| --- |
| 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. |
|
|
