将博导十年科研经验炼化为可直接调用的 AI 技能。从 Idea 构思到论文投稿,你的 AI 科研副导师。
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The open-source AI research workbench for scientific research and agent workflows. Local-first, model-agnostic desktop app with extensible skills, MCP tools and connectors, Python/R execution and traceable artifacts for reproducible research on macOS, Windows and Linux.
As part of model development, we evaluate our models on open research problems. We expanded these evaluations after performance on our existing mathematical evaluations saturated. Some outputs build upon earlier results produced by the models.
OpenScience is an open-source workbench for scientific research. Work through literature, data, code, experiments, and writing in the desktop app, browser workspace, or terminal.
PaperSpine5 — local-first, evidence-bound paper research, writing, figures, review and delivery. Download:
An LLM agent's capability is largely set by its harness: the prompts, control flow, tools, memory and context management around a frozen model.
app — the end-signal probe, public pages, and the live steered-model lab.
parser.addargument("--pdfpath", type=str, default='', help="if none, the bot will download from arxiv with query") parser.add
0-rc2, Mathlib, and Lake. With elan installed, fetch the mathlib cache and build the formalizations with:
Mimir — 一站式科研工作台插件:LaTeX 论文边写边编译、arXiv 文献管理、实验追踪、指标图表、GPU 服务器 SSH 任务编排,管理科研全周期。An open-source research workbench plugin for the whole research cycle.
Serenity-inspired Agent Skill for supply-chain bottleneck stock research
Help build ASI-Bench: we are actively seeking high-quality scientific task contributions.
Pretrain temporal dynamics once. Adapt with limited target data. Generate full predictive distributions in 1–4 steps.
A title is not a salary. Two people at the same nominal level are worth different amounts if they control different profit pools, customer relationships or technical bottlenecks — so the analysis starts from where the money is made, not from a salary survey.
让AI Agent 做真正会思考,有判断的深度调研,而不是会摆信息的汇总报告。一个即插即用的 skill 文件。
The algorithm is not polynomial-time. It's not even close. It's "subexponential-time" which is the same class as the best factoring algorithms.