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Inverse Cross-spectral Neural Networks for Multivariate Time Series

arXiv:2610.06630v1 Announce Type: cross Abstract: CoVariance Neural Networks and their extensions have emerged as effective tools for processing multivariate data, deriving graph shift operators direc

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Inverse Mixed-Integer Programming: Learning Constraints then Objective Functions

DGX agent

arXiv:2510.04455v3 Announce Type: replace-cross Abstract: Data-driven inverse optimization for mixed-integer linear programs (MILPs), which seeks to learn an objective function and constraints consist

tutorialsarxiv-cs-lg

6 Oct 2026

Investigating Query-Insensitive Behavior in Spatio-Temporal Video Grounding

arXiv:2610.06018v1 Announce Type: new Abstract: Spatio-temporal video grounding (STVG) aims to localize objects or events described by natural language queries in both space and time. Existing STVG mo

Investigating Spatiotemporal Redundancy in Video Transformer for Collision Anticipation

arXiv:2610.04727v1 Announce Type: new Abstract: In worker-equipment proximity monitoring, video transformers are widely used for collision anticipation and have demonstrated strong performance. Howeve

InvestigationWorlds: An Agentic Environment for Legal Investigation

arXiv:2610.04129v1 Announce Type: cross Abstract: We introduce InvestigationWorlds, an agentic environment for legal investigation. We build on an underused artifact of U.S. civil litigation: the summ

agentsarxiv-cs-lg

Investors pattern matching on old movies is going to lead to a recession. There is no network effect here and no reason to think that large …

Investors pattern matching on old movies is going to lead to a recession. There is no network effect here and no reason to think that large language models will be anything other than a brutal commodi

safetygary-marcus--x

Local Ai

IRSTD-Agent: Agentic Infrared Small Target Detection via Zoom-Guided Interaction Learning

arXiv:2610.05342v1 Announce Type: new Abstract: Infrared small-target detection plays an important role in maritime monitoring and aerial surveillance. Although multimodal large language models (MLLMs

local-aiarxiv-cs-cv

Local Ai

Is Escalation Worth It? On the Depth of LLM Cascades

arXiv:2605.06350v2 Announce Type: replace Abstract: LLM cascades, in which a cheap model defers to an expensive one on low-confidence queries, are widely used to reduce inference cost. Given a pool of

local-aiarxiv-cs-lg

Isotropic Gaussian Processes Improve Vanilla Bayesian Optimization in High Dimensions

arXiv:2610.05780v1 Announce Type: cross Abstract: High-dimensional Bayesian optimization (BO) often fits Gaussian process (GP) surrogates from far fewer observations than input dimensions. Modern Vani

JASPER: Special Session on Joint Reliability And Security Assessment of SPlit Computing for Edge Robustness

arXiv:2610.04396v1 Announce Type: cross Abstract: Split Computing (SC) enables efficient deployment of Deep Neural Networks (DNNs) by partitioning inference between edge devices and cloud servers. How

JLD: Perceptual Distance Through A Jacobian Lens

arXiv:2610.05967v1 Announce Type: new Abstract: Image compression, restoration, and generation all require a way to measure how different two images look to a person. Pixel error ignores how people se

Joint Class-Time Learning for Video Classification with Multi-Instance Partial-Label Learning

arXiv:2610.06234v1 Announce Type: new Abstract: Multi-instance partial-label learning (MIPL) addresses inexact supervision in both the instance and label spaces, which can be applied to video classifi

Joint Estimation of Common-Slope Decay Rates and Spatial Amplitudes Using Parameterized Nonnegative Matrix Factorization

arXiv:2610.05549v1 Announce Type: new Abstract: We formulate joint estimation of common-slope decay rates and amplitudes from room impulse responses (RIRs) as parameterized nonnegative matrix factoriz

Joint Precision Neural Networks: Task-Aware Dependency and Predictive Learning

arXiv:2610.06023v1 Announce Type: new Abstract: Exploiting meaningful latent structures from data to solve downstream tasks is a fundamental challenge in signal processing and machine learning. While

KALEIDO: Input-Space Adaptation of a Vision Model for Time-Series Forecasting Through Gated Fold Geometries

arXiv:2610.04786v1 Announce Type: cross Abstract: Time-series foundation models buy zero-shot forecasting with large temporal corpora; a vision model needs none, since a natural image implicitly embed

