projected content, but most systems assume that the projector pose, target region,

local-aiarxiv-cs-cv

Manifold Generative Annealing for Contact Constraint-aware Impedance Control with Reinforcement Learning Prior

arXiv:2610.21140v1 Announce Type: new Abstract: Contact-rich robotic tasks require coordinated motion and impedance under uncertain interaction dynamics and physical constraints. While recent methods

Mind the Tail: Data Selection Through Sparse Concepts

arXiv:2610.21007v1 Announce Type: new Abstract: Data selection reallocates training support across visual concepts, including objects, attributes, color, texture, and backgrounds. Different allocation

Hardware

MJev-Spatial: System One Spatial Intelligence via Native Attention Matching

arXiv:2610.22276v1 Announce Type: new Abstract: Spatial intelligence lets an agent perceive and act in the physical world, and many of its judgments must be made at interaction rates: which spatial re

hardwarearxiv-cs-cv

Pairwise AUC Optimization Needs Corrective Power: A Unified View

model-releasesarxiv-cs-lg

arXiv:2610.21743v1 Announce Type: new Abstract: Direct pairwise AUC-surrogate training is attractive because it trains toward the ranking metric used at test time, especially under class imbalance. Th

Patch where the text is new: a compression view of where byte-level models should spend compute

arXiv:2610.22100v1 Announce Type: new Abstract: Byte-level language models such as the Byte Latent Transformer (BLT) run their large global model once per patch and start a patch where a small model's

Perceive, Predict, and Plan: a Unified World Action Model for Egocentric Dexterous Manipulation

arXiv:2610.22363v1 Announce Type: new Abstract: We introduce P^{3}-WAM, a unified world action model for egocentric dexterous manipulation that learns 3D perception, action-conditioned prediction, and

PiCo: Physics-Informed Concept Probing for Geo-Foundation Models

arXiv:2610.20973v1 Announce Type: new Abstract: Geo-foundation models (GFMs) are typically evaluated by downstream task predictive performance, revealing little about which physical features they are

Race to Recovery: Designing Advanced Safety Systems for Autonomous Driving via Learning Autonomous Racing

arXiv:2610.20873v1 Announce Type: new Abstract: Vehicles that lose control at the handling limit through icing, hydroplaning, or an abrupt evasive maneuver fall outside the operating envelope of produ

RAG-RST: Retrieved Exemplars as In-Context Discourse Schema for RST Parsing

model-releasesarxiv-cs-ai

arXiv:2610.22150v1 Announce Type: new Abstract: In Rhetorical Structure Theory (RST), each edge of a discourse tree carries a nuclearity label, marking which span holds the central content, and a rela

Agents

Reinforcement Learning-driven Diffusion Models: Language, Vision, and Beyond

arXiv:2610.21539v1 Announce Type: new Abstract: Diffusion models (DMs) have steadily demonstrated outstanding performance in high-fidelity, controllable generation across diverse scenarios, following

agentsarxiv-cs-ai

Representation Redistribution in Sparse Autoencoders

arXiv:2610.21240v1 Announce Type: cross Abstract: Sparse autoencoders (SAEs) decompose language model activations into sparse features, but prompt transformations designed to preserve the underlying r

safetyarxiv-cs-ai

Scene Reconstruction Using Ubiquitous Wireless Signals via Diffusion Posterior Inference Over physics-Grounded Representations

arXiv:2610.22120v1 Announce Type: new Abstract: Knowing the layout of the physical world matters for many applications, yet the sensors we rely on for it (e.g., cameras and LiDAR) can fail in darkness

researcharxiv-cs-lg

Scientific feedback reveals productive regimes beyond training conventions

arXiv:2512.15756v2 Announce Type: replace-cross Abstract: Human-imposed design conventions can restrict the alternatives represented in scientific training data and shape the search preferences learne

safetyarxiv-cs-ai

Seeing Both the Forest and the Trees: Reusing Holistic 3D Priors for Part-Decomposed Generation

arXiv:2610.21850v1 Announce Type: new Abstract: Generating 3D assets with explicit parts requires two properties at once: geometric detail inside each part and structural coherence across the whole ob

SelectPFN: Amortized Bayesian Variable Selection for Sparse Nonlinear Regression with Prior-Data Fitted Networks

arXiv:2610.21950v1 Announce Type: cross Abstract: Tabular foundation models based on prior-data fitted networks (PFNs) predict on a new data set in a single forward pass, but they cannot report which

researcharxiv-cs-lg

Semantic Alignment of EEG and Text: A Contrastive Learning Framework for Decoding Imagined Speech

arXiv:2610.20766v1 Announce Type: new Abstract: Imagined speech decoding from EEG offers a promising route toward naturalistic brain-computer interfaces. Traditional closed-set classifiers treat seman

safetyarxiv-cs-lg

Stabilizing the Modality Gap: Null-Space Projected Continual Learning for Vision-Language Models

arXiv:2507.19839v2 Announce Type: replace-cross Abstract: The modality gap is intrinsic to multimodal alignment, where embeddings from different modalities remain partially separated in a shared repre