Agentic language models compose multi step reasoning chains, yet intermediate steps can be corrupted by inconsistent context, retrieval errors, or adversarial inputs, which makes post hoc evaluation too late because errors propagate before detection. We introduce a diagnostic that requires no additional training and uses only the forward pass to emit a binary accept or reject signal during agent execution. The method analyzes token graphs induced by attention and computes two spectral statistics in early layers, namely the high frequency energy ratio and spectral entropy. We formalize these signals, establish invariances, and provide finite sample estimators with uncertainty quantification. Under a two regime mixture assumption with a monotone likelihood ratio property, we show that a single threshold on the high frequency energy ratio is optimal in the Bayes sense for detecting context inconsistency. Empirically, the high frequency energy ratio exhibits robust bimodality during context verification across multiple model families, which enables gating decisions with overhead below one millisecond on our hardware and configurations. We demonstrate integration into retrieval augmented agent pipelines and discuss deployment as an inline safety monitor. The approach detects contamination while the model is still processing the text, before errors commit to the reasoning chain.

The company is pursuing aggressive legal tactics against its opponents. On September 12, Jay Edelson received what he expected to be a standard legal document. Edelson is a lawyer representing the parents of Adam Raine; they are suing OpenAI, alleging that their 16-year-old son took his life at the encouragement of ChatGPT. OpenAI's lawyers had some inquiries for the opposing counsel, which is normal. For instance, they requested information about therapy Raine may have received, and Edelson complied.

When you purchase through links in our articles, we may earn a small commission. Google Drive's shared files and folders can now have permissions auto-expire Google Drive users can now grant temporary access to a file or folder, which is automatically revoked after a specified date. Google is now making it possible to set expiration dates on shared files and folders in Google Drive, reports Digital Trends . The feature applies to both shared drives and "My Drive," where users can grant temporary access to a file or folder that's automatically revoked after a certain date. This also means that temporary editing rights in "My Drive" no longer disappear completely when the period expires.

If the US Has to Build Data Centers, Here's Where They Should Go A new analysis tries to calculate the coming environmental footprint of AI in the US and finds that the ideal sites for data centers aren't where they're being built. A data center for cryptocurrency mining, cloud services, and AI computing in Stutsman County, North Dakota.Video: halbergman/Getty Images Tech companies have invested so much money in building data centers in recent months, it's actively driving the US economy--and the AI race is showing no signs of slowing down. Meta chief Mark Zuckerberg told President Donald Trump last week that the company would spend $600 billion on US infrastructure--including data centers--by 2028, while OpenAI has committed already to spending $1.4 trillion. An extensive new analysis looks at the environmental footprint of data centers in the US to get a handle on what, exactly, the country might be facing as this buildout continues over the next few years--and where the US should be building data centers to avoid the most harmful environmental impacts. The study, published in the journal Nature Communications on Monday, uses a variety of data, including demand for AI chips and information on state electricity and water scarcity, to project the potential environmental impacts of future data centers through the end of the decade. The study models a number of different possible scenarios on how data centers could affect the US and the planet--and cautions that tech companies' net zero promises aren't likely to hold up against the energy and water needs of the massive facilities they're building.

AI may blunt our thinking skills - here's what you can do about it There is growing evidence that our reliance on generative AI tools is reducing our ability to think clearly and critically, but it doesn't have to be that way Socrates wasn't the greatest fan of the written word. Famous for leaving no texts to posterity, the great philosopher is said to have believed that a reliance on writing destroys the memory and weakens the mind . Some 2400 years later, Socrates's fears seem misplaced - particularly in light of evidence that writing things down improves memory formation . A growing number of psychologists, neuroscientists and philosophers worry that ChatGPT and similar generative AI tools will chip away at our powers of information recall and blunt our capacity for clear reasoning. What's more, while Socrates relied on clever rhetoric to make his argument, these researchers are grounding theirs in empirical data.

