Private by design

Find papers that fit your expertise.

Compare permitted submission metadata with your research profile using an embedding model that runs entirely in this browser.

Step 1

Build your expertise profile

Each item is embedded separately so matches can point back to its strongest influence.

Step 2

Import permitted paper metadata

CSV and JSON stay in memory unless you explicitly enable local persistence.

Expected fields: paper ID, title, and ideally abstract, topics, track, preference.

No papers imported yet.

Step 3

Generate local rankings

The model will use WebGPU when available, with a WASM fallback.

Ready.

Step 4

Review candidates

Read the paper metadata and make your own judgment before recording a preference.

No rankings yet.

Local persistence

Keep this session on this device

Enabled by default. Profile text, paper metadata, scores, statuses, and notes are stored only in this browser’s IndexedDB. Turn it off to remove the saved copy.

This session will be saved locally.

Export

Take your decisions back to HotCRP

Use blank or an integer from −20 to 20. Conflict-marked papers are omitted; declare conflicts through the official conference process.

Technical overview

How this works

The ranking is a local semantic comparison, not an assessment of reviewer fitness.

01

Build comparable text

Your weighted expertise items and each paper’s title and abstract are normalized and split into overlapping chunks when they exceed the model’s practical context size. Imported conference topics remain available only as display/filter metadata.

02

Embed locally

The app uses the quantized all-MiniLM-L6-v2 sentence-transformer model. It produces 384-dimensional vectors in a Web Worker, using WebGPU when available and a local WebAssembly fallback otherwise. Model files are served with the app.

03

Measure topical proximity

Chunk vectors are mean-pooled and normalized. Cosine similarity compares each paper with every profile item. The final score blends the weighted profile centroid (70%) with the strongest individual expertise match (30%).

04

Keep judgment human

The strongest matching profile items explain each base score. Interested decisions add a small similarity boost, while not-interested papers and manual negative phrases apply stronger penalties. Similarity never becomes a bid automatically, and conflicts remain part of the official conference process.

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