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arXiv:2608.00985v1 Announce Type: new Abstract: The rapid growth of single-cell transcriptomic data has enabled the development of foundation models pretrained primarily by reconstructing masked expre

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Local Ai

Beyond Global Latents: Chunk-Based Sparse Grid VAE for Scalable 3D Modeling

DGX agent

arXiv:2608.02016v1 Announce Type: new Abstract: Sparse voxel grids preserve the spatial structure needed for detailed 3D reconstruction, but their memory still grows rapidly with resolution as active

local-aiarxiv-cs-cv

4 Aug 2026

Beyond Illumination: A Conditional Mutual Information-Guided Network for Low-Light Image Enhancement

arXiv:2608.01886v1 Announce Type: new Abstract: Low-light image enhancement (LLIE) seeks to restore structural fidelity, natural color rendition, and proper exposure from images captured under inadequ

Beyond Lanes: Traffic Flow Dynamics in Disordered Conditions Based on High-Resolution Trajectory Data

arXiv:2608.00602v1 Announce Type: cross Abstract: Disordered traffic flow is characterized by weak or non-existent lane discipline in the presence of strong vehicle heterogeneity and continuous latera

Beyond Magnitude and Shape: A Direction-Aware Loss for Time Series Forecasting

arXiv:2608.01857v1 Announce Type: new Abstract: The direction of change --- whether a series will move up or down --- is often as important as its exact value in decisiondriven applications such as ri

Beyond Modern Asymptotics for Log-Likelihood Ratios in Logistic Regression

arXiv:2608.02507v1 Announce Type: cross Abstract: We characterize the finite sample behavior of the log-likelihood ratio statistic in binary logistic regression, uniformly over both the design and the

Applications

Beyond Noise: A Hypothesis Testing Approach to Robust Feature Selection

arXiv:2511.20851v3 Announce Type: replace-cross Abstract: Feature selection remains difficult in modern high-dimensional settings, and established methods such as Boruta and Recursive Feature Eliminat

applicationsarxiv-cs-lg

Beyond On-Policy Exploration: Integrating External Policy Rollouts for Reinforcement Learning in Diffusion Language Models

arXiv:2608.01717v1 Announce Type: new Abstract: Recent reinforcement learning methods for diffusion large language models (dLLMs) commonly rely on on-policy rollouts generated by the target dLLM itsel

safetyarxiv-cs-lg

Beyond Random Partitioning: Unsupervised Spatio-Temporal Stratification for Cohort Balancing in Longitudinal Medical Imaging

arXiv:2608.00073v1 Announce Type: new Abstract: Rigorous dataset partitioning is a foundational, yet frequently overlooked, prerequisite for reliable deep learning in longitudinal medical imaging. Nai

safetyarxiv-cs-cv

Beyond Scaffold Splits: Structural-Frontier Evaluation Reveals Hidden Failures in ADMET Models

arXiv:2607.10729v2 Announce Type: replace Abstract: Molecular property models are commonly evaluated by holding out Bemis-Murcko scaffolds, yet a scaffold identifier is only one notion of chemical unf

Beyond Static Anchors: Bounded Prototype Conditioning for Language-Free Medical Anomaly Detection

arXiv:2608.00442v1 Announce Type: new Abstract: Medical anomaly detection identifies abnormal images and localizes lesions under scarce supervision while generalizing across organs and modalities. Exi

local-aiarxiv-cs-cv

Beyond Symmetric Fusion: Exploiting Task-Dependent Modality Strengths for RGB-Event Small Object Detection

arXiv:2608.01302v1 Announce Type: new Abstract: State-of-the-art RGB-Event detectors improve the detection of small, fast-moving objects by combining complementary features from RGB and Event data, ye

Beyond Token-Level Cross-Entropy: Frechet Distributional Post-Training for Autoregressive Image Generation

arXiv:2608.00562v1 Announce Type: new Abstract: Autoregressive image generators are commonly pretrained with token-level cross-entropy under teacher forcing, yet evaluated by the distributional qualit

bFaaaP: An Inclusive, Head-Angle Piano-Pedal Interaction that Quantitatively Reproduces a Pianist's Intended Pedalling -- Foot-Free, for Acoustic and Electronic Pianos

arXiv:2608.00633v1 Announce Type: cross Abstract: Expressive piano performance depends on the sustain (damper) pedal, operated by foot, excluding players who cannot readily use their feet: wheelchair

local-aiarxiv-cs-ro

BiCAA: Bidirectional Credit Assignment for Search-Augmented Agent

arXiv:2608.01321v1 Announce Type: new Abstract: Multi-step search is a fundamental capability for search agents, enabling them to iteratively acquire, refine, and integrate external evidence for compl

safetyarxiv-cs-cl

Agents

Bicycle Acrobatics with Reinforcement Learning

arXiv:2608.00880v1 Announce Type: new Abstract: Bicycle robots are fast and energy efficient, but their simple mechanical design and their underactuated and non-holonomic dynamics make highly agile ma

agentsarxiv-cs-ro

BiKAN: Restoring Collapsed Basis of Binary Kolmogorov--Arnold Networks

arXiv:2608.01490v1 Announce Type: new Abstract: Binarizing a polynomial Kolmogorov--Arnold Network (KAN) not only changes parameter precision, but also alters the function space available to each laye

