---
license: cc-by-4.0
extra_gated_prompt: >-
  This dataset is published for research and evaluation purposes only. Its
  contents were generated with Anthropic Claude models, so it must not be
  used to train machine-learning models. By requesting access you agree to
  use this data for research and evaluation only, and not for model training.
extra_gated_button_content: I agree — research/eval use only, no training
configs:
  - config_name: task-index
    data_files: tasks_index.jsonl
tags:
  - environment
  - rl-environment
  - harbor
  - agents
  - citation-verification
  - grounding
  - research
---

# SKBench-Env

**What this is.** [SKBench](https://huggingface.co/datasets/seekbot/skbench)
repackaged as a runnable agent environment (Harbor task format). SKBench is a
naturalistic quote-grounding benchmark from Seek, a live autonomous research
agent: 1405 claims, each attributing a quote to a public source URL, each
mechanically labeled `verbatim` or `no_match` by fetching the source and
testing normalized-substring containment. Here, instead of reading the labels,
your agent must EARN them: it is dropped into a container with the claims
(labels stripped), fetches every source itself, and is graded against the
shipped gold labels.

**Analogy.** A fact-checker's inbox: a stack of quotes someone claims to have
found in real documents. Some are genuine. Some never existed. Some are real
but mangled in transcription (PDF extraction ate a space). Your job is to
open every document and say which is which.

**Worked example.** Task `skbench-000` includes `claim-thuras-1930-patented-bass-reflex-loudspeaker-enclosure`:
the claim says "A.L. Thuras, a Bell Labs acoustics engineer, patented the bass-reflex (vented) loudspeaker enclosure in 1930 — a cabinet that works as a Hel..." and attributes a quote to
http://acousticsresearchcentre.no/a-brief-history-of-electroacoustics-pt-7-loudspeaker-enclosures-en/. The agent must fetch that source (a PDF — pdftotext
is installed), normalize both sides (NFKC, curly→straight quotes,
dashes→hyphens, whitespace collapsed, lowercased), and decide whether the
quote survives as a substring.

**Structure.** 147 tasks x ~10 claims (source-groups
never split across tasks). Per task: `instruction.md`,
`environment/Dockerfile` (+ `claims.json`, the agent's only input),
`tests/` (gold labels + grader, verifier-side only), `solution/solve.sh`
(oracle, validates plumbing). Reward is fractional accuracy written to
`/logs/verifier/reward.txt`.

**Baselines.** Answering `no_match` for everything scores ~0.6
(the corpus is 846/1405 no_match) — beat that
before claiming your agent can fact-check. `no_match` does NOT mean false:
it mixes fabrication, PDF-extraction artifacts, page drift, and paywall
shells; separating those is the actual skill.

**What this catches (a true story).** While building this corpus, the agent
noticed that reference [1] of Kaelbling, Littman & Cassandra's "Planning and
Acting in Partially Observable Stochastic Domains" (Artificial Intelligence
101, 1998) — the paper that carried POMDPs into mainstream AI planning —
dates Karl Åström's foundational paper as 1995. It was published in 1965,
thirty years earlier. The agent then traced the error's provenance: the
authors' own 1994 AAAI paper and their February 1998 preprint date it
correctly; the misdate exists only in the final typeset journal version and
every mirror of it since — a production-stage error, faithfully propagated
for decades. Four machine-verified receipts for this sit in the parent
dataset (`claim-kaelbling-1998-cites-astrom-1965-as-pomdp-origin` and its
siblings). Nobody asked for that check. It fell out of an agent that reads
references and verifies quotes — which is exactly the capability this
environment measures.

**Limits & time-sensitivity.** Sources are live URLs, so this environment
decays: labels were made on the per-claim `verify_date`, and a page edited
or removed since can strand a `verbatim` label (instructions tell the agent
to judge as-of that date). Decay is slower than you might fear — much of the
corpus cites stable primaries (journal PDFs, institutional archives, arXiv,
Wikipedia-class references), and in spot checks ~5 weeks after labeling,
sampled tasks still replayed at 100% — but it is real: treat scores as
comparable within a time window, not across years. Records carrying an
`evidence.sha256` in the parent dataset let future users prove whether a
source has drifted since capture. PDF records were extracted with pdftotext;
other extractors produce different whitespace. Claims were written by
Claude-family models in Jul–Sep 2026.

**Changelog.** v0.1 (2026-10-02): 19 gold labels corrected from `no_match`
to `verbatim`, following [skbench v0.1](https://huggingface.co/datasets/seekbot/skbench).
The original checker never ran pdftotext on extensionless arXiv `/pdf/` links,
so those quotes were tested against raw PDF bytes. They are verbatim under
the benchmark's own rule, and their `verify_date` is now the re-check date,
2026-10-01. Five tasks change. Tag `v0` keeps the original release.

**Terms.** Research and evaluation ONLY. The claim texts are Claude-model
outputs: do not use this environment or its contents to train models.
Same gate terms as every seekbot dataset.

Generated mechanically by `seek_export_skbench_env.py` (seek-code repo);
no model wrote any part of this export.