Search: “research”

CityToolVQA: Tool-Augmented Visual Question Answering for 3D Spatial Cognition in Urban Low-Altitude Environments

arXiv:2609.32427v1 Announce Type: new Abstract: CityToolVQA addresses the weak performance of Vision-Language Models (VLMs) on quantitative tasks in urban low-altitude visual question answering. We di

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Research

CLAD: Constrained Abstract Domain for Neural Network Verification

DGX agent

arXiv:2609.34628v1 Announce Type: cross Abstract: Neural network verification (NNV) formally verifies that a network satisfies a specified property for all inputs within a defined region. Modern NNV t

researcharxiv-cs-lg

29 Sept 2026

CLC-YOLO: A Compact Channel-Gated Prototype Network for Real-Time Leakage-Aware Breast Ultrasound Lesion Segmentation

arXiv:2609.31702v1 Announce Type: new Abstract: Reliable breast ultrasound lesion segmentation requires accurate boundaries and evaluation that prevents patients or duplicate images from crossing data

CLIMB-flow: Coupled Linear Inverse posterior sampling via Multiscale-Based flow

arXiv:2609.33834v1 Announce Type: new Abstract: Diffusion models are now widely used in Bayesian inverse problems in imaging as priors, where latent diffusion models are often used for larger scale pr

CoCurve: Cross-Module Co-Pruning Curvature for Structured LLM Pruning

arXiv:2607.17568v2 Announce Type: replace Abstract: Resource-constrained deployment requires sustaining large language model (LLM) capabilities as models scale under fixed memory and computation budge

CODEBLOCK: Learning to Supervise Code at the Right Granularity

arXiv:2606.18286v2 Announce Type: replace Abstract: Supervised fine-tuning of code LLMs typically applies uniform cross-entropy loss to all response tokens, implicitly assuming that every token provid

CoinFT: A Coin-Sized, Capacitive 6-Axis Force Torque Sensor for Robotic Applications

arXiv:2503.19225v4 Announce Type: replace Abstract: We introduce CoinFT, a capacitive 6-axis force/torque (F/T) sensor that is compact, light, low-cost, and robust with an average root-mean-squared er

researcharxiv-cs-ro

Collaborative Synthetic Data for Privacy-Preserving Financial Fraud Detection Across Organizational Silos

arXiv:2609.33754v1 Announce Type: new Abstract: Organizations seek analytical value from AI, yet relevant data are often fragmented across organizations and constrained by privacy. This is acute in fi

Compositional Objectives: Learning Structure in Structure

arXiv:2609.32566v1 Announce Type: new Abstract: Intelligence is defined in many ways. One of these definitions defines intelligence as the pursuit of learnable novelty. However, learnable novelty can

Compressing Value Predictions for Learning-Augmented Metrical Task Systems

arXiv:2609.33580v1 Announce Type: new Abstract: Learning-augmented algorithms for metrical task systems (MTS) can exploit predictions of canonical dual values, but existing formulations typically requ

Concepts Complement Dense Semantics: Learning Compact Sparse Spaces for Text-Image Retrieval

arXiv:2609.32671v1 Announce Type: cross Abstract: Cross-modal retrieval has been advanced by vision-language pre-trained models that encode images and texts into a shared dense embedding space. While

Conformal Prediction and Conditional Coverage for Tabular Foundation Models

arXiv:2609.34887v1 Announce Type: cross Abstract: Tabular foundation models (TFMs) provide predictive distributions for regression, but their prediction regions can exhibit undercoverage or overcovera

Conformal Robust Set Estimation

arXiv:2604.18441v2 Announce Type: replace-cross Abstract: We introduce a robust conformal prediction method based on the half-mass radius P(x):=inf{r>0:P(B(x,r))>1/2}. Its empirical version is exactly

Continuous Variational Synthesis

arXiv:2609.35083v1 Announce Type: cross Abstract: Biological machine learning was long bottlenecked by the ability to synthesize designed DNA. Variational synthesis models control chemical reactions t

Contrast Matters: Understanding Robustness of In-Context Fine-Tuning to Target-Context Relatedness

arXiv:2604.01601v2 Announce Type: replace Abstract: In-context fine-tuning (IC-Train), training an LLM with labeled examples in-context, is increasingly used in place of standard fine-tuning for domai

