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Twist Flow for Inverse Problems

arXiv:2610.09281v1 Announce Type: new Abstract: In Bayesian inverse problems, posterior sampling requires generating samples that are consistent with given observations while capturing the range of pl

U-Space: Uncovering When and Why Uncertainty Arises in Language Models

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arXiv:2610.09087v1 Announce Type: new Abstract: Large language models are informing decisions with ever-higher stakes. As the consequences of their errors grow, a central question becomes harder to ig

researcharxiv-cs-cl

8 Oct 2026

Unbounded Characteristic and Universal Kernels

arXiv:2610.09731v1 Announce Type: cross Abstract: Kernel methods are among the most powerful tools in machine learning and statistics, with a large number of successful applications. Their immense suc

UniCSI Towards a Universal Wi-Fi CSI Encoder for Ubiquitous Human Sensing

arXiv:2610.09559v1 Announce Type: new Abstract: Wi-Fi sensing promises to turn the everyday wireless signals that already surround us into ubiquitous sensors for human sensing. However, a fundamental

Unified Multi-plane Autoregressive Diffusion for 3D Multi-contrast MRI Synthesis

arXiv:2610.09376v1 Announce Type: new Abstract: Acquiring a complete set of magnetic resonance imaging (MRI) contrasts is time-intensive and uncomfortable for patients, despite the diagnostic value of

VIS-Ground: Video Interactive Storytelling with Contextual Grounding

arXiv:2610.09326v1 Announce Type: new Abstract: Video interactive storytelling enables viewers to actively steer how a video unfolds. However, once we allow viewers to intervene during generation, a n

What a Reporting Convention Hides: A Matched-Budget Audit of Quantum Natural Gradient with an Exactly Computed Metric

arXiv:2610.09425v1 Announce Type: new Abstract: Several published comparisons of variational quantum optimizers time only runs that reach a target loss, or read the verdict at a single target. Either

What Can a Gaussian Process Design Test

arXiv:2610.10122v1 Announce Type: new Abstract: A Gaussian process (GP) model can agree with the data for two reasons: its assumptions are right, or the chosen inputs could never have shown that they

What Makes Synthetic Hard Negatives Work in Vision-Language Pretraining?

arXiv:2610.09700v1 Announce Type: new Abstract: Synthetic hard negatives generated in the representation space have proven effective for unimodal self-supervised learning, but transferring this idea t

When Rank Rises as LLMs Degrade

arXiv:2610.09647v1 Announce Type: cross Abstract: Post-training adapts language models in non-stationary environments. Practitioners monitor representation health with RankMe and related spectral stat

When Should an In-Context Learner Expand Its Hypothesis Space?

arXiv:2610.09471v1 Announce Type: new Abstract: Learning systems adapt quickly inside a familiar family of models. The harder step comes earlier: deciding, from observations that could be noise, an ex

Which Rollout Taught It That? BehaviorTrace and the Limits of Training-Data Attribution in Online RL

arXiv:2610.10422v1 Announce Type: cross Abstract: When reinforcement learning teaches a language model a new behavior, can we find the training rollouts that taught it? And when an attribution method

Why Forget-Only Unlearning Needs Memorization

arXiv:2610.10519v1 Announce Type: new Abstract: Machine unlearning asks for a deletion algorithm whose output is close to retraining from scratch without the selected forget examples. In this work, we

WxFM-XL: Adapting Univariate Foundation Models to Multi-Station Weather Forecasting

arXiv:2610.10057v1 Announce Type: new Abstract: With the rise of univariate time series foundation models (e.g., Sundial, Timer), initial efforts have been made to extend them to multivariate settings

Your Prompt Should Do More: Effects of Retrieval Instructions in Embedding Models

arXiv:2610.10508v1 Announce Type: new Abstract: Prompted embedding models have recently received increasing attention, particularly for retrieval, where detailed retrieval instructions are provided as

Zero-Shot Brain MRI Inpainting with 2.5D Unconditional Flow Priors

arXiv:2610.08983v1 Announce Type: new Abstract: Generative inpainting of brain MRI volumes is essential for synthesizing healthy tissue in pathological regions, improving the accuracy and reliability

2D Spatial Reasoning with Adaptive Neural Cellular Automata

arXiv:2610.08518v1 Announce Type: new Abstract: Many modern learning approaches are still struggling with spatial reasoning tasks, i.e. they lack the ability to utilize geometric information of percei

