Search: “pretraining”

Hierarchical Book Organization for Learning-Resource Discovery using Dual-Path Graph Convolutions

model-releasesarxiv-cs-lg

arXiv:2512.21076v2 Announce Type: replace-cross Abstract: The growing availability of books and textual materials in digital learning environments necessitates reliable semantic organization to suppor

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Research

HiFiC-G: Adapting HiFiC for Hi-C Contact Matrices

DGX agent

arXiv:2608.21446v1 Announce Type: cross Abstract: We study whether the loss design of High-Fidelity Generative Image Compression (HiFiC), a GAN-based neural codec originally built for natural photogra

researcharxiv-cs-cv

25 Aug 2026

Hints, Critics, and Teachers: Prior Injection for Sparse-Reward RL in Vision-Language Math Reasoning

arXiv:2608.21811v1 Announce Type: new Abstract: Reinforcement learning for vision-language math reasoning starves under sparse reward: on a pool of 20,830 visual-math problems where Qwen2-VL-2B answer

safetyarxiv-cs-ai

Large-Small Model Collaboration for Zero-Shot Surgical Phase Recognition

arXiv:2608.22879v1 Announce Type: new Abstract: Task-specific lightweight models for surgical phase recognition excel at capturing temporal dynamics but generalize poorly under domain shift. Conversel

LD4WAM: Learning Latent Dynamics from Human Videos for World Action Models

arXiv:2608.22403v1 Announce Type: new Abstract: Human video is playing an increasingly central role in training World Action Models (WAMs), owing to its diversity and low collection cost relative to t

safetyarxiv-cs-ro

Localization-Infused Vision-Language Semantic Fusion for Text-Guided Medical Image Segmentation

arXiv:2607.16327v2 Announce Type: replace Abstract: Medical image segmentation is essential for modern computer-aided medicine. Recently, text-guided segmentation has shown promise by incorporating cl

MOCLIP: A Foundation Model for Large-Scale Nanophotonic Inverse Design

arXiv:2511.18980v2 Announce Type: replace-cross Abstract: Foundation models (FM) are transforming artificial intelligence by enabling generalizable, data-efficient solutions across different domains f

researcharxiv-cs-ai

Mover360: Controllable Object Manipulation in 360{eg} Panoramic Images

arXiv:2608.23238v1 Announce Type: new Abstract: We present Mover360, a controllable object manipulation framework for 360{eg} images. Unlike perspective images, 360{eg} images in equirectangular proje

Tutorials

Multi-Task Learning for Non-Canonical Phoneme Recognition via Articulatory Feature Decomposition

arXiv:2608.22273v1 Announce Type: cross Abstract: Pathological and more broadly non-canonical speech present significant challenges for automatic phoneme recognition due to systematic deviations from

tutorialsarxiv-cs-ai

NeST: Neighborhood-aware semantic alignment and temporal modulation for LLM based time series forecasting

arXiv:2412.04806v2 Announce Type: replace-cross Abstract: Adapting Large Language Models (LLMs) trained on discrete text data, to forecast continuous time series signals is challenging. While finetuni

On the Threat Model of Weird Generalization and Emergent Misalignment

arXiv:2608.23476v1 Announce Type: new Abstract: Narrow fine-tuning on small, domain-specific datasets can produce broad and surprising changes in model behavior-a phenomenon called weird generalizatio

researcharxiv-cs-cl

PEAfowl: Perception-Enhanced Multi-View Vision-Language-Action for Bimanual Manipulation

arXiv:2601.17885v2 Announce Type: replace-cross Abstract: Bimanual manipulation in cluttered scenes requires policies that remain stable under occlusions, viewpoint changes and scene variations. Exist

local-aiarxiv-cs-ai

PepLLM: ESM-Guided Llama for Structured Protein-Peptide Binding Interface Analysis

arXiv:2608.21367v1 Announce Type: cross Abstract: Protein-peptide interactions are central to cellular regulation and peptide-based drug discovery, yet existing computational methods mainly focus on i

Pre-Decoding Acoustic Triage for Budgeted Vision-Language Captioning of Untrimmed Egocentric Video

arXiv:2608.22359v1 Announce Type: cross Abstract: Automatically analyzing hours-long egocentric video is increasingly essential for progress monitoring, quality control, and safety in logistics, const

safetyarxiv-cs-ai

Q-Learning with Stable Infinite-Dimensional Linear Function Approximation

arXiv:2608.22636v1 Announce Type: new Abstract: Q-learning with linear function approximation can be unstable because an arbitrary approximation architecture need not preserve the Bellman contraction.

safetyarxiv-cs-lg

Reference-free Human-Object Interaction Editing

arXiv:2503.09130v2 Announce Type: replace-cross Abstract: This paper presents InteractEdit, a novel framework for reference-free Human-Object Interaction (HOI) editing that tackles the challenging tas

