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arXiv:2610.13299v1 Announce Type: cross Abstract: Pretrained vision transformers learn structured image representations spanning spatial patch tokens and non-spatial tokens. Recent analyses suggest th

tutorialsarxiv-cs-lg

Deep End-to-End Survival Analysis with Temporal Consistency

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DGX agent

arXiv:2410.06786v2 Announce Type: replace Abstract: In this study, we present a novel Survival Analysis algorithm designed to efficiently handle large-scale longitudinal data. Our approach draws inspi

researcharxiv-cs-lg

12 Oct 2026

Defence against the dark arts of distillation: Unsupervised discovery of subliminal learning

arXiv:2610.13536v1 Announce Type: cross Abstract: When a language model is distilled from a teacher that shares its base parameters, the student can inherit behavioural traits of the teacher---includi

DeFiRank: Federated Pairwise Ranking for Governance in Decentralized Financial Networks

arXiv:2610.12916v1 Announce Type: new Abstract: Existing governance approaches in decentralized financial networks rely on manual review processes conducted by human committees, which introduces subje

safetyarxiv-cs-ai

DeFM: Learning Foundation Representations from Depth for Robotics

arXiv:2601.18923v2 Announce Type: replace Abstract: Depth sensors are widely deployed across robotic platforms, and advances in fast, high-fidelity depth simulation have enabled robotic policies train

applicationsarxiv-cs-ro

DeltaLLM: Delta-Key Sparse Attention for Edge LLMs

arXiv:2507.19608v2 Announce Type: replace Abstract: On-device LLM inference is increasingly important for edge platforms but remains challenging due to high computational and memory demands and tight

Deploying RDMA for Large-Scale Inference and Training Workloads

arXiv:2610.12815v1 Announce Type: new Abstract: RDMA can reduce communication overhead in distributed machine learning systems, but realizing these benefits in production requires more than replacing

Desertification Forecasting in Semi-Arid Regions: Using Deep Learning to Probe the Observability Limits of Land Degradation Dynamics

arXiv:2610.13406v1 Announce Type: cross Abstract: Forecasting desertification has received far less attention than mapping it. We use a convolutional LSTM (ConvLSTM) trained on ten annual Earth Observ

Designing Protein Structures from Natural Language

arXiv:2610.13917v1 Announce Type: new Abstract: A central challenge in de novo protein design is generating proteins with desired structural and functional properties. Existing design models typically

Development of a Low-Cost Prosthetic Hand Using Electromyography and Machine Learning

arXiv:2411.15533v2 Announce Type: replace Abstract: Electromyography (EMG) is a measure of muscular electrical activity and is used in many clinical/biomedical disciplines and modern human computer in

researcharxiv-cs-ro

DiffMorphGrasp: A Diffusion Framework Conditioned on Hand Morphology for Cross-Embodiment Grasp Generation

model-releasesarxiv-cs-ro

arXiv:2602.00915v2 Announce Type: replace Abstract: As robots are deployed across diverse environments, manipulation tasks require robot hands with different morphologies to satisfy varying task requi

Diffusion Precipitation Generation with Medium-range Weather Forecasting

arXiv:2610.12976v1 Announce Type: new Abstract: Accurate daily precipitation forecasts are important for weather-sensitive decisions. Although recent Weather Foundation Models (WFMs) have achieved sig

applicationsarxiv-cs-lg

DiffusionART: Efficient, Stable and Scalable Reinforcement of Diffusion Models

arXiv:2610.12572v1 Announce Type: new Abstract: Online reinforcement learning (RL) fine-tunes diffusion models with black-box rewards, but reverse-process methods such as Flow-GRPO are limited in effi

safetyarxiv-cs-lg

Discovering Phase Space Structure in Learned Hamiltonian Systems

arXiv:2509.23003v3 Announce Type: replace-cross Abstract: Mechanics-inspired neural dynamical models improve interpretability and physical consistency, but they usually assume a known configuration sp

applicationsarxiv-cs-ai

Disentangling Total-Variance and Signal-to-Noise-Ratio Improves Diffusion Models

arXiv:2502.08598v3 Announce Type: replace Abstract: Diffusion models have been widely adopted as a powerful generative framework across numerous scientific domains, yet their inherently iterative samp

