Embodied-AI-Daily: an arXiv keyword digest for VLN, VLA and SLAM work
📚这个仓库是在arxiv上收集的有关VLN，VLA，World Model，SLAM，Gaussian Splatting,非线性优化等相关论文。每天都会自动更新！issue区域是最新10篇论文
At a glance
- What is it?
- Embodied-AI-Daily is a GitHub repository that runs a keyword search against arXiv every day and writes the results into its README, a papers/ directory and its issue tracker. It is a reading queue, not a library, and its usefulness depends entirely on how well its keyword lists match your research area.
- Who is it for?
- Adopt it if you already read arXiv by keyword search and want that search performed for you, with an issue feed limited to the newest ten papers and a papers/ directory holding up to eighty per keyword. Do not adopt it as a citation source, as a substitute for a reference manager, or if your topic is absent from its keyword lists, since the repository retrieves by keyword and nothing else.
- Can I use it commercially?
- Not without permission. GitHub finds no licence file in the repository, and without a licence all rights are reserved by default: you may read the code but not reuse it. Check the README, or ask the authors, before using it.
- Is it still maintained?
- Yes. The repository last received commits 4 days ago.
- What is it written in?
- Mainly HTML, according to GitHub's language statistics.
Answers come from the project's GitHub data, last synced on September 15, 2026, and from our analysis. They are not legal advice.
DEEP OPEN-SOURCE ANALYSIS
The problem it addresses is search fatigue, not literature management
arXiv does not offer a saved search that lands in your inbox in a form you can skim. Embodied-AI-Daily fills that gap for one specific research neighbourhood: vision-and-language navigation, vision-language-action models, world models, SLAM, Gaussian Splatting and nonlinear optimization, according to the repository description. The README states the project fetches papers from arXiv based on predefined keywords, and that each section of the README corresponds to one search keyword. The audience is narrow and identifiable: a graduate student or research engineer who already knows which keywords matter to them and wants a daily list rather than a curated feed. The repository is written primarily in HTML, which tells you something about where the effort goes. The output is a rendered page, not a Python package. The homepage at luohongkun.top/Embodied-AI-Daily/ is the intended reading surface, and the README is the machine-readable mirror of it. If you want a tool you can import and query, this is not that. If you want a page you open with coffee, it is exactly that.
How the daily fetch is organised, and where each artefact lives
The pipeline is a keyword search followed by three outputs, and the README describes the split explicitly. Each README section holds up to twenty papers for its keyword. The full list, up to eighty per keyword, goes into the papers/ directory. The issue area carries the latest ten papers. That is a deliberate three-tier design: the README stays short enough to scroll, the directory holds the archive, and the issue tracker gives a low-friction feed that GitHub will email to anyone who clicks Watch. The README recommends exactly that, telling readers to click Watch on the repo for daily email notifications. The AI summaries are the second stage. The README's support section states that each paper's AI deep summary calls the DeepSeek API, and that this costs money. The acknowledgements credit Research-Paper-Skills for the reading report prompt structure used by the deep-summary feature, and cv-arxiv-daily as the inspiration for the automated daily-fetch workflow. So the architecture is two borrowed ideas glued together: a fetch loop from one project, a summarisation prompt from another. The README records a last update date of 2026-09-10, and the repository's last push is 2026-09-09, which is consistent with a scheduled job rather than manual editing.
What the rendered entries actually contain
The README's Continuous Learning table shows the entry format. Each row has a title linked to an arXiv abs page, a date, an abstract inside a collapsible details block, and a comment column that also collapses. The abstract is the arXiv abstract, shown verbatim, with a Show summary control. The comment column carries the author-supplied metadata, for example page count, figure and table counts, and venue acceptance lines such as an acceptance to a conference. This is a plain table, not a database. There is no per-paper tagging beyond the keyword section it appears under, no BibTeX export visible in the material, and no deduplication logic described. A paper matching two keywords will plausibly appear twice, once in each section, though the README does not say whether that is filtered. The collapsible blocks matter for load time: a page with eighty abstracts per keyword across several keywords would be heavy if everything rendered open. The design choice to hide both abstract and comment behind details elements is the reason the page is usable at all.
