dsh-chatpaper

dsh-chatpaper

After DSH's release, I plan to gradually accumulate the tools I use frequently as DSH plugins.

ChatPaper has brought me a lot of convenience — it saves a great deal of time searching for papers and doing research. So, to adapt it into a DSH plugin, I spent some time modifying the code with AI and published it to GitHub, hoping to bring the same convenience to more people.

arXiv paper tools for DeepSeek Harness (DSH), provided as native tools — no Python, no MCP server. Paper text is extracted primarily from arXiv's official HTML rendering (LaTeXML), with ar5iv and PDF fallbacks.

This project is a DSH plugin port referencing ChatPaper: the tool contracts and query logic are kept consistent, but it now runs directly on DeepSeek Harness's native interfaces. See Acknowledgments at the end for the source and citation.

Tools

Tool	What it does
search_arxiv	Search arXiv. Natural language (e.g. "agent as a judge") is matched as an all: phrase, falling back to an AND match over its content words when nothing is found; native arXiv query syntax (ti:transformer, cat:cs.CL AND all:agent) passes through untouched.
summarize_paper	Download a paper, extract its text, and summarize it with an LLM under fixed headings (Background, Methods, Results, Conclusions).
translate_paper	Translate a paper's full text chunk by chunk; long papers can run in the background.
generate_survey	Write a literature survey from search results — abstract-level (fast) or full-text deep mode (one summary per paper, concurrent, background-capable).

Background runs (run_in_background: true) register standard DSH jobs: the call returns a job id immediately, and after completion the result (or failure detail) is retrieved with the harness's built-in job_output tool. A single read is capped at 20000 bytes.

search_arxiv needs no API key. The three LLM-backed tools use the harness LLM service (ctx.llm) — the provider, model, credentials, retry policy, and routing configured on the web Models page are all reused directly, with nothing to configure inside the plugin. When provider/model are left empty, they resolve at call time to the deployment's deepseek route and its default model; explicit configuration takes precedence.

Install

From npm (prebuilt, no install-time build authorization needed):

dsh plugin --profile <name> add dsh-chatpaper

From GitHub source (runs a prepare build; pnpm requires an allowBuilds grant in the profile's pnpm-workspace.yaml — the npm route is preferred):

dsh plugin --profile <name> add github:puppet2004/dsh-chatpaper

The bundle registers one chatpaper row; after installation, every session of that profile sees these 4 tools.

Configuration

Add or override the row in your profile's cordis.patch.yml:

- insert:
    - id: chatpaper
      name: dsh-chatpaper
      config:
        provider: ''
        model: ''

Key	Default	Description
provider	''	Harness LLM provider route; empty resolves to the deployment's deepseek route (deepseek-official in the official deployment).
model	''	Harness LLM model id; empty resolves to the adapter's advertised default model.
cacheDir	~/.dsh-chatpaper-cache	Paper-body cache directory, keyed by versioned arXiv id.
surveyConcurrency	4	Concurrent per-paper summaries in deep survey mode.
summaryChars	15000	Text slice fed to a summary.
surveyPaperChars	8000	Text slice fed to a deep-survey per-paper summary.
translateChunkChars	6000	Size of one translation chunk.
translateMaxChars	60000	Cap on source characters to translate.
searchDelayMs	3000	Delay before each arXiv API request (rate-limit courtesy).
searchRetries	3	arXiv API retry count.
downloadTimeoutMs	120000	Paper-body download timeout.

How paper text is fetched

1. Official HTML — https://arxiv.org/html/<versioned-id>; structured extraction preserves section headings and math (<math alttext> → LaTeX).
2. ar5iv — https://ar5iv.labs.arxiv.org/html/<id> for older papers without official HTML.
3. PDF fallback — pdfjs-dist text extraction for papers with no HTML at all.

Each tier's output is validated (an empty shell or fewer than 200 extracted characters falls through to the next tier). Extracted text is cached, so repeated calls are instant.

Model Experience

What the model sees

4 native tools with their declared schemas. search_arxiv returns structured paper metadata (id, title, authors, date, categories, pdf URL, abstract) rendered as a numbered list. The LLM tools return structured results whose render shows the summary/translation/survey text; surveys also carry an [n]-indexed reference list.

Token and KV-cache effects

Tool schemas are fixed, so the model-facing definition prefix is stable across requests. Tool results are plain text; no binary or image payloads enter context. Paper bodies are truncated (summaryChars/surveyPaperChars/translateMaxChars) before every LLM call, keeping prompts bounded. Cache hits avoid re-downloading and re-extracting entirely.

Acknowledgments

This project is a port of the open-source ChatPaper project. The search query logic, paper-summarization prompts, and survey workflow follow the original design, re-implemented on DeepSeek Harness native interfaces (ctx.llm, ctx.tools, ctx.jobs). Thanks a lot for ChatPaper's work. If you use this plugin or build on it, please also cite the original project:

@misc{ChatPaper,
  author={Yongle Luo, Rongsheng Wang, Peter Gam, Jiaxi Cui, circlestarzero, Shiwen Ni, Jaseon Quanta, Qingxu Fu, Siyuan Hou},
  title = {ChatPaper: Use LLM to summarize papers.},
  year = {2023},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/kaixindelele/ChatPaper}},
}