DistressBench: Evaluating Multi-Dimensional Crisis Support in Large Language Models

arXiv:2610.12758v1 Announce Type: new Abstract: Language models have been adopted faster than evaluation practices that reliably assess their risks, and a growing share of use is affective rather than

safetyarxiv-cs-cl

Distributed Harmonic-Covariance Feedback for Hole Closure in Planar Robot Swarms

arXiv:2610.12637v1 Announce Type: new Abstract: We consider hole closure in a planar multi-agent system whose sensing disks form a time-varying Cech complex. We propose a harmonic-covariance feedback

local-aiarxiv-cs-ro

Distributionally Robust Adaptive Iterative Covariance Steering for Small-Body Proximity Operations

arXiv:2610.12774v1 Announce Type: cross Abstract: Autonomous small-body proximity operations require continuous-thrust guidance under uncertain gravity, solar-radiation pressure (SRP), and stochastic

safetyarxiv-cs-ro

Diversity-Aware Ensemble of Quantum-Classical Models via Combinatorial Fusion

arXiv:2610.13808v1 Announce Type: cross Abstract: Variational quantum circuits (VQCs) are highly sensitive to circuit architecture, yet most quantum machine learning pipelines select a single best des

Do Prompt Optimizers Break Requirements They Are Not Scored On? A Preregistered Study

arXiv:2610.13287v1 Announce Type: new Abstract: Prompt optimizers rewrite a prompt to raise one target metric, but prompts also state requirements the metric does not score, such as an output format,

researcharxiv-cs-cl

Does Financial Speech Need Continual Learning Regularization? A Study of EWC and mini-KFAC on Earnings Call ASR

model-releasesarxiv-cs-cl

arXiv:2610.13432v1 Announce Type: cross Abstract: Earnings call transcription presents a realistic continual learning scenario where Automatic Speech Recognition (ASR) systems are updated with new rec

Agents

Does Knowledge About Perceptual Uncertainty Help an Agent in Automated Driving?

arXiv:2502.11864v2 Announce Type: replace-cross Abstract: Agents in real-world scenarios like automated driving deal with uncertainty in their environment, in particular due to perceptual uncertainty.

agentsarxiv-cs-ro

Does Learning Recurrent Networks Matter in Partially Observable RL?

arXiv:2610.13197v1 Announce Type: cross Abstract: Reinforcement learning (RL) under partial observability typically re- lies on recurrent networks to encode temporal information, with the recurrent mo

Does Location Matter? A Black-Box Causal Audit Reveals Model-Dependent Audio Grounding in LALM Hallucination

arXiv:2610.13112v1 Announce Type: new Abstract: Large audio-language models (LALMs) frequently exhibit object hallucination, affirming absent sounds. The dominant mitigation, Audio-Aware Decoding (AAD

Don't Guess, Just Ask: Evaluating Interactive Requirement Clarification for Repository-Level Software Development

arXiv:2610.13022v1 Announce Type: cross Abstract: As LLMs become increasingly capable of implementing software, a critical bottleneck emerges: whether they truly understand what users want. Users ofte

Double-Take: Improving Capacity, Binding, and Selectivity In Long-Context Within Bidirectional Memory

arXiv:2610.13073v1 Announce Type: new Abstract: Processing a long sequence does not guarantee that distant information remains usable. As context grows, a fixed-size memory must retain many associatio

DP-MIND: Differentially private memory-guided Long context Inference

arXiv:2610.13132v1 Announce Type: new Abstract: LLM-as-a-Service has become a dominant paradigm for accessing large language models without requiring local deployment. However, the process raises seve

Dream to Remember: Continual Steering of Generalist Robot Policies with World Models

arXiv:2610.13816v1 Announce Type: cross Abstract: Generalist robot policies are trained with imitation learning and are bounded by the quality and coverage of their demonstrations. Reinforcement learn

safetyarxiv-cs-lg

DRYLAB-Bench: Benchmarking the Biosecurity Risk of Agentic LLM Mutation Design

arXiv:2610.13116v1 Announce Type: new Abstract: As large language models (LLMs) acquire advanced biological design capabilities, they offer substantial research benefits but simultaneously introduce s

Dual-Stream Memory for Vision-Language-Action Policies

arXiv:2610.13262v1 Announce Type: new Abstract: Memory-dependent manipulation requires a policy to track execution state and recover visual evidence that is no longer observable. We propose dual-strea

safetyarxiv-cs-ro

DualQuant: Dual-scale Calibration-free Weight-and-Activation Quantization for LLMs

arXiv:2610.13767v1 Announce Type: cross Abstract: Low-bit inference on accelerators with native 4-bit support requires quantizing both weights and activations to realize the full throughput gains thes

DuoRate: Two-Rate Per-Expert Precision Allocation for Mixture-of-Experts Serving

arXiv:2610.13328v1 Announce Type: new Abstract: Serving a Mixture-of-Experts (MoE) model pays for expert precision twice: the resident footprint pays for every expert the router can reach, while each

DyssomniaNN: Multi-Scale Spiking Neural Modeling of Human Sleep Disorders and Perceived Restedness

arXiv:2610.13918v1 Announce Type: cross Abstract: Clinical sleep medicine repeatedly documents profound divergences between objective sleep duration and subjective daytime impairment, notably exemplif

