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Problem

Autonomous AI agents are showing promise in conducting machine learning research. However, these agents often lack “operational knowledge”—the practical, domain-specific know-how that bridges the gap between understanding a technique and applying it effectively. This knowledge currently resides in the vast landscape of code repositories and research papers, but it’s scattered, difficult to access, and too large to efficiently use during task execution.

Image: How to test agent experience changes without shipping them — Microsoft DevBlogs

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Overview

This week is shaping up to be a fascinating one at the intersection of AI, transportation, and enterprise workflows. The continued development and deployment of large language models (LLMs) are having a visible impact, particularly regarding enterprise adoption and the evolving landscape of startup funding. Tesla’s audacious Cybercab launch is generating buzz (and some skepticism), while Shopify’s “gisting” technique provides a practical glimpse into how companies are optimizing LLM inference costs. Alongside these forward-looking stories, we see a reminder of the ongoing need for robust compliance and security measures, highlighted by TikTok’s avoidance of congressional scrutiny.

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Problem

Training AI tutors that adapt to individual student needs is incredibly valuable, but gathering data on what tutoring methods work best for each student is currently difficult – both time-consuming and expensive. Existing solutions haven’t fully cracked the code: some student simulators accurately model behavior but fail at understanding explanations, while others excel at following guidance but aren’t very good at mimicking actual student competence.

Image: Planetary prediction engine: Automating global models via Earth AI — Google Research

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Overview

This week’s headlines reveal a complex landscape for data scientists and ML engineers. We’re seeing rapid advancements in model architectures (OpenAI’s Astra & Gemini 3.8 Flash), growing concern about safety (Astra’s reasoning technique), practical applications across various industries (Swiggy, REI, Amazon, healthcare organizations), and ongoing regulatory scrutiny (Google facing ad business adjustments). The race for AI dominance continues – Palo Alto Networks’ acquisition of Console highlights the demand for automation tools, while MapQuest’s unexpected resurgence showcases how even legacy tech can benefit from principled stances. Finally, ethical considerations around funding initiatives are also impacting sentiment towards companies in this space.

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Problem

Existing video generation models frequently treat audio and visuals as separate components, either omitting audio entirely or generating it in a later step. This disconnected approach misses the opportunity to model how visual elements and sounds naturally influence each other within a scene. DreamX-Creator aims to tackle this by enabling joint audio-video generation – creating both sight and sound simultaneously from a single input.

Image: A milestone in expanding access to AI — OpenAI Blog

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Overview

The tech landscape this week is a mixed bag, showcasing both continued investment in emerging technologies and some growing pains with established platforms. We’re seeing significant activity in India’s energy sector, as well as news around data security at Apple, pricing changes from Google, and new funding for AI startups – all pointing to rapid evolution across different domains. The ongoing maturation of cloud infrastructure is also evident through DoorDash’s Flux platform and GCP workload identity improvements, while the edge computing space continues its ascent with compelling browser-based AI demonstrations. Finally, OpenAI demonstrates continuing support for safety measures and expands access via ChatGPT Ads.

개요

paper-to-slides는 Cursor 에이전트 스킬입니다. 논문 PDF(또는 arXiv 링크)와 PPTX 템플릿을 넣으면, Cursor 채팅에서 발표용 슬라이드를 만들고 PowerPoint에서 바로 수정 가능한 deck-editable.pptx를 생성합니다.

template.pptx  +  paper.pdf  →  Cursor 채팅  →  deck-editable.pptx

• GitHub: dschloe/paper2ppt
• Cursor Marketplace: 제출 준비 완료 — 승인 전까지는 아래 로컬 설치로 바로 사용

한·영 논문 모두 지원합니다. Python 앱이 아니라, Cursor 에이전트가 논문을 읽고 deck.md를 작성한 뒤 템플릿 색·폰트에 맞춰 PPTX를 렌더링합니다.

설치

Marketplace 승인을 기다리지 않아도 됩니다. 아래 중 하나를 선택하세요.

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Problem

Large language models (LLMs) are increasingly being used in complex, multi-step tasks that require them to manage lots of information across many turns. The standard approach – storing all previous interactions as context – quickly becomes unsustainable because the context window grows and grows. Existing “proactive context management” techniques try to solve this by allowing LLMs to edit their own working memory (e.g., searching, deleting, or summarizing). However, these methods are limited; they offer a basic toolset, don’t support complex planning or long-term memory, and struggle with how to best learn which edits are most effective during training.

Image: Chess.com launched a poker site and is planning even more classic games — The Verge

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Overview

This week’s headlines showcase a complex interplay of technological advancements, regulatory pushback, and evolving business models within the data science and ML engineering landscape. We’re seeing continued fragmentation in global tech policy, particularly around robotics and AI surveillance. Meanwhile, companies are aggressively experimenting with new monetization strategies – from Grindr’s controversial subscription tier to Chess.com venturing into poker. The open-source ecosystem continues to flourish with resources for asynchronous coding agents and microcar innovation, while OpenAI’s continued expansion indicates a heightened emphasis on education and global adoption of AI tools. The ethical implications surrounding rapid technological deployment remain prominent as we witness increased scrutiny on practices like robotaxis and turbine usage.

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Problem

Current vision-language models excel at recognizing and describing physical events, but they struggle with the deeper task of reasoning about how those events unfold and how to predictably influence them. This boils down to a lack of explicit representation of the underlying physics – things like object states, how objects interact (dynamics), and measurable parameters governing their behavior. Essentially, these models understand what happens but not necessarily why.