Certified holistic nutritionist Robin DeCicco explains how pumpkin's antioxidants and carotenoids help protect against cell damage and reduce inflammatory conditions.

There's a surprisingly competitive global race on to grow a 3,000-pound pumpkin. Ian (left) and Stuart Paton pose with a giant pumpkin in their nursery in the New Forest, Hampshire. Breakthroughs, discoveries, and DIY tips sent every weekday. The pumpkin's name was Muggle and it weighed as much as a bull moose. At 2,819 pounds and over 21 feet in circumference, this enormous gourd claimed the dual titles of "heaviest pumpkin" and "largest pumpkin by circumference" in the on October 4, 2025.

Brandon Dawson's prize-winning pumpkin weighs as much as a bison. Breakthroughs, discoveries, and DIY tips sent every weekday. After narrowly missing the title last year, electrical vehicle engineer Brandon Dawson won the top prize at the Safeway World Championship Pumpkin Weigh-Off in Half Moon Bay, California. His humongous gourd weighed a staggering 2,346 pounds. The annual pumpkin weighing contest has been likened to the Super Bowl of pumpkin growing.

Keep fake spider webs close to your house, and ditch the real pumpkins if you live near wildlife. Breakthroughs, discoveries, and DIY tips sent every weekday. With spooky season just on the horizon, Halloween decorations are beginning to pop up everywhere--tombstones, pumpkins, and of course, tons and tons of fake spiderwebs . Amidst all the autumnal celebrations, it's easy to forget those who not only can't join in on the celebration, but might even be threatened by the decorations: wildlife. While Jennifer Bloodgood, a Cornell University wildlife veterinarian, hasn't personally witnessed it before, she tells that she agrees with the dangers of some Halloween decorations. "Birds would definitely be the major concern," she says, referring specifically to fake spider webs.

Theory of Mind (ToM), the ability to attribute mental states to others, is fundamental for human social intelligence and a critical capability for advanced Artificial Intelligence. Recent advancements in Large Language Models (LLMs) have shown promising performance on ToM benchmarks, raising the question: Do these benchmarks necessitate explicit human-like reasoning processes, or can models succeed through alternative strategies? We investigate this question empirically by applying Reinforcement Learning (RL) and Supervised Fine-Tuning (SFT) to LLMs of varying scales (0.5B to 7B parameters) and evaluating them across multiple ToM datasets. Our results reveal a scale-dependent impact of RL: while RL significantly improves accuracy and fosters high-quality, interpretable, and transferable belief-tracking reasoning in larger models (7B), it leads to "reasoning collapse" in smaller models ($\leq$3B), where high accuracy and generalization ability are achieved via drastically shortened, less meaningful responses. Surprisingly, further SFT achieves competitive and generalizable performance across these benchmarks, often matching or exceeding RL models in accuracy, despite not being explicitly trained to produce structured reasoning traces. These findings highlight a critical discrepancy between benchmark accuracy and the nature of learned reasoning. Our work suggests that current ToM benchmarks may be solvable without requiring the explicit, human-like simulation of mental states they were designed to probe. LLMs, particularly when scale is limited or training signals focus solely on output correctness, may leverage alternative rules effective for benchmark data structures.

Language models (LMs) are powerful tools for natural language processing, but they often struggle to produce coherent and fluent text when they are small. Models with around 125M parameters such as GPT-Neo (small) or GPT-2 (small) can rarely generate coherent and consistent English text beyond a few words even after extensive training. This raises the question of whether the emergence of the ability to produce coherent English text only occurs at larger scales (with hundreds of millions of parameters or more) and complex architectures (with many layers of global attention). In this work, we introduce TinyStories, a synthetic dataset of short stories that only contain words that a typical 3 to 4-year-olds usually understand, generated by GPT-3.5 and GPT-4. We show that TinyStories can be used to train and evaluate LMs that are much smaller than the state-of-the-art models (below 10 million total parameters), or have much simpler architectures (with only one transformer block), yet still produce fluent and consistent stories with several paragraphs that are diverse and have almost perfect grammar, and demonstrate reasoning capabilities. We also introduce a new paradigm for the evaluation of language models: We suggest a framework which uses GPT-4 to grade the content generated by these models as if those were stories written by students and graded by a (human) teacher. This new paradigm overcomes the flaws of standard benchmarks which often requires the model's output to be very structures, and moreover provides a multidimensional score for the model, providing scores for different capabilities such as grammar, creativity and consistency. We hope that TinyStories can facilitate the development, analysis and research of LMs, especially for low-resource or specialized domains, and shed light on the emergence of language capabilities in LMs.

This is the latest in my series of screencasts demonstrating how to use the tidymodels packages. If you are a tidymodels user, either just starting out or someone who has used the packages a lot, we are interested in your feedback on our priorities for 2022. The survey we fielded last year turned out to be very helpful in making decisions, so we would so appreciate your input again! Today's screencast is great for someone just starting out with workflowsets, the tidymodels package for handling multiple preprocessing/modeling combinations at once, with this week's #TidyTuesday dataset on giant pumpkins from competitons. Here is the code I used in the video, for those who prefer reading instead of or in addition to video.

If you make a purchase by clicking one of our links, we may earn a small share of the revenue. However, our picks and opinions are independent from USA TODAY's newsroom and any business incentives. The spookiest holiday of all is just around the corner--and you're going to need more than a few pumpkins scattered around your porch to properly decorate. Halloween is on Thursday, Oct. 31, which means you've only got a few weeks to add an eerie flair to your home before hoards of trick-or-treaters arrive. From smart plugs to spider webs, we've rounded up 10 essentials you'll need to decorate for this Halloween.

We hypothesize that enhancing computer tutors to respond to student uncertainty over and above correctness is one method for increasing both student learning and self-monitoring abilities. We explore this hypothesis using data from an experiment with a wizarded spoken tutorial dialogue system, where tutor responses to uncertain and/or incorrect student answers were manipulated. Our results suggest that monitoring and responding to student uncertainty has the potential to improve both cognitive and metacognitive student abilities.