This is new one DeepSeek-R1 dear Sunny Sharma .

China’s cheap, open AI model DeepSeek thrills scientists

DeepSeek-R1 performs reasoning tasks at the same level as OpenAI’s o1 — and is open for researchers to examine...

"A Chinese-built large language model called DeepSeek-R1 is stepping up as an affordable rival to ‘reasoning’ models such as OpenAI’s o1. R1 matched o1’s performance on certain tasks in chemistry, mathematics and coding, and has been released as ‘open-weight’, which means that researchers can study and build on the algorithm. “The openness of DeepSeek is quite remarkable,” says AI researcher Mario Krenn, since OpenAI’s models are “essentially black boxes”..."

https://www.nature.com/articles/d41586-025-00229-6

What are the best AI tools for research?

There are many large language models to choose from; some excel at coding, whereas others are better for synthesizing information...

"With new impressive AI tools dropping constantly, it can be hard to keep up. In this guide, Nature spoke to researchers about which tools to use when (with many adding the perennial caveat that all models make mistakes, so use their outputs with care).

• o3-mini (the reasoner): OpenAI’s speedy model relies on a ‘chain of thought’ process to simulate human reasoning. That makes it good at technical tasks, such as solving coding issues, reformatting data, or picking apart unfamiliar concepts in a new mathematical proof.
• DeepSeek (the all-rounder): DeepSeek-R1 is available through an API at a fraction of the cost of its competitors and is open weight, meaning that although its training data have not been released, anyone can download the underlying model and tailor it to their specific research project. Its fortes are maths problems and writing code, though it’s also good at tasks such as generating hypotheses.
• Llama (the workhorse): Llama has long been a go-to LLM for the research community. Researchers have built on Llama models to make LLMs that predict materials’ crystal structure, as well as to simulate the outputs of a quantum computer, as it’s well-suited for adapting to the specialized language of scientific projects.
• Claude (the coder): In Silicon Valley, many people swear by Claude 3.5 Sonnet for writing code. Its models also get plaudits for their writing style, too.
• Olmo (the really open one): Researchers who want to understand what’s going on under the hood of an LLM will need something even more transparent than the models offered by Llama and DeepSeek. The top-performing model that comes with the algorithm’s training data plus the code used to train the models is OLMo 2."

https://www.nature.com/articles/d41586-025-00437-0

AI tools are spotting errors in research papers: inside a growing movement

"...Both projects want researchers to use their tools before submitting work to a journal to avoid mistakes entering the literature. The impact could be huge, but the methods also generate a lot of false positives and could have worrying unintended consequences; for example, if they are used to target certain fields...

The projects have tentative support from academic sleuths who work in research integrity..."

https://www.nature.com/articles/d41586-025-00648-5

Why an overreliance on AI-driven modelling is bad for science

Without clear protocols to catch errors, artificial intelligence’s growing role in science could do more harm than good...

https://www.nature.com/articles/d41586-025-01067-2

A scientist’s guide to AI tools

"As artificial intelligence (AI) continues to evolve, more tools are being built to meet the needs of students and scientists. Nature explores how to harness AI to streamline various parts of the research process, from sharpening your literature review to streamlining your statistics. Among these are tools such as Paperpal and Thesify, which check academic manuscripts against journal submission guidelines, and Elicit, which helps to summarize papers at speed..."

https://www.nature.com/articles/d41586-025-01069-0

‘Open source’ AI isn’t truly open — here’s how researchers can reclaim the term

Many firms are misusing the ‘open source’ label. The scientific community, which relies on transparency and replicability, must resist this trend...

https://www.nature.com/articles/d41586-025-00930-6

Hello,

I am Oshin, a master’s student at Imperial College London, studying Economics. Along with a couple of AI engineers, I am building a platform to help researchers innovate more effectively. Our MVP is ready and we are looking for the first users; completely FREE OF ANY COSTS (except for just a few minutes of you providing us feedback)

Features included in the MVP:

Smart Summaries delivers structured, context-aware research paper summaries, optimized for scientific clarity across text and visuals, unlike generic outputs by current AI tools.

Research Paper Recommendations & Literature Review Automation uses semantic search to find and rank relevant papers, then generates a preliminary literature review and detailed summaries to aid researchers in quickly identifying valuable work—without losing academic nuance.

Please let me know if you would be willing to give the product a shot.

