ReMe/plugins/daily_paper/README.md

Daily Paper Plugin

中文

Daily Paper selects three papers from the Hugging Face Papers weekly and monthly rankings, downloads their arXiv PDFs, and produces detailed Chinese reading notes plus a roughly five-minute Chinese brief. This directory is an independent Python distribution. Its daily-paper entry point exposes five Step backends and complete Job defaults through the package's plugin.yaml; enable it explicitly with plugins=["daily-paper"] after installation.

Quick start

The workflow requires Python 3.11 or later, an available AgentScope LLM, and network access to Hugging Face Papers and arXiv.

python -m pip install "reme-ai[core]>=0.4.1.8"
reme plugins install reme-daily-paper
export LLM_API_KEY="your-api-key"
export LLM_MODEL_NAME="qwen3.7-plus"
export LLM_BASE_URL="https://your-provider.example/v1"
reme start plugins='["daily-paper"]' job=daily_paper

The built-in LLM component defaults to:

• model: qwen3.7-plus
• endpoint: no built-in LLM_BASE_URL; set the OpenAI-compatible endpoint required by your provider
• environment variables: LLM_API_KEY, LLM_MODEL_NAME, and LLM_BASE_URL

Daily Paper uses the application's default LLM and default AgentScope wrapper. Select and Analyze call the wrapper without tools, while Digest receives only the read-only memory_search and read ReMe Job tools.

The default workspace is .reme/ beneath the process working directory. Override it with workspace_dir=... or the application configuration you use alongside the plugin.

Migrating from the built-in cookbook

Daily Paper is no longer imported or configured by the core reme-ai distribution. Install this package and enable daily-paper explicitly instead of running config=daily_cookbook to obtain the Daily Paper Jobs:

# Before
reme start config=daily_cookbook job=daily_paper

# Now
reme plugins install reme-daily-paper
reme start plugins='["daily-paper"]' job=daily_paper

The workflow schemas have moved from reme.schema.daily_paper to reme_daily_paper.schema. The arXiv and Hugging Face clients formerly under reme.utils are plugin implementation details under reme_daily_paper; applications that used those modules directly must install this package and update their imports. Existing workspace notes, downloaded PDFs, and indexes are not migrated or rewritten. Point the application at the same workspace_dir to keep using them.

Pipeline

Hugging Face weekly/monthly rankings
                 │
                 ▼
Collect ──► Rank ──► Select 3 ──► Analyze PDFs ──► Digest ──► DingTalk (optional)
                                      │               │
                                      ├─ PDFs         ├─ daily brief
                                      └─ paper notes  └─ day index

1. Collect

daily_paper_collect_step concurrently fetches:

• the Hugging Face weekly ranking for the run date's ISO week;
• the monthly ranking for the run date's calendar month; and
• Hugging Face Daily Papers for exactly the previous calendar day.

The weekly and monthly results are merged by arXiv ID while preserving both ranks. The step then excludes papers found in yesterday's list or in the arxiv_id frontmatter of daily/<date>/*.md within the previous history_days.

If a Markdown file with kind: daily-paper-brief already exists and force=false, generation is skipped; the saved brief can still proceed to DingTalk delivery. The job fails when no eligible papers remain.

2. Rank

daily_paper_rank_step uses reciprocal-rank fusion:

score = 1 / (rrf_k + monthly_rank)
      + weekly_weight / (rrf_k + weekly_rank)

A missing rank contributes zero. Papers are ordered by fused score, upvotes, and arXiv ID. The pool is capped at candidate_limit, and Rank applies no topic preference.

3. Select

daily_paper_select_step sends candidate metadata to a tool-free AgentScope agent and requires exactly three items:

{"papers": [{"arxiv_id": "2601.01234", "reasoning": "A specific, verifiable reason"}]}

All IDs must be unique and belong to the candidate pool, and every reason must be non-empty. A validation failure is returned to the agent for one retry. Only a non-empty topics value injects a personalized subject preference into the selection prompt; it does not change the fixed count of three papers.

4. Analyze

daily_paper_analyze_step processes the selected papers in order:

1. validates a modern YYYY.NNNN or YYYY.NNNNN arXiv ID;
2. downloads the PDF to resource/papers/<arxiv-id>.pdf;
3. reuses an existing target whose header is %PDF-;
4. extracts paginated text with pypdf, bounded by max_pdf_pages and max_pdf_chars;
5. sends metadata, selection reasoning, and PDF text to a tool-free agent; and
6. writes a Chinese note to daily/<date>/<Chinese-title>.md.

Downloads use a temporary file and atomically replace the target only after validating the PDF header. They are also bounded by max_pdf_bytes. There is no OCR fallback, so scanned or textless PDFs fail. When extraction is truncated, the note records pdf_text_truncated: true in its frontmatter.

5. Digest

daily_paper_digest_step uses the three in-memory analyses as the factual source for the Chinese brief. It also searches and, when needed, reads earlier daily notes to identify related coverage; those notes may only support contextual wikilinks, not add facts about the current papers. The agent returns title, desc, and body. The code then:

• strips model-generated YAML frontmatter if present;
• normalizes the Chinese title for use as a filename;
• keeps model-generated wikilinks only when they point to existing daily/ Markdown files dated before the run date;
• deterministically appends wikilinks to all three source notes;
• writes daily/<date>/<Chinese-brief-title>.md; and
• rebuilds the daily/<date>.md day index.

Final response metadata includes the date, week/month scopes, selected arXiv IDs, selection reasons, note/PDF/brief paths, source counts, and exclusion counts.

