All entries

Distribution-Aware Diffusion-LLM for Robust Ultra-Long-Term Time Series Forecasting

arXiv:2606.23391v1 Announce Type: new Abstract: Time series forecasting is a fundamental machine learning task. Recent work has explored Large Language Models (LLMs) for this purpose due to their stro

Distribution-Aware Robust Bilevel Optimization: Quantile-Guided Huber Updates in Two-Timescale Stochastic Approximation

DGX agent

arXiv:2606.22436v1 Announce Type: new Abstract: Bilevel optimization (BLO) is fundamental to hierarchical decision-making but suffers from critical instability under heavy-tailed stochastic noise. Exi

model-releasesarxiv-cs-lg

23 Jun 2026

Distributional Regression with Tabular Foundation Models: Evaluating Probabilistic Predictions via Proper Scoring Rules

arXiv:2603.08206v5 Announce Type: replace Abstract: Modern tabular foundation models such as TabPFN and TabICL naturally produce full predictive distributions, while the benchmarks used to evaluate th

DiT-Reward: Generative Representations for Text-to-Image Reward Modeling

arXiv:2606.23626v1 Announce Type: new Abstract: Can representations learned for image generation also support the evaluation of generated images? We study text-to-image reward prediction as a downstre

DivCon-NeRF: Diverse and Consistent Ray Augmentation for Few-Shot NeRF

arXiv:2503.12947v3 Announce Type: replace Abstract: Neural Radiance Field (NeRF) has shown remarkable performance in novel view synthesis but requires numerous multi-view images, limiting its practica

researcharxiv-cs-cv

Do Activation Monitors Survive Model Updates? Benchmarking, Predicting, and Repairing Activation-Monitor Staleness

arXiv:2606.15980v2 Announce Type: replace Abstract: Activation monitors -- lightweight probes trained on a language model's internal representations -- are an increasingly common layer in deployment s

Do Location Encoders Capture Spatial Effects? A GeoShapley Benchmark Across Scales

arXiv:2606.23453v1 Announce Type: new Abstract: Location encoders transform geographic coordinates into high dimensional embeddings for downstream machine learning, but it is unclear how well these re

Do Modern Video-LLMs Need to Listen? A Benchmark Audit and Scalable Remedy

arXiv:2509.17901v4 Announce Type: replace Abstract: Speech and audio encoders developed over years of community effort are routinely excluded from video understanding pipelines, not because they fail,

Applications

Do Rigid-Body Simulators Dream of Soft Robots? Learning Contact-Rich Manipulation for Tendon-Driven Continuum Robots

arXiv:2606.22397v1 Announce Type: new Abstract: Learning contact-rich, whole-body manipulation for soft continuum robots is held back by the lack of simulation infrastructure that has accelerated rigi

applicationsarxiv-cs-ro

Tutorials

Do Sparse Autoencoders Learn Meaningful Concept Hierarchies?

arXiv:2606.22994v1 Announce Type: new Abstract: Sparse autoencoders (SAEs) have become an important tool for unsupervised concept discovery in large models. To make the resulting feature spaces more i

tutorialsarxiv-cs-lg

Does RoPE Prevent or Degrade Retrieval Heads? A Mechanistic Analysis Across Model Families

arXiv:2606.21249v1 Announce Type: new Abstract: Retrieval heads, attention heads that copy information from earlier context to the current position, have been proposed as the mechanistic substrate for

Domain Adaptation Under Wireless Network Constraints: When Does It Become Green?

arXiv:2606.23047v1 Announce Type: cross Abstract: The deployment of data-driven models in 6G wireless networks is increasingly challenged by frequent distribution shifts that degrade performance over

Domain-incremental audio classification using domain-specific experts and prototype classifier

arXiv:2606.22952v1 Announce Type: cross Abstract: This technical report presents submission systems for Task 7(domain-incremental audio classification) of the DCASE 2026 Challenge. The main obstacle i

Don't Tell the Answer, Truly Guide the Reasoning During RL Rollouts

arXiv:2510.09388v2 Announce Type: replace Abstract: Reinforcement Learning (RL) has become a key driver for enhancing the long chain-of-thought (CoT) reasoning capabilities of Large Language Models (L

Double-Diffusion: Balancing Speed, Accuracy, and Uncertainty in Probabilistic Forecasting for Urban Sensor Networks

arXiv:2506.23053v3 Announce Type: replace Abstract: Urban sensor networks need forecasts that are accurate, carry useful uncertainty, and refresh fast enough to act on as new readings arrive. These go

DP-FedSOFIM: Differentially Private Federated Stochastic Optimization using Regularized Fisher Information Matrix

arXiv:2601.09166v3 Announce Type: replace Abstract: Differentially private federated learning (DP-FL) often suffers from slow convergence under tight privacy budgets because the noise required for pri

