오늘의 AI 소식

원제 — OpenAI fires three safety researchers for "mishandling research information"

“Later: <a href="https:&#x2F;&#x2F;www.cnbc.com&#x2F;2026&#x2F;10&#x2F;09&#x2F;openai-fired-researchers-ai-concerns.html" rel="n…”

나온 때 2026-10-09

가져온 때 2026-10-09 23:01 UTC · 53aea05dc6d18ffd

뜯은 자리 — 목록뿐 (원문을 못 읽었다)

원제 — Asana cuts model costs 76x in browser tests with GPT-6.1 Sol

“Using GPT-6 Astra in Codex, Asana made its browser agent 76x cheaper and 5x faster in tests to offer customers more capable mod…”

대상 주장 — Using GPT-6 Astra in Codex, Asana made its browser agent 76x cheaper and 5x faster in tests to offer customers more capable models.

나온 때 2026-10-09

가져온 때 2026-10-09 23:01 UTC · 0f1eab57236c05e2

뜯은 자리 — 목록뿐 (원문을 못 읽었다)

원제 — Sophos cuts threat investigation time by 96% with OpenAI Daybreak

“Discover how Sophos uses OpenAI’s Daybreak to cut cyber-threat investigation time by 96% and automate 52% of MDR cases while …”

대상 주장 — Discover how Sophos uses OpenAI’s Daybreak to cut cyber-threat investigation time by 96% and automate 52% of MDR cases while preserving human oversight.

나온 때 2026-10-09

가져온 때 2026-10-09 23:01 UTC · c2bd9140cf451f0f

뜯은 자리 — 목록뿐 (원문을 못 읽었다)

원제 — Qwen/Qwen-Image-2.1-Turbo

제목과 링크만 주는 소스라 인용할 것이 없다.

대상 주장 — The text-to-image example uses 1680 × 2512 portrait resolution; the editing example uses 2048 × 2048.

나온 때 2026-10-09

가져온 때 2026-10-09 23:01 UTC · 7cad2807a6dba4a4

뜯은 자리 — 모델 카드

원제 — LegalOn halves Codex costs while maintaining development speed

“LegalOn cut estimated daily Codex costs by 65% while maintaining development speed. It matched Astra, Sol, and Luna to tasks an…”

대상 주장 — LegalOn cut estimated daily Codex costs by 65% while maintaining development speed.

나온 때 2026-10-08

가져온 때 2026-10-09 23:01 UTC · a13e16f89e5be705

뜯은 자리 — 목록뿐 (원문을 못 읽었다)

원제 — wheresryan22/anatomy

“A Claude Code skill that turns a technical idea into a detailed, interactive isometric figure of a real machine. It makes SVG w…”

나온 때 2026-10-08

가져온 때 2026-10-11 23:01 UTC · 30a09c855d09c4c2

계정이 적어 둔 문자열 — 계정이 비워 뒀다

뜯은 자리 — README

원제 — mhtsec/ARTEX

“AI 自主渗透测试系统 | 百度“agent+”攻防挑战赛冠军项目”

대상 주장 — 点「更新」后：下载当前平台的发布包 → 比对 Release 的 SHA256SUMS → 用 -h 冒烟测试新二进制 → 暂存为 artex.

나온 때 2026-10-08

가져온 때 2026-10-09 23:01 UTC · 0cc27db3d1a52caa

계정이 적어 둔 문자열 — 계정이 비워 뒀다

뜯은 자리 — README

원제 — ConwayResearch/Underdog-Saluki-27B-1.0

제목과 링크만 주는 소스라 인용할 것이 없다.

대상 주장 — Qwen3.8-27B in under 8 GB, tuned to keep tool calling intact.

나온 때 2026-10-08

가져온 때 2026-10-09 23:01 UTC · fd535ff0fdcb55b5

뜯은 자리 — 모델 카드

원제 — Phocinae/Phocinae-Largha-150M-v1

제목과 링크만 주는 소스라 인용할 것이 없다.

대상 주장 — 55.0% fewer LLM calls, 21.0 ms per decision on an RTX 5090.

나온 때 2026-10-08

가져온 때 2026-10-09 23:01 UTC · 53fb1f4f0b1a1150

뜯은 자리 — 모델 카드

원제 — DreamTrue: Action-Faithful Robot World Model with Counterfactual Post-Training

“We present DreamTrue, a multi-view, cross-embodiment robot world model for action-faithful and physically plausible video predi…”

대상 주장 — On AgiBot, DreamTrue attains state-of-the-art action following, while reducing the human-assessed interaction defect rate from from 48.12% to 6.25%.

