Search: “research”

Structured-Noise Masked Modeling for Video, Audio and Beyond

arXiv:2503.16311v2 Announce Type: replace-cross Abstract: Masked modeling has emerged as a robust self-supervised learning framework. However, most methods rely on random masking, which disregards the

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Supervise What Decides Success: Criterion-Aligned Auxiliary Losses for Latent World-Model Planning

DGX agent

arXiv:2610.01224v1 Announce Type: new Abstract: Latent world models plan by scoring candidate action sequences with distances in latent space. However, task success is judged by physical quantities, w

researcharxiv-cs-lg

2 Oct 2026

Surface-volume self-supervised representation learning of brain MRI for genetic discovery

arXiv:2610.02114v1 Announce Type: new Abstract: Existing genome-wide association studies (GWAS) of brain imaging provide predefined or deep-learning-derived imaging phenotypes, yet these phenotypes co

Synthetic training for long-tail haemorrhagic lesion segmentation in data-scarce settings

arXiv:2610.01542v1 Announce Type: new Abstract: Cerebral microbleeds (CMBs) and cortical superficial siderosis (cSS) are imaging markers of cerebral small vessel disease, but their automated segmentat

TacDyn-WAM: Learning Implicit Tactile Dynamics in a Heterogeneous Visuo-Tactile World Action Model

arXiv:2610.00638v1 Announce Type: new Abstract: World action models improve robotic manipulation by conditioning actions on predicted futures, yet existing tactile variants largely inherit video-gener

Target-Dependent Limits of Causal Repair: A Leading-Log Frontier in a Gaussian Model

arXiv:2610.00424v1 Announce Type: cross Abstract: Knowing how much a causal predictor could improve need not reveal the gain of the repair actually learned. We quantify this gap in a scalar Gaussian c

Task-Adaptive Grounded 3D-Programmers Using 2D VLMs

arXiv:2610.02021v1 Announce Type: cross Abstract: Recent vision-language models (VLMs) exhibit remarkable generalization and reasoning abilities, yet 3D understanding in these models is limited by dat

Temporally-Resolved Token Attribution Reveals the Generation Dynamics of Diffusion Language Models

arXiv:2610.01177v1 Announce Type: cross Abstract: This work presents Diffusion Layer Integrated Gradients (DLIG), a token attribution method for diffusion language models (DLMs) that extends Integrate

The AI Assessment Sandbox Configurator: A Framework to Support Technical Assessment in AI Regulatory Sandboxes

arXiv:2610.01539v1 Announce Type: new Abstract: The EU's Artificial Intelligence Act requires all Member States to establish AI Regulatory Sandboxes (AIRS) by August 2027: supervised environments brin

The COTe score: A decomposable framework for evaluating Document Layout Analysis models

arXiv:2603.12718v3 Announce Type: replace Abstract: Document Layout Analysis (DLA) is the process by which a page is parsed into meaningful elements, often using machine learning models. Typically, th

The Curvature of Regret in Contextual Linear Optimization

arXiv:2610.01980v1 Announce Type: new Abstract: Decision-focused learning for linear optimization is complicated by the discontinuity of the optimizer, where small cost errors may leave the decision u

The Effect of Gait Stability Based on Two Types of Impact Strategies for Two-Link Walking and Brachiating Robots

arXiv:2610.01004v1 Announce Type: cross Abstract: In this paper, we explore the impulsive dynamics common to single-joint, two-link models of walking and brachiating gaits with respect to slope and sw

The Geometry of Time: Horizon-Independent Feasibility and Repair for STL

arXiv:2610.00199v1 Announce Type: cross Abstract: Signal Temporal Logic control synthesis frequently encounters physical infeasibility due to actuator limits or flawed task deadlines. Standard optimiz

The Life Cycle of a Massive Activation: Stochastic Birth, Weight-Decay-Driven Growth, and Competitive Consolidation

arXiv:2610.00423v1 Announce Type: cross Abstract: Massive activations, residual-stream coordinates with magnitudes far larger than typical activations, are associated with attention sinks in transform

The Price of Correlated Tests: How Strict Should a Model Release Gate Be?

arXiv:2610.00993v1 Announce Type: cross Abstract: Before a machine learning model ships, it often has to pass a suite of automated tests. Requiring every test to pass looks safe, yet it can reject man

