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...
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...
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...
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.
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."
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.
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...
COMPOT: Calibration-Optimized Matrix Procrustes Orthogonalization for Transformers Compression
arXiv:2602.15200v1 Announce Type: new Abstract: Post-training compression of Transformer models commonly relies on truncated singular value decomposition (SVD). However, enforcing a single shared subspace can degrade accuracy even at moderate compression. Sparse dictionary learning provides a more flexible union-of-subspaces representation,...
MAVRL: Learning Reward Functions from Multiple Feedback Types with Amortized Variational Inference
arXiv:2602.15206v1 Announce Type: new Abstract: Reward learning typically relies on a single feedback type or combines multiple feedback types using manually weighted loss terms. Currently, it remains unclear how to jointly learn reward functions from heterogeneous feedback types such as...
BindCLIP: A Unified Contrastive-Generative Representation Learning Framework for Virtual Screening
arXiv:2602.15236v1 Announce Type: new Abstract: Virtual screening aims to efficiently identify active ligands from massive chemical libraries for a given target pocket. Recent CLIP-style models such as DrugCLIP enable scalable virtual screening by embedding pockets and ligands into a shared...
Fast and Effective On-policy Distillation from Reasoning Prefixes
arXiv:2602.15260v1 Announce Type: new Abstract: On-policy distillation (OPD), which samples trajectories from the student model and supervises them with a teacher at the token level, avoids relying solely on verifiable terminal rewards and can yield better generalization than off-policy distillation....
Hybrid Federated and Split Learning for Privacy Preserving Clinical Prediction and Treatment Optimization
arXiv:2602.15304v1 Announce Type: new Abstract: Collaborative clinical decision support is often constrained by governance and privacy rules that prevent pooling patient-level records across institutions. We present a hybrid privacy-preserving framework that combines Federated Learning (FL) and Split Learning (SL) to...
A Scalable Curiosity-Driven Game-Theoretic Framework for Long-Tail Multi-Label Learning in Data Mining
arXiv:2602.15330v1 Announce Type: new Abstract: The long-tail distribution, where a few head labels dominate while rare tail labels abound, poses a persistent challenge for large-scale Multi-Label Classification (MLC) in real-world data mining applications. Existing resampling and reweighting strategies often disrupt...
Directional Reasoning Trajectory Change (DRTC): Identifying Critical Trace Segments in Reasoning Models
arXiv:2602.15332v1 Announce Type: new Abstract: Understanding how language models carry out long-horizon reasoning remains an open challenge. Existing interpretability methods often highlight tokens or spans correlated with an answer, but they rarely reveal where the model makes consequential reasoning turns,...
Doubly Stochastic Mean-Shift Clustering
arXiv:2602.15393v1 Announce Type: new Abstract: Standard Mean-Shift algorithms are notoriously sensitive to the bandwidth hyperparameter, particularly in data-scarce regimes where fixed-scale density estimation leads to fragmentation and spurious modes. In this paper, we propose Doubly Stochastic Mean-Shift (DSMS), a novel...
Fairness over Equality: Correcting Social Incentives in Asymmetric Sequential Social Dilemmas
arXiv:2602.15407v1 Announce Type: new Abstract: Sequential Social Dilemmas (SSDs) provide a key framework for studying how cooperation emerges when individual incentives conflict with collective welfare. In Multi-Agent Reinforcement Learning, these problems are often addressed by incorporating intrinsic drives that encourage...
Logit Distance Bounds Representational Similarity
arXiv:2602.15438v1 Announce Type: new Abstract: For a broad family of discriminative models that includes autoregressive language models, identifiability results imply that if two models induce the same conditional distributions, then their internal representations agree up to an invertible linear transformation....