Email: [email protected]

The Future of Scientific Writing: AI Tools, Benefits, and Ethical Implications

"Artificial Intelligence (AI) transforms scientific writing by improving efficiency, accessibility, and quality. This study evaluated the applications, benefits, and challenges of AI tools, including Elicit, Perplexity, Consensus, ChatGPT, and Grammarly, in the literature review, information organization, and textual clarity enhancement. A narrative review and practical analysis were conducted, assessing the tools based on synthesis capabilities, accessibility, and accuracy. Results showed that AI tools optimize literature analysis and enhance the clarity of scientific texts, particularly for non-native English-speaking researchers. However, limitations include technical inaccuracies, excessive standardization of writing style, and ethical concerns regarding authorship and accountability. The study concludes that while AI can significantly support scientific writing, its adoption should be accompanied by stringent human oversight and adherence to ethical guidelines to maintain academic integrity."

Article The Future of Scientific Writing: AI Tools, Benefits, and Et...

Ethical guidelines for the use of generative artificial intelligence and artificial intelligence-assisted tools in scholarly publishing: a thematic analysis

"This paper identified inconsistencies in guidelines for using AI in scientific research and publications, emphasizing the need for unified ethical standards. Based on an analysis of policies from top academic publishers, it proposes guidelines that distinguish between the accepted use of AI-assisted tools and the cautious use of generative AI tools. The study under-scores human authors’ responsibility for research integrity and advocates for a common best practice guideline to ensure consistency and uphold research ethics in the scientific community. " Article Ethical guidelines for the use of generative artificial inte...

Can AI help authors prepare better risk science manuscripts?

"Scientists, publishers, and journal editors are wondering how, whether, and to what extent artificial intelligence (AI) tools might soon help to advance the rigor, efficiency, and value of scientific peer review. Will AI provide timely, useful feedback that helps authors improve their manuscripts while avoiding the biases and inconsistencies of human reviewers? Or might it instead generate low-quality verbiage, add noise and errors, reinforce flawed reasoning, and erode trust in the review process? This perspective reports on evaluations of two experimental AI systems: (i) a “Screener” available at http://screener.riskanalysis.cloud/ that gives authors feedback on whether a draft paper (or abstract, proposal, etc.) appears to be a fit for the journal Risk Analysis, based on the guidance to authors provided by the journal (https://www.sra.org/journal/what-makes-a-good-risk-analysis-article/); and (ii) a more ambitious “Reviewer” (http://aia1.moirai-solutions.com/) that gives substantive technical feedback and recommends how to improve the clarity of methodology and the interpretation of results. The evaluations were conducted by a convenience sample of Risk Analysis Area Editors (AEs) and authors, including two authors of manuscripts in progress and four authors of papers that had already been published. The Screener was generally rated as useful. It has been deployed at Risk Analysis since January of 2025. On the other hand, the Reviewer had mixed ratings, ranging from strongly positive to strongly negative. This perspective describes both the lessons learned and potential next steps in making AI tools useful to authors prior to peer review by human experts."

https://onlinelibrary.wiley.com/doi/10.1111/risa.70055?af=R

http://screener.riskanalysis.cloud:8701/

https://www.sra.org/journal/what-makes-a-good-risk-analysis-article/

http://aia1.moirai-solutions.com:8702/

Principles for Responsible AI Usage in Research

"In a rapidly evolving AI landscape, policymakers, publishers, and researchers have to continuously re-define responsible practices for research. Our consensus describes the fundamental principles required for responsible AI usage to guide everyday research and the development of future policies..."

Preprint Principles for Responsible AI Usage in Research

Revolutionizing Research Methodologies: The Emergence of Research 5.0 through AI, Automation, and Blockchain

"This integrative literature review (ILR) explores the significant impact of incorporating artificial intelligence (AI), automation, and blockchain technology into research methodologies, collectively known as Research 5.0. The study addresses the shortcomings of traditional research methods, which need help managing the complexities and demands of modern scientific inquiry, thereby affecting the reliability and efficiency of research across various fields. The ILR aims to critically assess how these advanced technologies can enhance research processes, guided by a conceptual framework centered on AI, automation, and blockchain. The research method involved a comprehensive literature review and the analysis of qualitative data to identify patterns, challenges, and opportunities for implementing these technologies. The findings reveal that while AI significantly improves research efficiency and accuracy, it also introduces challenges such as algorithmic bias and transparency issues, which can be mitigated through Research 5.0 Explainable AI (RXAI) framework and comprehensive researcher training. Automation enhances consistency but risks reducing human oversight, necessitating hybrid systems that blend human expertise with automated precision. Blockchain strengthens data integrity and transparency yet faces complexity and energy consumption challenges, underscoring the need for scalable and sustainable solutions. The study concludes that while Research 5.0 technologies offer substantial potential, their successful integration requires careful consideration of ethical, technical, and operational challenges. Future research should focus on developing transparent AI systems, hybrid automation models that retain human judgment, and scalable blockchain solutions to advance research methodologies effectively."

