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FedSumUp: User-Auditable Privacy-Aware Representation Sharing for Resource-Constrained Federated Intelligence

arXiv:2610.21194v1 Announce Type: cross Abstract: Existing federated data condensation methods face privacy risks, as uploaded synthetic data may preserve sensitive information and remain vulnerable t

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FlowBudget: Training-Free Sampling-Budget Estimation for Flow Matching Models

DGX agent

arXiv:2610.20636v1 Announce Type: cross Abstract: Sampling from continuous flow-based generators such as flow matching models requires numerical ODE or SDE integration, making inference cost depend on

researcharxiv-cs-cv

16 Oct 2026

Forecast lead time and the timing of crew deployment for weather-driven power outages using AI weather models

arXiv:2610.20918v1 Announce Type: cross Abstract: Utilities request mutual aid crews to restore power after severe storms, and requesting early secures more crews but relies on less accurate forecasts

Fractional Rotation, Full Potential? Investigating Performance and Convergence of Partial RoPE

arXiv:2603.11611v2 Announce Type: replace-cross Abstract: Rotary Positional Embedding (RoPE) is widely used in transformers to encode relative positional information, yet the effect of applying RoPE t

From Observation to Intervention: Distilling Self-Generated In-Context Demonstrations via Executable Causal Intervention

arXiv:2610.21149v1 Announce Type: new Abstract: Self-generated In-Context Learning (ICL) enables LLMs to synthesize their own few-shot demonstrations, often outperforming manual prompts. However, exis

From Sketch to Scene: Relightable Garment Reconstruction from Marked Sketches

arXiv:2610.21865v1 Announce Type: cross Abstract: Sketching is one of the most natural ways for people to express visual ideas. Unlike the text prompts that most generative systems rely on today, a sk

FusionAudit: Pathway-Conditioned Robustness Auditing for Native Multimodal Models

arXiv:2610.21667v1 Announce Type: new Abstract: As vision-language models (VLMs) transition from frozen-encoder pipelines to native multimodal architectures, the visual channel increasingly serves as

Gen4P: Video Generators are 4D Perceivers

arXiv:2610.22371v1 Announce Type: new Abstract: We introduce Gen4P, a unified framework for recovering 4D geometry and motion from monocular video. We show that a pretrained video generator can be rep

Generalization Guarantees of Randomized Block Coordinate Descent for Nonconvex Learning

arXiv:2610.22213v1 Announce Type: new Abstract: Randomized block coordinate descent (RBCD) is a popular optimization algorithm for many large-scale machine learning problems. The existing theoretical

Global-in-time Spectral Duhamel-Picard Method for Post-Exposure Bake Simulation and Physics-Informed Neural Operators for EUV Lithography

arXiv:2610.20655v1 Announce Type: cross Abstract: We present a global-in-time spectral Duhamel-Picard method with polynomial expansions for post-exposure bake (PEB) simulation. The coupled acid-quench

GNN-Guided Local Refinement for Efficient Layout Decomposition with DSA-MP

arXiv:2610.22214v1 Announce Type: new Abstract: Multiple patterning lithography (MPL) has been widely used to improve lithography resolution in advanced nodes, but it faces cost limits as feature size

Graph Homomorphism Distortion: A structural (pseudo)-metric for attributed graphs

arXiv:2511.03068v5 Announce Type: replace Abstract: A large driver of the complexity of graph learning is the interplay between structure and features. When analyzing the expressivity of graph neural

GraphBEAT: Graph-based Bucketized Event Aggregation Over Time for Early Risk Stratification of Gastrointestinal Cancers

arXiv:2610.22014v1 Announce Type: cross Abstract: Longitudinal electronic health records (EHRs) contain complementary diagnostic and laboratory information about patient health. However, irregularity,

Halve-Then-Greedy: Adaptive Sampling for Posterior Success with Near-Optimal Instance Dependence

arXiv:2610.20997v1 Announce Type: new Abstract: We study fixed-budget adaptive experimentation aimed at maximizing the probability of obtaining sufficient posterior evidence that a recommended design'

Hard Way, The Only Way: Provable Merits of Training on Hardest Examples

arXiv:2610.21201v1 Announce Type: new Abstract: Despite a surge of empirical interest in the ``unreasonable effectiveness'' of drastic data pruning for AI systems, a unified theoretical understanding