6. DingTalk

The final dingtalk_markdown_send_step is optional. With no conversation IDs it is a no-op. When configured, it strips frontmatter and sends the brief body to each group in order:

DINGTALK_APP_KEY=your-app-key
DINGTALK_APP_SECRET=your-app-secret
DINGTALK_ROBOT_CODE=your-robot-code
DINGTALK_CONVERSATION_IDS=cid-group-one,cid-group-two

A failed recipient does not prevent later attempts; the step reports a combined failure after trying every group.

Outputs

.reme/
├── daily/
│   ├── YYYY-MM-DD.md
│   └── YYYY-MM-DD/
│       ├── <Chinese-paper-title>.md   # three, kind: daily-paper-analysis
│       └── <Chinese-brief-title>.md   # one, kind: daily-paper-brief
└── resource/
    └── papers/
        └── <arxiv-id>.pdf

Each successful generation writes three analysis notes and one brief. A forced rerun can leave unrelated or previously selected analysis notes in the same day directory; ReMe does not delete them as cleanup. Filenames come from the agent's Chinese titles. The implementation removes unsafe path characters and resolves title collisions. Markdown and PDF outputs are written through same-directory temporary files and atomic replacement.

Parameters and defaults

Public job parameters:

Parameter	Default	Purpose
date	""	Run date; empty uses today in the app timezone, otherwise requires YYYY-MM-DD
force	false	Regenerate even when the day's brief exists
use_hf_mirror	false	Use the Hugging Face mirror from HF_MIRROR_URL, or https://hf-mirror.com when unset
topics	""	Topics to prioritize during selection
weekly_weight	0.7	Weekly contribution to RRF
history_days	30	Prior recommendation exclusion window

Step-level settings on the daily_paper job:

Setting	Default	Purpose
candidate_limit	20	Maximum candidates sent to Select
rrf_k	60	RRF constant
hf_timeout	600 seconds	Timeout for one Hugging Face request
hf_max_retries	3	Maximum Hugging Face attempts
pdf_timeout	600 seconds	arXiv PDF download timeout
max_pdf_bytes	52428800	PDF limit, 50 MiB
max_pdf_pages	35	Maximum extracted pages
max_pdf_chars	300000	Maximum extracted PDF characters sent to the agent

Mirrors

The data clients use httpx's default environment handling, so HTTP_PROXY, HTTPS_PROXY, and NO_PROXY take effect when present. The two data sources reach a mirror differently: Hugging Face is gated on the use_hf_mirror job parameter, while arXiv is driven by its environment variable alone.

# The plugin's daily_paper_cron Job enables the mirror by default; set false to use the official service
DAILY_PAPER_USE_HF_MIRROR=false

# Read only when the manual or scheduled job enables the mirror; defaults to https://hf-mirror.com when unset
HF_MIRROR_URL=https://hf-mirror.com

# Optional override; the code defaults to https://arxiv.org when unset
ARXIV_MIRROR_URL=https://export.arxiv.org

# Path-prefixed relay URLs are also supported
# HF_MIRROR_URL=http://relay-host:18080/hf
# ARXIV_MIRROR_URL=http://relay-host:18080/arxiv

HF_MIRROR_URL must implement the /papers/..., /api/daily_papers, and /api/papers/... routes used by the current client. ARXIV_MIRROR_URL must implement /pdf/<arxiv-id>. A path prefix in either base URL is preserved, and a trailing slash is optional. There is no fallback chain: whichever base URL a client selects is the only one it tries.

Behavior change: HF_MIRROR_URL used to redirect Hugging Face traffic on its own. It is now read only when the job runs with use_hf_mirror=true; otherwise the official service is used and the client logs a warning that the variable was ignored. Pass use_hf_mirror=true for manual requests. The plugin's daily_paper_cron Job enables the mirror by default; set DAILY_PAPER_USE_HF_MIRROR=false to make that scheduled job use the official service.

Running the workflow

Generate a brief for a specific date:

reme start \
  plugins='["daily-paper"]' \
  job=daily_paper \
  date=2026-08-06 \
  topics="Agent memory" \
  history_days=30

Force a rerun; valid local PDFs are still reused:

reme start plugins='["daily-paper"]' job=daily_paper date=2026-08-06 force=true

Start the HTTP service and scheduled jobs:

reme start plugins='["daily-paper"]'

With the default ReMe configuration, the HTTP service listens on 127.0.0.1:2333. daily_paper_cron runs every day at 08:00 in the application timezone, prioritizes the topic 大模型长期记忆, and uses the Hugging Face mirror by default. Set DAILY_PAPER_USE_HF_MIRROR=false to use the official service. Override the service address through normal ReMe configuration or startup arguments.

curl -s http://127.0.0.1:2333/daily_paper \
  -H 'Content-Type: application/json' \
  -d '{"date":"2026-08-06","force":false,"topics":"Agent memory"}'

Failures and reruns

• Hugging Face HTTP failures use exponential backoff up to hf_max_retries attempts; invalid response payloads fail immediately.
• Fewer than three candidates, invalid agent selection, invalid/oversized/textless PDFs, or empty agent output stop the job.
• Papers are analyzed sequentially; PDFs and notes completed before a failure remain on disk.
• force=true regenerates the selected notes and the brief while reusing valid PDFs; it does not remove other notes already present in that day's directory.
• The multi-file workflow is not transactional and has no global per-date execution lock.

Tests

The focused unit tests mock Hugging Face, arXiv, AgentScope, and DingTalk boundaries and do not call real services:

python -m pip install -e packages/reme_ai_studio -e ".[dev,core]" -e plugins/daily_paper
python -m pytest plugins/daily_paper -v