Hardware

Bole: Efficient Tree Speculation for Hybrid-Attention Language Models

arXiv:2608.01651v1 Announce Type: cross Abstract: Hybrid-attention large language models combine full attention with recurrent linear attention to reduce long-context inference costs, yet their autore

hardwarearxiv-cs-cl

Boosting Generalizable Depth Estimation in Endoscopy by Mixture of Lightweight Experts and Intrinsic Image Alignment

arXiv:2608.00415v1 Announce Type: new Abstract: Depth estimation is a significant task for 3D perception in endoscopic surgeries. However, illumination interference and feature diversity in various en

Breaking Diversity Collapse in Spiking Pseudo-Ensembles for Efficient OOD Detection in Remote Sensing

arXiv:2608.01090v1 Announce Type: new Abstract: Spiking Neural Networks (SNNs) are attractive for resource-constrained remote-sensing systems, but reliable out-of-distribution (OOD) detection remains

Breaking Self-Attention Failure: Rethinking Query Initialization for Infrared Small Target Detection

arXiv:2601.02837v2 Announce Type: replace Abstract: Infrared small target detection (IRSTD) faces significant challenges due to low signal-to-noise ratios, extremely small target sizes, and complex cl

Breaking the Horizontal Prior: From Long-Tailed Orientation Bias to Roll-Robust Monocular Depth Estimation

arXiv:2608.00678v1 Announce Type: new Abstract: Despite recent advances in Monocular Depth Estimation, state-of-the-art depth foundation models remain vulnerable to robustness issues. Particularly, ev

Breaking the Statistical Similarity Trap in Extreme Convection Detection

arXiv:2509.09195v2 Announce Type: replace-cross Abstract: Current evaluation metrics for deep learning weather models create a 'Statistical Similarity Trap', rewarding blurry predictions while missing

safetyarxiv-cs-cv

BRIC-Net: Boundary-Reliable Illumination-Color Interaction for Remote Sensing Image Deshadowing

arXiv:2608.00682v1 Announce Type: new Abstract: Shadows in remote sensing images obscure surface appearance and disrupt radiometric continuity, reducing the reliability of visual interpretation and do

Bridging the English-Arabic Medical Knowledge Gap: Targeted Low-Rank Adaptation via Causal Layer Selection

model-releasesarxiv-cs-cl

arXiv:2608.00207v1 Announce Type: new Abstract: Large Language Models (LLMs) perform strongly in English medical tasks but degrade substantially in Arabic, a gap widely attributed to limited training

Bridging the Sim-to-Real Gap in Parallel-Link Leg Mechanisms via Simulator-Side Dynamics Normalization

arXiv:2608.01697v1 Announce Type: new Abstract: This paper addresses the sim-to-real gap in dynamics arising when a parallel-link mechanism is represented by a serial-tree surrogate in simulation. Con

safetyarxiv-cs-ro

BRiG-AFA: Bellman Risk-to-Go Learning for Non-Myopic Active Feature Acquisition

arXiv:2608.02305v1 Announce Type: new Abstract: Active feature acquisition (AFA) asks which unobserved feature to measure next for each test instance under a budget. Greedy rules are easy to train but

CAAT: Contact-Aware Attention Scaling and Tactile Masking for Data-Efficient Contact-Rich Manipulation

arXiv:2608.01102v1 Announce Type: new Abstract: In contact-rich manipulation, visual observations primarily guide motion in free space, whereas tactile observations become particularly informative dur

safetyarxiv-cs-ro

CADENA: Stepwise CAD Reverse Engineering

arXiv:2608.00799v1 Announce Type: new Abstract: Computer-Aided Design (CAD) underpins modern engineering, yet converting existing shapes into editable models still demands substantial expert effort. M

CalibBEV: LiDAR-Camera Calibration via BEV Alignment

arXiv:2608.02309v1 Announce Type: new Abstract: We present CalibBEV, a novel Bird's Eye View (BEV) alignment approach for LiDAR-camera calibration. Our method unifies LiDAR and camera data into a shar

safetyarxiv-cs-cv

Caliber: Cross-Architecture Extraction-Cost Control for Score-Returning APIs

arXiv:2608.01023v1 Announce Type: new Abstract: We present Caliber, an output-perturbation defense against model extraction that formulates noise selection as a calibration problem: how much the defen

Calibrated Similarity and Graph Clustering for Open-Set Animal Re-Identification

arXiv:2608.02469v1 Announce Type: new Abstract: AnimalCLEF26 addresses discovery-oriented animal re-identification, where systems must both attach query images to known individuals and discover unseen

