NEAR AI Research
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Research from NEAR AI on confidential computing, verifiable inference, secure agents, and the infrastructure required for user-owned AI. • 5 items • Updated
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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.
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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.parquetplus that split's attachment files. - This repackaging: same terms as upstream, gated. Content unmodified; the
additions are the assembled
promptcolumn,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}
}
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