나온 때 2026-10-07

가져온 때 2026-10-09 23:01 UTC · 074b3efb8c207b56

뜯은 자리 — 초록

원제 — Multi-Agent Egocentric World Model with Fine-Grained Embodied Interaction

“Egocentric world models predict first-person observations conditioned on an agent's actions, but most focus on a single agent. …”

나온 때 2026-10-07

가져온 때 2026-10-09 23:01 UTC · 984783c99f6c9c63

뜯은 자리 — 초록

원제 — OuroWorld: Bringing Any 3D World Alive as Diverse, Endlessly Looping 3D Cinemagraphs

“Recent 3D world models generate photorealistic, explorable scenes that remain frozen in time. OuroWorld is a mask-free framewor…”

대상 주장 — On 39 reconstructed and generated scenes, OuroWorld outperforms all baselines and wins 70.8%-99.0% of user-study comparisons.

나온 때 2026-10-07

가져온 때 2026-10-09 23:01 UTC · 2df83b14d4e56c3d

뜯은 자리 — 초록

원제 — Foundations of Large Language Models

“This is a book about large language models. As indicated by the title, it primarily focuses on foundational concepts rather tha…”

나온 때 2026-10-07

가져온 때 2026-10-09 23:01 UTC · 5a28cbe0f0dba767

뜯은 자리 — 초록

원제 — Memento 3: Model-Based Recursive Self-Improvement through Reflective Rulebooks

“Learning to act in unfamiliar environments requires agents to infer how the world works and revise that understanding as new ev…”

대상 주장 — On ARC-AGI-3, the single-model agent clears every level of all 25 public games, achieves a mean Relative Human Action Efficiency (RHAE) of 100.0, and uses 44% of the human action count.

나온 때 2026-10-07

가져온 때 2026-10-09 23:01 UTC · 2ec664cc3165cff7

뜯은 자리 — 초록

원제 — Beyond Spatio-Temporal Priors: A Generalizable Approach for Dense Correspondence Matching

“Dense correspondence matching has historically been bounded by simplifying spatio-temporal priors, such as smooth motion and ri…”

나온 때 2026-10-07

가져온 때 2026-10-09 23:01 UTC · 928c5916c8745d83

뜯은 자리 — 초록

원제 — MiMo-V2.6: Scaling Reinforcement Learning Towards Self-Improvement

“Reinforcement learning (RL) is the central training paradigm for advancing large foundation models towards self-improvement. Th…”

대상 주장 — We scale RL compute along three dimensions: (1) larger batches and higher throughput, with an asynchronous training that consumes 1,568 samples and 2.7-3.7B tokens per step at context len…

나온 때 2026-10-07

가져온 때 2026-10-09 23:01 UTC · 1b5db0f1008c3cfe

뜯은 자리 — 초록

원제 — TokenRouter: Efficient Serving System for Token-Level LLM Routing

“Large language model (LLM) routing distributes inference work across different models, advancing the cost-quality Pareto fronti…”

대상 주장 — Across diverse routing algorithms, workloads, and model pairs, TokenRouter achieves 2.01-64.15x higher decoding throughput than existing systems, substantially advancing the serving effic…

나온 때 2026-10-07

가져온 때 2026-10-09 23:01 UTC · e1c4e3f59be4b797

뜯은 자리 — 초록

원제 — REMORY: Learning Residual Memory for Context Compaction

“Long-horizon agents compact their history to continue within a finite context window, but a textual summary alone may not suppo…”

대상 주장 — On SummHay, REMORY improves source attribution at nearly unchanged insight coverage and approaches the full-context joint score using only 5.2% of the input positions.

나온 때 2026-10-07

가져온 때 2026-10-09 23:01 UTC · d6956d1b7ab9450f

뜯은 자리 — 초록

원제 — Learn2Play Bench: How Well Do LLM Agents Learn from Experience in Unfamiliar Environments?

“Learning from experience is essential for LLM agents to adapt to unfamiliar and dynmaic environments. Evaluating this ability i…”

대상 주장 — (2) Human agent gap: Top-performing human players achieve higher peak scores than the evaluated agents.

나온 때 2026-10-07

가져온 때 2026-10-09 23:01 UTC · 2a9358bd3d19b21d

뜯은 자리 — 초록

원제 — Introducing Playground: Create and play custom games

“Playground is a new experimental gaming platform that lets you create, play, and share custom games.”

