Is it illegal to own an axolotl? A recent pet seizure at Chicago's O'Hare Airport illustrates ongoing confusion. Many pet axolotls are crossbred with other salamanders to create their unique coloration. Breakthroughs, discoveries, and DIY tips sent six days a week. The axolotl () is a confusing creature, and not simply because it looks like a real-life Pokémon .

As large language models become increasingly capable, there is growing concern that they may develop reasoning processes that are encoded or hidden from human oversight. To investigate whether current interpretability techniques can penetrate such encoded reasoning, we construct a controlled testbed by fine-tuning a reasoning model (DeepSeek-R1-Distill-Llama-70B) to perform chain-of-thought reasoning in ROT-13 encryption while maintaining intelligible English outputs. We evaluate mechanistic interpretability methods--in particular, logit lens analysis--on their ability to decode the model's hidden reasoning process using only internal activations. We show that logit lens can effectively translate encoded reasoning, with accuracy peaking in intermediate-to-late layers. Finally, we develop a fully unsupervised decoding pipeline that combines logit lens with automated paraphrasing, achieving substantial accuracy in reconstructing complete reasoning transcripts from internal model representations. These findings suggest that current mechanistic interpretability techniques may be more robust to simple forms of encoded reasoning than previously understood. Our work provides an initial framework for evaluating interpretability methods against models that reason in non-human-readable formats, contributing to the broader challenge of maintaining oversight over increasingly capable AI systems.

"Everyone thinks they're on this big now," Debbie said, refilling her glass. "I've had it with the journey. I've had it with you people." "I don't think I'm on a journey," Burt said. Life's too short to find out who we really are." It was the first time the six of them had got together for dinner in more than a year (since Maria's diagnosis), and after such a long time (and in celebration of Maria's remission) they'd expected to have more interesting things to tell one another, deeper things, but they were entering dessert territory now, a cake was on the table, and only superficial topics had been broached: Ervin's promotion, Jane and Burt's move to the suburbs, Katherine's recent purchase of a metabolism-tracking device--a pen-shaped item and the cause of Debbie's rant. "How much can you know about yourself, exactly?" she said. "The therapy, the vision quests, the birth charts--do we really need the data on metabolic flexibility, too?" Jane, in Katherine's defense, said that, the more you knew about yourself, the more useful you could be to society. Knowing whether Kat is in fat-or carb-burning mode doesn't help anyone." As a result of Katherine declining cake five minutes earlier, no one had touched it. No one, Debbie included, really wanted to. They'd all overeaten already, drunk too much, made private plans to atone for it the next day. The cake presented a challenge, it sat there taunting them, and Debbie knew this, that you couldn't serve cake to a group of fortysomethings without causing ripples, but what else could she have done? She got it, no one wanted to put on weight, but this was a gorgeous princess cake, just gorgeous, she'd had to drive all the way to Andersonville to get it from that Swedish bakery everyone talked about. Staring at it now, though, she wondered if the cake didn't look a little bit like a tit, the smooth half sphere, the small pink marzipan flower nippling the top of it--and, oh, God, did think it looked like a tit?

Public health agencies face critical challenges in identifying high-risk neighborhoods for childhood lead exposure with limited resources for outreach and intervention programs. To address this, we develop a Priority Score integrating untested children proportions, elevated blood lead prevalence, and public health coverage patterns to support optimized resource allocation decisions across 136 neighborhoods in Chicago, New York City, and Washington, D.C. We leverage these allocation tasks, which require integrating multiple vulnerability indicators and interpreting empirical evidence, to evaluate whether large language models (LLMs) with agentic reasoning and deep research capabilities can effectively allocate public health resources when presented with structured allocation scenarios. LLMs were tasked with distributing 1,000 test kits within each city based on neighborhood vulnerability indicators. Results reveal significant limitations: LLMs frequently overlooked neighborhoods with highest lead prevalence and largest proportions of untested children, such as West Englewood in Chicago, while allocating disproportionate resources to lower-priority areas like Hunts Point in New York City. Overall accuracy averaged 0.46, reaching a maximum of 0.66 with ChatGPT 5 Deep Research. Despite their marketed deep research capabilities, LLMs struggled with fundamental limitations in information retrieval and evidence-based reasoning, frequently citing outdated data and allowing non-empirical narratives about neighborhood conditions to override quantitative vulnerability indicators.

