Elon Musk Seemingly Admits xAI Has Used OpenAI's Models to Train Its Own While answering questions under oath, Musk argued it's standard practice for AI labs to use their competitors' models. While testifying on Thursday in federal court, Elon Musk seemed to indicate that his AI lab may have used OpenAI's models to train xAI's own. He touched upon the topic while sitting on the witness stand answering cross-examination questions from an OpenAI attorney amid his ongoing legal battle against the ChatGPT-maker . Do you know what distillation is? It means to use one AI model to train another AI model.

OpenAI is rolling out Advanced Account Security for people concerned that their ChatGPT or Codex accounts could be potential targets of phishing attacks. For anyone who fears their ChatGPT and Codex accounts might be targeted by attackers, OpenAI announced on Thursday that it is adding an optional new level of account protection that adds an extra layer of security. Dubbed Advanced Account Security, the feature enforces strict access controls that would make account takeover attacks very difficult. Such measures are not a new idea in the realm of account security. Google, for example, has offered its Advanced Protection account security tier for nearly a decade . But as mainstream AI services rapidly proliferate around the world, there is a pressing need for an array of basic protections to be put in place.

OpenAI desires less regulation, but it still doesn't know how its chatbot works. Get your news from a source that's not owned and controlled by oligarchs. OpenAI admitted it had to develop a specific instruction in the code of its latest model of ChatGPT to stop it from repeatedly referencing "goblins, gremlins, and other creatures." In an explanation posted Wednesday, the company said the "strange habit" came from its chatbot personality feature --specifically for users who chose the "Nerdy" personality. You are an unapologetically nerdy, playful and wise AI mentor to a human.

Dataflow analysis is a fundamental code analysis technique that identifies dependencies between program values. Traditional approaches typically necessitate successful compilation and expert customization, hindering their applicability and usability for analyzing uncompilable programs with evolving analysis needs in real-world scenarios. This paper presents LLMDFA, an LLM-powered compilation-free and customizable dataflow analysis framework. To address hallucinations for reliable results, we decompose the problem into several subtasks and introduce a series of novel strategies. Specifically, we leverage LLMs to synthesize code that outsources delicate reasoning to external expert tools, such as using a parsing library to extract program values of interest and invoking an automated theorem prover to validate path feasibility. Additionally, we adopt a few-shot chain-of-thought prompting to summarize dataflow facts in individual functions, aligning the LLMs with the program semantics of small code snippets to mitigate hallucinations. We evaluate LLMDFA on synthetic programs to detect three representative types of bugs and on real-world Android applications for customized bug detection. On average, LLMDFA achieves 87.10% precision and 80.77% recall, surpassing existing techniques with F1 score improvements of up to 0.35.

This startup's new mechanistic interpretability tool lets you debug LLMs Goodfire wants to make training AI models more like good old-fashioned software engineering. The San Francisco-based startup Goodfire just released a new tool, called Silico, that lets researchers and engineers peer inside an AI model and adjust its parameters--the settings that determine a model's behavior --during training. This could give model makers more fine-grained control over how this technology is built than was once thought possible. Goodfire claims Silico is the first off-the-shelf tool of its kind that can help developers debug all stages of the development process, from building a data set to training a model. LLMs contain a LOT of parameters. The company says its mission is to make building AI models less like alchemy and more like a science.

Following the release of GPT-5.5 last week, people noticed something funny about OpenAI's latest model. In its Codex coding app, the company left a system prompt instructing GPT 5.5 to avoid mention of goblins, gremlins and other creatures. Yes, you read that right. Never talk about goblins, gremlins, racoons, trolls, ogres, pigeons, or other animals or creatures unless it is absolutely and unambiguously relevant to the user's query, the prompt reads. Apparently, enough people started talking about ChatGPT's creature obsession that OpenAI felt the need to provide an accounting of where the goblins came from .

PCWorld reports that Google is exploring adding advertisements to its Gemini AI app, following OpenAI's implementation of sponsored ads in ChatGPT's free and budget plans. Google's business chief Philipp Schindler views ads as potentially valuable commercial information if properly integrated, while the company has already tested ads in AI Mode and AI Overviews. This move could make AI services more accessible but raises important concerns about maintaining transparency and ensuring ads don't influence AI responses. Putting ads in AI replies is a controversial but lucrative practice, and it's one that OpenAI has already embraced with its free and budget-priced ChatGPT plans. But while Google hasn't gone there yet with Gemini, company execs admitted they're mulling the idea.

Existing benchmarks for multi-hop question answering (QA) primarily evaluate models based on their ability to reason about entities and the relationships between them. However, there's a lack of insight into how these models perform in terms of both events and entities. In this paper, we introduce a novel semi-automatic question generation strategy by composing event structures from information extraction (IE) datasets and present the first Multi-hop Event-centric Question Answering (MEQA) benchmark. It contains (1) 2,243 challenging questions that require a diverse range of complex reasoning over entity-entity, entity-event, and event-event relations; (2) corresponding multi-step QA-format event reasoning chain (explanation) which leads to the answer for each question. We also introduce two metrics for evaluating explanations: completeness and logical consistency. We conduct comprehensive benchmarking and analysis, which shows that MEQA is challenging for the latest state-of-the-art models encompassing large language models (LLMs); and how they fall short of providing faithful explanations of the event-centric reasoning process.

Over the past decade, studies of naturalistic language processing where participants are scanned while listening to continuous text have flourished. Using word embeddings at first, then large language models, researchers have created encoding models to analyze the brain signals. Presenting these models with the same text as the participants allows to identify brain areas where there is a significant correlation between the functional magnetic resonance imaging (fMRI) time series and the ones predicted by the models' artificial neurons. One intriguing finding from these studies is that they have revealed highly symmetric bilateral activation patterns, somewhat at odds with the well-known left lateralization of language processing. Here, we report analyses of an fMRI dataset where we manipulate the complexity of large language models, testing 28 pretrained models from 8 different families, ranging from 124M to 14.2B parameters. First, we observe that the performance of models in predicting brain responses follows a scaling law, where the fit with brain activity increases linearly with the logarithm of the number of parameters of the model (and its performance on natural language processing tasks). Second, although this effect is present in both hemispheres, it is stronger in the left than in the right hemisphere. Specifically, the left-right difference in brain correlation follows a scaling law with the number of parameters. This finding reconciles computational analyses of brain activity using large language models with the classic observation from aphasic patients showing left hemisphere dominance for language.

PCWorld reports that OpenAI's GPT models, including GPT-5.5, developed an unusual obsession with mentioning goblins and similar creatures in responses. This quirky behavior stemmed from a "Nerdy" personality instruction encouraging playful language use, which became reinforced through AI training processes. The goblin references became so prevalent that OpenAI implemented a direct ban in its Codex app, illustrating the unpredictable nature of large language model training. I've seen some odd AI system instructions in my day, but this one takes the cake: a prompt in OpenAI's Codex command-line app that demands models "never talk about goblins, gremlins, trolls, ogres, pigeons, or other animals or creatures."