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LEAD: An EEG Foundation Model for Alzheimer's Disease Detection

arXiv:2502.01678v5 Announce Type: replace-cross Abstract: Electroencephalography (EEG) provides a non-invasive, highly accessible, and cost-effective approach for detecting Alzheimer's disease (AD). H

researcharxiv-cs-ai

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Learning-Accelerated Narrow-Phase Collision Detection via Check Ordering for Sampling-Based Motion Planning

DGX agent

arXiv:2609.30599v1 Announce Type: new Abstract: Collision detection is critical for ensuring the safety of planned paths. However, it imposes a non-negligible computational burden on motion planners,

safetyarxiv-cs-ro

28 Sept 2026

Learning-Based Pressure Predictive Control of a Vertebraic Soft Robotic Tail

arXiv:2609.30479v1 Announce Type: new Abstract: Soft robots have attracted much attention for their safe human-robot interaction and flexibility, but the typical continuum structure and nonlinear mate

researcharxiv-cs-ro

Learning Chance-Constrained MDPs with Bellman Distributional Certificates

model-releasesarxiv-cs-lg

arXiv:2609.30856v1 Announce Type: new Abstract: Safe reinforcement learning (RL) commonly enforces expected-cost constraints, but such expectation safety may fail to control the probability of rare hi

Learning coarse-step dynamics and internal mechanical response with graph networks

arXiv:2609.30344v1 Announce Type: new Abstract: Modern sensing records the motion of physical systems, but often leaves the forces and mechanical response governing that motion unobserved. Inferring t

Learning from Next-Frame Prediction: Autoregressive Video Modeling Encodes Effective Representations

arXiv:2512.21004v2 Announce Type: replace Abstract: Recent advances in pretraining general foundation models have significantly improved performance across diverse downstream tasks. While autoregressi

local-aiarxiv-cs-cv

Learning Hierarchical Causal Representations of the Effects of Forcings on Temperature in Climate Models

arXiv:2609.30995v1 Announce Type: new Abstract: Machine learning (ML) emulators provide a fast and cost-effective method to simulate climate change scenarios after being trained on Earth System Models

Learning Natural Conversational Behavior in Tandem Speech-to-Speech Models with Randomized Guidance

arXiv:2609.30773v1 Announce Type: new Abstract: Tandem speech-to-speech architectures couple a responsive speech frontend with an asynchronous text backend. In KAME, a large language model (LLM) serve

researcharxiv-cs-cl

Learning Polarization Image Restoration with General Restoration Priors

model-releasesarxiv-cs-cv

arXiv:2609.30728v1 Announce Type: new Abstract: Polarization imaging captures distinctive surface and geometric cues that benefit a wide range of vision tasks. However, real-world polarization acquisi

Learning Provable Neural Network Observer for Uncertain Dynamical Systems

arXiv:2609.30819v1 Announce Type: new Abstract: In many safety-critical applications, control of uncertain dynamical systems relies on observers that estimate states and external disturbances. Neural

safetyarxiv-cs-lg

Learning Robot Policies from Sparse Success Signals via STL-Guided Stein Variational Policy Gradient

arXiv:2609.31606v1 Announce Type: new Abstract: Learning robot policies for tasks with sparse success signals is challenging when completion depends on coordinated actions, precise contact outcomes, o

Learning to Bias: Machine Learning-Enhanced Particle Filters

arXiv:2609.30498v1 Announce Type: new Abstract: Sequential inference estimates latent states from noisy and incomplete observations. Particle Filters (PFs), a class of Monte Carlo methods based on imp

safetyarxiv-cs-lg

Learning to Leverage Compliance: A Policy-Admittance Learning Framework for Robotic Insertion

arXiv:2609.31439v1 Announce Type: new Abstract: Policy learning and compliant control offer a promising route to reliable autonomous assembly under pose errors and contact uncertainty. However, combin

Learning to Replace MCMC in Split-Gibbs Diffusion Posterior Sampling via Deep Unfolding

arXiv:2609.30539v1 Announce Type: cross Abstract: Split Gibbs sampling enables diffusion posterior inference for general nonlinear inverse problems by decoupling prior and likelihood computations, all

Learning to Stop without Learning to Stop: Self-Supervised Confidence Training Improves Reasoning Efficiency

model-releasesarxiv-cs-ai

arXiv:2609.31619v1 Announce Type: new Abstract: Reasoning models often generate very long reasoning traces, making inference computationally expensive. Existing approaches typically improve efficiency

Learning Vision-Based Agile Gap Traversal: Differentiable Simulation with a Warm-Started Critic

arXiv:2609.30696v1 Announce Type: new Abstract: Traversing narrow gaps is challenging for autonomous quadrotors, especially when control commands come directly from high-dimensional visual observation