Tight Transition Time Bounds for Separable Logistic Regression at the Edge of Stability

arXiv:2610.01459v1 Announce Type: new Abstract: We study logistic regression on linearly separable data under gradient descent with a large constant stepsize eta. Such dynamics may exhibit a character

Toward Humanoid Robots in Construction: A Teleoperation Feasibility Study

arXiv:2610.00718v1 Announce Type: new Abstract: We present a teleoperation system that enables a single operator to perform construction tasks on a Unitree G1 humanoid, combining extended reality (XR)

Towards Physical Underwater Robotic Assistance for Scuba Diver Movement in Confined Spaces

arXiv:2610.01906v1 Announce Type: new Abstract: Scuba divers are taught to control their depth to avoid rapid ascents and descents, which could result in serious injuries such as gas embolisms and bar

TRACE: Privacy-Preserving Next-Best-View Selection over Distributed 3D Gaussian-Splat Maps

arXiv:2610.00822v1 Announce Type: new Abstract: Share the light, not the map. We study next-best-view selection for a team of robots, each of which builds its own 3D Gaussian Splatting map and keeps i

Training-Aware Target Coverage for Synthetic Data Selection

arXiv:2610.00814v1 Announce Type: cross Abstract: Synthetic data are increasingly used to scale LLM training, yet more synthetic data do not necessarily produce better models. Useful synthetic data mu

Trajectory Stitching for Solving Inverse Problems with Flow-Based Models

arXiv:2602.08538v2 Announce Type: replace-cross Abstract: Flow-based generative models have emerged as powerful priors for solving inverse problems. One option is to directly optimize the initial late

Transferable Graph Metanetworks

arXiv:2610.00420v1 Announce Type: cross Abstract: A weight space network (or metanetwork) takes the weights of another neural network as input and predicts properties of it. Most prior work trains suc

Uncertainty-Aware Learning from Multi-Expert Interval Targets

arXiv:2610.00102v1 Announce Type: new Abstract: Many machine learning (ML) applications rely on expert labels, and qualified experts may provide different but plausible interpretations of the same obs

Understanding Latent Diffusability via Fisher Geometry

arXiv:2604.02751v3 Announce Type: replace Abstract: Diffusion models often degrade in latent spaces, yet the formal causes remain poorly understood. We quantify latent-space diffusability via the rate

UniGuardian: A Unified Defense for Detecting Prompt Injection, Backdoor Attacks and Adversarial Attacks in Large Language Models

arXiv:2502.13141v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are vulnerable to attacks like prompt injection, backdoor attacks, and adversarial attacks, which manipulate prom

Universal Byte-Level Encoding: UTF-8/UTF-16 Routing to Reduce Cross-Script Token-Budget Disparities

arXiv:2610.01984v1 Announce Type: cross Abstract: Byte-level byte-pair encoding (BBPE) tokenizers are attractive for multilingual large language models (LLMs) because they cover all Unicode text. In U

VANDAM: Viewing a nucleotide sequence with DNA molecular priors

arXiv:2610.00411v1 Announce Type: new Abstract: Contemporary Genomic Foundation Models (GFMs) rely on a DNA-as-a-string paradigm that employs masked token prediction objectives for pretraining. Howeve

Variational Streaming Flow: Probabilistic Forecasting in Physical Time

arXiv:2610.00976v1 Announce Type: new Abstract: Probabilistic forecasting is important for predicting complex dynamical systems because intrinsic randomness and incomplete observations can cause the s

VASC: Value-Aware Sparse Attention with Cross-Layer Memory for Efficient 3D Reconstruction

arXiv:2610.01013v1 Announce Type: new Abstract: Feed-forward 3D vision models such as VGGT have achieved remarkable progress, unifying camera estimation and dense scene reconstruction in a single pass

Verbalized and Internal Probabilities Are Coupled in Large Language Models

arXiv:2610.00827v1 Announce Type: cross Abstract: Large language models carry an internal notion of uncertainty in their sampling distribution, i.e., the probabilities they place on generating one ans

VETO: Video Efficient Token Optimization for Vision Language Models

arXiv:2610.01785v1 Announce Type: cross Abstract: Processing long videos with Vision-Language Models (VLMs) is bottlenecked by the quadratic cost of visual tokens, making long-form inference prohibiti

VideoSTF: Stress-Testing Output Repetition in Video Large Language Models

arXiv:2602.10639v2 Announce Type: replace Abstract: Video Large Language Models (VideoLLMs) have achieved strong performance on video understanding tasks, yet existing benchmarks evaluate only what mo

