Geometry-Aware Uncertainty Quantification via Conformal Prediction on Manifolds
arXiv:2602.16015v1 Announce Type: new Abstract: Conformal prediction provides distribution-free coverage guaranties for regression; yet existing methods assume Euclidean output spaces and produce prediction regions that are poorly calibrated when responses lie on Riemannian manifolds. We propose \emph{adaptive geodesic conformal prediction},...
Extracting and Analyzing Rail Crossing Behavior Signatures from Videos using Tensor Methods
arXiv:2602.16057v1 Announce Type: new Abstract: Railway crossings present complex safety challenges where driver behavior varies by location, time, and conditions. Traditional approaches analyze crossings individually, limiting the ability to identify shared behavioral patterns across locations. We propose a multi-view tensor...
Feature-based morphological analysis of shape graph data
arXiv:2602.16120v1 Announce Type: new Abstract: This paper introduces and demonstrates a computational pipeline for the statistical analysis of shape graph datasets, namely geometric networks embedded in 2D or 3D spaces. Unlike traditional abstract graphs, our purpose is not only to...
Investigating GNN Convergence on Large Randomly Generated Graphs with Realistic Node Feature Correlations
arXiv:2602.16145v1 Announce Type: new Abstract: There are a number of existing studies analysing the convergence behaviour of graph neural networks on large random graphs. Unfortunately, the majority of these studies do not model correlations between node features, which would naturally...
ASPEN: Spectral-Temporal Fusion for Cross-Subject Brain Decoding
arXiv:2602.16147v1 Announce Type: new Abstract: Cross-subject generalization in EEG-based brain-computer interfaces (BCIs) remains challenging due to individual variability in neural signals. We investigate whether spectral representations offer more stable features for cross-subject transfer than temporal waveforms. Through correlation analyses across...
Differentially Private Non-convex Distributionally Robust Optimization
arXiv:2602.16155v1 Announce Type: new Abstract: Real-world deployments routinely face distribution shifts, group imbalances, and adversarial perturbations, under which the traditional Empirical Risk Minimization (ERM) framework can degrade severely. Distributionally Robust Optimization (DRO) addresses this issue by optimizing the worst-case expected...
Discrete Stochastic Localization for Non-autoregressive Generation
arXiv:2602.16169v1 Announce Type: new Abstract: Non-autoregressive (NAR) generation reduces decoding latency by predicting many tokens in parallel, but iterative refinement often suffers from error accumulation and distribution shift under self-generated drafts. Masked diffusion language models (MDLMs) and their remasking samplers...
Graphon Mean-Field Subsampling for Cooperative Heterogeneous Multi-Agent Reinforcement Learning
arXiv:2602.16196v1 Announce Type: new Abstract: Coordinating large populations of interacting agents is a central challenge in multi-agent reinforcement learning (MARL), where the size of the joint state-action space scales exponentially with the number of agents. Mean-field methods alleviate this burden...
ModalImmune: Immunity Driven Unlearning via Self Destructive Training
arXiv:2602.16197v1 Announce Type: new Abstract: Multimodal systems are vulnerable to partial or complete loss of input channels at deployment, which undermines reliability in real-world settings. This paper presents ModalImmune, a training framework that enforces modality immunity by intentionally and controllably...
Training-Free Adaptation of Diffusion Models via Doob's $h$-Transform
arXiv:2602.16198v1 Announce Type: new Abstract: Adaptation methods have been a workhorse for unlocking the transformative power of pre-trained diffusion models in diverse applications. Existing approaches often abstract adaptation objectives as a reward function and steer diffusion models to generate high-reward...
Bayesian Quadrature: Gaussian Processes for Integration
arXiv:2602.16218v1 Announce Type: new Abstract: Bayesian quadrature is a probabilistic, model-based approach to numerical integration, the estimation of intractable integrals, or expectations. Although Bayesian quadrature was popularised already in the 1980s, no systematic and comprehensive treatment has been published. The...
SEMixer: Semantics Enhanced MLP-Mixer for Multiscale Mixing and Long-term Time Series Forecasting
arXiv:2602.16220v1 Announce Type: new Abstract: Modeling multiscale patterns is crucial for long-term time series forecasting (TSF). However, redundancy and noise in time series, together with semantic gaps between non-adjacent scales, make the efficient alignment and integration of multi-scale temporal dependencies...