Hybrid Generative-Tagging Decoding for Efficient Product Attribute-Value Extraction

arXiv:2610.21329v1 Announce Type: new Abstract: Attribute-value extraction is important for building high-quality product catalogs in e-commerce. Existing systems either rely on generative or tagging

Identifying Effective Strategies in Prompt Evolution for Stress and Emotion Prediction

arXiv:2610.21407v1 Announce Type: new Abstract: Early stress detection and risk prediction are crucial for timely mental health assessment and support. While large language models have recently shown

In-Context Learning Before Specialisation: Theory of Learning in a Solvable Topic Model

arXiv:2610.21901v1 Announce Type: cross Abstract: Sequence models can generalise in fundamentally distinct ways: extrapolating structures seen in training, or using their prompt to adapt to novel task

International Multi-Centre Validation of Unsupervised Domain Adaptation for Precise Discrimination between Normal and Abnormal Retinal Development

arXiv:2610.21719v1 Announce Type: new Abstract: Arrested retinal development can cause childhood visual impairment, but grading optical coherence tomography scans is difficult to standardise across de

Judging the Judges: Confidence and Disagreement in Rubrics as Rewards

arXiv:2610.21179v1 Announce Type: cross Abstract: Rubrics as Rewards (RaR) extends reinforcement learning to non-verifiable domains by using an LLM judge to score responses against a structured rubric

KNOU-Flood: Advancing global streamflow forecasting with lead-aware probabilistic machine learning

arXiv:2610.20640v1 Announce Type: new Abstract: Global streamflow forecasting supports flood preparedness, yet data-driven models trained on reanalysis lose skill when driven by operational forecasts,

Knowledge That Is Linear Before Fine-Tuning Generalizes After It

arXiv:2610.22300v1 Announce Type: cross Abstract: Not all facts a language model can recall support further learning equally well. We distinguish active facts, which support generalization beyond thei

LAMP: Layerwise Adaptive Mixing of Predictors for Training-Free Few-Shot Vision-Language Adaptation

arXiv:2610.22182v1 Announce Type: new Abstract: Few-shot adaptation often relies on an encoder's final global representation, leaving complementary information in intermediate layers underused. Combin

Language Models Share a Readout Bottleneck in Working Memory

arXiv:2610.21176v1 Announce Type: cross Abstract: Large language models (LLMs) reproduce a human working-memory signature on the N-back task: performance declines as memory load N increases, and error

LEAP: Gradient Flow Augmented Lagrangian Trajectory Optimization for Contact Constrained Robots

arXiv:2610.20936v1 Announce Type: new Abstract: Generating dynamically feasible motions for legged and manipulating robots requires solving a large, nonlinear, non-convex optimal control problem (OCP)

Learning Not To Interfere: Adapting Experts For Model Merging

arXiv:2603.13467v2 Announce Type: replace-cross Abstract: Model merging has shown that multitask models can be created by directly combining the parameters of different models that are each specialize

Learning to Search for Executable Patternmaking from Certified Programs

arXiv:2610.21579v1 Announce Type: cross Abstract: Recent models generate sewing patterns from images or text, and program-based representations make these patterns executable. These systems produce a

Let's Ask Gauss: Improved One-Run Privacy Auditing

arXiv:2606.12733v3 Announce Type: replace Abstract: Privacy auditing provides an important safeguard by estimating the actual information leaked by a model, thus ensuring that theoretical privacy guar

LEV: Thinking Fast and Deep with Looped Decision Models

arXiv:2610.20951v1 Announce Type: new Abstract: Decision models answer typed questions about an input with one probability per option, computed in a single forward pass without generating text. Like t

Lexical Reference Frames: Interpreting LM Residual Streams in Native Semantics

arXiv:2610.22169v1 Announce Type: new Abstract: The vocabulary of a language model is its native semantics: the embedding and the unembedding fix the meaning of a state at the two ends of the residual

Linear Independence of Polynomial Compositions with Bounded Input Degree

arXiv:2610.20825v1 Announce Type: cross Abstract: We prove the weak composition-independence conjectures of Kohn, Marchetti, Massarenti and Mella. The proof uses deformations of rational curves and a

LLM-Based Weak Annotation for Cybersecurity Tweet Classification: A Teacher-Student Framework

arXiv:2610.21254v1 Announce Type: new Abstract: Cyber threat intelligence (CTI) relies on the timely identification of emerging threats, enabling organizations and security analysts to support situati

