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Defensive Sufficiency in a Stackelberg Model of AI Security

arXiv:2610.09892v2 Announce Type: replace-cross Abstract: Feedback from automated testing, human red teaming, and incident response can strengthen an AI system's defenses when discovered failures lead

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Deflating the Hessian: Rank-4 W4A4 Quantization for Multimodal Diffusion Transformers

DGX agent

arXiv:2610.11315v1 Announce Type: cross Abstract: In diffusion transformers, low-rank branches can mitigate 4-bit weight--activation (W4A4) post-training quantization (PTQ) loss by decomposing each we

researcharxiv-cs-cv

9 Oct 2026

Density Ratio Estimation with Stein Displacement Fields

arXiv:2610.12437v1 Announce Type: cross Abstract: Density ratios quantify distribution shift from a probability-mass point of view, whereas displacement fields describe, from a dynamical point of view

Detecting Spin in Clinical Trials with Large Language Models

arXiv:2610.11845v1 Announce Type: new Abstract: Spin in clinical trials includes reporting practices that distort the presentation of results. This is particularly critical in medicine, where spin is

DiffuPlex: Accelerating Full-Duplex Spoken Dialog Models via Rolling Masked Diffusion

arXiv:2610.12214v1 Announce Type: cross Abstract: Recent full-duplex spoken dialog models enable simultaneous listening and speaking, but fine-grained models still advance their backbone autoregressiv

Diffusion Meta-Prompting and Steering for Generalizable Foundation Model Adaptation

arXiv:2610.11067v1 Announce Type: new Abstract: Prompt learning is a popular method for adapting foundation models, but learned prompts are typically task-specific and fail to generalize to new classe

Diffusion Removes Langevin's Conditioning Dependence: A Sharp Gaussian Analysis

arXiv:2610.12052v1 Announce Type: cross Abstract: Despite their empirical success, why diffusion models overcome the bottlenecks of classical score-based samplers remains unclear. In this work, we lev

Dino Forcing Flow Models: Do not denoise what you can predict

arXiv:2610.11751v1 Announce Type: new Abstract: Co-denoising pretrained representations such as DINO can substantially improve the training speed and quality of flow matching models, but it introduces

Discovering Global False Negatives On the Fly for Self-supervised Contrastive Learning

arXiv:2502.20612v2 Announce Type: cross Abstract: In self-supervised contrastive learning, negative pairs are typically constructed using an anchor image and a sample drawn from the entire dataset, ex

Disentangling Linguistic and Paralinguistic Information with Routed Sparse Autoencoders

arXiv:2610.10865v1 Announce Type: new Abstract: Self-supervised speech encoders contain linguistic and paralinguistic information in a shared, entangled representation space. We combine a TopK sparse

Distributed Relative Localization Based on Ultra-WideBand and LiDAR for Multi-robot with Limited Communication

arXiv:2610.11141v1 Announce Type: new Abstract: Relative localization is crucial for a multi-robot system to collaboratively perform tasks, such as exploration and formation. However, this is highly c

researcharxiv-cs-ro

Do Flatter Minima Drive Better Generalization? An Algorithmic Separation in Grokking

arXiv:2610.11206v1 Announce Type: new Abstract: Flat loss landscapes have long been linked to better generalization in neural networks. However, its role as a causal mechanism for generalization is le

Do Not Train Away Uncertainty: Early Uncertainty Anchored Calibration

arXiv:2610.12048v1 Announce Type: new Abstract: Deep neural networks, including large language models, have achieved remarkable performance across various tasks. However, they are prone to overconfide

Does an Illumination Prior Help Face-Swap Detection? A Controlled Study of Temporal Self-Blended Images

arXiv:2610.11706v1 Announce Type: new Abstract: Self-blended images are widely used to train face-swap detectors, but primarily capture blending artifacts. We investigate whether adding illumination i

DPPM: Dual-Path Parametric Memory for Personalized Language Models

arXiv:2610.11776v1 Announce Type: new Abstract: Long-term personalization requires language models to use interaction history to track users' preferences across sessions. Parametric memory encodes thi

Dynamics as Code: On Model Compression via Dynamic System

arXiv:2610.11115v1 Announce Type: new Abstract: The escalating size of pretrained neural networks has rendered model compression a prerequisite for deployment under stringent memory and compute constr

