pl-vision-nips

Vision version of the OpenReview-ICLR and arXiv PaperLens datasets. Anonymous release for double-blind review.

Each row is one unique paper. We release all extracted papers — not every paper here is used in our downstream training/eval sets. The papers that are used are denoted by the references field, which lists every internal (release_name, release_split) pair the paper belongs to (a single paper can belong to multiple). The accompanying reconstruction.py in the anonymous code release reads this field to materialize the original sharegpt data.json for any of the publishable vision keys.

Configs (subsets)

• arxiv — papers from arXiv (per_venue + 21k families + residual + the arXiv side of combined).
• openreview-iclr — papers from ICLR via OpenReview (balanced_original + max_rejects + train_50pct/75pct + the iclr side of combined).

from datasets import load_dataset
ds_arxiv = load_dataset("anonuser231357/pl-vision-nips", "arxiv",           split="papers")
ds_iclr  = load_dataset("anonuser231357/pl-vision-nips", "openreview-iclr", split="papers")

Schema

field	type	description
paper_id	string	arXiv id or OpenReview submission id
title	string	paper title
content	string	prompt-stripped body (title + abstract + <image> placeholders (one per page))
metadata	string	JSON blob — venue, year, authors, ratings, decision, …
label	string	"Accept" or "Reject"
references	list<list<string>>	each entry is [release_name, release_split] — the internal splits this paper belongs to
images	list<struct<bytes,path>>	one PNG per page, bytes inline

Reconstructing the sharegpt data.json files

reconstruction.py (in the anonymous code release) rebuilds any of the publishable internal keys (e.g. arxiv_50_50_21k_vision_..._y24up_test) byte-identically from this dataset. Point --hf_vision_repo at this repo:

python scripts/reconstruction.py \
    --hf_vision_repo anonuser231357/pl-vision-nips \
    --dataset_keys arxiv_50_50_balanced_per_venue_vision_wmetadata_filtered24480_train

Reconstructed files land in ./data/ by default (override with --data_root <path>): data/<dataset_key>/data.json (sharegpt rows), data/dataset_info.json (LlamaFactory entry), and data/images_{arxiv,iclr}/<paper_id>/page_*.png for the per-page PNGs.

The release ships a manifest.json sidecar mapping each internal dataset_info.json key → (release_name, release_split, columns, file_name), so reconstruction reproduces conversations, _metadata, accept_reject_label (where applicable), and image bytes exactly.

Reconstructing the full vision tree produces one PNG per page, creating over 1M files for the whole release — run on a fileset with inode headroom.

License & citation

License: other. Citation withheld for anonymous review.