AI-powered research idea evaluation platform. Evaluate your research pitch quality using models trained on institutional publication traces.
AI Research Assistant Claude Scholar: Open Source, Free, and Citation-Verified | João Cláudio Nunes Carvalho posted on the topic | LinkedIn
🚨 Professors are going to hate this. Someone just open sourced an AI that writes research papers from idea to publication. Conference-ready. Citation-verified. Free. It's called Claude Scholar. An AI-powered research system that handles every step of the academic workflow. Idea to publication. Fully automated. No advisor at 2am. No staring at blank LaTeX files. No crying during rebuttal season. Here's what's inside this thing: → AI brainstorms research topics, reviews literature, and finds gaps nobody has explored → Runs statistical analysis on your experiments. t-tests, ANOVA, ablation studies. Publication-ready figures. → Writes your paper section by section. Abstract to conclusion. Conference-formatted. → Verifies every citation through multi-layer validation so AI never hallucinates a reference → Strips robotic AI language and adds human voice so reviewers can't tell → Self-reviews your draft with a 6-point quality checklist before you submit → Parses reviewer comments, classifies each one, and drafts your entire rebuttal Here's the wildest part: It supports NeurIPS, ICML, ICLR, ACL, AAAI, Nature, Science, Cell, and PNAS. Downloads the conference template, strips sample content, and gives you a clean LaTeX structure ready to write into. The creator says it covers 90% of the academic research lifecycle. Research assistants charge $30 to $60/hour. Conference paper consultants charge $5,000+. Graduate programs cost $50K/year. This is free. All of it. 100% Open Source.
AI Research Agents Narrow Scientific Exploration
AI research agents now support large-scale AI-assisted scientific discovery. We examine whether AI-generated ideas broaden scientific exploration or primarily reinforce existing work. Using five agent frameworks and five large language models, we generate 219,655 ideas for different scientific fields. Across experiments, four consistent patterns emerge. First, AI-generated ideas are more concentrated than human-authored papers within the same research area. Second, they remain much closer to starting literature than later human follow-on work does. Third, AI-generated ideas align less with future human research. Last, AI-generated ideas are located in lower-impact regions of the historical scientific landscape. Overall, current AI research agents appear better suited to local elaboration than to broadening scientific exploration.
Exploring AI’s growing role in scientific peer review
Stanford computer scientist James Zou discusses AI’s strengths at spotting errors and limitations in judging research significance.
Introducing Elicit Research Agent: Rigor that powers decisions - Elicit
Elicit Research Agent is an AI environment built for high-stakes decisions
No. 030: Google Deep Review, AI research taste, AI in dissertations - The Hallway Track
The Hallway Track No. 030 - How AI is affecting the practice of science
Consensus: AI for Research
Consensus is an AI academic search engine for peer-reviewed literature—your research OS for finding, organizing, and analyzing science 10x faster.
Scientific Paper Planner - AI Research Planning
Structure your scientific research with AI-powered guidance
Governing the scholarly AI Commons – Open Future
New report explores how academic communities can shape AI governance in research and publishing, from regulation to community-led approaches.
Artificial intelligence tools expand scientists’ impact but contract science’s focus
Developments in artificial intelligence (AI) have accelerated scientific discovery1. Alongside recent AI-oriented Nobel prizes2–9, these trends establish the role of AI tools in science10. This advancement raises questions about the influence of AI tools on scientists and science as a whole, and highlights a potential conflict between individual and collective benefits11. To evaluate these questions, we used a pretrained language model to identify AI-augmented research, with an F1-score of 0.875 in validation against expert-labelled data. Using a dataset of 41.3 million research papers across the natural sciences and covering distinct eras of AI, here we show an accelerated adoption of AI tools among scientists and consistent professional advantages associated with AI usage, but a collective narrowing of scientific focus. Scientists who engage in AI-augmented research publish 3.02 times more papers, receive 4.84 times more citations and become research project leaders 1.37 years earlier than those who do not. By contrast, AI adoption shrinks the collective volume of scientific topics studied by 4.63% and decreases scientists’ engagement with one another by 22%. By consequence, adoption of AI in science presents what seems to be a paradox: an expansion of individual scientists’ impact but a contraction in collective science’s reach, as AI-augmented work moves collectively towards areas richest in data. With reduced follow-on engagement, AI tools seem to automate established fields rather than explore new ones, highlighting a tension between personal advancement and collective scientific progress.
Weighted DVF: A Simple Model for Scoring and Prioritizing Ideas
Discover a powerful yet straightforward approach to evaluating new ideas by balancing desirability, viability, and feasibility. Learn how…
The AI Roles Continuum: Blurring the Boundary Between Research and...
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AI is not superhuman
What metaphor should drive the field of AI research?
No. 002: Hallucinated citations at scale, ICML catches AI reviewers, scholarly infrastructure gaps - The Hallway Track
The Hallway Track No. 002 - How AI is affecting the practice of science
Open Source AI Policy Landscape — RedMonk Analysis
How foundations and projects are responding to AI-generated contributions. This analysis surveys 88 major organizations.
AI Is Changing Who Wins Research Grants
A new study from the Northwestern Innovation Institute finds that proposals showing stronger signs of AI-assisted writing were more likely to receive support from the National Institutes of Health, while also tending to align more closely with ideas that had already been funded.
Augmenting Research Ideation with Data: An Empirical Investigation...
Recent advancements in large language models (LLMs) demonstrate strong potential for generating novel research ideas, yet such ideas often struggle with feasibility and effectiveness. In this...