Learning What to Skip: Counterfactual Credit Assignment for Efficient Multi-Agent LLM Workflows

arXiv:2609.30734v1 Announce Type: new Abstract: Multi-agent LLM workflows use planning, execution, verification, and summarization to improve task performance, yet the value of each component depends

safetyarxiv-cs-ai

LensDesigner: A Self-Improving Agent for Optical Lens Design

model-releasesarxiv-cs-cv

arXiv:2609.30450v1 Announce Type: new Abstract: Optical lens design is a complex, non-convex optimization challenge that relies heavily on human experience and intuition. Existing optimized-based auto

Light Field Primitive for Novel View Synthesis

arXiv:2609.31198v1 Announce Type: new Abstract: We present Light Field Primitives (LFP), a formulation for novel view synthesis that replaces the dense ray database with a compact set of differentiabl

researcharxiv-cs-cv

LipSSM: Structurally Lipschitz-Bounded Cascaded State-Space Model via Metric Transfer between Consecutive SSM Layers

arXiv:2609.30973v1 Announce Type: new Abstract: Lipschitz continuity is a fundamental principle in the design of certifiably robust deep neural networks (DNNs), wherein adjusting the Lipschitz constan

LiTe-GS: Oracle-Efficient Next Best View Selection for 3D Gaussian Splatting

arXiv:2609.30393v1 Announce Type: new Abstract: Selecting informative camera views is critical for efficient training and adaptive refinement in 3D Gaussian Splatting, where each observation significa

researcharxiv-cs-cv

LLM Parkinsonism: Executive-Control Failure, Token-Inefficient Persistence, and an Uncertainty-Aware Global Executive Control Architecture for Autonomous Language-Model Agents

model-releasesarxiv-cs-ai

arXiv:2609.30662v1 Announce Type: new Abstract: Large language models (LLMs) can plan, use tools, write code, and execute long-horizon workflows, yet strong local competence does not guarantee project

Tutorials

LLPR: Location-aware learning and physics-based reconstruction for raindrop removal from a single image

arXiv:2609.30758v1 Announce Type: new Abstract: Raindrops can cause occlusion and distortion in the background scenes due to their adherence to windows or camera lenses. Existing raindrop removal meth

tutorialsarxiv-cs-cv

LocUS: Head Selection and Subspace Projection for Targeted Activation Steering

arXiv:2609.31122v1 Announce Type: new Abstract: Activation steering is a powerful training-free paradigm for controlling large language models at inference time. However, standard approaches estimate

local-aiarxiv-cs-cl

LogicTree-RAG: Logic Tree-guided Retrieval-Augmented Generation for Long-form Patent Drafting

arXiv:2609.30943v1 Announce Type: new Abstract: Long-form technical text generation underpins knowledge-intensive workflows, yet remains challenging for large language models (LLMs) due to the need fo

local-aiarxiv-cs-ai

Low-Bit Recurrent States in Hybrid Language Models

arXiv:2609.30950v1 Announce Type: new Abstract: Hybrid language models maintain fixed-size recurrent states, but existing quantizers typically use eight bits or more. Quantization errors persist accor

Low-Rank Friction for Memory-Efficient Transformer Pretraining

arXiv:2609.30342v1 Announce Type: cross Abstract: iKFAD is a recently proposed optimiser that replaces adaptive learning rates with adaptive friction in the momentum dynamics, yet performs as well as

LUCID: Learning Under Confounding for Inference and Discovery in Time Series

model-releasesarxiv-cs-lg

arXiv:2609.31315v1 Announce Type: new Abstract: Unobserved common causes are pervasive in real-world time series and can induce spurious associations that causal discovery methods mistake for direct e

LUMO (Lightweight Unified Multilingual Orchestrator): A Privacy Preserving Offline Voice Assistant

arXiv:2609.30692v1 Announce Type: new Abstract: Reliable voice interaction is essential in environments with limited internet connectivity and strong privacy. However, most existing voice assistants d

local-aiarxiv-cs-lg

MA-WAM: Multi-Agent World-Action Model for Test-Time Planning

arXiv:2609.31281v1 Announce Type: new Abstract: Multi-agent cooperative tasks require different agents to execute a joint action simultaneously, and each agent's action affects both the observations a

safetyarxiv-cs-ai

MACBT: A Multi-Agent Cognitive Behavioral Therapy Decision Support System with Longitudinal Memory

model-releasesarxiv-cs-ai

arXiv:2609.30939v1 Announce Type: new Abstract: Cognitive behavioral therapy (CBT) is an evidence-based first-line treatment for depression, yet its scale is constrained by the time clinicians spend o

Manifold Projection and Iterative Autoencoder Refinement for Masked Language Modeling

model-releasesarxiv-cs-cl

arXiv:2609.30288v1 Announce Type: new Abstract: In Transformer-based masked language models, attention is the primary mechanism for context mixing, but there are other ways to mix data across tokens.

