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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 18

Think Locally, Refine Globally for Memory-Efficient 3D Reconstruction

We propose LoG-VGGT, a memory-efficient framework for long-sequence 3D reconstruction that balances local temporal modeling with global camera consistency. Instead of relying on full global attention, our method introduces cross-window attention at a small subset of transformer…

arXiv

Sep 18

GVPO++: Group Variance Policy Optimization for LLM Post-Training and On-Policy Distillation

Post-training plays a pivotal role in enhancing the reasoning capabilities and task-specific expertise of large language models (LLMs). Despite recent advances in post-training methods, such as Group Relative Policy Optimization (GRPO), their practical deployment remains…

arXiv

Sep 18

Decision-Focused Learning for Mean-Variance Portfolio Optimization via KKT-Based Reformulation

Mean-variance portfolio optimization (MVO) is a central framework in data-driven asset management. A widely adopted approach is a two-stage framework that first predicts expected returns and then solves the optimization problem based on these predictions, with the predictive…

arXiv

Sep 18

Tracing the Evidence Behind Zero-Shot Time-Series Forecasting: A Source-First Taxonomy and Audit Framework

Zero-shot time-series forecasting (TSF) is often described as forecasting without target-specific parameter updates, but that training-status condition does not specify what evidence the system may use. A frozen language model prompted with serialized values, a time-series…

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