parallax-dsh

Comprehensive arXiv literature search for your agent, on a local corpus.

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Parallax is a DeepSeek Harness (dsh) plugin for finding the arXiv papers on a topic from a local corpus. It harvests arXiv metadata into SQLite. The agent then searches it with set operations (keyword recall, union, subtract, AI screening and classification), and each intermediate set shows up in the dsh sidebar, where you can open it.

A typical graph: recall routes → merge → AI screening → AI classification. The semantic-search routes shown here are from Parallax online; this plugin recalls by keyword.

A general-purpose agent searches the open web through a black-box search API: it sees only the top-k results, can't tell you how many papers a topic really has, and spends effort cleaning up noise. Parallax works the other way round. The agent recalls exhaustively from a corpus with known boundaries, keeps every intermediate result as a set you can inspect, and leaves per-paper judgement to Jev, a judge model that returns calibrated probabilities rather than free-text verdicts.

• Local arXiv corpus: harvest the categories you care about day by day, with a coverage calendar that shows exactly which days are in.
• Auditable set graph: every step is an immutable node, so you can see what each one kept, what it dropped, and why.
• AI screening and classification: Jev judges thousands of papers in minutes, trialled on a sample first, and never pays twice for the same paper.
• A research skill included: a literature-search playbook for reading lists, surveys, method and trend searches, loaded only when needed.

The corpus, the graphs and every judgement stay in a SQLite file on your machine.

How it works

Searching papers is not like ranking a feed, and a recall → coarse filter → fine filter funnel isn't enough:

• It needs several recall routes. A topic goes by several names, each name becomes one keyword route, and the routes have to be merged and deduplicated.
• It needs review and repair. Human or agent, the first pass misses things. Review has to be able to drop false positives in bulk and add back what was missed.

Merging, deduplicating and removing are set operations. So in Parallax the unit the agent works with is a set of papers, and every set is an immutable node in a graph:

• Corpus root: a virtual starting point (a category scope or date range of the local corpus), so the whole library never has to be materialized as a set.
• Rule filter: filters by arXiv category, submission date, or keywords in title, abstract or authors (substring or whole-word).
• Named list: arXiv ids listed by hand, for papers you already know or ones the review found missing.
• Union / subtract: merges recall routes, and removes false positives.
• AI screening: the agent turns your intent into inclusion and exclusion criteria, and Jev gives each paper a probability of inclusion (kept at p ≥ 0.5 by default).
• AI classification: splits a screened set into 2–12 classes, single-label or multi-label (a paper can belong to two classes).

Screening is where a plain LLM gets slow and expensive: it has to write a verdict for every paper, and long batches of structured output break easily. Jev answers typed questions with probabilities directly, so it is both faster and cheaper:

Screening 10,000 papers: about 2 minutes with Jev versus about 20 with a general LLM (LLM time estimated from a 1,000-paper benchmark).

The graph is drawn live in the Build graph tab of the right sidebar. Click any node to see which papers it kept, which it excluded, and the probability behind each judgement.

Keeping sets in a database with indexes, rather than having the agent track them as text or JSON in the conversation, buys two things. It scales: a single Jev run takes up to 20,000 candidates. And it moves large, deterministic work into code, which holds up against model hallucination and against details lost when the context gets compacted. Because nodes are immutable, a change is always a new node, and the full chain of reasoning stays on the graph.

Plugin or online?

This plugin is the local, open-source edition of Parallax. [Parallax online](https://parallax.ink) runs the same set-graph search with a larger corpus and more of the work around it:

| | This plugin | Parallax online |

|---|---|---|

| Corpus | The categories and days you harvest yourself | All arXiv subjects since 2020, ready to search |

| Recall | Keyword, category, date | Keyword, category, date, and semantic search |

| Screening and classification | Jev, with your own API key | Jev |

| After the search | Export the set as JSON | Generate a field survey from the papers, plus a reading view and daily / weekly / monthly progress reports |

| Where it runs | Your machine, inside DeepSeek Harness | In the browser, nothing to install |

Pick the plugin if you want everything local or want to change how it works. If you just want to see what a Parallax search feels like, [sign up for Parallax online](https://parallax.ink) and try it there first.

Quick start

> Just want to try it? [Parallax online](https://parallax.ink) needs no install or harvesting.

Requirements

• DeepSeek Harness with a profile that includes the web UI (@deepseek-ai/dsh-web-app)
• Node.js ≥ 22.5 (the plugin uses the built-in node:sqlite, so there is no native module to compile)
• A Jev API key for AI screening and classification (optional: rule filters and set operations work without one)

Build

git clone https://github.com/Zilize/parallax-dsh.git
cd parallax-dsh
npm install
npm run build

Install into a profile

A dsh profile is a named setup (which plugins and components are loaded), stored as a folder under ~/.dsh/profiles/. Each way of running dsh uses its own profile; on a typical