Article Revolutionizing Research Methodologies: The Emergence of Res...

"We are facing a quiet but profound crisis. Students are increasingly equipped with powerful AI tools, like ChatGPT, yet many are losing the crucial ability to think critically and creatively. If you’re involved in supervising student research or guiding academic writing, you’ve likely observed this shift. Thesis-writing, despite technological advancements, often remains rigid, formulaic and disconnected from meaningful thought.

Let’s pause and ask ourselves: have we fundamentally misunderstood what thesis-writing should achieve? What if the core goal isn’t ticking boxes or applying techniques but nurturing deep, reflective thinking?

My recent research is designed to help educators foster meaningful intellectual growth through thesis-writing in the age of generative AI..."

https://blogs.lse.ac.uk/impactofsocialsciences/2025/06/27/how-students-should-write-a-thesis-in-the-ai-age/

OpenAI's o3 tops new AI league table for answering scientific questions

SciArena uses votes by researchers to evaluate large language models’ responses on technical topics...

o3, an artificial intelligence (AI) model developed by the creators of ChatGPT, has been ranked the best AI tool for answering science questions in multiple fields, according to a benchmarking platform launched last week...

https://www.nature.com/articles/d41586-025-02177-7

‘Another DeepSeek moment’: Chinese AI model Kimi K2 stirs excitement

Excitement is growing among researchers about a second, powerful artificial intelligence (AI) model to emerge from China, after DeepSeek shocked the world with its launch of R1 in January...

The latest version of the chatbot, developed by start-up Moonshot AI, is open for researchers to build on...

https://www.nature.com/articles/d41586-025-02275-6

AI in Scientific Research: Empowering Researchers with Intelligent Tools

"This article explores the transformative impact of artificial intelligence (AI) on scientific research across various disciplines. It examines how AI-driven tools are revolutionizing data analysis, simulation, and hypothesis generation, particularly in fields such as genomics, climate science, and materials science. The article discusses the acceleration of discovery processes through AI, highlighting its role in enabling sophisticated analysis of complex datasets, developing predictive models, and facilitating automated experimentation. Ethical considerations, including the need for transparency and reproducibility in AI-assisted research, are addressed. The synergy between human creativity and AI capabilities is explored, emphasizing how AI augments human ingenuity and fosters interdisciplinary collaboration. Case studies illustrate successful implementations of AI in scientific inquiry, demonstrating its potential to enhance research methodologies and outcomes. The article also looks ahead to the prospects of AI in scientific research, considering emerging technologies and the evolving role of AI in the scientific process. By providing a comprehensive overview of AI's current applications and future potential in scientific research, this article underscores the pivotal role of AI in advancing scientific knowledge and addressing complex global challenges."

Article AI in Scientific Research: Empowering Researchers with Intel...

OpenAI launches reasoning LLM that you can download and tweak

One version of the gpt-oss large language model can run on a laptop, and performs nearly as well as the company’s most powerful models...

Researchers will be able to download it and customize it...

https://openai.com/index/gpt-oss-model-card/

https://www.nature.com/articles/d41586-025-02495-w

Beyond AlphaFold: how AI is decoding the grammar of the genome

Scientists are seeking to decipher the role of non-coding DNA in the human genome, helped by a suite of artificial-intelligence tools...

A fast-growing suite of artificial intelligence (AI) tools aim to help researchers decipher the grammar of the genome — especially the vast portion that does not code for proteins. Think AlphaFold, but for regulatory DNA, which are sequences that control gene expression. “How proteins are encoded in the genome, the code of how genes are expressed, when and where, how much — is one of the most fascinating problems in biology,” says computational biologist Stein Aerts...

https://www.nature.com/articles/d41586-025-02621-8

Swiss open-source AI is ‘for the public’

There’s a buzzy new AI model in town, but it’s not from OpenAI or DeepSeek. Apertus, Latin for open, is a product of the Swiss government — its source code, training data and model weights are all available online and it was designed to comply with Swiss copyright laws. Apertus was developed by public institutions ETH Zurich, the Swiss Federal Technology Institute of Lausanne and the Swiss National Supercomputing Centre. “Currently, Apertus is the leading public AI model: a model built by public institutions, for the public interest. It is our best proof yet that AI can be a form of public infrastructure like highways, water, or electricity," computer scientist Joshua Tan said...

https://www.engadget.com/ai/switzerland-launches-its-own-open-source-ai-model-133051578.html