Environment Conservation Land Asexual parasitic plants break biology's rules Mushroom-looking'Balanophora' plants mostly live underground feeding off of tree roots. Breakthroughs, discoveries, and DIY tips sent every weekday. Learning how plants use the sun and water to make their own food is a staple of biology class and makes life on Earth possible. Still, not all of the over 300,000 known plant species are food-producing powerhouses that reproduce sexually. Instead, plants like those in the genus are asexual parasites.

Large Language Models (LLMs) are increasingly employed as AI tutors due to their scalability and potential for personalized instruction. However, off-the-shelf LLMs often underperform in educational settings: they frequently reveal answers too readily, fail to adapt their responses to student uncertainty, and remain vulnerable to emotionally manipulative prompts. To address these challenges, we introduce CoDAE, a framework that adapts LLMs for educational use through Chain-of-Thought (CoT) data augmentation. We collect real-world dialogues between students and a ChatGPT-based tutor and enrich them using CoT prompting to promote step-by-step reasoning and pedagogically aligned guidance. Furthermore, we design targeted dialogue cases to explicitly mitigate three key limitations: over-compliance, low response adaptivity, and threat vulnerability. We fine-tune four open-source LLMs on different variants of the augmented datasets and evaluate them in simulated educational scenarios using both automatic metrics and LLM-as-a-judge assessments. Our results show that models fine-tuned with CoDAE deliver more pedagogically appropriate guidance, better support reasoning processes, and effectively resist premature answer disclosure.

The development of biological data analysis tools and large language models (LLMs) has opened up new possibilities for utilizing AI in plant science research, with the potential to contribute significantly to knowledge integration and research gap identification. Nonetheless, current LLMs struggle to handle complex biological data and theoretical models in photosynthesis research and often fail to provide accurate scientific contexts. Therefore, this study proposed a photosynthesis research assistant (PRAG) based on OpenAI's GPT-4o with retrieval-augmented generation (RAG) techniques and prompt optimization. Vector databases and an automated feedback loop were used in the prompt optimization process to enhance the accuracy and relevance of the responses to photosynthesis-related queries. PRAG showed an average improvement of 8.7% across five metrics related to scientific writing, with a 25.4% increase in source transparency. Additionally, its scientific depth and domain coverage were comparable to those of photosynthesis research papers. A knowledge graph was used to structure PRAG's responses with papers within and outside the database, which allowed PRAG to match key entities with 63% and 39.5% of the database and test papers, respectively. PRAG can be applied for photosynthesis research and broader plant science domains, paving the way for more in-depth data analysis and predictive capabilities.

Although large language models demonstrate remarkable question-answering performances, revealing the intermediate reasoning steps that the models faithfully follow remains challenging. In this paper, we propose FAME (FAithful question answering with MontE-carlo planning) to answer questions based on faithful reasoning steps. The reasoning steps are organized as a structured entailment tree, which shows how premises are used to produce intermediate conclusions that can prove the correctness of the answer. We formulate the task as a discrete decision-making problem and solve it through the interaction of a reasoning environment and a controller. The environment is modular and contains several basic task-oriented modules, while the controller proposes actions to assemble the modules. Since the search space could be large, we introduce a Monte-Carlo planning algorithm to do a look-ahead search and select actions that will eventually lead to high-quality steps. FAME achieves state-of-the-art performance on the standard benchmark. It can produce valid and faithful reasoning steps compared with large language models with a much smaller model size.

The proliferation of Large Language Models (LLMs), such as ChatGPT, has raised concerns about their potential impact on academic integrity, prompting the need for LLM-resistant exam designs. This article investigates the performance of LLMs on exams and their implications for assessment, focusing on ChatGPT's abilities and limitations. We propose guidelines for creating LLM-resistant exams, including content moderation, deliberate inaccuracies, real-world scenarios beyond the model's knowledge base, effective distractor options, evaluating soft skills, and incorporating non-textual information. The article also highlights the significance of adapting assessments to modern tools and promoting essential skills development in students. By adopting these strategies, educators can maintain academic integrity while ensuring that assessments accurately reflect contemporary professional settings and address the challenges and opportunities posed by artificial intelligence in education.

If you follow me, you've seen I'm fascinated with GPT-3 both as a tool for productivity and as a tool for information retrieval through natural questions. You've also seen that GPT-3 often provides correct answers to a question, but sometimes it does not and it can even be misleading or confusing because its answer appears confident despite being wrong. In some cases, but not always, when it cannot find a reasonable completion (i.e. it "doesn't know" the answer) it tells you so, or it just doesn't provide any answer. I showed you that factual accuracy can be improved by fine-tuning the model, or more easily, by few-shot learning. But it isn't easy to decide what information to use in these procedures, let alone how to apply it.

A robot piloted by a ball of algae can swim through water and move around obstacles, powered only by photosynthesis. Neil Phillips at the University of the West of England, UK, and his colleagues wanted to build a robot with no electronic parts, meaning it wouldn't interfere with any electromagnetically sensitive measurement instruments. The team inserted a marimo, a ball of algae that forms naturally in freshwater currents, inside a 3D-printed plastic spherical shell equipped with vents.

Showcasing America's most promising young scientists and engineers, the Blavatnik Family Foundation and the New York Academy of Sciences today named 31 finalists for the world's largest unrestricted prize honoring early-career scientists and engineers. Three winners of the Blavatnik National Awards for Young Scientists – in life sciences, chemistry, and physical sciences and engineering – will be announced on July 20, each receiving $250,000 as a Blavatnik National Awards Laureate. The finalists, culled from 298 nominations by 157 United States research institutions across 38 states, have made trailblazing discoveries in wide-ranging fields, from the neuroscience of addiction to the development of gene-editing technologies, from designing next-generation battery storage to understanding the origins of photosynthesis, from making improvements in computer vision to pioneering new frontiers in polymer chemistry. Descriptions of the honorees' research are listed below. "Each day, young scientists tirelessly seek solutions to humanity's greatest challenges," said Len Blavatnik, founder and chairman of Access Industries, and head of the Blavatnik Family Foundation. "The Blavatnik Awards recognize this scientific brilliance and tenacity as we honor these 31 finalists. We congratulate them on their accomplishments and look forward to their continued, future discoveries and success." President and CEO of the New York Academy of Sciences Nicholas B. Dirks said: "Each year, it is a complete joy to see the very'best of the best' of American science represented by the Blavatnik National Awards Finalists."

Warmer temperatures may benefit parts of the Amazon rainforest, suggesting that the tropical ecosystem may be more resistant to climate change than once thought. It had previously been thought that water stress brought on by global warming and the drying out of the soil and air would broadly harm the plants of the Amazon. This would lead to reduced photosynthesis -- the chemical process by which plants make food and absorb in carbon dioxide -- and help accelerate climate change. However, US researchers found that wetter areas of the world's largest rainforest actually grow leaves more efficient at photosynthesis when exposed to dry air. The team warned that there is a limit to this, however, and that excessively warm temperatures would still cause damage to even these resilient parts of the forest.

There are only a few ingredients needed for algae to take over: carbon dioxide, light, and water. The ancient microorganism is thriving thanks to record heat waves and fertilizers washed away into nearby waters. But what if a fourth ingredient -- artificial intelligence -- could transform the gooey sludge from a growing pest into a tool to fight climate change? A team of researchers at the AI technology company Hypergiant sees algae as a weapon that can be harnessed for our benefit. They recently built an AI-powered machine, the EOS bioreactor, that takes advantage of algae's ability to capture carbon dioxide through photosynthesis.