DynaTE: Accelerating Diffusion LLMs via Dynamic Token Execution

arXiv:2610.11284v1 Announce Type: cross Abstract: Diffusion-based LLMs (dLLMs) have recently emerged as a promising alternative to autoregressive (AR) LLMs by enabling bidirectional parallel refinemen

Easy to anticipate, hard to compute: boundary dependence finds the computed outputs that entropy patching misses

arXiv:2610.11790v1 Announce Type: new Abstract: Byte-level language models such as the Byte Latent Transformer (BLT) group bytes into patches and run their large global model once per patch. BLT start

Efficient Multi-Granularity Knowledge Transfer for Radiology Report Generation

arXiv:2610.11303v1 Announce Type: new Abstract: Radiology report generation can automatically generate clinical descriptions from X-ray images, thereby significantly improving the efficiency of radiol

Efficient Reasoning via Constrained Optimization in Latent Space

arXiv:2609.34181v2 Announce Type: replace Abstract: Large Reasoning Models (LRMs) have shown remarkable reasoning capabilities, yet they still suffer from overthinking, generating redundant reasoning

EgoPhys: Estimating Peak Contact Force and Mechanical Work from Egocentric Manipulation Video

arXiv:2610.11347v1 Announce Type: new Abstract: Physically grounded manipulation of articulated objects requires understanding both the maximum forces encountered during contact and the work performed

Enabling Quantum Natural Language Processing for Hindi Language

arXiv:2312.01221v2 Announce Type: replace Abstract: Quantum Natural Language Processing (QNLP) is taking huge leaps in solving the shortcomings of classical Natural Language Processing (NLP) technique

Epistemic Disturbance in the Graph Model for Conflict Resolution: State-Preserving Actions, Four-Valued Assessments, and the Distinction between Capability and Intention

arXiv:2610.11690v1 Announce Type: cross Abstract: In the graph model for conflict resolution (GMCR), a decision maker (DM) either moves the conflict to another state or does nothing. The basic definit

Equal Path Cost, Unequal Output Effects: Understanding Perturbation Propagation in Diffusion Models

arXiv:2610.11380v1 Announce Type: new Abstract: Diffusion models have achieved remarkable success in generative modeling, with their sampling procedures routinely modified to control generation and im

Explaining the Saliency Map Sparsity of Adversarially-Trained Neural Networks

arXiv:2610.10666v1 Announce Type: new Abstract: Understanding why deep neural networks make a given prediction is of great importance for their safe deployment. In computer vision, saliency maps, whic

Explanation Multiplicity in SHAP: Characterization and Assessment

arXiv:2601.12654v3 Announce Type: replace-cross Abstract: SHAP explanations are widely used in high-stakes settings to justify decisions, yet they can differ substantially across repeated runs, even w

FastJEV: Understanding Redundancy for Compact JEV Inference

arXiv:2610.11379v1 Announce Type: new Abstract: JEV models make multimodal decisions by directly scoring candidates. Although the common context is encoded once, candidate evaluation can still repeat

Feature Space Adaptation for Effortless Gaussian Process Flows

arXiv:2610.11459v1 Announce Type: cross Abstract: Outside the linear-Gaussian regime, conditional sampling from Gaussian processes (GPs) is challenging. Recent methods such as FlowGP (Moss et al., (20

Few-Step Generation via Data-Space Iteration

arXiv:2610.12102v1 Announce Type: cross Abstract: Flow matching has emerged as a scalable paradigm for training high-quality generative models, but sampling from the learned probability flow requires

Fixed-Reference Pose Residuals for Measuring Cross-Dataset Cue Transfer in Human-Robot Interaction Anticipation

arXiv:2610.12245v1 Announce Type: new Abstract: Social and service robots in public spaces need to anticipate which nearby person is about to approach and touch them, so that a response can be prepare

researcharxiv-cs-ro

Fractional Heat Kernel for Semi-Supervised Graph Learning with Small Training Sample Size

arXiv:2510.04440v2 Announce Type: replace Abstract: We develop a source-driven fractional heat-kernel framework for semi-super-vised graph learning that combines nonlocal propagation with sustained la

Fresco++: Frequency-Guided and Canonical-Consistent Optimization for Fine-Grained Head Avatar Modeling

arXiv:2610.11412v1 Announce Type: new Abstract: We propose Fresco++, a unified optimization framework for fine-grained and view-consistent head avatar reconstruction. Head avatar optimization is typic