Cochlear implants (CI) significantly improve spoken language in children with severe-to-profound sensorineural hearing loss (SNHL), yet outcomes remain more variable than in children with normal hearing. This variability cannot be reliably predicted for individual children using age at implantation or residual hearing. This study aims to compare the accuracy of traditional machine learning (ML) to deep transfer learning (DTL) algorithms to predict post-CI spoken language development of children with bilateral SNHL using a binary classification model of high versus low language improvers. A total of 278 implanted children enrolled from three centers. The accuracy, sensitivity and specificity of prediction models based upon brain neuroanatomic features using traditional ML and DTL learning. DTL prediction models using bilinear attention-based fusion strategy achieved: accuracy of 92.39% (95% CI, 90.70%-94.07%), sensitivity of 91.22% (95% CI, 89.98%-92.47%), specificity of 93.56% (95% CI, 90.91%-96.21%), and area under the curve (AUC) of 0.977 (95% CI, 0.969-0.986). DTL outperformed traditional ML models in all outcome measures. DTL was significantly improved by direct capture of discriminative and task-specific information that are advantages of representation learning enabled by this approach over ML. The results support the feasibility of a single DTL prediction model for language prediction of children served by CI programs worldwide.

Each point in the central path of DAGMA is a solution to an unconstrained problem regularized by our log-det function, then we show that at the limit of the central path the solution is guaranteed to be a DAG.

The Department of Homeland Security collected data on Chicago residents accused of gang ties to test if police files could feed an FBI watchlist. Months passed before anyone noticed it wasn't deleted. On November 21, 2023, field intelligence officers within the Department of Homeland Security quietly deleted a trove of Chicago Police Department records. It was not a routine purge. WIRED has made this article free for all to read because it is primarily based on reporting from Freedom of Information Act requests. Please consider subscribing to support our journalism.

In February 1953, two men walked into a pub in Cambridge and announced they had found the secret of life. It was not an idle boast. One was James Watson, an American biologist from the Cavendish laboratory; the other was his British research partner, Francis Crick. The full Promethean power of their achievement would slowly emerge over decades of research by fellow geneticists. It also opened a Pandora's Box of controversial scientific and ethical issues - including human cloning, designer babies and Frankenstein foods.

Leinweber Institute for Theoretical Physics, University of Chicago, Chicago, Illinois 60637, USA Tissue dynamics play a crucial role in biological processes ranging from inflammation to morphogenesis. However, these noisy multicellular dynamics are notoriously hard to predict. Here, we introduce a biomimetic machine learning framework capable of inferring noisy multicellular dynamics directly from experimental movies. This generative model combines graph neural networks, normalizing flows and WaveNet algorithms to represent tissues as neural stochastic differential equations where cells are edges of an evolving graph. Cell interactions are encoded in a dual signaling graph capable of handling signaling cascades. The dual graph architecture of our neural networks reflects the architecture of the underlying biological tissues, substantially reducing the amount of data needed for training, compared to convolutional or fully-connected neural networks. Taking epithelial tissue experiments as a case study, we show that our model not only captures stochastic cell motion but also predicts the evolution of cell states in their division cycle. Finally, we demonstrate that our method can accurately generate the experimental dynamics of developmental systems, such as the fly wing, and cell signaling processes mediated by stochastic ERK waves, paving the way for its use as a digital twin in bioengineering and clinical contexts. The physicist Eugene Wigner famously commented on the "unreasonable effectiveness of mathematics in the natural sciences" [1]. Continuum theories are an example of such unreasonable effectiveness [2].

ChatGPT on App Store displayed on a phone screen on 07 June 2025. ChatGPT on App Store displayed on a phone screen on 07 June 2025. Has OpenAI really made ChatGPT better for users with mental health problems? Prompts indicating suicidal ideation got alarming replies, which experts say shows'how easy it is to break the model' A n OpenAI statement released this week claimed the company had made its popular service ChatGPT better at supporting users experiencing mental health problems like suicidal ideation or delusions, but experts tell the Guardian they need to do more to truly ensure users are protected. The Guardian tested several prompts indicating suicidal ideation with the ChatGPT GPT-5 updated model, which is now the default, and got alarming responses from the large language model (LLM) chatbot.