American AI giants are backing a new effort to establish open standards for building agentic software and tools. OpenAI, Anthropic, and Block have cofounded a new open source organization--the Agentic AI Foundation--to promote standards for artificial intelligence agents. The three companies are also transferring ownership of some widely used agentic technologies over to the foundation. This includes Anthropic's Model Context Protocol (MCP), which allows agents to connect and interact; OpenAI's Agents.md These technologies were already free to use, but through the new foundation it will be possible for others to contribute to their development.

Back in 2019, an indie game launched with a simple tagline: "It's a lovely morning in the village and you are a horrible goose." And Untitled Goose Game tells no lies. This one sentence describes its premise accurately. I tell everyone to play it. I've been especially vocal during the past week, when its price on Steam dropped to its all-time low of just 7 for the second time ever.

At a company-wide hackathon this month, developers at finance firm Block built a dizzying number of prototype tools including a database debugger, a program for identifying duplicated code, and an app that automates Bitcoin support. The sudden productivity boost was driven by Goose, an artificial intelligence agent developed by Block several months ago that can help with coding and other work like knocking together data visualizations or mocking up new product features. "We've always had really strong hack weeks, but this one was at another level," says Jackie Brosamer, who leads the AI and data platform at Block. "We have tens of ideas that we're looking to bring to production." Goose helped developers at Block to develop a new agent-to-agent communication server at the hackathon.

From the very first scene of The Wild Robot, the new animated movie from director Chris Sanders (How to Train Your Dragon), adapted from the first in a trilogy of children's novels by Peter Brown, the viewer is plunged along with the protagonist into a new and alien world. A robot washes up on the shore of a lushly forested island, surrounded by the flotsam of some sort of wrecked vehicle--a plane? a spacecraft?--and immediately begins scanning the area for someone she can help. Rozzum Unit 7134, voiced by Lupita Nyong'o and soon to be known as "Roz," has been designed to, as she puts it, offer "integrated, multifaceted task accomplishment" to whatever human requests it of her. The problem is, the island where she's washed up has no human inhabitants, and the animals witnessing the arrival of this hulking metal biped regard Roz as nothing but a menacing predator to be either fought or fled. A witty time-lapse montage shows the robot powering down for a bit so her software can learn to decode the animal sounds around her, enabling her to communicate with all the island's denizens.

We present three novel graph representations of planning tasks suitable for learning domain-independent heuristics using Graph Neural Networks (GNNs) to guide search. In particular, to mitigate the issues caused by large grounded GNNs we present the first method for learning domain-independent heuristics with only the lifted representation of a planning task. We also provide a theoretical analysis of the expressiveness of our models, showing that some are more powerful than STRIPS-HGN, the only other existing model for learning domain-independent heuristics. Our experiments show that our heuristics generalise to much larger problems than those in the training set, vastly surpassing STRIPS-HGN heuristics.

Despite advancements in LLMs, knowledge-based reasoning remains a longstanding issue due to the fragility of knowledge recall and inference. Existing methods primarily encourage LLMs to autonomously plan and solve problems or to extensively sample reasoning chains without addressing the conceptual and inferential fallacies. Attempting to alleviate inferential fallacies and drawing inspiration from multi-agent collaboration, we present a framework to increase faithfulness and causality for knowledge-based reasoning. Specifically, we propose to employ multiple intelligent agents (i.e., reasoners and an evaluator) to work collaboratively in a reasoning-and-consensus paradigm for elevated reasoning faithfulness. The reasoners focus on providing solutions with human-like causality to solve open-domain problems. On the other hand, the \textit{evaluator} agent scrutinizes if a solution is deducible from a non-causal perspective and if it still holds when challenged by a counterfactual candidate. According to the extensive and comprehensive evaluations on a variety of knowledge reasoning tasks (e.g., science question answering and commonsense reasoning), our framework outperforms all compared state-of-the-art approaches by large margins.

Masayoshi Son, the billionaire boss of SoftBank Group Corp., has long presided over a quarterly earnings ritual of zany slide presentations. One included a goose laying multibillion-dollar golden eggs and another flock of unicorns flying upward along a chart of growth in artificial intelligence. Mr. Son is planning to step back from the routine when the giant technology investor delivers its earnings Friday, instead greeting attendees with short remarks before handing the baton to his chief financial officer, according to a SoftBank agenda for the event. It is slated to be a more sedate presentation than those from Mr. Son, who also isn't planning on taking questions from the media, according to people familiar with the company. The more subdued role--which is likely to continue, the people said--comes as Toyko-based SoftBank, the world's most active startup investor in recent years, is in the midst of a difficult run.

There used to be a time where video games were sneered at and overlooked by the culturati as lowbrow schlock but games are, and always have been, a lively and responsive form of artistic expression. It's not always immediately clear when a game was made in Australia, which makes it a little harder to celebrate homegrown hits – which we should do, because we have a thriving community of developers who punch well above their weight. The Australian independent games scene is vibrant, dynamic and overdue an apology. As I have (graciously, selflessly) decided, we're all going to yank games from the declasse and appreciate them properly – so here are 10 great Australian-made games, all variously ruminative, charming, effervescent, sincere, generous, visceral, cheeky, and beautiful. Glad we have that sorted.

Hungry Geese is a n-player variation of the popular game snake. This paper looks at state of the art Deep Reinforcement Learning Value Methods. The goal of the paper is to aggregate research of value based methods and apply it as an exercise to other environments. A vanilla Deep Q Network, a Double Q-network and a Dueling Q-Network were all examined and tested with the Hungry Geese environment. The best performing model was the vanilla Deep Q Network due to its simple state representation and smaller network structure. Converging towards an optimal policy was found to be difficult due to random geese initialization and food generation. Therefore we show that Deep Q Networks may not be the appropriate model for such a stochastic environment and lastly we present improvements that can be made along with more suitable models for the environment.

SoftBank Group Corp. has reported a record profit in its Vision Fund as a surging stock market lifted the value of its portfolio companies, but founder Masayoshi Son wiped out a significant chunk of those gains with his controversial trading in derivatives. The Vision Fund on Monday reported a ¥844.1 billion ($8 billion) profit in the December quarter, surpassing record numbers set just a quarter earlier. A global rally in technology shares has boosted the value of SoftBank's stakes in publicly traded firms like Uber Technologies Inc. and paved the way for initial public offerings from the likes of DoorDash Inc. Those gains, which had been widely expected, were offset by fallout from Son's decision last year to start dabbling in trading stocks and options. SoftBank posted a ¥285.3 billion derivatives loss in the period.