Agents

Can AI Agents Simulate A/B Test Outcomes? A Validation Framework for Agentic Experimentation

arXiv:2608.02345v1 Announce Type: new Abstract: A/B testing remains the standard for rolling out new features in the technology industry. Each experiment, however, consumes real traffic, engineering e

agentsarxiv-cs-cl

Can Humans Dream of Electric Sheep? Human-Written Samples for Fine-Grained Vision-and-Language Hallucination Benchmarking

arXiv:2608.01021v1 Announce Type: cross Abstract: In an age of rapid model turnover, how do we make hallucination evaluation more perennial? We explore whether human-written hallucination samples coul

researcharxiv-cs-cl

Can Language Models Identify Shadow Trading Targets? An NLP Evaluation of SEC Enforcement Theory

arXiv:2608.01322v1 Announce Type: new Abstract: Shadow trading -- trading in a peer firm's securities on the basis of material nonpublic information (MNPI) about an 'economically linked' company -- is

researcharxiv-cs-cl

Applications

Can Urban Blight Be Accessed with Vision-language Models: A Case Study in Detroit

arXiv:2608.01753v1 Announce Type: new Abstract: Addressing urban blight has seen increased focus in the past 15 years. Assessing urban blight is essential for guiding urban planning, targeting rehabil

applicationsarxiv-cs-cv

Can You Trust the Confidence? ConfBench for Vision-Language Models on Document Extraction

model-releasesarxiv-cs-cl

arXiv:2608.01792v1 Announce Type: cross Abstract: Intelligent document processing (IDP) with vision-language models (VLMs) hinges on confidence scores trustworthy enough to route extractions between a

Capability Provenance in Language Models: A Case Study in Social Reasoning

model-releasesarxiv-cs-cl

arXiv:2606.19625v2 Announce Type: replace Abstract: We use training-data attribution as an interpretable tool for capability discovery, mapping which regions of the pretraining corpus support social-r

CAPEval: A Decoupled Caption Evaluation across Understanding and Generation

arXiv:2608.02589v1 Announce Type: new Abstract: Captions serve as a primary supervision signal for both multimodal understanding and text-to-image generation. However, previous evaluations treat the c

CAPMix: Robust KPI Anomaly Detection for AIOps in Noisy and Dynamic Environments

arXiv:2509.06419v2 Announce Type: replace Abstract: Time-series anomaly detection is crucial in AIOps for maintaining large-scale service reliability. In production, streams of Key Performance Indicat

Cardiovascular Digital Twins from Physics Based to Data Driven Approaches

arXiv:2608.02135v1 Announce Type: cross Abstract: Cardiovascular digital twins aim to create patient-specific computational models that evolve with clinical data to support diagnosis, prognosis, and t

Applications

CARE: A Cascaded Framework for Efficient and Reliable Time Series Anomaly Detection

arXiv:2608.01885v1 Announce Type: new Abstract: While deep learning models have achieved state-of-the-art performance in time series anomaly detection, their complex architectures incur substantial in

applicationsarxiv-cs-lg

CARNet: Channel-Adaptive Receiver Network for Robust NextG Communications

arXiv:2608.02172v1 Announce Type: cross Abstract: Neural receivers have been recognized as a promising paradigm for the next-generation (NextG) communications. However, due to the reliance on a static

CascadeLUT: Information-Ordered Streaming Inference for Bandwidth-Constrained FPGAs

arXiv:2608.00720v1 Announce Type: cross Abstract: Mapping neural networks to FPGAs enables low-latency, energy-efficient inference, particularly for lookup table (LUT)-based models that eliminate mult

local-aiarxiv-cs-lg

Tutorials

Causal Inference with Unstructured Treatments

arXiv:2608.00657v1 Announce Type: cross Abstract: Causal inference usually concerns a scalar treatment, yet in many problems the treatment is unstructured: a text, an image, or a sequence of clinical

tutorialsarxiv-cs-lg

CAVE: Competence-Aware Visual Boundary Evidence Alignment for Video Temporal Grounding

arXiv:2608.02078v1 Announce Type: new Abstract: Large vision-language models (LVLMs) have achieved substantial performance gains in Video Temporal Grounding (VTG) through reinforcement learning (RL).

safetyarxiv-cs-cl

CDG-MAE: Cross-view Masked Modeling using Diffusion Generated Views

arXiv:2506.18164v2 Announce Type: replace Abstract: Cross-view masked autoencoding has emerged as a powerful pretext task for learning dense correspondences, which are essential for applications such

local-aiarxiv-cs-cv

Celty: SpMspV GPU Kernel and SIMT Co-Design for Efficient Dual-Sparse LLM Inference

arXiv:2608.01536v1 Announce Type: cross Abstract: Large Language Models (LLMs) increasingly rely on sparsity to reduce inference cost, but most prior work targets a single sparsity source-either weigh

local-aiarxiv-cs-lg