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Pulse · finance.yahoo.com

finance.yahoo.com · Oct 31

Here's the Real Winner of the AI Build-Out

With Anthropic's initial public offering (IPO) quickly approaching, many investors are wondering if it could be the best AI stock to buy right now. While I'm not sure how its IPO

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…

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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

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…

arXiv

Sep 18

P$^3$-SAM: SAM with Perceptual Parallel Prompt for Few-Shot Strip Steel Surface Defect Segmentation

Few-shot semantic segmentation (FSS) of strip steel surface defects (S$^3$D) has posed significant challenges distinct from natural scenes. Unlike natural images, S$^3$D task exhibits unique characteristics including low local contrast, uneven illumination, and complex…

arXiv

Sep 18

DENSE: Distilling Agent Trajectories into Evidence-Grounded Shortcut Trees for Self-Refinement

Online agent deployments accumulate execution trajectories at massive scale and behavioral diversity, for which predefined annotation criteria hardly exist. Extracting useful evidence therefore demands costly manual annotation or verifier signals that fail to scale, leaving…

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