대상 주장 — @keyframes gemini-draw-border-timeline{0%{--border-draw-percent:0%;--border-mask-color:transparent;--btn-bg-color:var(--btn-bg-start);--gradient-angle:var(--start-angle,145deg)}85%{--bord…

나온 때 2026-10-07

가져온 때 2026-10-09 23:01 UTC · 6f521205a43d3a67

뜯은 자리 — 원문 페이지

원제 — LosaLosSantos/aurelio-finance

“Open-source personal finance app with an AI financial advisor: track your net worth, investments, ETFs, cash and debts on your …”

나온 때 2026-10-06

가져온 때 2026-10-09 23:01 UTC · 984d32f396b5debc

계정이 적어 둔 문자열 — 계정이 비워 뒀다

뜯은 자리 — README

원제 — zhongerxin/iPhone-use

“让 Codex 通过 USB 操作真实 iPhone：引导安装、App 自动化、实时屏幕与截图回退。”

나온 때 2026-10-06

가져온 때 2026-10-09 23:01 UTC · bdb641bc7307d99d

계정이 적어 둔 문자열 — 계정이 비워 뒀다

뜯은 자리 — README

원제 — U-Space: Uncovering When and Why Uncertainty Arises in Language Models

“Large language models are informing decisions with ever-higher stakes. As the consequences of their errors grow, a central ques…”

나온 때 2026-10-05

가져온 때 2026-10-09 23:01 UTC · b917d3ea1ea8f81a

뜯은 자리 — 초록

원제 — LiquidAI/d1-omni-600M

제목과 링크만 주는 소스라 인용할 것이 없다.

대상 주장 — top answer on 0.8% of text and 1.7% of audio rows.

나온 때 2026-10-05

가져온 때 2026-10-11 23:01 UTC · c0297536843caeee

뜯은 자리 — 모델 카드

원제 — speridlabs/iris-3b

제목과 링크만 주는 소스라 인용할 것이 없다.

대상 주장 — Restoration tiles large outputs into overlapping 1024×1024 windows.

나온 때 2026-10-05

가져온 때 2026-10-10 23:01 UTC · fa4451995d49a027

뜯은 자리 — 모델 카드

원제 — perplexity-ai/pplx-decider-v1.1-27b

제목과 링크만 주는 소스라 인용할 것이 없다.

대상 주장 — model, that boost the Decision Index score from 56.4 to 61.56, now outperforming Jev by more than 3.5 points, while using the same backbone.

나온 때 2026-10-05

가져온 때 2026-10-10 23:01 UTC · 2db5bbc68088a7bf

뜯은 자리 — 모델 카드

원제 — nerkyor/Qwen3.8-27B-Coder390-EfficientThink-Opus5.5-GPT6Astra-Grok4.7-DSV4Pro-K3-SFT-RLOO-MTP-DFlash2

제목과 링크만 주는 소스라 인용할 것이 없다.

대상 주장 — For NInfer W4A4, the 2 LCB empty answers are problems 11 and 15, both truncated with no code; the 2 GPQA empty answers are problems 79 and 127, both truncated with an empty pred.

나온 때 2026-10-04

가져온 때 2026-10-09 23:01 UTC · 1c8c0b72263dc3b9

뜯은 자리 — 모델 카드

원제 — In-context Robot Learning Made Simple: A Democratized Recipe for Manipulation Tasks

“We study robotic in-context learning (ICL), an emerging paradigm that enables robots to infer and execute tasks from visual dem…”

나온 때 2026-09-28

가져온 때 2026-10-09 23:01 UTC · 8e9c091792f18ffc

뜯은 자리 — 초록

원제 — Quoting Muse AI Agent

“Bad news on the MX Keys Mini pickup. Usman showed up at your building around 9:15 and waited, messaged a bunch of times, and no…”

나온 때 2026-09-28

가져온 때 2026-09-28 05:05 UTC · e576682f7f3370bc

뜯은 자리 — 목록뿐 (원문을 못 읽었다)

원제 — FT: Corporate America rejects overpriced frontier, embraces open models

“&#32; submitted by &#32; /u/chocolateUI [link] &#32; [comments]”

나온 때 2026-09-28

가져온 때 2026-09-28 05:05 UTC · 2bd288bbf1adef1e

뜯은 자리 — 목록뿐 (원문을 못 읽었다)