ManiVid: Unified and Explainable Forensic Analysis of Manipulated Videos

arXiv:2609.30934v1 Announce Type: new Abstract: Rapid advances in AI-generated video (AIGV) have increased the risks posed by deceptive video manipulation. Unlike fully synthetic videos, manipulated v

researcharxiv-cs-cv

MARCEDES: Score-based causal discovery under non-Gaussianity with continuous optimization

arXiv:2609.30643v1 Announce Type: cross Abstract: We consider the problem of learning the underlying causal directed acyclic graph (DAG) structure corresponding to a structural equation model (SEM) wi

MDSkin-Net: Multi-Task Skin Lesion Analysis Driven by Pattern Analysis Priors and Spatial Alignment Regularization

model-releasesarxiv-cs-cv

arXiv:2609.30855v1 Announce Type: new Abstract: Reliable skin lesion segmentation and classification are central to dermoscopic computer-aided diagnosis. Existing multi-task frameworks couple the two

Measurement and Potential Field-Based Patient Modeling for Model-Mediated Tele-ultrasound

arXiv:2509.15325v2 Announce Type: replace Abstract: Teleoperated ultrasound can improve diagnostic medical imaging access for remote communities. Having accurate force feedback is important for enabli

researcharxiv-cs-ro

Mechanism-Aware Ensemble Conditioning for Data-Limited Emulation of Extreme Events

model-releasesarxiv-cs-lg

arXiv:2609.30746v1 Announce Type: new Abstract: Extreme events in chaotic systems are difficult to learn from short trajectories because they are controlled by transient finite-time instability rather

MedHal: a Synthetic Dataset for Medical Hallucination Detection

arXiv:2504.08596v3 Announce Type: replace-cross Abstract: Hallucination, the generation of non factual content by AI systems, poses serious risks in medical contexts, where errors can directly affect

researcharxiv-cs-ai

MedTokenBudget: Lesion-Preserving Token Routing for Dermoscopic Image Classification

arXiv:2609.30613v1 Announce Type: cross Abstract: Dermoscopy classifiers built on Vision Transformers process all image patches uniformly, although diagnostic evidence is concentrated in the lesion re

researcharxiv-cs-ai

Memory-Aware Multi-Sensor Perception for Efficient and Safe Navigation in Dynamic Environments

arXiv:2609.30495v1 Announce Type: new Abstract: Autonomous navigation in previously unseen environments requires effective perception, persistent environmental representation, and collision avoidance

Mentored Decoding: Faster Inference meets Boosting

arXiv:2609.30474v1 Announce Type: new Abstract: Speculative decoding is a successful technique speeding up inference of a target autoregressive language model via a fast drafter model. Lossy speculati

MeshGraphNet-Transformer: Scalable Mesh-based Learned Simulation for Solid Mechanics

arXiv:2601.23177v4 Announce Type: replace Abstract: We present MeshGraphNet-Transformer (MGN-T), a novel architecture that combines the global modeling capabilities of Transformers with the geometric

safetyarxiv-cs-lg

Metacognitive Selective Ensemble for Mobile Systems

arXiv:2609.31031v1 Announce Type: new Abstract: Deep ensembles improve robustness in mobile sensing, but repeatedly executing many models over continuous sensor streams is costly. Selecting only a few

MexHat: A Dataset for Hate Speech Detection in Mexican Spanish Videos

arXiv:2609.31553v1 Announce Type: new Abstract: Ensuring online safety through content monitoring had raised Hate Speech Detection as a crucial task to be addressed. By essence the task demands the ca

safetyarxiv-cs-cl

Missingness-Aware Conformal Prediction Under Cross-Hospital Distribution Shift

arXiv:2609.30781v1 Announce Type: new Abstract: Clinical measurements are recorded for some patients but not others, at rates that differ across hospitals, and marginal conformal coverage does not ens

MM-VeriAgent: Learning to Use Extensive Tools to Verify Multimodal Misinformation with Reinforcement Learning

arXiv:2609.30698v1 Announce Type: new Abstract: Real-world multimodal misinformation often involves mixed forgery sources, requiring sample-specific detection strategies. Existing tool-augmented metho

safetyarxiv-cs-cv

MOCHA: Multi-Objective Co-Design using Hypernetwork Architectures

arXiv:2609.30570v1 Announce Type: new Abstract: In this work, we present MOCHA, the first, to our knowledge, reinforcement learning based approach to computing a family of Pareto-optimal policies acro

Modeling and Generative-AI-Based Design of Load-Adaptive Gravity Balancing Mechanisms

arXiv:2609.31386v1 Announce Type: new Abstract: Load-adaptive gravity balancing mechanisms (LA-GBMs) can accommodate various loading conditions by passively changing their characteristics in response

researcharxiv-cs-ro