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Beyond Verbalized Confidence: Calibrating Reasoners with Differentiable Readouts

arXiv:2609.34857v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) trains reasoning models to produce correct answers, but does not ensure that their stated confiden

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Beyond Volume Overlap: Surface Matching for Topology-Aware Coronary Artery Segmentation

DGX agent

arXiv:2609.31740v1 Announce Type: new Abstract: Accurate coronary artery segmentation on coronary computed tomography angiography (CCTA) is essential for diagnosing coronary artery disease. Deep netwo

researcharxiv-cs-cv

29 Sept 2026

Bidirectional Information Flow (BIF) - A Sample Efficient Hierarchical Gaussian Process for Bayesian Optimization

arXiv:2505.11294v3 Announce Type: replace Abstract: Hierarchical Gaussian Process (H-GP) models divide problems into different subtasks, allowing different components to address each part, making them

BiMoGen: Bidirectional Motion-Text Generation via Unified Masked Discrete Diffusion

arXiv:2609.35407v1 Announce Type: new Abstract: Text-to-motion generation and motion-to-text captioning are two fundamental tasks in human motion modeling, both grounded in the same underlying motion-

Bregman geometry-aware split Gibbs sampling for Bayesian Poisson inverse problems

arXiv:2511.12257v2 Announce Type: replace-cross Abstract: This paper proposes a novel Bayesian framework for solving Poisson inverse problems by devising a Monte Carlo sampling algorithm which account

Bridging Stochastic Flow Maps and Boltzmann Generators with Normalizing Flows

arXiv:2609.31978v1 Announce Type: cross Abstract: Generating independent, equilibrium samples of molecular systems at scale remains a central obstacle in computational statistical mechanics. Boltzmann

BudgetVerify: Budget-Tiered Verification for Financial QA

arXiv:2609.33052v1 Announce Type: cross Abstract: Financial question answering often requires precise numerical extraction, unit handling, and arithmetic over tables and text, but applying expensive v

Building Transformation Layers for Riemannian Neural Networks

arXiv:2609.35436v1 Announce Type: cross Abstract: Recently, deep neural networks on manifold-valued representations have garnered significant attention across various machine learning applications. On

Byzantine-Robust Federated RAG via Aligned Calibration and Fixed-Membership Conformal Prediction

arXiv:2609.33037v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) lets language models answer questions more accurately by consulting relevant documents. Many valuable collections

CacheRepair: Learning to Repair Cross-Chunk Context in RAG for KV Cache Fusion

arXiv:2609.35139v1 Announce Type: new Abstract: Multi-document retrieval-augmented generation (RAG) requires a language model to process multiple retrieved text chunks before answering a question. Pre

CalibHyper: Chance-Corrected Relational Hypergraphs for Few-Shot Molecular Property Prediction

arXiv:2609.33342v1 Announce Type: new Abstract: Molecular property prediction is central to drug development and materials discovery, but experiments are costly and labeled data are scarce. Context-aw

Calibrated Derivative-Process Sensitivity for Gaussian-Process Variable Selection

arXiv:2609.33549v1 Announce Type: cross Abstract: Automatic relevance determination (ARD), the default tool for variable selection in Gaussian-process (GP) regression, ranks inputs by inverse lengthsc

CAME: Company-Aware Evidence-Memory Experts for Interpretable Quarter-Ahead Revenue Forecasting

arXiv:2609.33143v1 Announce Type: cross Abstract: Quarter-ahead revenue forecasting requires company-scale numerical accuracy, strict temporal validity, and company-specific interpretation of narrativ

Can Attack Difficulty Be Characterized Before Optimization? A Study of Pre-optimization Difficulty in Person-Vanishing Attacks

arXiv:2609.34501v1 Announce Type: new Abstract: Adversarial attacks against object detectors are traditionally studied from an optimization perspective, where attack difficulty is regarded as an outco

Can Protein-Derived Knowledge Improve Pathology Foundation Models?

arXiv:2609.33178v1 Announce Type: new Abstract: Molecularly guided pathology foundation models (PFMs) exploit transcriptomic or proteomic information to enrich whole-slide image (WSI) representations,

