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configs:
- config_name: default
data_files:
- split: test
path: tasks.jsonl
tags:
- agents
- tool-use
- retrieval
- synthetic
pretty_name: Knowledge-Worker Search Bench
---
# Knowledge-Worker Search Bench
40 multi-channel retrieval tasks over realistic synthetic knowledge-worker
environments, generated with [Tonic Fabricate](https://www.tonic.ai/fabricate).
Each task drops an agent into one persona's work world — mail (Outlook or
Gmail), Slack, Google Docs, calendar, attachments — and asks a question a real
chief-of-staff-style assistant would get: *"brief me for tomorrow's sync"*,
*"where did we land on the renewal, and what forced the timeline?"*. Answering
requires finding and synthesizing evidence scattered across channels; grading
is per-claim (an LLM judge checks each rubric claim is conveyed and grounded
in cited artifacts).
**Code, docs, and eval harness:** github.com/TonicAI/knowledge_worker_search_bench
(a [verifiers](https://github.com/PrimeIntellect-ai/verifiers) environment).
This dataset stores the tasks and the environment data; the GitHub repo is the
central source.
## Contents
| path | what it is |
|---|---|
| `tasks.jsonl` | the 40 tasks, one browsable row each (prompt, rubric claims, evidence ids, difficulty, reference rewards) |
| `tasks/<task_id>.json` | full runtime task specs (what the harness loads) |
| `<persona>.db` | 10 persona SQLite environments — mail, Slack, docs, calendar tables the agent's tools query |
## Row schema (`tasks.jsonl`)
- `task_id`, `persona`, `shape` (`multi_hop_chain` 32 / `thread_synthesis` 6 / `attachment_lookup` 2), `difficulty` (easy 20 / medium 18 / hard 2, banded by mean reward across a six-model panel), `today` (the persona's current date)
- `system` — persona framing; `prompt` — the user request + output contract
- `rubric_claims` — what a correct answer must convey; `evidence_artifact_ids` — where the ground truth lives (resolvable in the persona DB); `evidence_channels` — channel spread of the evidence
- `reference_rewards` — per-task rewards from the published six-model panel (unified per-claim grader, single rollout)
## Reference results
| model | mean reward | pass@1 |
|---|--:|--:|
| gpt-5.5 | 0.92 | 62.5% |
| claude-opus-4.7 | 0.89 | 70.0% |
| claude-sonnet-4.6 | 0.70 | 30.0% |
| claude-haiku-4.5 | 0.56 | 20.0% |
| gpt-5.4-mini | 0.46 | 12.5% |
| Qwen3.6-35B-A3B | 0.39 | 17.5% |
Every task is verified solvable (≥1 frontier model scores ≥0.8), yet the slate
separates models cleanly.
## The environments
Persona DBs are fully synthetic worlds: consistent orgs, characters,
storylines, and timelines woven across `outlook__*` / `gmail__*` / `slack__*` /
`google_docs__*` / `*calendar__*` tables. No real people, companies, or data.
The eval harness exposes them through read-only product-shaped tools
(search/read for each channel + attachment parsers); agents never query SQL
directly.
## Usage
```python
# Browse tasks
from datasets import load_dataset
ds = load_dataset("TonicAI/knowledge-worker-search-bench", split="test")
# Run the eval (see the GitHub repo)
pip install knowledge-worker-search-bench # or: pip install -e . from the repo
python scripts/run_eval.py --models claude-haiku-4-5
```
The harness resolves persona DBs and task specs from this dataset
automatically via `huggingface_hub` when they aren't bundled locally.
|