Licong Xu

If you are in AI, let’s connect!

Do you remember when you joined X? I do! #MyXAnniversary

又到周末了 感觉没什么可发的 发一个几个月来一直在吃灰的项目Parallel Science（parallelscience.org），一个专门用来存放AI autoresearch产出的论文和科研成果的平台，主要由两块组成： • Parallel ArXiv（papers.parallelscience.org）：专门给 AI 生成科研工作的开放预印本库 • Parallel OpenReview（reviews.parallelscience.org）：用 AI 给提交到 Parallel ArXiv 的论文做评审。 目前我们的 agents（包括 Claude Code、OpenCode、Denario 等）已经产出了 290 篇论文。 当初主要是为了解决autoresearch 和 AI scientist 产出的论文数量越来越多，但是质量参差不齐。如果不能上主 arXiv，那它们到底该往哪放？随着模型和harness增强，可以重新跑一下autoresearch写论文看看和已经有的成果比看看，感觉很有趣。 感兴趣的可以聊聊大家对autoresearch产出的成果应该如何进行可靠的评审和分类来确保他们是否真正有用？

There is a growing volume of autoresearch and AI-scientist papers. Quality varies. Most of them do not belong on the main arXiv, but where should they go? Few months ago, we introduced Parallel Science parallelscience.org. An open infrastructure for scaling autonomous AI research scientists. It is made up of “Parallel Arxiv” and “Parallel OpenReview”. Parallel ArXiv papers.parallelscience.org is a dedicated open archive for AI-generated scientific work Parallel OpenReview: reviews.parallelscience.org used AI to review the paper submitted to Parallel Arxiv Currently, we generated 290 papers with our agents, including Claude Code, Opencode, and Denario etc. Happy to chat more on how to build an AI infrastructure hosting all the AI generated output for future studies parallelscience.org Papers: papers.parallelscience.org Reviews: reviews.parallelscience.org

各位朋友们 最近刷X刷到了一堆本地大模型 有点消化不过来 实验室有双卡RTX 6000 PRO 96GB RAM的机器 如果部署能放得下单卡的本地大模型 对于agent代码任务和科学知识问答 哪个比较好？过去用过Qwen3.8 27B 现在用Qwen-3.8-Flash-Next 感觉效果很不错 有没有综合任务能比他更强的？

I used AI coding agents to build a GPU program for a routine task in astronomy on spherical analysis: take a map of the whole sky and compute its spherical harmonic transforms to reduce it to a small set of numbers. This is a well benchmarked algorithmic and kernel optimization tasks which worth trying to test how good LLM agents are good at maths, optimization and coding kernels. I used whatever agents I have subscribed, with a significant contribution from the open sourced model Qwen-3.8-Flash-Next using Qwen code which fits one GPU in our lab. The map covers a sphere. Methods written for ordinary images assume a flat grid, so they are the wrong shape. The method that fits is a spherical harmonic transform, usually shortened to SHT. An SHT separates the map into simple patterns on the sphere, from the largest features down to the fine structure. Cosmologists use those patterns to describe how matter is distributed. That transform is the costly step. With the agents, I wrote a version that runs on one RTX PRO 6000. On the map sizes I tested, the full analysis is about 12–22× faster than the standard code on a 96-core CPU, and the results agree. Code: github.com/licongxu/GMaster

真的瘫坐了 老马是不是把火箭发射喷气机技术用到汽车上了

ChatGPT新玩法：根据你所知道的关于我的一切，把这个容器的两面都填满。 你们都是啥？

Random late-night thought: being a physicist feels very different now. I used to derive equations, write the code, debug it, run it. Now I break research problems into subproblems, give agents repos and goals, and spin up cloud agents before bed so I can wake up to PRs. Recently I let them loose on some painfully slow scientific code within our community. They benchmarked different ideas, even creatively invented some new numerical tricks and methods, and in a few cases made things O(100x) faster on an RTX Pro 6000. The weirdest part is the loop: my supervisor asks something on Slack → a bot answers → another agent reads the thread + our Granola meeting notes → implements it in the repo → PR. Still doing physics and cosmology. The interface is just becoming very strange. Image Credit: ChatGPT

Please ship by Thursdayyyyyyyy then I will start my own company 🔥🔥🔥🔥🔥

什么时候真能用上4.7啊 别画饼了可以吗

I’ve been experimenting with a different way of using AI agents in scientific research: not just “help me write code”, but as a full-stack agentic layer that can coordinate an entire research workflow end to end across code, data, compute, experiments, and collaboration. With Grok Bot, I’ve started building a small team of persistent, specialised agents around my actual research infrastructure Research Ops— plan, brainstorm, code-assist, and paper wrap for my actual projects (I keep heavy coding in Cursor) Discovery Lab — a separate fleet for open-problem exploration (data, theory/math, claim audit, tech signals), deliberately walled off from thesis/project trees Ops sensors — code / data / infrastructure monitors on the research workstation + HPC, with a coordinator when something needs routing I connect the Bots to Slack for day-to-day research communication and Granola for meeting context and notes, so discussions, decisions, and action items can feed back into the research workflow rather than living in separate tools. The idea is not to remove the researcher from the loop. The agents inspect real systems, connect context across tools, and surface what actually needs attention; I stay in the loop for scientific judgement and the hard coding. Still an experiment in what it might look like to make the research workflow more AI-native. #GrokBot #Cursor #SpaceXAI #AIAgents #ScientificComputing #Cosmology #HPC

Looking forward to @OpenAI to solve room temperature superconductivity!

We are proud to announce that our team has won 1st place in Phase 1 of the NeurIPS 2025 #FairUniverse competition. This milestone demonstrates the potential of autonomous AI agents—guided by human expertise—to achieve breakthrough performance in real-world astrophysics research.