DPLAN: Minimal Connectivity to Floorplan Generation

arXiv:2606.21159v1 Announce Type: cross Abstract: Automated floor plan generation is an important problem in computational architectural design. The goal is to construct a floor plan from user-defined

researcharxiv-cs-ro

DPO Unchained: Your Training Algorithm is Secretly Disentangled in Human Choice Theory (and its Loss' Convexity is Dispensable)

arXiv:2507.07855v4 Announce Type: replace Abstract: Normative theories allow one to elicit key parts of a ML algorithm from first principles, which is crucial at a time of championed scrutiny for ML w

DR-Mamba: Automatic Inference-Time Domain Adaptation for Document Image Binarization via Sample-Conditioned Detail-Background Suppression

arXiv:2606.22625v1 Announce Type: new Abstract: Degraded document image binarization is sensitive to domain shifts caused by paper aging, bleed-through, stains, shadows, and uneven illumination, and t

Applications

DreamUV: Unwrap Artist-like UV by End-to-End Flow Matching

arXiv:2606.22445v1 Announce Type: new Abstract: UV parameterization is a fundamental step in 3D content creation, yet producing production-ready UV layouts remains challenging due to the gap between g

applicationsarxiv-cs-cv

DrivingVoxels: Compositional Sparse Voxel Rasterization for Dynamic Driving Scene Reconstruction

arXiv:2606.23031v1 Announce Type: new Abstract: Reconstructing dynamic urban scenes remains challenging due to the unbounded nature of driving environments and the presence of multiple dynamic objects

Drowning in Routine: Signal Dilution in Multi-Turn Agent Training

arXiv:2606.22164v1 Announce Type: new Abstract: Multi-turn agents interleave consequential decisions with routine execution: some actions change the downstream return distribution, while others are ne

agentsarxiv-cs-lg

DrugBench: Evaluating AI Control Protocols for Medication Harm Mitigation

arXiv:2606.20663v1 Announce Type: cross Abstract: Large Language Models have the potential to expand and improve the access to clinical information by enabling new ways of interacting with medical kno

DT-GOL: Dual-Track Geometric Online Learning in Nonstationary Environment with Label Delay

arXiv:2606.22950v1 Announce Type: new Abstract: Online learning is crucial for handling complex data streams in big data applications. Recent research has begun to focus on dynamic scenarios, i.e., no

Dual-Attention Convolution Experts for Sparse Tensor Completion

arXiv:2606.21427v1 Announce Type: new Abstract: Tensor factorization (TF) has been widely adopted for high-dimensional sparse data completion tasks. Despite significant progress, neural TF methods oft

Dual-Stream EEG Decoding for 3D Visual Perception

arXiv:2606.22182v1 Announce Type: new Abstract: This paper explores a novel brain decoding model for 3D shape perception through a dual pathway architecture mirroring biological vision. Our bio-inspir

researcharxiv-cs-cv

DUET: Decentralized Bilevel Optimization without Lower-Level Strong Convexity

arXiv:2606.21153v1 Announce Type: cross Abstract: Decentralized bilevel optimization (DBO) provides a powerful framework for multi-agent systems to solve local bilevel tasks in a decentralized fashion

agentsarxiv-cs-lg

Duet: Dual-Robot Understanding via Efficient Teaching

model-releasesarxiv-cs-ro

arXiv:2606.20990v1 Announce Type: new Abstract: Dual-robot collaboration enables tasks that exceed the reach and payload of a single robot, such as collaboratively transporting objects across environm

Applications

Durability-Aware Multi-Objective Optimization of the Jansen Linkage: Trading Gait Quality Against Joint Wear

arXiv:2606.22129v1 Announce Type: new Abstract: The Jansen linkage is a single-degree-of-freedom planar leg mechanism whose eleven 'holy numbers' were evolved by Theo Jansen to optimize the foot-path

applicationsarxiv-cs-ro

DVL-DeepONet: A Physics-Guided Operator Learning for Resilient Underwater Navigation

arXiv:2606.23502v1 Announce Type: new Abstract: Autonomous Underwater Vehicles (AUVs) rely heavily on the fusion of inertial sensors and Doppler velocity logs (DVLs) for navigation. In standard autono

agentsarxiv-cs-ro

dVLA-RL: Reinforcement Learning over Denoising Trajectories for Discrete Diffusion Vision-Language-Action Models

arXiv:2606.23623v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models have established a powerful paradigm for generalist robotic manipulation by grounding control into the semantic reas

safetyarxiv-cs-ro

Dynamic estimation of slowly varying sequences

arXiv:2606.23655v1 Announce Type: new Abstract: We consider the problem of sequentially approximating functions of each element in a slowly-varying sequence, i.e. one where the magnitude alpha_i of th