RiskWorld: Object-Centric Latent World Modeling for Autonomous Driving Risk Identification

arXiv:2608.21414v1 Announce Type: cross Abstract: Autonomous driving risk identification aims to determine which observed object is likely to become safety-critical to the ego vehicle. Existing approa

local-aiarxiv-cs-lg

Robust Bimanual Vision-Language-Action Models via Embarrassingly Simple Modality Masking

arXiv:2608.22419v1 Announce Type: cross Abstract: Query-based Vision-Language-Action (VLA) models offer low-latency inference that is attractive for bimanual robotic manipulation, but we observe that

Applications

RobustGS: Unified Boosting of Feedforward 3D Gaussian Splatting under Low-Quality Conditions

arXiv:2508.03077v2 Announce Type: replace Abstract: Feedforward 3D Gaussian Splatting (3DGS) overcomes the limitations of optimization-based 3DGS by enabling fast and high-quality reconstruction witho

applicationsarxiv-cs-cv

Hardware

Self-Calibrating Dense Displacement Fields for Reliable Co-Registration of Large Optical Satellite Imagery

arXiv:2608.22300v1 Announce Type: new Abstract: Co-registration underlies nearly every multi-temporal and multi-sensor use of optical satellite imagery, and operational products still carry documented

hardwarearxiv-cs-cv

SymmAdapt: Symmetrical Flow Matching for Source-Free Domain Adaptation in Medical Image Segmentation

arXiv:2608.22532v1 Announce Type: new Abstract: Domain shift across imaging modalities and acquisition sites remains a significant barrier to the clinical deployment of segmentation models. Source-fre

researcharxiv-cs-cv

Tabular foundation models for non-tabular tasks

arXiv:2608.22594v1 Announce Type: new Abstract: Tabular foundation models (TFMs) have recently emerged as a promising paradigm for machine learning on tabular data, offering the ability to generalize

The Geometry of Low-Resource Language Representations

arXiv:2608.23358v1 Announce Type: new Abstract: The performance gap between low- and high-resource languages in LLMs is widely known, but it remains unclear which internal model factors drive these di

researcharxiv-cs-cl

Training-Free Pseudo-Fusion for Composed Image Retrieval with Diffusion Models and Multimodal Large Language Models

arXiv:2608.23102v1 Announce Type: new Abstract: Composed Image Retrieval (CIR) is an emerging paradigm in content-based image retrieval that enables users to formulate compositional queries by combini

researcharxiv-cs-cv

Vision-Language Models for Occupational Physical Exposure Assessment: Estimating External Hand Forces in Manual Material Handling Tasks from RGB Video

arXiv:2608.22586v1 Announce Type: cross Abstract: External hand forces are important inputs to biomechanical analyses of occupational physical exposure and injury risk, yet continuous force measuremen

local-aiarxiv-cs-ai

Tutorials

What Does CLIP Learn for Regional Geolocalization? Probing Visual Cues and Scene Configuration After Adaptation

arXiv:2608.21761v1 Announce Type: new Abstract: Large collections of street-view imagery provide rich visual information about urban environments, but extracting fine-grained geographic information fr

tutorialsarxiv-cs-ai

WorldToken: Time-First Sequence Modeling for Robotic Imitation Learning

model-releasesarxiv-cs-ro

arXiv:2608.22591v1 Announce Type: new Abstract: Robot policies receive heterogeneous observations at each decision step, yet sequence models differ in how they organize these inputs over time. We intr

A Neurosymbolic Approach for Constructing Planning Domain Models from Clinical Narratives

arXiv:2608.21186v1 Announce Type: new Abstract: Surgical procedures such as laparoscopic appendectomy are complex, high-stakes processes, yet formalizing their workflows for decision support remains a

Bern2Edge: A Neurosymbolic Compiler for Edge Deployment via Bernstein Polynomial Networks

arXiv:2608.20497v1 Announce Type: new Abstract: Deploying high-accuracy neural networks on resource-constrained edge devices remains challenging, as existing approaches treat training, compression, an

BF1: A Causal Dyadic Sparse-Attention Retrofit for Efficient Long-Context Transformers

arXiv:2608.20427v1 Announce Type: cross Abstract: Dense causal attention remains expensive at long context even when implemented with highly optimized exact kernels. We study BF1, a deterministic bloc

local-aiarxiv-cs-ai

Deep Models, Shallow Alignment: Uncovering the Granularity Mismatch in Neural Decoding

arXiv:2601.21948v2 Announce Type: replace Abstract: Neural visual decoding is a central problem in brain-computer interface research, aiming to reconstruct human visual perception and to elucidate the

Fine-tuning LLMs for Tourist Trajectory Prediction using Field Experiment Data

model-releasesarxiv-cs-lg

arXiv:2608.20830v1 Announce Type: cross Abstract: Evaluating mobility interventions at tourist destinations requires predicting visitor behavior under varying conditions. Traditional methods struggle