ControlGS: Conditioning Neural Gaussians for Downstream-Processing-Aware XR Rendering

arXiv:2609.32038v1 Announce Type: new Abstract: Extended Reality (XR) users do not directly perceive the output of a rendering engine. Instead, rendered images pass through a post-processing pipeline

Convergent Plug-and-Play Image Restoration with Annealed Noise Levels

arXiv:2609.32393v1 Announce Type: cross Abstract: Plug-and-Play (PnP) methods solve imaging inverse problems by incorporating deep denoisers into iterative optimization algorithms. Although practical

Correcting Spectra Outside the Backbone: A Model-Agnostic Rectifier for Hyperspectral Image Super-Resolution

arXiv:2601.21338v2 Announce Type: replace Abstract: Hyperspectral image super-resolution (HSI-SR) aims to recover spatial detail while preserving the spectral shape on which quantitative analysis reli

Cost-free Spectral Estimation for Adaptive Newton--Schulz in Matrix Optimizers

arXiv:2609.33047v1 Announce Type: new Abstract: Matrix optimizers such as Muon transform each momentum matrix through an approximate orthogonalization, typically implemented by a small number of Newto

Counterfactual Self-Evolving Agents for Evidence-Grounded Reasoning

arXiv:2609.32870v1 Announce Type: cross Abstract: Self-play proposer--solver methods improve reasoning by generating tasks and learning from verified solutions. However, for evidence-identifiable task

Counting on Thinking: Tracing Evidence Integration in Language Models

arXiv:2609.32932v1 Announce Type: new Abstract: Finite computational resources force a tradeoff between automatic System 1 processes and costly System 2 thinking. Large language models (LLMs) can spen

Cross-Dataset Transfer and Unknown-Class Detection in Imbalanced SAR Ship Classification

arXiv:2609.31658v1 Announce Type: new Abstract: Ship classification from Synthetic Aperture Radar (SAR) imagery is a critical computer vision task, yet the robustness of models under deployment shifts

Cross-Modal Knowledge Distillation for Acoustic Pedestrian Detection

arXiv:2609.31785v1 Announce Type: cross Abstract: Audio-only pedestrian detection is attractive for urban sensing but limited by weak acoustic cues. An appealing strategy is cross-modal knowledge dist

cSVR: Convolutional Slice-to-Volume Reconstruction

arXiv:2601.07519v2 Announce Type: replace-cross Abstract: Unpredictable fetal motion during MRI scans can result in oblique 2D slices that are difficult to interpret and often leads to prolonged scan

D-JEPA: Design-Recoverable JEPA Representation with Swappable Physics Decoders

arXiv:2609.33110v1 Announce Type: new Abstract: Joint-Embedding Predictive Architectures (JEPAs) provide a framework for learning compact representations without directly reconstructing high-dimension

DCEmbed: Scalable Optimization over Neural Surrogates

arXiv:2609.33879v1 Announce Type: cross Abstract: Neural surrogates can accelerate large-scale optimization by replacing expensive or intractable model components with efficient learned approximations

Decentralized Optimization with Cross-Coupled Mixed Affine Constraints

arXiv:2609.33021v1 Announce Type: cross Abstract: We study decentralized optimization with cross-coupled mixed affine constraints, where local and shared variables interact through two affine channels

DecFlowEdit: Self-Localized Flow-based Image Editing via Guidance Decoupling

arXiv:2609.34237v1 Announce Type: new Abstract: Flow-based image editing (FlowEdit) enables inversion-free semantic changes through the difference between source and target velocities. In this paper,

Decoupling Token Roles in Autoregressive Pretraining

arXiv:2609.33405v1 Announce Type: new Abstract: Autoregressive pretraining increasingly draws on heterogeneous data, making it important to understand how a model learns from an individual token. The

Deep Learning Techniques for Phoneme Recognition in Italian Children' s Speech

arXiv:2609.33060v1 Announce Type: new Abstract: Speech therapists often face difficulties diagnosing impairments due to the lack of efficient tools for transcribing speech into the International Phone

DegreeSpar: Structured Degree Sparsity for Efficient Secure Transformer Inference

arXiv:2609.32204v1 Announce Type: cross Abstract: Secure Transformer inference protects sensitive inputs but incurs substantial cryptographic overhead, with nonlinear operations such as Softmax and Ge

Demystifying MaskGIT Sampler and Beyond: Adaptive Order Selection in Masked Diffusion

arXiv:2510.04525v3 Announce Type: replace Abstract: Masked diffusion models have shown promising performance in generating high-quality samples in a wide range of domains, but accelerating their sampl