Getting it running means forking a scheduled job, not installing a package
There is no install command in the supplied material. No pip install, no Dockerfile, no setup.py, no CLI entry point appears in the README. What the repository offers is a running instance plus the source that produces it. To run your own copy, the realistic path is to fork the repository, edit the keyword list that drives the search, and let the existing automation continue on your fork. The README does not document the keyword configuration file, the scheduling mechanism, or the environment variable that holds the DeepSeek API key. That is the single biggest gap in the documentation. The support section implies the key exists, since it says the summaries call a paid API, but it never names the variable or the file where it is set. If you fork and the summaries stop appearing, the material gives you no troubleshooting path. What you can verify without guessing: the README links to a papers/ directory, so that directory must exist in the tree, and the issue tracker is described as carrying the newest ten papers, so the automation must have issue-creation permission on the token it uses.
The failure modes are keyword drift and API cost
Two constraints are visible in the material. The first is coverage. Retrieval is keyword-based, so a paper that describes itself with different vocabulary will not surface. Work labelled as embodied navigation rather than VLN, or as pose estimation rather than SLAM, sits outside the net unless someone adds the term. The README does not describe any semantic expansion or embedding-based matching, only predefined keywords. The second constraint is money. The README states plainly that AI deep summaries call the DeepSeek API and that this is a paid service, and the support section asks for donations to cover server and API costs. A fork with a long keyword list and daily runs will incur a bill proportional to the number of papers fetched, and the README gives no per-paper cost figure. There is also a licence problem. The repository's licence is unknown in the supplied metadata, and no licence file is referenced in the README. Without a stated licence, the default position is that all rights are reserved, which affects whether you may redistribute a fork or reuse the summarisation prompt. That is a question for the repository owner, not something to assume either way.
How it differs from cv-arxiv-daily, the project it credits
The README names cv-arxiv-daily as the inspiration for the daily-fetch workflow, and the difference between the two is worth stating precisely because they look similar from the outside. cv-arxiv-daily is a computer vision tracker. Embodied-AI-Daily narrows the keyword set to embodied AI topics: VLN, VLA, world models, SLAM, Gaussian Splatting and nonlinear optimization. The second difference is the summarisation layer. cv-arxiv-daily presents paper metadata; this project adds a DeepSeek-generated deep summary per paper, with the prompt structure borrowed from Research-Paper-Skills. That is the actual product distinction: not the fetch, which is a solved problem, but the Chinese-language structured summary sitting on top of each abstract. A reader who only wants titles and links gains nothing from the switch. A reader who wants a first-pass summary before deciding whether to open the PDF is the person this repository is built for, and the paid API is the cost of serving them.
Maintenance and upgrade cost for a fork
A fork inherits three ongoing costs. The scheduled job must keep running, which means the CI minutes or server the README's donation request is paying for. The DeepSeek key must stay funded, or summaries silently degrade to plain abstracts, and the material does not describe any alerting for that case. The keyword list must be maintained by hand, because the README describes keywords as predefined and gives no discovery mechanism. On the upgrade side, there are no retrieved releases, so there is no versioned artefact to pin and no changelog to read. Updates arrive as commits to main. That makes a fork cheap to create and awkward to keep in sync if you modify the fetch logic, since you will be resolving conflicts against a moving branch. The pragmatic split is to fork only the keyword configuration and leave the pipeline untouched, accepting that the automation's internals are undocumented.
Editorial conclusion
Adopt it if you already read arXiv by keyword search and want that search performed for you, with an issue feed limited to the newest ten papers and a papers/ directory holding up to eighty per keyword. Do not adopt it as a citation source, as a substitute for a reference manager, or if your topic is absent from its keyword lists, since the repository retrieves by keyword and nothing else. Before relying on it, open the papers/ directory and the issue tracker for your own keyword, compare what appears there against an arXiv search you run yourself, and check whether the AI summaries are present or missing for that day.
Community notes