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Pulse · ppc.land

ppc.land · Nov 1

Bot tools caught 3.1% to 14.8% of disguised AI agents in Resemble AI test

RAD-S1 flagged at least 98.8% of the same agents, free for sites up to 1m sessions. No sample size or human error rate backs either figure. Who can check it?

Pulse · laorotava.es

laorotava.es · Nov 1

how to create a female ai model for adults 18+ with privacy controls

El Museo de Arte Sacro 'Tesoros de La Concepción' alberga una de las colecciones más valiosas de Canarias relacionadas con la liturgia y el culto. Sus diferentes estancias están llenas de piezas y contenidos muy diversos, como imágenes, pinturas, mobiliario ritual, andas procesionales, vasos sagrados, custodias, cantorales, instrumentos musicales y ornamentos. Una visita por sus instalaciones supone todo un recorrido por un legado patrimonial de cinco siglos de historia. (Virtual tour…

Pulse · note.com

note.com · Nov 1

October 2026: A Summary of Key News in the AI Industry — What Can We Entrust to the 'AI Agents' That Have Arrived in Japan?

Meta's 'Muse' on October 23, the Japanese version of Apple's 'Siri AI' on the 26th, and Amazon's 'Alexa+' on the 29th. In just one week, three AI services that act on behalf of humans—such as checking schedules and performing long-running tasks—were launched in Japan.

Pulse

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

Oct 28

Embodied at Sea: Imitation Learning for Multi-Task Underwater Manipulation

Behavior Cloning has demonstrated promising generalization capabilities for robotic manipulation in terrestrial environments. However, marine visual distortions and dynamic currents create a severe domain shift that remains a key bottleneck in adapting generalist architectures…

GitHub

Oct 28

rul-lang/rul

Rul (.rl) — Zero-Syntax 128-Bit Schedule- & Proof-Carrying Binary Programming Language & Execution Engine for Autonomous AI Agents (5.8ns/node O(N) Verifier, 6.4µs Zero-LLVM AVX2 ELF64 AOT Compiler)

Convex-SKM-Net: Low-Latency Block Halfspace Corrections for Neural Convex Constraint Satisfaction

Fast inference and strict constraint satisfaction are essential for learning-based decision-making. Existing methods often rely on constraint-specific projections or feasible-set parameterizations, while iterative repair can be costly. We introduce Convex-SKM-Net, a convex…

arXiv

Oct 28

GearUp: DAG-aware Acceleration for Serverless Inference Serving via Slack-guided Pre-loading

Serverless platforms increasingly host machine learning (ML) inference workflows, modeled as directed acyclic graphs (DAGs) of interdependent functions. Beyond container startup, each ML node must load libraries, model weights, and CUDA kernels before it can run. These costs…

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