Clarification on Theorem 1 R2: The first sentence "Given that h

We thank all the reviewers for their valuable comments. Below, we address the detailed comments of each reviewer. The margin range m is chosen to be around cos 30 0.15. Then other samples that are not well-learned will dynamically contribute more. Figure 1, Sec. 3.5, and other comments: Thank you for the suggestions.

SoftBank Group's investments in AI will be financed through a loan in which Mizuho Bank, Sumitomo Mitsui Banking and JPMorgan Chase serve as lead underwriters -- a sign of the Japanese tech investor's ability to secure financing for its outsized ambitions. The one-year 15 billion bridge loan, one of the biggest borrowings SoftBank has pulled off to date, will be financed by 21 banks and includes 1.35 billion from Mizuho, 1.25 billion from SMBC and 1 billion from JPMorgan, according to people familiar with the matter. It also includes a combined 950 million from HSBC Holdings and Barclays and 850 million jointly from seven banks including Goldman Sachs Group Inc., MUFG Bank and Credit Agricole, said the people, who asked not to be named as the details of the financing remain private. Representatives of the banks also declined to comment.

Progress in Reinforcement Learning (RL) algorithms goes hand-in-hand with the development of challenging environments that test the limits of current methods. While existing RL environments are either sufficiently complex or based on fast simulation, they are rarely both. Here, we present the NetHack Learning Environment (NLE), a scalable, procedurally generated, stochastic, rich, and challenging environment for RL research based on the popular single-player terminalbased roguelike game, NetHack. We argue that NetHack is sufficiently complex to drive long-term research on problems such as exploration, planning, skill acquisition, and language-conditioned RL, while dramatically reducing the computational resources required to gather a large amount of experience. We compare NLE and its task suite to existing alternatives, and discuss why it is an ideal medium for testing the robustness and systematic generalization of RL agents. We demonstrate empirical success for early stages of the game using a distributed Deep RL baseline and Random Network Distillation exploration, alongside qualitative analysis of various agents trained in the environment.

The best performance is achieved when the threshold τ is set to 0.45.

If the AI lovefest of Google I/O 2025 were a TV show, you might be tempted to call it It's Always Sunny in Mountain View. But here's a better sitcom analogy for the event that added AI Mode to all U.S. search results, whether we want it or not. It's The Good Place, in which our late heroes are repeatedly assured that they've gone to a better world. A place where everything is fine, all is as it seems, and search quality just keeps getting better. Don't worry about ever-present and increasing AI hallucinations here in the Good Place, where the word "hallucination" isn't even used.

US chip exports controls have been a "failure", the head of Nvidia, Jensen Huang, told a tech forum on Wednesday, as the Chinese government separately slammed US warnings to other countries against using Chinese tech. Successive US administrations have imposed restrictions on the sale of hi-tech AI chips to China, in an effort to curb China's military advancement and protect US dominance of the AI industry. But Huang told the Computex tech forum in Taipei that the controls had instead spurred on Chinese developers. "The local companies are very, very talented and very determined, and the export control gave them the spirit, the energy and the government support to accelerate their development," Huang told media the Computex tech show in Taipei. "I think, all in all, the export control was a failure."

A benchmark 2. Benchmark: For benchmarks, the supplementary materials must ensure that all results are easily reproducible (i.e.

Automated scientific discovery promises to accelerate progress across scientific domains. However, developing and evaluating an AI agent's capacity for endto-end scientific reasoning is challenging as running real-world experiments is often prohibitively expensive or infeasible.

Hacked AI-powered chatbots threaten to make dangerous knowledge readily available by churning out illicit information the programs absorb during training, researchers say. The warning comes amid a disturbing trend for chatbots that have been "jailbroken" to circumvent their built-in safety controls. The restrictions are supposed to prevent the programs from providing harmful, biased or inappropriate responses to users' questions. The engines that power chatbots such as ChatGPT, Gemini and Claude – large language models (LLMs) – are fed vast amounts of material from the internet. Despite efforts to strip harmful text from the training data, LLMs can still absorb information about illegal activities such as hacking, money laundering, insider trading and bomb-making.