Kandinsky 6.0 Video: Foundation Models for Synchronized Video and Audio Generation

arXiv:2610.05608v1 Announce Type: new Abstract: We present Kandinsky 6.0 Video, a family of foundation diffusion models for synchronized text-to-audio-video generation, comprising Kandinsky 6.0 Video

safetyarxiv-cs-cv

Keepsake: Selective Spatial Memory for Long-Horizon Video Generation

arXiv:2610.06588v1 Announce Type: new Abstract: Long-horizon camera-controlled video generation relies on persistent memory to maintain scene consistency. Existing systems follow two strategies to ach

Kepler4D: Controllable Future Video Generation via 4D Scene State Evolution

arXiv:2610.04152v1 Announce Type: new Abstract: Video world models aim to preserve scene structure and predict how dynamic objects evolve beyond visual observations. We present Kepler4D, a framework f

applicationsarxiv-cs-cv

KESurv: A Kernel Ensemble Method for Patient-Specific Survival Prediction

arXiv:2610.06434v1 Announce Type: cross Abstract: Predicting patient-specific survival functions is crucial for clinicians in making informed decisions about patient care and treatment strategies. Amo

Local Ai

Kinematics-Centric Continuous Sign Language Retrieval with Gloss-Guided Boundary-Aware Alignment

arXiv:2610.05306v1 Announce Type: new Abstract: Sign language-text alignment remains a fundamental challenge for text-driven sign language understanding. Existing methods predominantly rely on appeara

local-aiarxiv-cs-cv

KineWorld: Action-Induced Transport Fields for Embodied World Modeling

arXiv:2610.06349v1 Announce Type: new Abstract: Embodied world models predict the visual consequences of candidate actions before execution. However, existing action-conditioned world models often ado

KITINet: KInetic Theory Inspired Inter-Channel Information Exchange for Residual Networks

model-releasesarxiv-cs-lg

arXiv:2505.17919v2 Announce Type: replace Abstract: Residual connections are foundational to modern neural networks, yet the standard additive update provides no explicit mechanism for structured inte

Knossos and Ariadne: Benchmarking and Learning Complete Diagram Topology Extraction with Vision-Language Models

model-releasesarxiv-cs-cv

arXiv:2610.04721v1 Announce Type: new Abstract: Structural diagrams are widely used to represent complex systems and relational information across scientific, engineering, procedural, and spatial doma

Kolmogorov-Arnold Networks for Personal Context Recognition on ExtraSensory

arXiv:2610.05250v1 Announce Type: new Abstract: Kolmogorov--Arnold Networks (KANs) have attracted increasing attention in recent years, with applications across a wide range of AI tasks. In this paper

KVE-KD: Key Visual Evidence-Guided Knowledge Distillation for Vision-Language Models

arXiv:2610.03842v1 Announce Type: new Abstract: Knowledge distillation is crucial for deploying vision-language models on resource-constrained devices. However, existing methods typically impose unifo

LA Tech Week starts Monday, and our monthly LA Agentic AI Meetup is on day two: Tuesday, October 13, 5 to 7pm at Gulp in Playa Vista. Commun…

LA Tech Week starts Monday, and our monthly LA Agentic AI Meetup is on day two: Tuesday, October 13, 5 to 7pm at Gulp in Playa Vista. Community member PJ Kedilaya is demoing ModelVantage, which tracks

agentspinecone--x

Label Agreement Does Not Measure Authorization

arXiv:2610.04544v1 Announce Type: cross Abstract: Many groups now delegate label ontology and metadata harmonization to agentic LLM pipelines. We built one and audited it. Our aggregate scores looked

agentsarxiv-cs-lg

Label-Free Coreset Selection with Foundation Models for Efficient Annotation in Computational Pathology

arXiv:2610.05987v1 Announce Type: new Abstract: Computational pathology has the potential to improve clinical outcomes through a demonstrated increase in diagnostic and prognostic accuracy. However, t

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Langevin Flow Maps: Efficient Molecular Dynamics and Transition Path Sampling

arXiv:2610.05998v1 Announce Type: new Abstract: Molecular dynamics simulations proceed by integrating the Langevin equations over many small femtosecond timesteps. This poses a challenge for estimatin

tutorialsarxiv-cs-lg

Language Model Activations Inhabit Privileged Error-Correcting Basins

arXiv:2610.04183v1 Announce Type: cross Abstract: Language models exhibit remarkable robustness, continuing to produce coherent text even when their activations are perturbed by interventions like lin

Large Stepsizes Federated Learning on Logistic Regression with Linearly Separable Data: The Case of Heterogeneous Devices

arXiv:2610.05915v1 Announce Type: new Abstract: This paper revisits the distributed learning problem for training a multinomial logistic regression model with the Federated Averaging (exttt{FedAvg}) a

