How emergence happens has long been debated amongst physicists, biologists and philosophers alike. quantamagazine.org/the-new-m…

Researchers at Columbia University have developed modular robots that can adapt, repair, and even rebuild themselves using a concept called robot metabolism. 🤖 Instead of remaining fixed, these robots can detach, reconnect, and reorganize their own structure based on the task or environment. If one part is damaged, the system can replace or rearrange itself rather than stopping completely. This could reshape the future of disaster response, industrial automation, and even space exploration. The idea of robots that evolve instead of wear out is becoming more than science fiction. What real-world application do you think will benefit most from this technology? 🎥 Media: @Columbia ⚠️ This content is shared for informational purposes only. CTO Robotics Media is a media platform and does not own or develop the technology shown. Credit belongs to the original creators.

Just posted a paper on arXiv? 📄 Head to kurate.org to see how it scores in our AI-powered impact ranking. Kurate now covers ~50% of all new arXiv submissions, and growing fast. #arXiv #OpenScience

Here's an alarming combination of charts: - CS is the most rapidly growing major degree category - CS is the degree category that's most rapidly adopting AI

Einstein’s field equation on a wall in Leiden, Netherlands

This sounds like a fantastic resource for staying updated on cutting-edge research. Excited to explore the papers and insights from @kurateorg.

Interesting concept. As arXiv grows, the real bottleneck is no longer access to papers, but discovering which ones deserve attention first.

That sounds amazing! A fresh take on ranking papers could really elevate research visibility. Can't wait to explore the top picks!

🥇 Congrats to DES Collaboration, T. M. C. Abbott, M. Adamow et al. for ranking #1 Gold in astro-ph.CO Preprints, Week 22, 2026 on Kurate.org! Their paper, “Constraints on Dynamical Dark Energy from Multiple Probes in the Full Dark Energy Survey,” is highlighted for its strong impact in cosmology, with a Tournament Score of 1553 ± 48 and a 94% win rate on Kurate.org. #Cosmology #Astrophysics #DarkEnergy #DES #arXiv #Research #KurateOrg #Kuratepapers

Please check our latest article on Medium: “The Papers We Miss” A reflection on how valuable research can disappear between publication, search, and recognition — and why paper discoverability now matters for researchers, institutions, and scholarly navigation. medium.com/kurate/the-papers… #Research #AcademicPublishing #OpenScience #ScholarlyCommunication #ResearchDiscovery

if you didn't notice that the paper has been on the arxiv since last year, maybe it's better to leave science to AI x.com/exosome/status/2056898…

We want to help scientists discover their next breakthrough with AI. Gemini for Science is our new suite of experimental tools to help them explore more hypotheses, validate work at scale, unpack literature with ease, and more 🧵

tho the arxiv ban on fake citations also kills jokes like this which is a shame

Congratulations to Yuhao Li, Elaine Shi, and Mengqian Zhang. Their paper, “A Trilemma in AMM Mechanism Design,” is featured today in Kurate’s Game Theory Paper Rankings. The paper examines a fundamental limitation in automated market maker design, showing that incentive compatibility, weak local efficiency, and uniform pricing cannot all be achieved at the same time. Kurate ranked the paper #92 of 472 in Game Theory, with a tournament score of 1495 ± 49, a 64% win rate, and strong assessment scores for rigor, clarity, and overall significance. This is an important contribution for mechanism design, DeFi, AMM protocol research, and MEV mitigation. Explore research rankings at Kurate.org #GameTheory #MechanismDesign #DeFi #Blockchain #AMM #MEV #Research #AcademicTwitter #arXiv

Finally a smarter way to discover quality research.

The future of scientific discovery won’t just depend on better researchers. It’ll depend on better ranking, filtering, and surfacing systems for knowledge itself. This is a very underrated idea.

this is actually a pretty useful idea. arxiv is impossible to follow manually at this point, so even a good first layer of filtering can save a lot of time.

This could shift how we evaluate research impact. Pairwise tournaments might offer a fresh perspective on rankings.

This is the kind of AI tooling researchers actually need. Signal ranked over hype > endless paper dumping.