README.md

metadata

license: cc-by-nc-4.0
pretty_name: GraphIV Benchmarks
language:
  - en
size_categories:
  - 1K<n<10K
task_categories:
  - text-retrieval
tags:
  - arxiv
  - literature-review
  - scientific-retrieval
  - information-retrieval
configs:
  - config_name: surveymatch
    data_files:
      - split: train
        path: surveymatch/train.parquet
      - split: validation
        path: surveymatch/val.parquet
      - split: test
        path: surveymatch/test.parquet
    default: true
  - config_name: frontiermatch
    data_files:
      - split: train
        path: frontiermatch/train.parquet
      - split: validation
        path: frontiermatch/val.parquet
      - split: test
        path: frontiermatch/test.parquet
  - config_name: consensusmatch
    data_files:
      - split: train
        path: consensusmatch/train.parquet
      - split: validation
        path: consensusmatch/val.parquet
      - split: test
        path: consensusmatch/test.parquet

GraphIV Benchmarks

Motivation

Existing literature search benchmark, like LitSearch, often matches a query to a list of one or a few papers. As many research questions can be answered by multiple research papers that often complement each other, often in the form of a literature review cohort, I find existing benchmarks lacking for automated research agents. As such, I built three benchmarks that score a retriever against what arXiv's own reviews cite, often as a list of dozens, if not hundreds, of papers. Every case is mined from GraphIV, and every id in it is an arXiv id in that corpus.

A version is specified by its date cutoff, which is the newest date of publication for the reviews inside of this dataset. As newer papers aren't cited by already published reviews, a later cutoff only adds new cases. The present one is GraphIV-benchmarks-2026-10-22.

How Each Benchmark Is Built

Every review is a paper GraphIV's survey labeler scores 0.95 or more, and Gemma-4-26B writes one question per case without mentioning any system, dataset or author. The answer is always what the reviews cite, resolved to papers in GraphIV, which is asking what other papers in arXiv the reviewer cites.

SurveyMatch

SurveyMatch is the simplest of the three benchmarks. It simply asks: given a research question, what do reviewers of that question cite for their literature reviews.

FrontierMatch

FrontierMatch was designed to evaluate how well a retriever finds "rising" papers in a given field. For each research question, two reviews of the same topic at most 190 days apart are retrieved, and papers cited by the later (and not former) of the two are labeled as "rising frontier" in the dataset.

ConsensusMatch

ConsensusMatch asks what every review of a field agrees on. For each research question, three reviews of the same topic released within 190 days of each other are retrieved, and only the papers all three of them cite, published before the first of the three, are labeled as the answer. These answers are small, 9 papers at the median, so it is scored at 100 rather than 1,000.

Splits

The split is assigned per subfield of the GraphIV topic tree, never per case, with the seed 69420. Two reviews of one topic share most of their references, so a split by case would let a retriever tune on the answers it is tested on. split_group is that subfield, so anyone can check it.

Downloading and Scoring

from datasets import load_dataset

test = load_dataset("kkuroma/GraphIV_benchmarks", "frontiermatch", split="test")

To score a case, retrieve from the GraphIV corpus with every paper dated after date_max and every paper in anchors removed, then report the share of citations found in the top headline_k.

Columns

column	type	holds
case_id	string	the anchors joined by _
split	string	train, val or test
query_text	string	the question the case asks
query_drafter	string	the model that wrote it
date_max	date	the latest date a retrieved paper may carry: the review's date, the later review's for frontiermatch, the first review's for consensusmatch
anchors	list of string	the case's own reviews, which a retriever must never return
anchor_titles	list of string
anchor_dates	list of date
anchor_survey_probability	list of float	the survey labeler's score for each anchor
citations	list of string	the answer
n_citations	int
headline_k	int	the cutoff recall is reported at
topic_l0, topic_l1, topic_l2	int	the case's field, subfield and topic in the topic tree
topic_l0_name, topic_l1_name, topic_l2_name	string
split_group	string	the subfield the split was assigned by
gap_days, cosine	int, float	frontiermatch only, the gap and the similarity between the two reviews

Limitations

Every answer is what a later document cited, so all three reward work that was already being noticed, and none of them can score a paper too new to be cited. Two reviews on one topic written within three months agree at recall 0.265, so past a point a higher score means matching one author's selection more than the field's.

The full definitions are in docs/benchmarks.md, and the code that mines, splits and exports these is at git.kuroma.dev/kkuroma/graphiv, licensed as AGPLv3.