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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…

Pulse

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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 22

Temporally Ordered Region-Token Mamba with Logit-Space Diffusion for Remote Sensing Change Detection

Remote sensing change detection requires both global reasoning across bitemporal images and precise localization of changed regions. However, dense attention is computationally expensive for high-resolution imagery, while conventional feature fusion and coarse decoding may…

arXiv

Sep 22

Learning Risk Scores Robust to Unobserved Confounders

We consider the problem of learning risk scores to prioritize individuals for scarce resources or interventions, from historical observational data affected by unobserved confounding. Decisions about who receives scarce resources are often guided by risk scores based on…

arXiv

Sep 22

A Systematic Evaluation of Infrastructure-Based Radar System for Highway Traffic Monitoring

Infrastructure-based radar systems offer robust and long-range solutions for traffic monitoring, yet their detection and tracking performance under real-world conditions remains insufficiently evaluated. This study introduces DRaT (Drone and Radar Trajectories), a dual-modality…

arXiv

Sep 22

MINER: Multi-crop INference-time Enhancement for Rare-Object Retrieval with Frozen Dual Encoders

Text-to-image retrieval with frozen dual encoders degrades when the query names a small, visually subordinate object in a cluttered scene: a single global image embedding underrepresents the localized visual evidence. We present MINER, a training-free inference framework that…

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