paper-distiller 1.2.0

Chat-first paper distillation: turn arXiv papers into an Obsidian-ready knowledge base.

paper-distiller

Chat-first paper distillation. Turn arXiv papers into an Obsidian-ready knowledge base — via REPL, one-shot commands, or natural language.

paper-distiller is a command-line tool that searches academic paper sources (arXiv + Semantic Scholar), downloads PDFs, has an LLM distill each one into a structured markdown note, and writes everything to a folder that opens directly in Obsidian.

v1.0 ships a single paper-distiller-chat command with three modes:

Mode	When to use
paper-distiller-chat (no args)	Interactive REPL — slash commands + natural-language input
paper-distiller-chat distill	One-shot: search a topic, distill N papers
paper-distiller-chat ask	One-shot: ask a research question, multi-round QA loop
paper-distiller-chat resume	One-shot: continue a paused/errored QA session

Output is plain markdown with YAML frontmatter and [[wikilink]] cross-references — no proprietary format, no lock-in. Graph view, Dataview, tags, and full-text search all work out of the box.

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Install

pip install paper-distiller

Requires Python 3.10+. From source:

git clone https://github.com/jesson-hh/paper-distiller
cd paper-distiller
pip install -e ".[dev]"

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Configure

paper-distiller needs an OpenAI-compatible LLM endpoint. Cheapest reliable option: Aliyun Bailian's qwen-plus (~¥0.02 per paper).

cp examples/example.env .env
# Edit .env — set PD_API_KEY, PD_BASE_URL, PD_MODEL

Env var	Required	Default	Purpose
PD_API_KEY	✓	—	Any OpenAI-compatible API key
PD_BASE_URL	✓	—	API endpoint base URL
PD_MODEL	✓	—	Model identifier
PD_PROVIDER_NAME		unspecified	Logging tag only
PD_PDF_TIMEOUT		60	PDF download timeout (seconds)
PD_MIN_SURVEY		2	Min articles before composing a session survey
PD_SS_API_KEY		(none)	Optional — higher Semantic Scholar rate limit

Provider quick reference

Provider	PD_BASE_URL	PD_MODEL
Aliyun Bailian (recommended)	https://dashscope.aliyuncs.com/compatible-mode/v1	qwen-plus
Aliyun Bailian (coding plan)	https://coding.dashscope.aliyuncs.com/v1	qwen3.5-plus
DeepSeek	https://api.deepseek.com/v1	deepseek-chat
OpenRouter	https://openrouter.ai/api/v1	qwen/qwen3.5-plus
Local Ollama	http://localhost:11434/v1	qwen2.5

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Use it

Interactive REPL (recommended)

paper-distiller-chat --vault /path/to/your/vault

You see a welcome banner with provider + vault info, then a prompt. Type slash commands or natural language:

> /help
[command list]

> /vault
Vault: /path/to/your/vault
  articles: 47
  surveys: 6
  ...

> /distill diffusion models --n 3
[live status table during execution]

> 帮我研究下扩散模型在长周期金融时序生成上的最新进展
[intent-router] Intent: ask  | confidence 9
  question: 扩散模型在长周期金融时序生成上的最新进展
Missing: max_rounds, per_round, max_cost_cny
Apply defaults (max_rounds=3, per_round=2, max_cost_cny=5.0) and run? [Y/n]
> Y
[live status table for 3-round QA loop]

> /quit
  (bye)

10 slash commands available: /distill, /ask, /resume, /sessions, /vault, /provider, /agents, /show, /help, /quit.

Natural-language input goes through an LLM intent-router that classifies into one of distill/ask/resume/show and proposes defaults for any missing parameters. You confirm before any expensive operation runs.

One-shot mode (good for scripts / cron)

Distill N papers on a topic:

paper-distiller-chat distill --vault /path/to/your/vault \
    --topic "diffusion models for finance" --n 5

Answer a question across multiple rounds:

paper-distiller-chat ask --vault /path/to/your/vault \
    --question "What are recent advances in long-horizon time-series diffusion?" \
    --max-rounds 3 --per-round 2 --max-cost-cny 5

Resume a paused / errored session:

paper-distiller-chat resume --vault /path/to/your/vault \
    --session-id 20260519-1635-a3f7

Use --dry-run on any subcommand to validate config without spending API budget.

Helpful flags

paper-distiller-chat [--vault PATH]
                     {distill | ask | resume}
                     [subcommand-specific flags]

paper-distiller-chat distill --help etc. show every flag for that subcommand.

