llm-paper-daily: A Daily LLM Paper Digest With Agent-Driven Subscription
Daily updated LLM papers. 每日更新 LLM 相关的论文，欢迎订阅 👏 喜欢的话动动你的小手 🌟 一个
At a glance
- What is it?
- llm-paper-daily is a Python repository that publishes a daily, categorized list of LLM and agent papers with arXiv links, linked code repositories and summaries. Its most distinctive design choice is that subscription setup is delegated to a coding agent rather than a human running commands.
- Who is it for?
- Adopt llm-paper-daily if you want a low-maintenance reading queue of LLM and agent papers and you already run a local agent such as OpenClaw, Codex or Claude Code, because the documented setup path assumes one. Do not adopt it if you need the underlying summarization pipeline to run on your own infrastructure; the README states the subscriber reads only the public feed-papers.json and does not run the scraping or summarization production flow locally.
- 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 3 days ago.
- What is it written in?
- Mainly Python, 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 gap llm-paper-daily tries to fill
The volume of LLM and agent preprints makes manual arXiv browsing a losing habit. llm-paper-daily addresses that by publishing a curated daily list rather than a raw feed. The README describes the repository as a place to get daily updates and categorization for LLM and agent research, with each entry carrying an arXiv address, a related GitHub repository where one exists, and a summary. The intended audience is the practitioner who wants a short, pre-filtered queue: the current month table shows roughly five to seven entries per day with institution attributions, so a reader can scan a day in under a minute. It is not a search tool or a citation database. It is a reading list with editorial selection baked in, and the value depends entirely on whether that selection matches your interests.
How the daily digest is assembled and published
The repository layout reveals a generated, marker-based README. Two HTML comment markers, paper-daily:readme:updates:start and paper-daily:readme:months:start, bracket the auto-filled regions, which means a script rewrites those blocks in place instead of regenerating the whole file. The updates block holds a collapsible details element listing the day's titles with a timestamp; the months block holds a per-month table with columns for date, paper, and links. Summaries are not inlined in the README. Each row links to a Markdown file under a summary/YYYY-MM/ path named after the arXiv identifier, for example summary/2026-09/2609.09153.md. That separation matters: the README stays small enough to render quickly, and the long Chinese-language summaries live in individual files. The README also states the repository is bilingual, with README.md in Simplified Chinese and README_en.md in English. The badge in the header encodes the last update time, so the README doubles as its own freshness indicator.
Subscription is handed to an agent, not to a cron line you write
This is the part worth studying. The README does not give a pip install followed by a config file. It gives a prompt. The user is instructed to send a block of Chinese text to a local OpenClaw, Codex or Claude Code instance, asking the agent to read SUBSCRIBE.md in the repository root, create the local configuration, preview the digest, install a scheduled task, and then report back the config file location, run time, language, number of items per push, and the verification result. According to the README, the agent uses a paper-subscribe skill bundled in the repository and reads only the public feed-papers.json. The README explicitly states the agent will not run the paper scraping or summarization production flow on your machine. So the architecture is split: the producer side generates feed-papers.json and the summary files, and the consumer side is a thin client that polls that JSON. The trade-off is real. You get a guided setup with no manual cron syntax, but you also get a setup path that depends on an agent correctly interpreting SUBSCRIBE.md, and the actual config keys and schedule format are not visible in the README itself. You have to open SUBSCRIBE.md to know what you are agreeing to.
What the feed actually contains, and what it does not
Looking at the September 2026 entries, the selection is tightly scoped to agent infrastructure: procedural graphs for agent execution, LLM agents for scenario-based testing of motion planners, progress reporting reliability, game-theoretic memory clearance, and a survey on graph-based personalized memory. Institution lines are included, which helps when you want to weigh a preprint before opening it. What the feed does not contain is equally clear. There is no scoring or ranking signal beyond inclusion itself, no deduplication note, and no indication of how papers are chosen. The summaries are written in Chinese even in the table shown, so a reader who does not read Chinese gets the titles, the arXiv links and the linked repositories but not the digest text. The README_en.md file exists, and the README states an English version is available, but the material reviewed here does not show whether the per-paper summary files are translated. Treat that as something to verify rather than assume.
Where this breaks down for a serious user
The design assumes the upstream producer keeps running. If the scheduled job behind feed-papers.json stops, your subscriber keeps polling a stale file and you may not notice, because the freshness badge lives in the README, not necessarily in the JSON your client reads. There is no documented fallback or alerting in the material reviewed. A second limitation is selection bias you cannot audit: the repository gives no criteria for what counts as an LLM or agent paper worth including, so you cannot tune the feed toward, say, evaluation work or inference efficiency. Third, the agent-driven setup is a single point of failure in a way a shell script is not. An agent that misreads a step in SUBSCRIBE.md may produce a config that runs at the wrong hour or pushes the wrong number of items, and the README's requested verification step exists precisely because that failure mode is plausible. If you want a feed you can debug with standard tools and no LLM in the loop, this is the wrong shape for you.
How it differs from running your own arXiv query
The obvious alternative is an arXiv API query or an RSS-to-email pipeline that you control. The difference in approach is who does the filtering. A self-built query gives you reproducible, auditable selection: you write the category filters and keywords, you own the schedule, and you can change the ranking whenever you like. llm-paper-daily inverts that. You accept someone else's editorial pass in exchange for zero curation effort, and you get an added artifact a raw query does not produce: a short summary per paper plus a linked GitHub repository when the authors have one, stored as a file under summary/YYYY-MM/. That is the actual product. If your reading habit is already served by a keyword query, this repository adds little. If your problem is that you never get around to writing the query, the pre-built digest plus the agent-assisted subscription removes that friction, at the cost of control.
Maintenance, licence and what to check before relying on it
The repository is not archived, the default branch is main, and the last push recorded is 2026-09-09T06:13:56Z, which matches the update timestamp shown in the README badge. The primary language is Python. No releases have been published, so there is no versioned artifact to pin; you are tracking a moving main branch. The licence field is not available in the material reviewed, and the README does not state a licence. That is a concrete blocker for anyone planning to redistribute the summaries or fold the feed into an internal product, because the terms are simply unknown. Read the LICENSE file in the repository root before doing anything beyond personal reading. On upgrade cost: because the consumer side only reads feed-papers.json, changes to the producer's scraping or summarization logic should not break your subscriber, unless the JSON schema changes. The schema is not documented in the README, so a field rename upstream would be silent until your client errors. If you build on this, validate the shape of feed-papers.json on each run rather than trusting it. The topics listed for the repository are agent, chatgpt, large-language-models, llm and rag, which is consistent with the papers shown, but topics are metadata and not a guarantee of future coverage.
Editorial conclusion
Adopt llm-paper-daily if you want a low-maintenance reading queue of LLM and agent papers and you already run a local agent such as OpenClaw, Codex or Claude Code, because the documented setup path assumes one. Do not adopt it if you need the underlying summarization pipeline to run on your own infrastructure; the README states the subscriber reads only the public feed-papers.json and does not run the scraping or summarization production flow locally. Before committing, read SUBSCRIBE.md in the repository root and confirm the digest preview step works with your agent, and check the repository's licence file directly, since the licence field is not available in the material reviewed here.
Community notes