A Belief-State World Model for Catheter Navigation under Sparse Fluoroscopy: A Planar Proof of Concept

arXiv:2610.08469v1 Announce Type: new Abstract: Endovascular catheter navigation relies on continuous fluoroscopy, exposing patients and clinical staff to ionizing radiation throughout the procedure.

researcharxiv-cs-ro

A BEMD-Based Quaternion Filtering Approach Sharp-to-Soft Kernel CT Image Conversion

arXiv:2610.07071v1 Announce Type: new Abstract: The quality of computed tomography (CT) images is significantly affected by the selection of reconstruction kernels: sharp kernels improve spatial resol

A Broader Look at Model Merging: Rethinking Implicit Regularization Induced by Task Arithmetic

arXiv:2610.07990v1 Announce Type: cross Abstract: Model merging aims to build a multi-task model cheaply by combining the weights of individual task-specific models. To perform well across multiple ta

A Decision-Focused Neural Optimization Framework for Personalized Route Reproduction from Vehicle Trajectories

arXiv:2610.07857v1 Announce Type: new Abstract: This study formulates individual route reproduction as a shortest-path problem over learned driver-specific latent link costs. The central idea is that,

A doctrine-grounded visual question answering dataset for Tactical Combat Casualty Care

arXiv:2610.07339v1 Announce Type: cross Abstract: Tactical Combat Casualty Care (TC3) requires responders to connect visual observations of injuries and interventions with established clinical guidanc

A Neural JKO Scheme for Hellinger-Kantorovich Gradient Flows via Monge-Growth Pairs

arXiv:2610.07602v1 Announce Type: cross Abstract: We develop a mesh-free neural JKO scheme for advection-reaction-diffusion equations with a gradient-flow structure in the Hellinger-Kantorovich (HK) g

A Single-Loop, Constant-Batch First-Order Penalty Method for Stochastic Bilevel Optimization

arXiv:2610.07290v1 Announce Type: cross Abstract: Recent advances in penalty-based methods for stochastic bilevel optimization (SBO) have eliminated the need for second-order derivative oracles. Howev

A Systematic Study of Semantic ID Spaces for Generative Information Retrieval

arXiv:2610.08732v1 Announce Type: cross Abstract: Generative Information Retrieval (GIR) has emerged as a transformative paradigm, shifting document retrieval from a traditional 'retrieve-and-rank' wo

Accelerating the Development of PLGA In Situ Forming Depots Through AI-Driven Multi-Objective Optimization

arXiv:2610.08368v1 Announce Type: cross Abstract: Developing long-acting injectable formulations requires the simultaneous optimization of drug loading, release kinetics, viscosity, injectability, sta

Activation Denoising: A Robustness View on Parallel vs Sequential LLM Quantization

arXiv:2610.07522v1 Announce Type: new Abstract: Post-training quantization is a powerful tool for compressing large language models. The most scalable methods quantize every layer in parallel, but qua

AdaLoop: Adaptive-Depth Latent Reasoning for Audio Language Models

arXiv:2610.06949v1 Announce Type: cross Abstract: Large audio language models answer questions about speech, sound, and music, yet their accuracy drops sharply on tasks that need fine-grained acoustic

Adaptive Bidirectional Task Interaction for Joint Segmentation and Classification of Breast Ultrasound

arXiv:2603.01295v2 Announce Type: replace-cross Abstract: Joint lesion segmentation and tissue classification in breast ultrasound are usually trained with a shared encoder, so the two branches stop e

Adaptive Gait Biofeedback With Participant-Held-Out Modeling and Participant-Specific Updating in Chronic Ankle Instability

arXiv:2610.07428v1 Announce Type: new Abstract: Adaptive gait biofeedback may support repeated practice in chronic ankle instability, but its evaluation must address model performance and human respon

Adaptive Model Inversion Attacks Generalize a Privacy-Robustness Tradeoff

arXiv:2610.07677v1 Announce Type: new Abstract: In this paper, we show that standard evaluations of high-resolution Model Inversion Attacks (MIAs) significantly underestimate training-data privacy lea

Adaptive Power Sampling for LLM Reasoning

arXiv:2610.08563v1 Announce Type: new Abstract: Sequence-level power sampling has recently emerged as a training-free approach to reasoning by sampling from a sharpened output distribution of a base l

