Search: “research”

AutoAdapt: Reliable Few-Shot Adaptation under Clinical Distribution Shifts

arXiv:2610.11162v1 Announce Type: new Abstract: Large pretrained clinical models provide a practical way to reuse learned prior knowledge across hospitals by adapting models to them. In practice, a ta

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Research

Autoregressive Retriever: Improving Query Understanding from Item Feedback for Universal Multimodal Retrieval

DGX agent

arXiv:2610.11666v1 Announce Type: cross Abstract: Universal multimodal retrieval typically encodes a query once and ranks independently indexed items by embedding similarity. This design supports effi

researcharxiv-cs-cv

9 Oct 2026

AyurParam: A State-of-the-Art Bilingual Language Model for Ayurveda

arXiv:2511.02374v2 Announce Type: replace-cross Abstract: Current large language models excel at broad, general-purpose tasks, but consistently underperform when exposed to highly specialized domains

Barron Optimal Transport I: Generative Modeling

arXiv:2610.10875v1 Announce Type: new Abstract: Motivated by recent applications in generative modeling and sampling, we introduce a framework for optimal measure transport where cost captures the not

Bayesian Optimisation under State-Preservation Constraints

arXiv:2610.12150v1 Announce Type: new Abstract: In many engineering design problems, the objective and constraints depend on the state: the solution of a PDE determined by the design parameters. We co

Bernoulli Flow Models: Self-Consistent Generative Modeling for Binary Data

arXiv:2610.11362v1 Announce Type: cross Abstract: Binary diffusion models typically require a large number of function evaluations (NFEs) to generate high-quality samples, making practical inference c

Beyond Action Entropy: Quotient-Space Exploration for Genome-Scale Metabolic Model Repair

arXiv:2610.11627v1 Announce Type: new Abstract: Repairing scientific models from functional observations differs fundamentally from supervised prediction: feedback may certify a solution without revea

Beyond Sequences: Distilling Structured Decision Memory for LLM Recommendation

arXiv:2610.11501v1 Announce Type: new Abstract: Despite the adoption of large language models (LLMs) in recommendation systems, prevailing approaches mostly model single-type behaviors (e.g., views or

Beyond Spatio-Temporal Priors: A Generalizable Approach for Dense Correspondence Matching

arXiv:2610.12421v1 Announce Type: new Abstract: Dense correspondence matching has historically been bounded by simplifying spatio-temporal priors, such as smooth motion and rigid geometry. While effec

Bi-FORK: Generative Modeling of High-Dimensional Bifurcating Systems

arXiv:2610.12449v1 Announce Type: cross Abstract: Bifurcations are ubiquitous in physical systems, from structural buckling to fluid and climate dynamics, yet they remain largely unexplored in deep le

Bi-temporal Image-driven Acute Stroke Evolution Analysis

arXiv:2602.07535v2 Announce Type: replace-cross Abstract: Acute ischemic stroke requires rapid treatment decisions that are strongly guided by emergency imaging. Admission computed tomography perfusio

BRACE: Differential Privacy for Dense Associative Memory with LSR Energy

arXiv:2610.11218v1 Announce Type: cross Abstract: Dense associative memory (DAM) provides an energy-based framework for memory retrieval with close connections to attention mechanisms in modern artifi

BudgetPix: Compute-Adaptive Tokenization for Pixel-Space Image Diffusion

arXiv:2610.12307v1 Announce Type: new Abstract: Most image generation models rely on uniform tokenization, allocating the exact same computational budget to equally-sized image patches. This static pa

C_4-Equivariant Flow Matching on Anisotropic Power-Diagram Graphs for Microstructure Generation

arXiv:2610.11549v1 Announce Type: new Abstract: Acquiring realistic microstructure data through Electron Backscatter Diffraction (EBSD) is costly and time consuming, often relying on specialised equip

CAFE: Causal Black-Box Testing of Machine Unlearning

arXiv:2509.16525v2 Announce Type: replace-cross Abstract: Machine learning models are increasingly deployed as software components that must evolve as requirements change. When specific training recor

Calibrating Ambiguity Set via Diagnostic Transport for Distributionally Robust Optimization

arXiv:2610.10793v1 Announce Type: cross Abstract: Distributionally robust optimization (DRO) protects decisions against distributional uncertainty by optimizing over an ambiguity set, but poorly align

Camera-Noise Residuals for Face-Swap Detection: Redundant, Not Complementary, and Why

arXiv:2610.11683v1 Announce Type: new Abstract: Fusing a learned camera-noise fingerprint with an RGB appearance backbone is an appealing route to generator-independent deepfake detection, because the

Can Large Language Models Reinvent Foundational Algorithms?

arXiv:2604.05716v2 Announce Type: replace Abstract: LLMs have shown strong potential to advance scientific discovery. Whether they possess the capacity for foundational innovation, however, remains an