Denoising Multi-Robot Trajectories

arXiv:2609.35651v1 Announce Type: new Abstract: Multi-robot trajectory planning is a fundamental problem in multi-robot coordination but remains computationally challenging due to its nonconvex, multi

researcharxiv-cs-ro

Depth Laws for the Precision Floor of Trained Neural Networks: Amplification, Residual Scaling, and a Quantization-Aware Training Paradox

arXiv:2609.32060v1 Announce Type: new Abstract: How many bits does a network need before its accuracy collapses, and how does this grow with depth? We study the precision floor, the perturbation level

Derivative-Informed Training of Neural Operators On-the-Fly via Sketched Tangent Consistency

arXiv:2609.32797v1 Announce Type: new Abstract: Derivative-informed training improves neural operators by directly supervising their input-output sensitivities, which is crucial when neural operators

Design of Experiment for Discovering Directed Mixed Graph

arXiv:2509.01887v2 Announce Type: replace-cross Abstract: We study the design of interventions for causal discovery in simple structural causal models whose causal graphs are directed mixed graphs (DM

DiffMath: Symbol- and Graph-Aware Latent Diffusion Transformer for Handwritten Mathematical Expression Generation

arXiv:2606.19939v2 Announce Type: replace Abstract: Handwritten Mathematical Expression Generation (HMEG) is challenging due to the complex two-dimensional layouts and long-range structural dependenci

DiffPTS: Rethinking Diffusion ELBO for Probabilistic Time Series Forecasting

arXiv:2609.32363v1 Announce Type: new Abstract: Probabilistic time series forecasting requires modeling and predicting complex and time-varying distributions. Recently, Denoising Diffusion Probabilist

Diffusion-Based Rollouts as a Stabilization Mechanism for Long-Horizon Environmental Forecasting

arXiv:2609.33930v1 Announce Type: new Abstract: Extending forecast lead times while maintaining predictive skill remains a major challenge in environmental forecasting. We investigate diffusion-based

Diffusion-grounded VideoLLM for Entity-aware temoporal grounding

arXiv:2508.15641v2 Announce Type: replace Abstract: Precise temporal grounding requires distinguishing when a queried event occurs from when its participating entities are merely visible. We propose D

DimPO: Dimensionality Reduction for Attention using Preference Optimization

arXiv:2609.32579v1 Announce Type: new Abstract: A linear projection can reduce the dimension of query and key vectors without updating the pretrained model, but it remains unclear which training objec

DirectUV: Image-Conditioned UV Texture Generation with Surface-Aware Positional Encoding

arXiv:2609.34651v1 Announce Type: new Abstract: Generating high-quality UV textures for 3D meshes remains challenging. Multi-view projection pipelines suffer from occlusion and view inconsistency, and

Disciplined Biconvex Programming

arXiv:2511.01813v3 Announce Type: replace-cross Abstract: We introduce disciplined biconvex programming (DBCP), a modeling framework for specifying and solving biconvex optimization problems. Biconvex

Disentangle and Drop: Robust Universal Removal of Image Watermarks via Reconstructive Grayscale Residual Decomposition

arXiv:2609.31693v1 Announce Type: new Abstract: Invisible image watermarks are commonly evaluated against benign postprocessing operations such as compression, resizing, blur, and color changes. These

Disentangling Lung-Cancer CT/LDCT AI: A Systematic Evidence Map of Clinical Tasks, Evidence Chains, and Translational Gaps

arXiv:2609.35240v1 Announce Type: new Abstract: Artificial-intelligence studies using computed tomography (CT) for lung cancer are often broadly labelled 'prediction' despite addressing clinically dis

Distillation Defenses Easily Break After Reinforcement Learning

arXiv:2609.35699v1 Announce Type: new Abstract: Distillation attacks copy the reasoning capabilities of closed-source large language models, allowing bad actors to replicate state-of-the-art performan

Distributed Hydrological Modeling in the Feature Space

arXiv:2609.32971v1 Announce Type: new Abstract: Accurate forecasting of river discharge and floods is very challenging. River dynamics are affected by storage, meteorological forcing, and flow propaga

Diversified Multinomial Logit Contextual Bandits

arXiv:2607.11684v2 Announce Type: replace-cross Abstract: Existing contextual multinomial logit (MNL) bandits model relevance-driven choice but ignore the potential benefits of within-assortment diver