Fury over Discord’s age checks explodes after shady Persona test in UK
Persona confirmed all age-check data from Discord's UK test was deleted.
FCC asks stations for "pro-America" programming, like daily Pledge of Allegiance
Brendan Carr wants "patriotic" shows for Trump's yearlong America 250 celebration.
India’s Sarvam launches Indus AI chat app as competition heats up
Sarvam's Indus chat app is currently available in beta.
The creator economy’s ad revenue problem and India’s AI ambitions
The creator economy is evolving fast, and ad revenue alone isn’t cutting it anymore. YouTubers are launching product lines, acquiring startups, and building actual business empires. In fact, MrBeast’s company bought fintech startup Step, and his chocolate business is outearning...
Why creators are ditching ad revenue for chocolate bars and fintech acquisitions
The creator economy is evolving fast, and ad revenue alone isn’t cutting it anymore. YouTubers are launching product lines, acquiring startups, and building actual business empires. In fact, MrBeast’s company bought fintech startup Step, and his chocolate business is out-earning...
Anthropic-funded group backs candidate attacked by rival AI super PAC
Dueling pro-AI PACs have centered around backing or targeting one New York congressional bid: Alex Bores, whose RAISE Act requires AI developers to disclose safety protocols and report serious system misuse.
InScope nabs $14.5M to solve the pain of financial reporting
The startup, founded by accountants who worked at Flexport, Miro, Hopin and Thrive Global, automates the difficulties of prepping financial statements.
Great news for xAI: Grok is now pretty good at answering questions about Baldur’s Gate
A new report from Business Insider reveals that high-level engineers at xAI were pulled off other projects to make sure Grok could answer detailed questions about the video game Baldur's Gate.
‘Toy Story 5’ takes aim at creepy AI toys: ‘I’m always listening’
Addictive, AI-enabled tablets are taking over, and also, Woody is balding in the new Toy Story movie, out June 19.
AI’s promise to indie filmmakers: Faster, cheaper, lonelier
AI expands access to filmmaking for resource-constrained creators. But as efficiency becomes the industry’s north star, creativity risks being overwhelmed by a deluge of low-effort, AI-generated content.
Peak XV raises $1.3B, doubles down on AI as global VC rivalry in India heats up
Peak XV says most of its new capital will target India as the firm prioritizes AI, fintech and cross-border bets while navigating recent partner departures.
UAE’s G42 teams up with Cerebras to deploy 8 exaflops of compute in India
Abu Dhabi-based tech company G42 has partnered with U.S.-based chipmaker Cerebras to deploy 8 exaflops of compute through a new system in India.
General Catalyst commits $5B to India over five years
The pledge marks a sharp jump from General Catalyst's earlier $500 million–$1 billion India earmark.
Reranker Optimization via Geodesic Distances on k-NN Manifolds
arXiv:2602.15860v1 Announce Type: new Abstract: Current neural reranking approaches for retrieval-augmented generation (RAG) rely on cross-encoders or large language models (LLMs), requiring substantial computational resources and exhibiting latencies of 3-5 seconds per query. We propose Maniscope, a geometric reranking method...
VDLM: Variable Diffusion LMs via Robust Latent-to-Text Rendering
arXiv:2602.15870v1 Announce Type: new Abstract: Autoregressive language models decode left-to-right with irreversible commitments, limiting revision during multi-step reasoning. We propose \textbf{VDLM}, a modular variable diffusion language model that separates semantic planning from text rendering. VDLM applies LLaDA-style masked diffusion over...
Every Little Helps: Building Knowledge Graph Foundation Model with Fine-grained Transferable Multi-modal Tokens
arXiv:2602.15896v1 Announce Type: new Abstract: Multi-modal knowledge graph reasoning (MMKGR) aims to predict the missing links by exploiting both graph structure information and multi-modal entity contents. Most existing works are designed for a transductive setting, which learns dataset-specific embeddings and...
Mitigating Gradient Inversion Risks in Language Models via Token Obfuscation
arXiv:2602.15897v1 Announce Type: new Abstract: Training and fine-tuning large-scale language models largely benefit from collaborative learning, but the approach has been proven vulnerable to gradient inversion attacks (GIAs), which allow adversaries to reconstruct private training data from shared gradients. Existing...
A Curious Class of Adpositional Multiword Expressions in Korean
arXiv:2602.16023v1 Announce Type: new Abstract: Multiword expressions (MWEs) have been widely studied in cross-lingual annotation frameworks such as PARSEME. However, Korean MWEs remain underrepresented in these efforts. In particular, Korean multiword adpositions lack systematic analysis, annotated resources, and integration into...
Missing-by-Design: Certifiable Modality Deletion for Revocable Multimodal Sentiment Analysis
arXiv:2602.16144v1 Announce Type: new Abstract: As multimodal systems increasingly process sensitive personal data, the ability to selectively revoke specific data modalities has become a critical requirement for privacy compliance and user autonomy. We present Missing-by-Design (MBD), a unified framework for...
Beyond Learning: A Training-Free Alternative to Model Adaptation
arXiv:2602.16189v1 Announce Type: new Abstract: Despite the continuous research and evolution of language models, they sometimes underperform previous versions. Existing approaches to overcome these challenges are resource-intensive, highlighting the need for alternatives that enable immediate action. We assume that each...
The Validity of Coreference-based Evaluations of Natural Language Understanding
arXiv:2602.16200v1 Announce Type: new Abstract: In this thesis, I refine our understanding as to what conclusions we can reach from coreference-based evaluations by expanding existing evaluation practices and considering the extent to which evaluation results are either converging or conflicting....
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...
A Koopman-Bayesian Framework for High-Fidelity, Perceptually Optimized Haptic Surgical Simulation
arXiv:2602.15834v1 Announce Type: new Abstract: We introduce a unified framework that combines nonlinear dynamics, perceptual psychophysics and high frequency haptic rendering to enhance realism in surgical simulation. The interaction of the surgical device with soft tissue is elevated to an...
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...
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...