Largest Rashomon sets of decision trees for robust contextual optimization

arXiv:2610.04385v1 Announce Type: cross Abstract: Many decision trees fit the same data almost equally well, yet they can route a query point to different leaves and induce different local empirical d

Last-Iterate Convergence Rate of Normalized Gradient Descent under Holder Smoothness

arXiv:2610.06070v1 Announce Type: cross Abstract: Normalized gradient descent is a widely studied adaptive optimization method. Most existing analyses focus on the best iterate or a weighted average o

Latent Flow Matching for Molecular Graph Generation

arXiv:2610.06468v1 Announce Type: new Abstract: Modern graph generative models typically operate directly in the discrete graph space, explicitly generating node and edge variables, which can become c

Latent-Lagrangian Neural Networks for Reduced Order Modeling of Non-autonomous Nonlinear Dynamical Systems

arXiv:2610.04723v1 Announce Type: cross Abstract: This work proposes a latent Lagrangian-based framework for reduced-order modelling of forced nonlinear dynamical systems. In contrast with conventiona

agentsarxiv-cs-lg

Latent Safety Filters: When a Lossy Encoder Admits a Transferable Certificate

arXiv:2610.04297v1 Announce Type: cross Abstract: Latent safety filters certify safety on a learned low-dimensional representation of the state, enabling constraints that resist analytic description.

safetyarxiv-cs-lg

Latent Score-Based Bayesian Cramer-Rao Bound Estimation for High-Dimensional Imaging Systems

model-releasesarxiv-cs-lg

arXiv:2610.03956v1 Announce Type: cross Abstract: We propose a data-driven framework for estimating the Bayesian Cramer-Rao bound (CRB) in high-dimensional imaging systems with complex, analytically i

Latent Similarity Gaussian Processes: A Theory-Grounded Approach to Personalized Suicide-Risk Forecasting for Clinical Decision-Support

arXiv:2610.06355v1 Announce Type: cross Abstract: Forecasting suicide risk is difficult due to the high heterogeneity of patients and the low base rate of suicide-related events (SREs). We present Lat

LawVu launches Lens to provide legal teams visibility into contract law

model-releasessiliconangle

New Zealand-based LawVu Ltd., a legal artificial intelligence startup, today announced the launch of Lens, an AI contract analysis tool that gives in-house legal teams visibility into risks and obliga

LD-EnFF: Latent-Dynamics Ensemble Flow Filtering for Data Assimilation with Sparse Observations

arXiv:2610.04034v1 Announce Type: new Abstract: Data assimilation combines model forecasts with noisy, incomplete observations to estimate the evolving state of a dynamical system. Existing methods fa

Learning Conditional Source Distribution via Flow Reversal for Temporal Flow Matching

arXiv:2610.05349v1 Announce Type: new Abstract: We introduce CNP-Flow, a flow matching framework for temporal generation that learns conditional source distributions through flow reversal. Whereas sta

Learning Coordinated Visuomotor Box-Pushing from Solo Demonstrations

arXiv:2610.05677v1 Announce Type: new Abstract: Multi-robot imitation learning, particularly in settings where visuomotor policies are deployed in a communication-free, onboard decentralised style, re

safetyarxiv-cs-ro

Learning Discriminative Geometry for Drifting Models

arXiv:2610.04703v1 Announce Type: cross Abstract: Recently proposed Drifting Models shift iterative distribution refinement from inference to training, enabling effective one-step generation. However,

Learning Granger Causality under Latent Confounding via Intervention-Induced Heterogeneity

arXiv:2510.19138v2 Announce Type: replace Abstract: Granger causality characterizes directed predictive dependencies in multivariate time series, but recovering such dependencies becomes challenging i

Learning in Continuous Games from Pairwise Preference Feedback

model-releasesarxiv-cs-lg

arXiv:2610.05428v1 Announce Type: cross Abstract: We study learning in continuous games when players receive only pairwise preference feedback, revealing which of two actions is preferred but neither

Learning Interpretable Differentiable Logic Networks for Tabular Regression

arXiv:2505.23615v2 Announce Type: replace Abstract: Neural networks (NNs) achieve outstanding performance in many domains; however, their decision processes are often opaque and their inference can be

Learning Interpretable Differentiable Logic Networks for Time-Series Classification

arXiv:2508.17512v2 Announce Type: replace Abstract: Differentiable logic networks (DLNs) have shown promising results in tabular domains by combining accuracy, interpretability, and computational effi

Learning Latent Protein Languages for Autoregressive Generation

arXiv:2610.03978v1 Announce Type: new Abstract: Autoregressive transformers remain comparatively weak for protein sequence and structure generation. We study the role of target representation: amino a