Hardware

From Attention Masks to Inert Zero-Vector Tokens: OAttention and O-Closure for Token Dynamics

arXiv:2608.21174v1 Announce Type: new Abstract: Attention masks are relation-level controls: they specify which query--source pairs may interact. They do not provide a representation-carried token sta

hardwarearxiv-cs-ai

Hardware

In the fight to defend openness in AI, the Marin project is a precious demonstration of openness in model training, with open code, data, re…

In the fight to defend openness in AI, the Marin project is a precious demonstration of openness in model training, with open code, data, recipes, even experimental results. Releasing AI research open

hardwareandrew-ng--x

Kinematic Knowledge Maps for Pattern Alignment: Structured Latent Representational Learning in Multimodal Gait Analysis

arXiv:2608.20969v1 Announce Type: new Abstract: Multimodal clinical AI is limited by weakly aligned inputs and the absence of domain-specific interpretable representations, particularly when learning

Learning-Based Measurement-Robust Control Barrier Functions for Obstacle Avoidance under State Estimation Error

arXiv:2608.20467v1 Announce Type: cross Abstract: Safety filters are an effective tool for enforcing constraints in safety-critical systems, but most existing methods assume perfect state information,

safetyarxiv-cs-ro

NeuroStrata: An Electroencephalographic Connectivity-Aware Deep Representation Learning Framework for Dynamic Brain Network Analysis of Mental Stress

arXiv:2608.20354v1 Announce Type: cross Abstract: This study introduces NeuroStrata, a connectivity-aware deep representation learning framework for EEG-based mental stress analysis using Time-Varying

researcharxiv-cs-ai

Applications

On the Transferability of Agricultural Weed Detection Under Cross-Field Distribution Shift

arXiv:2608.21254v1 Announce Type: new Abstract: Accurate agricultural weed detection in real-world field conditions is essential for precision agriculture, enabling targeted intervention and reducing

applicationsarxiv-cs-cv

PerturbRx: Learning Treatment-Conditioned Latent Transitions for Patient Drug Response Prediction

arXiv:2608.21349v1 Announce Type: cross Abstract: Scarce data and tumor heterogeneity limit patient-level cancer treatment-response prediction. Existing approaches predict response from pretreatment m

Semantically Compatible Knowledge Distillation for Cross-Domain Object Detection with Vision Foundation Models

arXiv:2608.20916v1 Announce Type: new Abstract: Vision foundation models (VFMs) offer strong generalization capabilities for domain-adaptive object detection (DAOD). However, existing VFM-based method

Six misconceptions about large language models: A minimal model and diagnostic taxonomy

arXiv:2608.20421v1 Announce Type: cross Abstract: Large language models (LLMs) are now embedded in scientific, educational, and governance workflows, with debates centering on their capabilities, mech

safetyarxiv-cs-ai

Truth Lies Deep: Countering Semantic Camouflage via Latent Intent Verification

arXiv:2608.20378v1 Announce Type: new Abstract: Safety alignment in Large Language Models (LLMs) is often superficial, relying on refusal mechanisms that trigger only at the final stages of generation

When does fusing hand-crafted knowledge with learned representations pay? A cost-normalized benchmark of stacking, substitution, and interference

arXiv:2608.21098v1 Announce Type: new Abstract: Fusing prior knowledge with data-driven learning is attractive where data is scarce, yet no controlled account says when it helps, is redundant, or harm

A Plug-in Interpretation of Conditioning in Score-Based Diffusion Models

arXiv:2608.19504v1 Announce Type: new Abstract: We propose a conditioning mechanism for diffusion models based on multi-speed joint diffusion of the target and the condition. The mechanism learns an u

researcharxiv-cs-cv

Agents

An Irreducible Quantum Advantage in Aligning World Models with Reality

arXiv:2608.19779v1 Announce Type: cross Abstract: World models provide digital simulacra of the true world, allowing agents to be trained and tested before costly real-world deployment. At each time s

agentsarxiv-cs-ai

CADRE: Stable, Parameter Efficient Adaptation of Medical Vision Language Models with Bounded Forgetting and Prior Drift

arXiv:2606.23487v2 Announce Type: replace Abstract: Medical vision-language models (VLMs) such as BiomedCLIP generalize broadly, but adapting them to a clinical service is as much a safety problem as

CarBench: A Comprehensive Benchmark for Neural Surrogates on High-Fidelity 3D Car Aerodynamics

model-releasesarxiv-cs-lg

arXiv:2512.07847v2 Announce Type: replace Abstract: Benchmarking has been the cornerstone of progress in computer vision, natural language processing, and the broader deep learning domain, driving alg

DeltaMomentum: A Key-Value based Anisotropic Momentum Update via Delta Rule

arXiv:2608.19491v1 Announce Type: cross Abstract: Most modern optimizers form their momentum as an exponential moving average (EMA) of past gradients, forgetting every direction at one fixed rate. How

researcharxiv-cs-cl