CARE: Constrained Attention Refinement for Fine-Grained Visual Classification via Teacher-Student Distillation

arXiv:2610.11153v1 Announce Type: cross Abstract: Fine-grained visual classification requires models to recognize subtle local traits while exposing the visual evidence behind their predictions. Class

Caught in the Act: Probes Effectively Detect Sabotage and Catch Unverbalized Deception

arXiv:2610.12445v1 Announce Type: cross Abstract: Recent incidents have highlighted the challenge of monitoring LLM agents and the danger of models deceiving people. We show that white-box deception d

Characterizing Statistical Separability in TP-CRIV for Probabilistic AI Models

arXiv:2610.11163v1 Announce Type: cross Abstract: Third-party challenge-response identity verification (TP-CRIV) enables an independent verifier to assess whether a claimant possesses a model identica

Cognitive Thermometers: Machine Learning and Logical Complexity

arXiv:2610.10724v1 Announce Type: new Abstract: How does the human mind represent semantic categories? Why do natural languages favor certain meanings over others? Prior explanations have relied on lo

Collaboratively Guided Adversarial Robust Distillation with Teacher-Favorable Examples

arXiv:2610.11306v1 Announce Type: new Abstract: Adversarial distillation transfers robustness from high-capacity teachers to compact students. Existing adversarial distillation methods mainly use teac

Compile the Table: Query-Calibrated Operator Compression for Tabular In-Context Learning

arXiv:2610.11784v1 Announce Type: new Abstract: Tabular in-context learning (ICL) has emerged as a training-free and accurate paradigm for tabular prediction, but current approaches to compressing its

Complexity of Grounded Semantics and Preferred Semantics in Finitary Argumentation Frameworks

arXiv:2610.12008v1 Announce Type: new Abstract: Abstract argumentation frameworks (AFs) introduced by Dung provide a formal foundation for non-monotonic reasoning in artificial intelligence. While dec

Composite Online-to-Nonconvex Conversion with Optimal Oracle Complexity

arXiv:2610.12328v1 Announce Type: new Abstract: We consider stochastic nonsmooth nonconvex composite optimization, which includes several important problems such as constrained optimization and the re

Conditional Transfer from Controlled Pretraining Mixtures to Code

arXiv:2610.11548v1 Announce Type: new Abstract: Synthetic tasks are increasingly used both as probes of language-model capability and as pretraining data. Both uses are often justified by loss reducti

Conformal Prediction under Partial Verification

arXiv:2610.10829v1 Announce Type: cross Abstract: Conformal prediction provides prediction sets with finite-sample guarantees, but the label verification required for calibration can be expensive. We

Constrained Dynamic Gaussian Splatting

arXiv:2602.03538v2 Announce Type: replace Abstract: While Dynamic Gaussian Splatting enables high-fidelity 4D reconstruction, its deployment is severely hindered by a fundamental dilemma: unconstraine

Constructing Structured Decision Sources for Consensus-Based Pseudo-Label Learning

arXiv:2610.11621v1 Announce Type: new Abstract: Consensus can make pseudo-label learning more reliable, but only when its predictors contribute genuinely different evidence. Multiple models that repea

Cost-Aware Mixture-of-Experts Coordination for Model Markets

arXiv:2610.11908v1 Announce Type: cross Abstract: Existing model marketplaces typically trade and select individual models as indivisible units, limiting their ability to exploit complementarities amo

Cova-PINN: Cross-Domain Conservation Physics-Informed Neural Network for Fluid-Solid Conjugate Heat Transfer in Complex Geometries

arXiv:2610.11108v1 Announce Type: new Abstract: Multi-domain physics-informed neural networks (PINNs) flexibly model medium-specific representations to solve fluid--solid conjugate heat transfer (CHT)

Credal Machine Learning for Risk-Averse Decision Making

arXiv:2610.12115v1 Announce Type: new Abstract: In many machine learning applications, it is necessary to guard against worst-case scenarios and predictions that could result in substantial losses. In

CRISP: Fixing Flying Pixels in Latent LiDAR Generation via Diffusion Decoding

arXiv:2610.11376v1 Announce Type: cross Abstract: Latent LiDAR pipelines suffer from flying pixels: convolutional VAEs blur sharp radial depth discontinuities, yielding edge depths that back-project t

Cross-species representation learning aligns mouse and human neural dynamics and tracks clinical drug efficacy

arXiv:2610.11222v1 Announce Type: cross Abstract: Preclinical models poorly predict human drug efficacy, particularly in neurological disorders. Neural activity offers a uniquely rich source of transl

DADP: Dynamic Activity-Dependent Pruning, A Reverse Hebbian-Inspired Structural Pruning Method

arXiv:2610.11853v1 Announce Type: new Abstract: Modern neural networks are heavily over-parameterized. This redundancy incurs substantial compute and memory overhead during training and inference. Exi

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

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

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

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