Can't Find Waldo: Evaluating VLMs' Sensitivity to Image Resolution and Detail Level

arXiv:2609.31706v1 Announce Type: new Abstract: Visual Language Models (VLMs) have achieved remarkable success across diverse tasks, yet they struggle with high-resolution inputs where critical inform

Cardinality-Stratified Interaction Decomposition for Interpretable Pairwise and Higher-Order Structure in Transactional Basket Data

arXiv:2609.34191v1 Announce Type: new Abstract: Transactional basket data can reveal associations among items, but observed co-occurrence conflates item-specific relations with basket-size structure a

Cartridges++: KV Cache Compression without Off-Context Derailment

arXiv:2609.35621v1 Announce Type: new Abstract: Serving long documents to a Large Language Model (LLM) repeatedly is expensive: computations grow with context length, and the memory footprint of the k

CAST: Reconstruction-Coupled Acceleration of Interactive World Models

arXiv:2609.34144v1 Announce Type: new Abstract: Interactive world models must respond quickly to controls while preserving scene consistency. Existing acceleration methods can miss heterogeneous contr

CAT-Free: Multi-View Pedestrian Localization without Calibration, Annotations, or Target-Scene Training via Adaptive Geometric Filtering

arXiv:2609.34302v1 Announce Type: new Abstract: Multi-camera pedestrian localization is useful for wide-area monitoring in public and commercial spaces. However, deploying these systems often requires

CATSplat: Context-Aware Transformer with Spatial Guidance for Generalizable 3D Gaussian Splatting from A Single-View Image

arXiv:2412.12906v3 Announce Type: replace Abstract: Recently, generalizable feed-forward methods based on 3D Gaussian Splatting have gained significant attention for their potential to reconstruct 3D

Causally Debiased Latent Action Model for Embodied Action-Conditioned World Models

arXiv:2607.09185v2 Announce Type: replace Abstract: Action-conditioned world models (ACWMs) aim to simulate future observations conditioned on embodied actions. However, learning controllable ACWMs re

Certification Frontiers for Gaussian LoRA: Independent Priors, Posterior Risk, and Prediction-Preserving Balancing

arXiv:2609.32271v1 Announce Type: new Abstract: Post-hoc Bayesian fine-tuning places Gaussians around trained low-rank adapters, yet a calibrated posterior does not by itself yield a useful generaliza

Certified Interface Aliases: Exact Collisions in Vision-Language Preprocessing, and When They Exist

arXiv:2609.33003v1 Announce Type: new Abstract: Vision-language verifiers and routers must distinguish errors repairable by more reasoning from those caused by visual evidence never reaching the langu

Certifying Interventional Agreement Among Observationally Equivalent Causal Models

arXiv:2609.32247v1 Announce Type: cross Abstract: Observationally equivalent causal models can still disagree about what happens under intervention, because interventions create inputs that never occu

CFCH: Coarse-Fine Collaborative Hierarchical Learning for Anterior Segment Disease Analysis

arXiv:2609.32559v1 Announce Type: new Abstract: Accurate classification of anterior segment diseases is crucial for ophthalmic screening and diagnosis. However, slit-lamp image analysis remains challe

Chameleon: Dynamic Format Adapter for Efficient Diffusion

arXiv:2609.33496v1 Announce Type: cross Abstract: Post-training quantization (PTQ) is the standard way to run modern diffusion models on memory-constrained accelerators, yet every existing diffusion P

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

CLAD: Constrained Abstract Domain for Neural Network Verification

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

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

Congrats to @hardmaru @risi1979 @yujin_tang and Risto! Neuroevolution is where I got started in AI, and neuroevolution is still a playground…

Congrats to @hardmaru @risi1979 @yujin_tang and Risto! Neuroevolution is where I got started in AI, and neuroevolution is still a playground for original ideas with neural networks. This book is worth

researchdavid-ha--x

ConRAG: Lightweight inference of multi-hop relations

arXiv:2609.35193v1 Announce Type: new Abstract: Understanding how two entities are connected often requires tracing multi-hop relations across documents to identify intermediate entities and supportin

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