Long-Tail Knowledge in Large Language Models: Taxonomy, Mechanisms, Interventions and Implications
arXiv:2602.16201v1 Announce Type: new Abstract: Large language models (LLMs) are trained on web-scale corpora that exhibit steep power-law distributions, in which the distribution of knowledge is highly long-tailed, with most appearing infrequently. While scaling has improved average-case performance, persistent failures...
Aladdin-FTI @ AMIYA Three Wishes for Arabic NLP: Fidelity, Diglossia, and Multidialectal Generation
arXiv:2602.16290v1 Announce Type: new Abstract: Arabic dialects have long been under-represented in Natural Language Processing (NLP) research due to their non-standardization and high variability, which pose challenges for computational modeling. Recent advances in the field, such as Large Language Models...
MemoryArena: Benchmarking Agent Memory in Interdependent Multi-Session Agentic Tasks
arXiv:2602.16313v1 Announce Type: new Abstract: Existing evaluations of agents with memory typically assess memorization and action in isolation. One class of benchmarks evaluates memorization by testing recall of past conversations or text but fails to capture how memory is used...
Memes-as-Replies: Can Models Select Humorous Manga Panel Responses?
arXiv:2602.15842v1 Announce Type: new Abstract: Memes are a popular element of modern web communication, used not only as static artifacts but also as interactive replies within conversations. While computational research has focused on analyzing the intrinsic properties of memes, the...
BamaER: A Behavior-Aware Memory-Augmented Model for Exercise Recommendation
arXiv:2602.15879v1 Announce Type: new Abstract: Exercise recommendation focuses on personalized exercise selection conditioned on students' learning history, personal interests, and other individualized characteristics. Despite notable progress, most existing methods represent student learning solely as exercise sequences, overlooking rich behavioral interaction...
B-DENSE: Branching For Dense Ensemble Network Learning
arXiv:2602.15971v1 Announce Type: new Abstract: Inspired by non-equilibrium thermodynamics, diffusion models have achieved state-of-the-art performance in generative modeling. However, their iterative sampling nature results in high inference latency. While recent distillation techniques accelerate sampling, they discard intermediate trajectory steps. This...
Fast Online Learning with Gaussian Prior-Driven Hierarchical Unimodal Thompson Sampling
arXiv:2602.15972v1 Announce Type: new Abstract: We study a type of Multi-Armed Bandit (MAB) problems in which arms with a Gaussian reward feedback are clustered. Such an arm setting finds applications in many real-world problems, for example, mmWave communications and portfolio...
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...
Axle Sensor Fusion for Online Continual Wheel Fault Detection in Wayside Railway Monitoring
arXiv:2602.16101v1 Announce Type: new Abstract: Reliable and cost-effective maintenance is essential for railway safety, particularly at the wheel-rail interface, which is prone to wear and failure. Predictive maintenance frameworks increasingly leverage sensor-generated time-series data, yet traditional methods require manual feature...
Deep TPC: Temporal-Prior Conditioning for Time Series Forecasting
arXiv:2602.16188v1 Announce Type: new Abstract: LLM-for-time series (TS) methods typically treat time shallowly, injecting positional or prompt-based cues once at the input of a largely frozen decoder, which limits temporal reasoning as this information degrades through the layers. We introduce...
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...
UCTECG-Net: Uncertainty-aware Convolution Transformer ECG Network for Arrhythmia Detection
arXiv:2602.16216v1 Announce Type: new Abstract: Deep learning has improved automated electrocardiogram (ECG) classification, but limited insight into prediction reliability hinders its use in safety-critical settings. This paper proposes UCTECG-Net, an uncertainty-aware hybrid architecture that combines one-dimensional convolutions and Transformer encoders...
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...
Amortized Predictability-aware Training Framework for Time Series Forecasting and Classification
arXiv:2602.16224v1 Announce Type: new Abstract: Time series data are prone to noise in various domains, and training samples may contain low-predictability patterns that deviate from the normal data distribution, leading to training instability or convergence to poor local minima. Therefore,...
Factored Latent Action World Models
arXiv:2602.16229v1 Announce Type: new Abstract: Learning latent actions from action-free video has emerged as a powerful paradigm for scaling up controllable world model learning. Latent actions provide a natural interface for users to iteratively generate and manipulate videos. However, most...
Online Prediction of Stochastic Sequences with High Probability Regret Bounds
arXiv:2602.16236v1 Announce Type: new Abstract: We revisit the classical problem of universal prediction of stochastic sequences with a finite time horizon $T$ known to the learner. The question we investigate is whether it is possible to derive vanishing regret bounds...
Regret and Sample Complexity of Online Q-Learning via Concentration of Stochastic Approximation with Time-Inhomogeneous Markov Chains
arXiv:2602.16274v1 Announce Type: new Abstract: We present the first high-probability regret bound for classical online Q-learning in infinite-horizon discounted Markov decision processes, without relying on optimism or bonus terms. We first analyze Boltzmann Q-learning with decaying temperature and show that...
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.
OpenAI, Reliance partner to add AI search to JioHotstar
The rollout includes two-way integration that surfaces streaming links directly inside ChatGPT.
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.
Beyond Binary Classification: Detecting Fine-Grained Sexism in Social Media Videos
arXiv:2602.15757v1 Announce Type: new Abstract: Online sexism appears in various forms, which makes its detection challenging. Although automated tools can enhance the identification of sexist content, they are often restricted to binary classification. Consequently, more subtle manifestations of sexism may...
ViTaB-A: Evaluating Multimodal Large Language Models on Visual Table Attribution
arXiv:2602.15769v1 Announce Type: new Abstract: Multimodal Large Language Models (mLLMs) are often used to answer questions in structured data such as tables in Markdown, JSON, and images. While these models can often give correct answers, users also need to know...
Seeing to Generalize: How Visual Data Corrects Binding Shortcuts
arXiv:2602.15183v1 Announce Type: cross Abstract: Vision Language Models (VLMs) are designed to extend Large Language Models (LLMs) with visual capabilities, yet in this work we observe a surprising phenomenon: VLMs can outperform their underlying LLMs on purely text-only tasks, particularly...
FrameRef: A Framing Dataset and Simulation Testbed for Modeling Bounded Rational Information Health
arXiv:2602.15273v1 Announce Type: cross Abstract: Information ecosystems increasingly shape how people internalize exposure to adverse digital experiences, raising concerns about the long-term consequences for information health. In modern search and recommendation systems, ranking and personalization policies play a central role...
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...
Near-Optimal Sample Complexity for Online Constrained MDPs
arXiv:2602.15076v1 Announce Type: new Abstract: Safety is a fundamental challenge in reinforcement learning (RL), particularly in real-world applications such as autonomous driving, robotics, and healthcare. To address this, Constrained Markov Decision Processes (CMDPs) are commonly used to enforce safety constraints...
Hybrid Feature Learning with Time Series Embeddings for Equipment Anomaly Prediction
arXiv:2602.15089v1 Announce Type: new Abstract: In predictive maintenance of equipment, deep learning-based time series anomaly detection has garnered significant attention; however, pure deep learning approaches often fail to achieve sufficient accuracy on real-world data. This study proposes a hybrid approach...
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,...