Dynamic multi-agent deep reinforcement learning-based pricing and incentivization approach in multimodal transportation networks

arXiv:2606.23257v1 Announce Type: new Abstract: In multimodal transportation systems, shared mobility services (SMSs) are promoted for their potential to enhance flexibility and reduce congestion. How

agentsarxiv-cs-lg

Dynamics, stability, and energy efficiency of an energy-recycling rimless wheel with spring-clutch legs

model-releasesarxiv-cs-ro

arXiv:2606.22073v1 Announce Type: new Abstract: This paper proposes an energy-recycling rimless wheel with spring-clutch legs. The proposed mechanism uses a lockable clutch to store part of the impact

Each Judge Its Own Yardstick: Discovering Per-VLM Taxonomies for Physical Video Evaluation

arXiv:2606.22918v1 Announce Type: new Abstract: Maintaining physical consistency in video generators and world models increasingly relies on vision-language models (VLMs) as automated judges that prov

Early-Exit Graph Neural Networks for Link Prediction

arXiv:2606.22167v1 Announce Type: new Abstract: Graph Neural Networks are great for link prediction in various network-like structures; however, the question of their speed/quality tradeoff has been b

Industry

Early land animals skipped the tadpole phase

Researchers analyzing rare, microscopic fossils of baby tetrapods discovered that the first animals to conquer the land completely skipped the tadpole phase. The finding means that the first land-dwel

industryars-technica

EasyUUV: An LLM-Enhanced Universal and Lightweight Sim-to-Real Reinforcement Learning Framework for UUV Attitude Control

arXiv:2510.22126v3 Announce Type: replace Abstract: Despite recent advances in Unmanned Underwater Vehicle (UUV) attitude control, existing methods still struggle with generalizability, robustness to

safetyarxiv-cs-ro

EBench: Elemental Diagnosis of Generalist Mobile Manipulation Policies

model-releasesarxiv-cs-ro

arXiv:2606.18239v2 Announce Type: replace Abstract: We present EBench, a simulation benchmark that diagnoses generalist mobile manipulation policies beyond a single success-rate scalar. EBench compris

ECGFlowCMR: Pretraining with ECG-Generated Cine CMR Helps Cardiac Disease Classification and Phenotype Prediction

arXiv:2601.20904v3 Announce Type: replace-cross Abstract: Cardiac Magnetic Resonance (CMR) imaging provides a comprehensive assessment of cardiac structure and function but remains constrained by high

EchoingPixels: Aliasing-Resistant Joint Token Reduction for Audio-Visual LLMs

arXiv:2512.10324v2 Announce Type: replace Abstract: Audio-Visual Large Language Models (AV-LLMs) face prohibitive computational costs of processing massive, redundant audio-visual tokens. Existing uni

researcharxiv-cs-cv

EEG Benchmarking Needs a Task Specification Layer: NeuroDoc for Rulebook-Guided, Executable Benchmark Construction

arXiv:2606.22925v1 Announce Type: new Abstract: Electroencephalography (EEG) foundation models increasingly rely on multi-dataset training and evaluation, yet public EEG datasets still lack a shared t

Efficient Continuous Semantic Mapping based on Spatio-Temporal Awareness

arXiv:2606.22672v1 Announce Type: new Abstract: Continuous semantic mapping allows autonomous robots to understand both the spatial structure and the semantic content of complex environments. However,

agentsarxiv-cs-ro

Local Ai

Efficient Document Tampering Localization with Multi-Level Discrepancy Features and Unified DCT-Quantization Embedding

arXiv:2606.22285v1 Announce Type: new Abstract: Localizing document tampering is extremely challenging, as manipulations are crafted to appear visually consistent and often leave only subtle traces th

local-aiarxiv-cs-cv

Efficient Network Inference via Hardware-Aware Architecture Search, Model Pruning & Quantization

arXiv:2606.23210v1 Announce Type: new Abstract: Embedded global navigation satellite system (GNSS) interference monitoring requires fast and memory-efficient inference to process large volumes of raw

Efficient Reinforcement Finetuning via Adaptive Curriculum Learning

arXiv:2504.05520v4 Announce Type: replace Abstract: Reinforcement finetuning (RFT) has shown great potential for enhancing the mathematical reasoning capabilities of large language models (LLMs), but

Local Ai

Efficient Traffic State Prediction With Dynamic Joint Spatio-Temporal Relation Inference

arXiv:2504.08061v2 Announce Type: replace Abstract: Traffic prediction is difficult due to the complex interplay of temporal evolution, spatial interactions, and delayed spatio-temporal propagation ov

local-aiarxiv-cs-cv

Efficient Training of Boltzmann Generators Using Off-Policy Log-Dispersion Regularization

arXiv:2602.03729v2 Announce Type: replace Abstract: Sampling from unnormalized probability densities is a central challenge in computational science. Boltzmann generators are generative models that en