모델 출시ENOpenAI Blog
Model ML completes finance work more efficiently with GPT-5.6 Sol
Model ML uses GPT5.6 Sol to carry finance work from research and analysis through editable, traceable PowerPoint decks and Excel workbooks.
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산업ENWired AI
The AI Slop Backlash Is Actually Having an Impact
Platforms are finally recognizing that people don’t want to consume AI slop. A growing number of sites and apps now have tools and policies to flag, label, and ban AIgenerated content.
산업ENThe Verge AI
Ford’s new AI assistant can check your fuel levels and tire pressure
Ford is rolling out a new AIpowered assistant that can answer questions about your Ford or Lincoln vehicle, such as how much fuel you'll need for your next road trip or whether your truck can tow that new motor boat.
산업ENWired AI
The Rise of the 1 am Job Interview
An AI interview is increasingly the first step of a hiring process. Since there’s no human on the other end, candidates are scheduling them whenever—even deep into the night.
연구ENHuggingFace Blog
Making Knowledge Distillation Cheap Enough to Run at Scale
Making Knowledge Distillation Cheap Enough to Run at Scale
모델 출시ENOpenAI Blog
Expanding Daybreak as the Cyber Defense Window Narrows
Meet GPT5.6Cyber, OpenAI’s cybersecurityspecific model available through Daybreak Red for authorized vulnerability research, exploit validation, and security testing.
모델 출시ENOpenAI Blog
Putting frontier cyber models in more trusted hands
Approved Daybreak partners can use OpenAI’s frontier cyber models to deliver authorized, governed cybersecurity services to customers.
연구ENMIT Technology Review AI
AI for science needs reasoning, not just data
Every few decades, someone announces that science has reached its end. In 1903, the revered physicist Albert Michelson wrote that the “facts of physical science have all been discovered.” In the 1980s, Stephen Hawking predicted that theoretical physics might be finished by the end of the century.
연구ENMIT Technology Review AI
These startups are chasing the next big thing in LLMs
MIT Technology Review’s What’s Next series looks across industries, trends, and technologies to give you a first look at the future. You can read the rest of them here.
연구ENArXiv AI
Towards Multi-Label Graph Foundation Models: from Single-Vector Representation Learning to Multi-Semantic Basis Learning
arXiv:2608.06394v1 Announce Type: new Abstract: Multilabel node classification is an important yet challenging task in graph learning, where nodes exhibit multiple semantics simultaneously.
연구ENArXiv AI
EntropyMoE: Entropy-Aware Sparse Expert Routing for Tokenizer-Free LLMs
arXiv:2608.06398v1 Announce Type: new Abstract: Recent bytelevel large language models LLMs have made tokenizerfree modeling increasingly competitive by grouping bytes into dynamically sized patches.
연구ENArXiv AI
Beyond Routing Weights: Faithful Response-Level Interpretation of Mixture-of-Experts Reward Models via Contribution Contrast
arXiv:2608.06400v1 Announce Type: new Abstract: Reward models are central to learning from human preferences, yet identifying what drives their predictions remains challenging.