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Pulse · msn.com

msn.com · Oct 31

Will AI provide a boost for XRP? The XRP blockchain ledger just hit 14 million in AI agent transactions.

The economic impact is small so far, but it could grow.

Pulse · timesofindia.indiatimes.com

timesofindia.indiatimes.com · Oct 31

Google is changing the way developers optimise apps for foldables: What it means for users

Google announced at the Android Dev Summit that starting next year, the Google Play Store will actively evaluate how well apps and games perform on foldable screens. While the detailed criteria for the evaluation process will arrive later this year, the company has said that apps which adapt cleanly to these flexible displays will earn prime promotional placement and enhanced visibility across the store.

Pulse · pluang.com

pluang.com · Oct 31

Pi Network updates app tools, token tracking, A...

Pi Network has launched four key updates ahead of its November 17-19 Ecosystem Summit, enhancing developer tools, token price tracking, AI-assisted app packaging, and identity verification services. Developers now have a redesigned portal for app settings, and the Pi BlockExplorer shows historical prices for testnet tokens. The SoloHost AI feature aids in preparing apps for Pi Desktop, while PiVerify adds advanced face and liveness checks for businesses. These improvements aim to support the…

Pulse · techbullion.com

techbullion.com · Oct 31

What Is an AI Gateway and Why Does It Matter for Enterprise Model Deployment?

How many AI models is your company using right now? Take a guess. Chances are the real number is higher than you assumed, and that’s not on you. Models sneak in sideways. A plugin someone installed, a team trial that never got switched off, a vendor who turned on an AI feature without much fanfare. […]

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Top picks this weekarXiv

arXiv

Oct 29

Elucidating the Design Space of LM Data Attribution

Data attribution methods for Language Models (LMs) are typically evaluated using downstream tasks like fact retrieval. However, existing evaluations vary multiple dataset properties simultaneously, making it impossible to isolate exactly why an attribution algorithm fails. To diagnose these failures, we explore the design space of LLM data attribution, with a primary focus on evaluation. We introduce the Controlled Attribution Benchmark (CATT) for fact tracing, in which we fine-tune LLMs on entirely fictitious knowledge, providing unambiguous ground truth for attribution. This synthetic setup allows us to systematically alter one dataset configuration at a time, such as document redundancy and lexical distractors. With this configurable evaluation, we demonstrate that redundancy and distractors consistently degrade attribution performance. Rather than proposing a new estimator, we examine two design choices that are underexplored: the loss used to compute gradients and how per-token attribution scores are aggregated into a document score. We find that the true causal evidence within a training document is highly localized, but standard methods dilute this evidence into noise by…

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arXiv

Sep 19

Whitening Inverts the Hierarchy: What the Norm of a Whitened Embedding Measures

Whitening a foundation-model embedding and using its squared norm as a training-free likelihood surrogate is motivated by the observation that whitened coordinates often appear approximately standard normal. We show that this observation follows from the projection central…

arXiv

Sep 19

DiagGen: Agentic Generation of Deformable Assets with Sim-based Diagnostics for Robotic Simulation

While simulation-ready deformable assets are essential for in-silico robotic manipulation tasks, existing generation frameworks typically assess physical plausibility after generation, leaving an object's simulated response unused as feedback for repairing upstream errors. We…

arXiv

Sep 19

When Does Adversarial Refinement Help? A Negative Result and Open Problem in Adapting R3GAN to Time Series Imputation

Diffusion models and transformers have supplanted GANs for multivariate time series imputation, largely on grounds of GAN training instability. R3GAN (NeurIPS 2024) removes that instability via regularized relativistic losses with provable convergence, raising a natural…

arXiv

Sep 19

An LLM-Assisted AutoML Framework for Intrusion Detection in IoT Networks

Internet of Things (IoT) systems are increasingly deployed in smart homes, transportation, energy systems, and critical infrastructure. This broad connectivity improves service intelligence, but also enlarges the attack surface of IoT networks. Machine Learning (ML)-based…

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