快速筛选
Quick screening
判断一篇论文是否值得继续投入时间。
Decide whether a paper deserves deeper time.
一套面向机器人、具身智能与 Agent 的结构化论文阅读工作流。
把论文读成可以验证、比较和迁移的研究材料。
普通摘要很容易丢掉页码、实验条件和失败边界。读完之后,我们往往记得一句结论,却说不清它由什么证据支持、能否迁移到自己的系统。
A conventional summary easily loses page references, experimental conditions and failure boundaries. After reading, we may remember a claim but cannot explain what supports it or whether it can transfer into our own system.
这套流程的目标,是让论文阅读从“看过”变成“可以验证、比较和继续积累”。
The goal is to move from having read a paper to being able to verify, compare and build on it.
核心结论没有回到页码、章节、公式、图或表。
Key claims are not tied back to pages, sections, equations, figures or tables.
看不到基线是否公平、消融是否充分、成本是否可以接受。
Baseline fairness, ablations, cost and deployment limits remain unclear.
每篇笔记格式不同,后续无法比较方法、数据和结论。
Inconsistent notes make it hard to compare methods, data and conclusions later.
组会、复现、方法迁移和方向综述需要不同结构。
Group meetings, reproduction, method transfer and surveys require different structures.
判断一篇论文是否值得继续投入时间。
Decide whether a paper deserves deeper time.
重建问题、方法、实验、消融、失败与复现风险。
Reconstruct the problem, method, experiments, ablations, failures and reproducibility risks.
提炼可以进入自己系统的机制和最小验证实验。
Extract mechanisms that can enter another system and define a minimum validation experiment.
用统一矩阵比较共识、分歧、方法演化和研究空白。
Compare consensus, disagreement, method evolution and research gaps in one matrix.
输入可以是 arXiv 链接、DOI、本地 PDF、论文题目,也可以是一组论文。把来源交给 Codex 后,再选择阅读模式即可。
Inputs can be an arXiv link, DOI, local PDF, title or a collection of papers. Give the source to Codex, then choose the reading mode.
读:https://arxiv.org/abs/2210.03629
Read: https://arxiv.org/abs/2210.03629
深读这三篇并做横向综述
Deep-read these three papers and create a synthesis
把这篇论文做成 12 页 PPT,重点讲方法和真机实验
Turn this paper into a 12-page deck focused on the method and real-robot experiments