Fast KV Compaction via Attention Matching
arXiv:2602.16284v1 Announce Type: new Abstract: Scaling language models to long contexts is often bottlenecked by the size of the key-value (KV) cache. In deployed settings, long contexts are typically managed through compaction in token space via summarization. However, summarization can...
Can courts excuse late removals to federal court?
As many law students learn in their civil procedure course, when a plaintiff files suit in state court asserting a claim over which a federal district court would have jurisdiction, […]The postCan courts excuse late removals to federal court?appeared first...
Nvidia deepens early-stage push into India’s AI startup ecosystem
Nvidia is working with investors, nonprofits, and venture firms to build earlier ties with India's fast-growing AI founder ecosystem.
Why these startup CEOs don’t think AI will replace human roles
The CEOs of Read AI and Lucidya told TechCrunch at Web Summit Qatar that they see AI tools replacing tasks, rather than workers.
YouTube’s latest experiment brings its conversational AI tool to TVs
YouTube is testing conversational AI on smart TVs, allowing viewers to ask the assistant questions related to the video they're watching on the big screen.
Reddit is testing a new AI search feature for shopping
A small group of users in the U.S. will start to see search results that include interactive product carousels with pricing, images, and direct where-to-buy links.
Reload wants to give your AI agents a shared memory
Reload announces a $2.275 million raise in a round led by Anthemis and the launch of its first AI employee, Epic.
Co-founders behind Reface and Prisma join hands to improve on-device model inference with Mirai
Mirai raised a $10 million seed round to improve how AI models run on devices like smartphones and laptops.
For open source programs, AI coding tools are a mixed blessing
AI coding tools have enabled a flood of bad code that threatens to overwhelm many projects. Building new features is easier, but maintaining them is just as hard.
Altman and Amodei share a moment of awkwardness at India’s big AI summit
When Prime Minister Narendra Modi prompted speakers at the event to join hands and raise them in a show of unity, all executives onstage obliged, except OpenAI's Sam Altman and Anthropic's Dario Amodei, who held their hands conspicuously apart.
Freeform raises $67M Series B to scale up laser AI manufacturing
“I think we're the only quote-unquote manufacturing company out there that has H200 clusters in a data center on site."
Reliance unveils $110B AI investment plan as India ramps up tech ambitions
Reliance has begun building multi-gigawatt AI data centers in Jamnagar, with more than 120 MW of capacity expected to come online in 2026.
OpenAI taps Tata for 100MW AI data center capacity in India, eyes 1GW
OpenAI also plans to expand its presence in India with new offices in Mumbai and Bengaluru later this year.
Avey-B
arXiv:2602.15814v1 Announce Type: new Abstract: Compact pretrained bidirectional encoders remain the backbone of industrial NLP under tight compute and memory budgets. Their effectiveness stems from self-attention's ability to deliver high-quality bidirectional contextualization with sequence-level parallelism, as popularized by BERT-style architectures....
How to Train Your Long-Context Visual Document Model
arXiv:2602.15257v1 Announce Type: cross Abstract: We present the first comprehensive, large-scale study of training long-context vision language models up to 344K context, targeting long-document visual question answering with measured transfer to long-context text. While several such strong are open-weight, namely...
The Information Geometry of Softmax: Probing and Steering
arXiv:2602.15293v1 Announce Type: cross Abstract: This paper concerns the question of how AI systems encode semantic structure into the geometric structure of their representation spaces. The motivating observation of this paper is that the natural geometry of these representation spaces...
Proactive Conversational Assistant for a Procedural Manual Task based on Audio and IMU
arXiv:2602.15707v1 Announce Type: cross Abstract: Real-time conversational assistants for procedural tasks often depend on video input, which can be computationally expensive and compromise user privacy. For the first time, we propose a real-time conversational assistant that provides comprehensive guidance for...
Learning Representations from Incomplete EHR Data with Dual-Masked Autoencoding
arXiv:2602.15159v1 Announce Type: new Abstract: Learning from electronic health records (EHRs) time series is challenging due to irregular sam- pling, heterogeneous missingness, and the resulting sparsity of observations. Prior self-supervised meth- ods either impute before learning, represent missingness through a...