LLMR: Knowledge Distillation with a Large Language Model-Induced Reward

arXiv:2409.12500v2 Announce Type: replace-cross Abstract: Large language models have become increasingly popular and demonstrated remarkable performance in various natural language processing (NLP) ta

Loud and Clear: Making Visual Evidence Count in Multimodal Document RAG

arXiv:2610.21318v1 Announce Type: cross Abstract: Multimodal document retrieval-augmented generation (RAG) has focused largely on retrieval, yet its answers depend on how well a vision-language model

Love for Believable AI: Artificial Partiality and Relationship Persistence as an Engineerable Stance

arXiv:2610.09895v1 Announce Type: cross Abstract: We study a behavioral mechanism for artificial partiality in conversational agents. The paper reports no human-subjects data and makes no claim about

Machine-learning-based neutrino-event generation constrained by experimental measurements

arXiv:2610.20870v1 Announce Type: cross Abstract: We present a generative surrogate for charged-current (nu_mu)-nucleus scattering that can be calibrated to measured differential cross sections withou

MAGMA: Multiplier-Augmented Geometric Motion Algorithm

arXiv:2610.21852v1 Announce Type: new Abstract: Solving optimal control problems for high-dimensional mechanical systems subject to constraints remains challenging due to nonlinear dynamics and non-co

MARS - A foundational map auto-regressor

arXiv:2610.20843v1 Announce Type: new Abstract: Map generation tasks feature extensive non-structural vectorized data (e.g., points, polylines, and polygons) and thus pose significant challenges to co

MetaExp: Learning to Self-Improve through Experience via Verifiable Cross-Task Environment Scaling

arXiv:2610.21492v1 Announce Type: new Abstract: Language-model agents can accumulate interaction feedback, but reliably turning that feedback into reusable experience remains challenging. Existing met

Mind the Delay: Latency-aware Query Routing for Heterogeneous LLM pools

arXiv:2607.18253v2 Announce Type: replace Abstract: Large language model (LLM) query routers typically assign each query to a suitable model that balances response accuracy and monetary cost. Subseque

Mind the Tail: Data Selection Through Sparse Concepts

arXiv:2610.21007v1 Announce Type: new Abstract: Data selection reallocates training support across visual concepts, including objects, attributes, color, texture, and backgrounds. Different allocation

MindEval2: Clinically Grounded Patients and Rubrics for Evaluating Language Models in Mental Health Support

arXiv:2610.22314v1 Announce Type: new Abstract: With people increasingly turning to AI chatbots for mental health support, the need to evaluate these tools is becoming more pressing. Recent benchmarks

Missed Detections and False Alarms in a CNN Technosignature Detector on Breakthrough Listen Data

arXiv:2610.22050v1 Announce Type: cross Abstract: Radio technosignature searches rely on machine learning detectors to shrink the candidate pool before a human looks at it. They are validated on held-

Mitigating Memorization Where It Happens

arXiv:2602.08901v2 Announce Type: replace Abstract: Large language models (LLMs) can memorize and reproduce training sequences verbatim, raising privacy and copyright concerns. Existing mitigation met

Mixture of Multi-Sequence Feature Difference for Contrast-Enhanced MRI Synthesis

arXiv:2610.21703v1 Announce Type: new Abstract: Contrast-enhanced magnetic resonance imaging (CE-MRI) is crucial for accurately diagnosing and segmenting brain tumors. However, the acquisition of CE-M

Modular Inverse Dynamics and Wrench Recovery for Closed-Chain Mechanisms Using Path Assembly

arXiv:2610.21400v1 Announce Type: new Abstract: Rigid closed chains can admit indeterminate internal reactions even when their motion is fully specified. This paper develops a modular inverse-dynamics

Navigation by Skylight Polarization via Rayleigh Model Optimization on a Road Vehicle

arXiv:2610.20797v1 Announce Type: new Abstract: Reliable absolute heading estimation is critical for road-vehicle navigation, particularly when magnetic, inertial, or GNSS-based references are degrade

No Single Tokenizer Feature Reliably Predicts Downstream Language Model Performance

arXiv:2610.21679v1 Announce Type: new Abstract: Tokenization is a critical component of the language modeling pipeline, but it is poorly understood. Tokenizers can impact downstream performance, but t