FreshMem: Brain-Inspired Frequency-Space Hybrid Memory for Streaming Video Understanding

arXiv:2602.01683v2 Announce Type: replace-cross Abstract: Transitioning Multimodal Large Language Models (MLLMs) from offline to online streaming video understanding is essential for continuous percep

From Chain-of-Thought to Loops: Non-Autoregressive Latent Reasoning via Looped Transformers

arXiv:2610.11472v1 Announce Type: new Abstract: Chain-of-thought (CoT) reasoning often improves language-model performance by giving models additional computation before answering. However, explicit C

From Language to Motion: Task-Conditioned Focal-Stack Trajectory Integration for Microscopic Robots

arXiv:2610.12241v1 Announce Type: new Abstract: Microscopic robots require accurate task geometry despite changes in language, parts, and focus. We present a semantic-to-physical framework that maps i

researcharxiv-cs-ro

From Log-Odds to Shapley Values: An Explanatory Geometry for the Weighted Naive Bayes Classifier

arXiv:2610.10642v1 Announce Type: cross Abstract: This paper studies the construction of an explanatory space for a weighted naive Bayes classifier from the supervised representation induced by the mo

From Pixels to Structure: Lightweight Vision-Language Models for Document OCR and Structured JSON Extraction

arXiv:2610.11818v1 Announce Type: cross Abstract: While massive, closed-source Vision-Language Models (VLMs) set strong benchmarks for document understanding, their dependence on commercial APIs limit

From Sparse Representations to Behavioral Insights for Multimodal Depression Assessment

arXiv:2610.11787v1 Announce Type: new Abstract: Multimodal depression assessment offers a promising approach to analyzing behavioral patterns associated with depression. However, existing methods ofte

From Video Clips to Creation Trajectory: Sora100K for AI-Native Video Creation

arXiv:2610.11770v1 Announce Type: new Abstract: AI-Native video creation is shifting from isolated video clips toward iterative video creation workflows. However, existing datasets remain largely vide

From What to Which: Decoding Modifier Grounding in Frozen MLLMs

arXiv:2610.12305v1 Announce Type: new Abstract: As Multimodal Large Language Models (MLLMs) can describe increasingly complex visual scenes, token-level grounding becomes crucial. Yet, when an MLLM ge

Ghost tasking for parametrized Gaussian Processes solving linear differential equations

arXiv:2610.12009v1 Announce Type: cross Abstract: Physics-informed machine learning has gained significant attention in recent years. In regimes of limited data, parametrized Gaussian processes have b

GPF-Net: Gated Progressive Fusion Learning for Polyp Re-Identification

arXiv:2512.21476v2 Announce Type: replace-cross Abstract: Colonoscopic Polyp Re-Identification (ReID) aims to match the same polyp across a large gallery of images captured from different viewpoints a

Graphons of Line Graphs

arXiv:2409.01656v4 Announce Type: replace-cross Abstract: We consider the problem of estimating graph limits, known as graphons, from observations of sequences of sparse finite graphs. In this paper w

Group Invariant Spectral Embedding

arXiv:2607.08987v2 Announce Type: replace Abstract: Spectral embedding methods are widely used for dimensionality reduction and clustering of high-dimensional datasets with intrinsic low-dimensional s

Harness Compilation: Which Decisions Should a Small Vision-Language Model Keep?

arXiv:2610.11231v1 Announce Type: new Abstract: Small vision-language models may be able to read external evidence yet struggle to obtain it. We introduce Harness Compilation (HC), an offline procedur

Healthy Counterfactual Generation via Diffusion Inpainting for Mammography Classification

arXiv:2610.12147v1 Announce Type: new Abstract: False negatives remain a critical limitation of computer-aided diagnosis (CAD) systems for breast cancer screening due to delayed detection and treatmen

How Is Automated Research Evaluated? A Survey of Benchmarks and Evaluation Practices

arXiv:2610.11877v1 Announce Type: new Abstract: Automated research systems support literature synthesis, ideation, experiments, writing, and peer review, but their evaluation is dispersed across tasks

How Many Directions Must a Truncated Diffusion Sampler Retain? Matching Bounds Under Power-Law Spectra

arXiv:2610.10640v1 Announce Type: cross Abstract: Diffusion samplers can reduce computation by generating selected spectral coordinates and filling the remaining directions with noise. How many direct