Altman believes the revenue will come from paid-for versions of ChatGPT, other companies using its data centres and people buying the hardware devices it is building. Altman believes the revenue will come from paid-for versions of ChatGPT, other companies using its data centres and people buying the hardware devices it is building. Sam Altman's bet: Can OpenAI's ambitions keep pace with industry's soaring costs? As investor jitters grow, the loss-making ChatGPT firm's vast spending commitments test the limits of Silicon Valley optimism Mon 10 Nov 2025 09.21 ESTFirst published on Mon 10 Nov 2025 09.00 EST It is the $1.4tn (£1.1tn) question. How can a loss-making startup such as OpenAI afford such a staggering spending commitment?

The threat landscape is being shaped by two seismic forces. To future-proof their organizations, security leaders must take a proactive stance with a zero trust approach. AI and quantum technologies are dramatically reconfiguring how cybersecurity functions, redefining the speed and scale with which digital defenders and their adversaries can operate. The weaponization of AI tools for cyberattacks is already proving a worthy opponent to current defenses. This includes using generative AI to create social engineering attacks at scale, churning out tens of thousands of tailored phishing emails in seconds, or accessing widely available voice cloning software capable of bypassing security defenses for as little as a few dollars. And now, agentic AI raises the stakes by introducing autonomous systems that can reason, act, and adapt like human adversaries.

'OK, so ChatGPT helps you write your grocery list. Does your individual convenience outweigh the societal harm it can cause?' 'OK, so ChatGPT helps you write your grocery list. Does your individual convenience outweigh the societal harm it can cause?' 'It shows such a laziness': why I refuse to date someone who uses ChatGPT It's the ultimate ick: trying to form a deep, lasting connection with a person who outsources original thought The Guardian's journalism is independent. We will earn a commission if you buy something through an affiliate link. I t was a setting fit for a Nancy Meyers film.

The personalization of black-box large language models (LLMs) is a critical yet challenging task. Existing approaches predominantly rely on context injection, where user history is embedded into the prompt to directly guide the generation process. However, this single-step paradigm imposes a dual burden on the model: generating accurate content while simultaneously aligning with user-specific styles. This often results in a trade-off that compromises output quality and limits precise control. To address this fundamental tension, we propose Reflective Personalization Optimization (RPO), a novel framework that redefines the personalization paradigm by decoupling content generation from alignment. RPO operates in two distinct stages: first, a base model generates a high-quality, generic response; then, an external reflection module explicitly rewrites this output to align with the user's preferences. This reflection module is trained using a two-stage process. Initially, supervised fine-tuning is employed on structured rewriting trajectories to establish a core personalized reasoning policy that models the transformation from generic to user-aligned responses. Subsequently, reinforcement learning is applied to further refine and enhance the quality of the personalized outputs. Comprehensive experiments on the LaMP benchmark demonstrate that RPO, by decoupling content generation from personalization, significantly outperforms state-of-the-art baselines. These findings underscore the superiority of explicit response shaping over implicit context injection. Moreover, RPO introduces an efficient, model-agnostic personalization layer that can be seamlessly integrated with any underlying base model, paving the way for a new and effective direction in user-centric generation scenarios.

Massive data collection holds the promise of a better understanding of complex phenomena and, ultimately, better decisions. Representation learning has become a key driver of deep learning applications, as it allows learning latent spaces that capture important properties of the data without requiring any supervised annotations. Although representation learning has been hugely successful in predictive tasks, it can fail miserably in causal tasks including predicting the effect of a perturbation/intervention. This calls for a marriage between representation learning and causal inference. An exciting opportunity in this regard stems from the growing availability of multi-modal data (observational and perturbational, imaging-based and sequencing-based, at the single-cell level, tissue-level, and organism-level). We outline a statistical and computational framework for causal structure and representation learning motivated by fundamental biomedical questions: how to effectively use observational and perturbational data to perform causal discovery on observed causal variables; how to use multi-modal views of the system to learn causal variables; and how to design optimal perturbations.