Datasets:

NEAR-AI
/

gaia

extra_gated_prompt: >-
  This is a re-packaging of the GAIA validation split, redistributed under
  upstream's requirement that it live only in a gated or private repository. By
  requesting access you agree to the same terms as
  https://huggingface.co/datasets/gaia-benchmark/GAIA — in particular, do not
  reshare this data in a crawlable format.
extra_gated_fields:
  I agree to not reshare this dataset outside of a gated or private repository: checkbox
task_categories:
  - question-answering
language:
  - en
tags:
  - agents
  - agentic-benchmark
  - evaluation
  - gaia
  - tool-use
size_categories:
  - n<1K
configs:
  - config_name: default
    data_files:
      - split: validation
        path: data/validation-*.parquet
      - split: no_multimodal
        path: data/no_multimodal-*.parquet
      - split: smoke
        path: data/smoke-*.parquet

GAIA validation split (nearai-bench packaging)

A flat, self-contained repackaging of the GAIA validation split — 165 questions with ground-truth answers, the only GAIA split whose answers are public (the test split's are withheld for the upstream leaderboard).

Questions, answers, levels and attachments are unmodified upstream content.

🔒 Gated — and it has to stay that way

Upstream's terms say: "you agree to not reshare this dataset outside of a gated or private repository on the HF hub" and "do not reshare the validation or test set in a crawlable format." This mirror is gated for exactly that reason. Do not make it public. If you need wider access, point people at gaia-benchmark/GAIA and let them accept the terms themselves.

Gating also protects the benchmark: GAIA is a live leaderboard, and crawlable validation answers are how it stops measuring anything.

Why this exists

Upstream ships per-split metadata.parquet alongside a flat directory of attachment files, so a harness has to resolve file_name against a second download. Here each question is one row, with its attachment inline as a deterministic base64 tar.gz, plus a ready-to-send prompt — GAIA's own reference system prompt already prepended, matching nearai-bench's adapter byte-for-byte.

from datasets import load_dataset
ds = load_dataset("NEAR-AI/gaia", split="validation")

Splits

Split	Rows	What it is
`validation`	165	The full public-answer split
`no_multimodal`	152	Text-only subset — drops questions needing image/audio/video understanding
`smoke`	8	Tiny subset for pipeline checks

no_multimodal and smoke are strict subsets of validation, and match suites/gaia-no-multimodal.toml / suites/gaia-smoke.toml upstream in nearai-bench.

Columns

Column	Type	Notes
`task_id`	string	GAIA task UUID
`question`	string	Verbatim upstream `Question`
`final_answer`	string	Ground truth (upstream `Final answer`)
`level`	int64	`1` \| `2` \| `3` — GAIA difficulty tier
`file_name`	string	Attachment filename, `""` when the question has none
`file_sha256`	string	Checksum of the attachment bytes
`assets_tar`	string	base64(tar.gz) of the attachment; `""` when none. 38 of 165 rows carry one
`prompt`	string	Ready to send: `system_prompt` + question (+ a note about the attachment when present)
`system_prompt`	string	GAIA's reference system prompt from the paper
`annotator_steps`	string	Human annotator's solution walkthrough
`annotator_num_steps`	string	Step count
`annotator_tools`	string	Tools the annotator needed
`annotator_num_tools`	string	Tool count
`annotator_time`	string	Wall-clock the annotator took

Running a question

import base64, io, tarfile, pathlib, tempfile

row = ds[0]
ws = pathlib.Path(tempfile.mkdtemp())
if row["assets_tar"]:
    blob = base64.b64decode(row["assets_tar"])
    with tarfile.open(fileobj=io.BytesIO(blob), mode="r:gz") as t:
        t.extractall(ws)          # lands at ws/<file_name>

answer = my_agent(row["prompt"], cwd=ws)   # prompt already carries the system preamble

Scoring

Extract the FINAL ANSWER: line from the response (fall back to the whole response if the model ignored the template), then apply upstream's question_scorer: numeric equality for numbers, element-wise compare for comma/semicolon-separated lists, normalized string equality otherwise.

Keep it bug-for-bug identical to upstream or your numbers stop being comparable to published GAIA results. The known quirk worth preserving: a ground truth like "3,676" is parsed as a two-element list, not the number 3676. A reference port lives in src/scoring.rs::gaia_match / gaia_extract_final_answer in nearai/benchmarks.

GAIA is search-heavy — ~76% of questions require web browsing per the paper, and ~30% need multi-modality — so scores mostly reflect whether your agent has a working search/fetch path. Compare across agents only with that held constant.

⚠️ Contamination warning

This split has public ground-truth answers, and annotator_steps contains full human solution walkthroughs. Do not train on it, and do not let it into a crawlable location. Treat it as an eval holdout.

Provenance & license

Upstream: gaia-benchmark/GAIA — Mialon et al., "GAIA: a benchmark for General AI Assistants" (arXiv:2311.12983). Built from 2023/validation/metadata.parquet plus that split's attachment files.
This repackaging: same terms as upstream, gated. Content unmodified; the additions are the assembled prompt column, file_sha256, and the base64 container format.
Packaged by: NEAR AI for nearai-bench (adapter: PR #317).

@misc{mialon2023gaia,
  title={GAIA: a benchmark for General AI Assistants},
  author={Gr{\'e}goire Mialon and Cl{\'e}mentine Fourrier and Craig Swift and
          Thomas Wolf and Yann LeCun and Thomas Scialom},
  year={2023}, eprint={2311.12983}, archivePrefix={arXiv}
}