New research suggests that reliance on AI assistants can have a negative impact on people's ability to think and problem solve. Using AI chatbots for even just for 10 minutes may have a shockingly negative impact on people's ability to think and problem-solve, according to a new study from researchers at Carnegie Mellon, MIT, Oxford, and UCLA. Researchers tasked people with solving various problems, including simple fractions and reading comprehension, through an online platform that paid them for their work. They conducted three experiments, each involving several hundred people. Some participants were given access to an AI assistant capable of solving the problem autonomously.

"Never talk about goblins, gremlins, raccoons, trolls, ogres, pigeons, or other animals or creatures unless it is absolutely and unambiguously relevant," reads OpenAI's coding agent instructions. OpenAI has a goblin problem. Instructions designed to guide the behavior of the company's latest model as it writes code have been revealed to include a line, repeated several times, that specifically forbids it from randomly mentioning an assortment of mythical and real creatures. "Never talk about goblins, gremlins, raccoons, trolls, ogres, pigeons, or other animals or creatures unless it is absolutely and unambiguously relevant to the user's query," read instructions in Codex CLI, a command-line tool for using AI to generate code. It is unclear why OpenAI felt compelled to spell this out for Codex --or indeed why its models might want to discuss goblins or pigeons in the first place.

OpenAI is racing to catch up to its greatest rival. OpenAI does not like to be left out. The week after Anthropic announced Claude Mythos Preview --an AI model that has put governments around the world on edge because of its potential ability to hack into banks, energy grids, and military systems--OpenAI shared a program that is uncannily similar. And just like Anthropic did with its model, OpenAI has, for cybersecurity purposes, restricted access to this new bot, called GPT-5.4-Cyber, to a small group of trusted users. This sequence has become something of a pattern: First Anthropic will make an announcement, and then OpenAI will follow suit.

OpenAI is refocusing its research efforts and throwing its resources into a new grand challenge. The San Francisco firm has set its sights on building what it calls an AI researcher, a fully automated agent-based system that will be able to go off and tackle large, complex problems by itself. OpenAI says that this new research goal will be its "North Star" for the next few years, pulling together multiple research strands, including work on reasoning models, agents, and interpretability .

Theproblemcontainsa prompt (blue), acanonicalsolutiontotheprompt (green), andafewtest-cases (black).

In this material, we will give more technical details as well as additional experiments to support the main paper. The overview of the proposed framework, DeWave, is illustrated in Figure 6. The dataset is split into training (80%), development (10%), and testing (10%) sets, comprising 10,874, 1,387, and 1,387 unique sentences, respectively, with no overlap. We release our implementation code through GitHub to contribute to this area. Section 3.3, where a 6-layer CNN encoder slides through the whole wave and gets the embedding The codex encoder shares the same structure with word-level features.

Codex can now manage several AI assistants to complete complex tasks. Since last spring, OpenAI has offered Codex . What started life as the company's response to Claude Code is becoming something more sophisticated with the release of a new dedicated macOS app. At its most basic form, Codex is a programming agent capable of writing code for users, but now it can also manage multiple AI assistants that can work together to complete more complex tasks. OpenAI gives an example of how this could work in practice.

WIRED analyzed more than 5,000 papers from NeurIPS using OpenAI's Codex to understand the areas where the US and China actually work together on AI research. The US and China are, by many measures, archrivals in the field of artificial intelligence, with companies racing to outdo each other on algorithms, models, and specialized silicon . And yet, the world's AI superpowers still collaborate to a surprising degree when it comes to cutting-edge research. A WIRED analysis of more than 5,000 AI research papers presented last month at the industry's premier conference, Neural Information Processing Systems ( NeurIPS), reveals a significant amount of collaboration between US and Chinese labs. The analysis found that 141 out of the 5,290 total papers (roughly 3 percent) involve collaboration between authors affiliated with US institutions and those affiliated with Chinese ones.

Figure 7: Actual example of how an anchor function impacts the generated solution. In this section, we provide additional experimental details and results for the experiments in Section 3. We include additional details for anchoring (Appendix A.1), the availability heuristic (Appendix A.3), Filtering prompts for longer canonical solutions. However, all components of the prompts from Section 3.3.2 We plot the analogous add-var results in Figure 10 and include full numerical results in Table 7. In this section, we augment Section 3.3.3