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What you get — a sample distilled article

---
title: "Convergence Rates of Conditional Flow Matching..."
category: articles
slug: cnf-convergence
tags: [generative-models, theory, distribution-estimation, arxiv-2024]
refs: [arxiv:2410.12345]
depth: full-pdf
---

# CFM 的样本复杂度上界

> **场合**: arxiv preprint, 2024 Oct
> **主题**: 给 CFM 训练给出第一个匹配 nonparametric minimax rate 的有限样本界
> **领域**: 统计 / 生成模型理论

## 一句话
作者证明 CFM 训练在 $\beta$-平滑目标密度下达到 $n^{-\beta/(2\beta+d)}$ 的 $W_2$ 收敛速度…

## 方法
核心是把 vector-field 估计误差 decompose 成 (1) approximation error 由 $\beta$-Hölder ball
覆盖控制 (2) statistical error 用 local Rademacher 处理 (3) discretization error 显式给…

## 与已有 wiki 的关联
对 [[cnf-convergence-distribution-learning]] 的分析路线是个自然的强化…

## 我的 take
最有意思的是 time-singularity 在 CFM 训练里其实从未出现…

Open the vault in Obsidian and this article cross-links automatically with everything else you've distilled.

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Vault layout

paper-distiller writes into a vault with these subdirectories (auto-created on first run):

Directory	Auto-written by tool	Description
articles/	✓	One file per paper
surveys/	✓	Multi-article surveys + qa-… final answer docs
techniques/, directions/, open-problems/, authors/	—	Reserved for human-curated notes

QA sessions persist resume state at <vault>/.paper_distiller/qa-sessions/<sid>/state.json.

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How it works

paper-distiller v1.0 is built around an async DAG of sub-agents:

Single-pass (distill):
  arxiv-searcher  ss-searcher          (parallel)
        └────┬────┘
        candidate-merger
              │
        candidate-ranker (LLM)
              │
        paper-processor × N            (parallel: fetch PDF → extract → distill LLM)
              │
        vault-writer
              │
        survey-composer (LLM, optional)

Multi-round (ask):
  ┌──────────────────────────────────────────────────────┐
  │  progress-reflector (LLM)                             │
  │      ↓                                                │
  │  [stop check: max_rounds / llm_done / llm_brake / ...] │
  │      ↓                                                │
  │  search → dedup → rank → distill × N → write          │
  └────────────────────────────────────────────────────────┘
                          ↓
                  answer-synthesizer (LLM) → surveys/qa-<slug>-<date>.md

11 agents, 4 stop reasons in QA mode, all wired together by a topological-level scheduler. For module structure, full data flow, and internal contracts, see docs/ARCHITECTURE.md.

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Cost

Aliyun Bailian qwen-plus pricing — roughly ¥2.1/M input tokens, ¥12.7/M output tokens.

Operation	Typical cost
1 paper distilled	¥0.02 ($0.003)
5-paper single-pass + survey	¥0.7 ($0.10)
3-round QA session @ 2 papers/round	~¥1.5–3
5-round QA session @ 3 papers/round	~¥4–8

paper-distiller-chat ask enforces --max-cost-cny (default ¥20). The cost number is for the circuit breaker — not billing-accurate.

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Customize the output

All 6 LLM prompts are plain markdown — edit them to change tone, structure, or output language. No Python changes needed.

• src/paper_distiller/prompts/{filter,article,survey}.md — distill mode
• src/paper_distiller/agents/prompts/route.md — intent router
• src/paper_distiller/qa/prompts/{reflect,answer}.md — QA mode

Defaults produce Chinese-primary notes with this 5-section structure: 一句话 / 问题动因 / 方法 / 关键结果 / 我的 take.

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Optional companion: semantic search via vault-mcp

paper-distiller does NOT ship its own semantic-search engine for your vault. To search by meaning (not keywords) from Claude Code, Cursor, or any MCP-aware agent, pair it with vault-mcp.

See docs/vault-mcp-recommendation.md for setup and rationale.

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Status & roadmap

v1.0.0 — beta. Chat-first architecture stable; 168 tests passing on Python 3.10 / 3.11 / 3.12.

Migration from v0.5

v0.5.x	v1.0
paper-distiller --topic X --n N	paper-distiller-chat distill --topic X --n N
paper-distiller-qa --question Y --max-rounds R	paper-distiller-chat ask --question Y --max-rounds R
(no resume command)	paper-distiller-chat resume --session-id <sid>
(no interactive mode)	paper-distiller-chat (no subcommand)

Flag names and defaults are otherwise preserved. See CHANGELOG for full details.

Coming

• v1.1 — citation-graph traversal: given a seed article, follow references / cited-by edges and rank them for inclusion.
• v1.2 — broaden sources beyond arxiv + SS: integrate browser-session scraping for ACM, IEEE, 知乎 etc.
• Later — per-vault paper-distiller.toml for custom category schemas; LEANN in-pipeline crosslink retrieval for vaults > 500 entries.

Known limitations

• arxiv.org and Semantic Scholar occasionally rate-limit (HTTP 429); QA sessions exit with error: search failed (resumable via paper-distiller-chat resume <sid>).
• Scanned-only PDFs fall through to abstract-only mode (PyMuPDF doesn't OCR — by design we flag rather than silently distill wrong text).

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Contributing

Issues and PRs welcome.

git clone https://github.com/jesson-hh/paper-distiller
cd paper-distiller
pip install -e ".[dev]"
pytest -v

CI runs the same matrix on every PR. For a tour of the codebase, see docs/ARCHITECTURE.md.

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License

MIT — see LICENSE.