VITA: A Multi-Source Vicinal Transfer Augmentation Method for Out-of-Distribution Generalization

arXiv:2204.11531v2 Announce Type: replace Abstract: Invariance to diverse types of image corruption, such as noise, blurring, or colour shifts, is essential to establish robust models in computer visi

VTV-FM: Flow Matching through Variational Terminal-Velocity Closure

arXiv:2610.00785v1 Announce Type: new Abstract: Flow matching (FM) learns generative transport by fitting continuous-time motion from a simple source distribution to the data distribution. Most existi

Wasserstein Gradient Flows and Forward-Only Diffusion Are Not Enough for Multimodal Sampling

arXiv:2610.02081v1 Announce Type: cross Abstract: There has been a proliferation of sampling algorithms based on Wasserstein gradient flows (WGF) and forward-only diffusion processes (FODP), often acc

Watch Your Speech: Text-aware Video-to-Speech Synthesis with Textual Conditioning

arXiv:2610.01012v1 Announce Type: new Abstract: Video-to-speech synthesis aims to generate natural-sounding speech from silent talking-face videos while ensuring phonetic accuracy. A fundamental chall

Weighted Data Selection: Sharp Upper-Half and Five-Dimensional Laws

arXiv:2610.00101v1 Announce Type: cross Abstract: How much risk does a small reweighted training support retain? For finite weighted least squares with the minimum-norm learner, we prove the exact law

What Can Analogy Tell Us About Artificial Consciousness?

arXiv:2610.01002v1 Announce Type: new Abstract: Who or what is conscious? Because subjective experience is directly accessible only in the first person, judgments about consciousness in other entities

What Makes Something Hard(er)? Explaining Question Difficulty in Natural Language

arXiv:2610.01627v1 Announce Type: cross Abstract: Difficulty is one of the most fundamental properties of a question: it determines whether the question can meaningfully discriminate between models of

When Do Attention-Head Ablations Support Causal Claims? Projection-Level Confounds, Floor Effects, and Matched Controls

arXiv:2610.00373v1 Announce Type: new Abstract: Attention-head ablation, zeroing a head and measuring the resulting change in task performance, is a common method for inferring which components of a l

When Does Exercise-Specific Joint Selection Help? An Audit of Evaluation and Control Design

arXiv:2610.01188v1 Announce Type: new Abstract: Exercise-specific joint selection can improve skeleton-based correctness classification, but what does that gain establish? We audit 1,057 repetitions f

When More Data Is Not Enough: The Context-Sufficiency Frontier in Generative AI Personalization

arXiv:2610.00654v1 Announce Type: new Abstract: Personalization has long relied on customer data to infer what an individual is likely to value. We call this customer evidence: the customer's historic

When Text-to-Image Helps Editing: The Effects of Conditioning During Denoising

arXiv:2610.01681v1 Announce Type: new Abstract: Unified models are trained for both instruction-based image editing and text-to-image (T2I) generation, but standard editing pipelines keep source-image

When the AI Leaves the Tailorshop: Measuring What an LLM Advisor Leaves Behind in Complex Problem Solving

arXiv:2610.00163v1 Announce Type: cross Abstract: Complex problem solving depends on acting effectively and understanding how a system works. AI advice may support these outcomes unequally. Two prereg

Which LLM to pick? Online Active Model Selection for Large Language Models

arXiv:2610.01592v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly applied to process streaming data, with practitioners relying on benchmarks to select the best model eve

WIPSNet: Deep Learning for Paediatric Wheeze Detection from Overnight Impedance Pneumography

arXiv:2610.00398v1 Announce Type: new Abstract: Overnight impedance pneumography (IP) is used to monitor paediatric respiratory health. Its current clinical readout, the Expiratory Variability Index (

XOR-Trellis: Ultra-Low-Complexity Dequantization and Curvature-Aware Hadamard-Free LLM Quantization

arXiv:2610.00432v1 Announce Type: cross Abstract: Trellis-coded quantization enables high-dimensional compression of large language model (LLM) weights at ultra-low bit widths without the exponentiall

Zero Flux: Flow-Based Comparison of High-Dimensional Discrete Distributions

arXiv:2610.01472v1 Announce Type: cross Abstract: Comparing two high-dimensional discrete distributions has always been a challenging task due to the exponentially growing state space and complex chan