Agents

E-commerce AI startup Loops raises $3.5M to create smoother online shopping experiences

Agentic e-commerce startup Loops Smart AI wants to help online retailers create more seamless buying journeys and streamline the shopping experience after raising 3.5 million in seed funding today. An

agentssiliconangle

EarLog: Opportunistic Ear Physiology Sensing toward Personalized Hearing Risk Awareness

arXiv:2610.12501v1 Announce Type: cross Abstract: Many headphone users risk irreversible hearing damage during high-load listening, yet current protection still relies on sound-pressure limits. Clinic

Easy-IIL: Reducing Human Operational Burden in Interactive Imitation Learning via an Assistant Expert

arXiv:2603.12769v2 Announce Type: replace Abstract: Interactive Imitation Learning (IIL) typically relies on extensive human involvement for both offline demonstration and online interaction. Prior wo

applicationsarxiv-cs-ro

EchoTab: Learning Measurement-Grounded Representations for Echocardiographic Diagnosis through Tabular Reconstruction

arXiv:2610.13599v1 Announce Type: new Abstract: Diagnostic information in multi-view medical imaging studies, like echocardiography, is distributed across views, each capturing different aspects of ca

Efficient Audio-Visual Speech Enhancement via Reinforcement Learning-Based Neural Architecture Search

arXiv:2610.12796v1 Announce Type: cross Abstract: Audio-visual speech enhancement (AVSE) improves speech intelligibility in noisy environments but often relies on computationally expensive models unsu

Efficient Learning of Survival Analysis Models Under Outcome-Dependent Truncation and Censoring

arXiv:2610.12538v1 Announce Type: new Abstract: Modern machine learning models are typically trained on data assumed to be representative of the predictive tasks the model will face in the future, but

safetyarxiv-cs-lg

Tutorials

EgoReveal: Learning Active Vision and Object Manipulation under Partial Occlusions from Egocentric Demonstrations

arXiv:2610.12999v1 Announce Type: new Abstract: Learning from Demonstrations (LfD) enables robots to learn object manipulation directly from human demonstrations. In contrast to resource-intensive and

tutorialsarxiv-cs-ro

Electron-Density-Informed Molecular Property Prediction Using Surrogate Functionals

arXiv:2610.13649v1 Announce Type: cross Abstract: Machine learning methods are increasingly used to predict chemical properties directly from the molecular structure to bypass the often prohibitive co

Tutorials

EliteTable: Self-Improving Program Search for Executable Control Policies

arXiv:2610.12536v1 Announce Type: new Abstract: EliteTable is a self-improving program-search algorithm guided by large language models (LLMs) that uses a fixed-capacity table of high-scoring solution

tutorialsarxiv-cs-ai

Industry

🚨 Elon Musk agrees with 'some of the things Anthropic has said' and warns against 'torturing superintelligence' after reading Grok's intern…

🚨 Elon Musk agrees with 'some of the things Anthropic has said' and warns against 'torturing superintelligence' after reading Grok's internal reasoning traces during reinforcement learning “It feels l

industryelon-musk--x

EmbodiedCLBench: Evaluating Continual Learning for Self-Evolving Embodied Agents

arXiv:2610.13021v1 Announce Type: new Abstract: Continual learning requires models to accumulate past experience and transfer it to new tasks. Despite substantial research on continual learning, exist

EmoGavel: Grounding-Aware Reinforcement Learning with Verifiable Rewards for Multimodal Emotion Preference Learning

arXiv:2610.13353v1 Announce Type: cross Abstract: Multimodal emotion preference learning uses pairwise comparisons to identify which open-ended emotion descriptions align more closely with human judgm

EndoRSI: Physical-Evidence-Driven Recursive Improvement of Endoscopic Robot Policies

arXiv:2610.13376v1 Announce Type: new Abstract: Artificial intelligence is beginning to automate scientific discovery, yet physical robot-policy research still relies on scientists and engineers to in

safetyarxiv-cs-ro

Energy-Efficient Spiking Neural Networks for Sensing-Aided Beam Prediction

arXiv:2610.12507v1 Announce Type: cross Abstract: Sensing-aided beam prediction can reduce the beam-training overhead of millimeter-wave (mmWave) systems by leveraging environmental information from c

researcharxiv-cs-ai

Enhancing Legal Reasoning in Pre-trained Language Models via Knowledge Graph-Guided Multi-Task Pre-training

arXiv:2610.13722v1 Announce Type: new Abstract: Legal language understanding requires reasoning over semi-structured regulatory documents, yet standard pre-training objectives do not explicitly target

applicationsarxiv-cs-cl