Metals & Mining
OmniJARVIS Unified Vision-Language-Action Tokenization Enables Open-World Instruction Following Agents
This paper presents OmniJARVIS, a novel Vision-Language-Action (VLA) model for open-world instruction-following agents in Minecraft. Compared to prior works that either emit textual goals to separate controllers or produce the control command directly, OmniJARVIS seeks a different path to ensure both strong reasoning and efficient decision-making capabilities via unified tokenization of multimodal interaction data.
Apple's App Store quietly turned into a gold mine for developers and businesses in the last 5 years
The Apple App Store carries millions of applications for a range of different use cases and devices, meeting people's everyday needs, from entertainment to productivity. A new study shows that the developers behind the apps are also winning big. The Apple-supported study, conducted by Professor Andrey Fradkin from Boston University Questrom School of Business and economist Jessica Burley, Ph.D., from Analysis Group, found that the US App Store ecosystem facilitated 406 billion in developer billings and sales in 2024. This amount reflects a doubling over the past five years, while the overall App Store ecosystem has tripled in the same period. Also: 6 things I did immediately after installing iOS 18.5 on my iPhone - and why you should too Apple said more than 90% of the billings and sales facilitated by the App Store did not have to pay Apple a commission.
Mars: Situated Inductive Reasoning in an Open-World Environment Jiaqi Li
Large Language Models (LLMs) trained on massive corpora have shown remarkable success in knowledge-intensive tasks. Yet, most of them rely on pre-stored knowledge. Inducing new general knowledge from a specific environment and performing reasoning with the acquired knowledge--situated inductive reasoning, is crucial and challenging for machine intelligence. In this paper, we design Mars, an interactive environment devised for situated inductive reasoning. It introduces counter-commonsense game mechanisms by modifying terrain, survival setting and task dependency while adhering to certain principles.
An NLP Benchmark Dataset for Assessing Corporate Climate Policy Engagement
As societal awareness of climate change grows, corporate climate policy engagements are attracting attention. We propose a dataset to estimate corporate climate policy engagement from various PDF-formatted documents. Our dataset comes from LobbyMap (a platform operated by global think tank InfluenceMap) that provides engagement categories and stances on the documents. To convert the LobbyMap data into the structured dataset, we developed a pipeline using text extraction and OCR. Our contributions are: (i) Building an NLP dataset including 10K documents on corporate climate policy engagement.
Mars: Situated Inductive Reasoning in an Open-World Environment Jiaqi Li
Large Language Models (LLMs) trained on massive corpora have shown remarkable success in knowledge-intensive tasks. Yet, most of them rely on pre-stored knowledge. Inducing new general knowledge from a specific environment and performing reasoning with the acquired knowledge--situated inductive reasoning, is crucial and challenging for machine intelligence. In this paper, we design Mars, an interactive environment devised for situated inductive reasoning. It introduces counter-commonsense game mechanisms by modifying terrain, survival setting and task dependency while adhering to certain principles.
Donald Trump Wants to Save the Coal Industry. He's Too Late.
This story was originally published by WIRED and is reproduced here as part of the Climate Desk collaboration. Last Tuesday, President Donald Trump held a press conference to announce the signing of executive orders intended to shape American energy policy in favor of one particular source: coal, the most carbon-intense fossil fuel. "I call it beautiful, clean coal," President Trump said while flanked by a crowd of miners at the White House. "I tell my people never use the word coal unless you put'beautiful, clean' before it." Trump has talked about saving coal, and coal jobs, for as long as he's been in politics.
Fox News AI Newsletter: White House record-keeping revamp
This photo posted by DOGE on Feb. 11, 2025, shows shelving and cardboard boxes which DODGE says workers at the underground mine facility use to store federal worker retirement papers. The White House announces that it will implement AI technology to improve efficiency in federal records keeping. HISTORIC EFFICIENCY: Fox News Digital has learned that the U.S. Office of Personnel Management (OPM) will post an updated Privacy Impact Assessment (PIA) at the close of business Wednesday that paves the way for artificial intelligence to improve government efficiency and enhance the federal record-keeping process. NOT IN KANSAS ANYMORE: The use of artifical intelligence to reimagine the classic film "The Wizard of Oz" will likely see mixed reactions from fans, experts told Fox News Digital. BAD-FAITH TACTICS: OpenAI escalated its legal battle with Elon Musk by countersuing the Tesla and xAI CEO, claiming in a lawsuit he "has tried every tool available to harm" the company.
Donald Trump Wants to Save the Coal Industry. He's Too Late
On Tuesday, President Donald Trump held a press conference to announce the signing of executive orders intended to shape American energy policy in favor of one particular source: coal, the most carbon-intense fossil fuel. "I call it beautiful, clean coal," President Trump said while flanked by a crowd of miners at the White House. "I tell my people never use the word coal, unless you put'beautiful, clean' before it." Trump has talked about saving coal, and coal jobs, for as long as he's been in politics. This time, he's got a convenient vehicle for his policies: the growth of AI and data centers, which could potentially supercharge American energy demand over the coming years.