Adaptive Semantic Communication for Wireless Image Transmission Leveraging Mixture-of-Experts Mechanism

arXiv:2604.02691v2 Announce Type: replace Abstract: Deep learning based semantic communication has achieved significant progress in wireless image transmission, but most existing schemes rely on fixed

Advantage of Entangled Learning Rules in Quantum Measurement Class Learning

arXiv:2610.07328v1 Announce Type: cross Abstract: Learning with data in the form of quantum states is of current interest and has led to a variety of problems that boil down to interaction with the av

Adversarial Training for Deep Hedging in Nonstationary Markets

arXiv:2610.07162v1 Announce Type: new Abstract: Deep hedging learns trading policies from historical or simulated market trajectories, yet under nonstationarity these training paths may not represent

Adversarially Trained Linear Transformers Are Optimal Robust In-Context Learners for Gaussian Mixtures

arXiv:2610.07754v1 Announce Type: cross Abstract: Adversarial training is one of the most reliable defenses against adversarial attacks, but its high computational cost must generally be paid anew for

Agreement Is Not Validity: Cross-Model LLM Consensus in Diagnosing Student Failure Modes in K-12 Math Tutoring Dialogue

arXiv:2610.08703v1 Announce Type: new Abstract: In K-12 mathematics tutoring, student-tutor dialogue provides rich evidence of learners' problem-solving processes and sources of difficulty. Learning a

AIMS: Anchor-Integrated Multi-View Synthesis for Scalable Novel View Rendering

arXiv:2610.07566v1 Announce Type: new Abstract: Feed-forward novel view synthesis methods achieve strong generalization from posed multi-view inputs, but scaling them to large input view sets remains

Align, Then Correct: Training-Free Two-Stage Low-Rank Compensation for Extremely Quantized Large Language Models

arXiv:2610.08164v1 Announce Type: cross Abstract: Low-rank quantization error compensation (LQEC) recovers the accuracy lost under aggressive weight quantization by attaching a closed-form rank-r adap

An overview of machine learning-enhanced iterative methods for systems of linear and nonlinear equations

arXiv:2610.07211v1 Announce Type: cross Abstract: Systems of equations arise in a wide range of scientific and engineering applications. The present work focuses on solvers for general systems of equa

Anchor Divergence for Semantic Geometry in Contrastive Learning

arXiv:2610.06919v1 Announce Type: new Abstract: This paper concerns how semantic context determines geometry in learned vector representations. Similarity is typically measured using cosine similarity

AnyBottle: A Recipe to Only Keep the Concepts You Really Need

arXiv:2610.08552v1 Announce Type: new Abstract: Concept bottleneck models (CBMs) make predictions inspectable and intervenable by routing them through human-interpretable concepts, but originally requ

Anytime-valid simulation-based hypothesis testing

arXiv:2610.08210v1 Announce Type: cross Abstract: For a given data distribution (X_t)_{t in N} sim Q i.i.d., we investigate the hypothesis testing problem: H_0: Q = P_0 vs. H_1: Q = P_1, for two diffe

APEX: Speculate smarter, not deeper

arXiv:2610.07780v1 Announce Type: new Abstract: Speculative decoding reduces large language model inference latency by drafting multiple tokens before target-model verification, but its effectiveness

ApexQuant: Data-Free Elastic Quantization by Residual Re-Isotropization

arXiv:2610.07904v1 Announce Type: new Abstract: We introduce ApexQuant, a calibration-free quantization method that recursively re-quantizes the residual error, serving as a refinement layer on top of

Are Language Models Script-Aware?

arXiv:2610.08037v1 Announce Type: new Abstract: Language models frequently generate outputs in unintended languages or scripts, a phenomenon known as off-target generation. While existing research has

Ariadne's Thread of LipSync: Unraveling Forgeries via Inconsistency between Lip Motions and Head Poses

arXiv:2610.08417v1 Announce Type: new Abstract: Recent advances in LipSync generation technology have led to the creation of highly realistic videos, posing severe societal risks. However, existing de

Atom-JEPA: Joint-Embedding Predictive Architecture for 3D Atomistic Systems

arXiv:2610.08400v1 Announce Type: cross Abstract: Large-scale self-supervised pretraining has reshaped modern machine learning, substantially advancing the ability of language and vision models to gen