> ## Documentation Index
> Fetch the complete documentation index at: https://docs.eigenai.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Full Dataset

> The full APEX Agent corpus — long-horizon, tool-using tasks across investment banking, management consulting, and law, synthesized from scratch by EigenData-CLI.

Inspired by the [APEX benchmark](https://www.mercor.com/blog/introducing-apex-agents/), the **APEX Agent** dataset was **synthesized entirely from scratch** by EigenData-CLI — every environment, task, and grading rubric is original. The full corpus spans three professional domains: **investment banking (IB)**, **management consulting (MC)**, and **law**, with **\~1,000 training-ready samples per domain** (task + successful agent trajectory + reward-verification function + environment), usable for either supervised fine-tuning or reinforcement learning. On top of these, each domain (law, IB, and MC) adds **\~1,000 hard-tier tasks released RL-only** — environment + reward-verification function, **no gold trajectory** — for reinforcement learning.

The difficulty, trajectory, and tool-usage sections below characterize the IB+MC portion in detail. The Law slice is summarized in the [Training utility](#training-utility) section.

<Note>
  Want to try it first? A free 10-task sample is available on the [Demo Samples](/products/eigendata-cli/datasets/apex-agent/demo) page.
</Note>

## What APEX Agent is

APEX Agent is a corpus of **long-horizon, tool-using tasks** in finance and law. Each task is a single user prompt describing a deliverable — a number, a memo, a filled-in spreadsheet, a legal analysis — paired with a **virtual workspace** mounted with the relevant files (PDFs, spreadsheets, documents, and correspondence). The agent works the task using tools for filesystem, PDF reading, spreadsheets, document editing, code execution, and correspondence (email, chat, calendar), then returns a final answer that is graded against a hand-written rubric.

Tasks fall into three streams:

* **IB (Investment Banking)** — questions about company filings, 10-Ks, equity research notes, and deal memos. PDF-heavy.
* **MC (Management Consulting)** — questions about financial models in Excel: navigating multi-tab workbooks, applying formulas, and producing analysis. Spreadsheet-heavy.
* **Law** — long-document analysis: locating relevant clauses, citing them correctly, and chaining textual facts into an argument. Text-retrieval-heavy.

## At a glance

| Property                 | Value                                                                                                                                                                                                                                        |
| ------------------------ | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| **Domains**              | Investment Banking, Management Consulting, Law                                                                                                                                                                                               |
| **Samples**              | \~1,000 per domain (≈3,000 total) — task + trajectory + reward verifier + environment, usable for SFT or RL. **Plus \~1,000 hard-tier RL-only tasks per domain** (law, IB, MC) — env + reward-verification function, **no gold trajectory**. |
| **Task format**          | Single prompt + virtual workspace + grading rubric                                                                                                                                                                                           |
| **Workspace file types** | PDF, XLSX, CSV, DOCX, PPTX; plus email (mbox) threads and chat channels                                                                                                                                                                      |
| **Agent tools**          | Filesystem, PDF reading, spreadsheets, documents, code execution, email, chat, calendar                                                                                                                                                      |
| **Grading**              | Rubric of 3–6 criteria per task; `reward = criteria passed ÷ total`                                                                                                                                                                          |

## What's inside

| Component            | Description                                                                                                                                                                                                                                                                                                                            |
| -------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| **Samples**          | The unit of release. Each sample bundles a task, the workspace environment, and a reward-verification function. **\~1,000 samples per domain** additionally include a successful agent trajectory (SFT-ready, also RL-trainable). The hard-tier samples lack a gold trajectory but remain RL-trainable via the environment + verifier. |
| **Environments**     | A self-contained virtual workspace per task — domain files organized into folders (e.g. one management-consulting environment holds 104 files across 7 categories).                                                                                                                                                                    |
| **Tasks & rubrics**  | Each task ships a prompt, a gold response, and 3–6 rubric criteria the answer must satisfy.                                                                                                                                                                                                                                            |
| **Trajectories**     | Agent rollouts with chain-of-thought reasoning and tool calls — the shortest pass-rate-1.0 rollout per task is shipped as the trajectory in the corresponding sample.                                                                                                                                                                  |
| **Reward verifiers** | Executable verification functions that score rollouts against the task rubric — usable as a reward signal for RL.                                                                                                                                                                                                                      |
| **Tool schemas**     | Definitions for the filesystem, spreadsheet, document, code-execution, and correspondence (email, chat, calendar) tools available to the agent.                                                                                                                                                                                        |

## Difficulty profile

To characterize the dataset's difficulty distribution, IB+MC tasks are difficulty-classified by running a strong open-weight baseline agent. Each task is assigned to one of three tiers — where "solve" means a perfect score, every rubric criterion passed:

| Tier       | Definition                                                                   |
| ---------- | ---------------------------------------------------------------------------- |
| **Easy**   | The baseline agent solves it unaided.                                        |
| **Medium** | The baseline fails unaided, but succeeds when given short protocol guidance. |
| **Hard**   | Neither mode solves it.                                                      |

| Tier       |    IB |    MC |     Total |     Share |
| ---------- | ----: | ----: | --------: | --------: |
| **Easy**   |   898 |   718 | **1,616** | **40.1%** |
| **Medium** |   243 |   236 |   **479** | **11.9%** |
| **Hard**   |   931 |   999 | **1,930** | **48.0%** |
| **Total**  | 2,072 | 1,953 | **4,025** |  **100%** |

Across the IB+MC corpus the difficulty gradient is broad: a large solvable core plus a substantial **hard tier (\~48%)** that defeats both the baseline and current frontier models. Easy- and medium-tier samples ship with successful trajectories (SFT-ready, also RL-trainable); hard-tier samples ship without trajectories but remain **RL-trainable** via the environment and reward verifiers.

## How challenging is the data

As a reference point, frontier closed-source models were evaluated on sampled subsets of the **IB+MC** corpus.

**Balanced pilot — 100 tasks (50 IB + 50 MC):**

| Model    |   All |    IB |    MC | Avg reward |
| -------- | ----: | ----: | ----: | ---------: |
| opus-4.7 | 69.0% | 82.0% | 56.0% |      0.890 |
| gpt-5.5  | 62.0% | 68.0% | 56.0% |      0.825 |

**Hardest-tier subset — 200 tasks:**

| Model    | Pass rate |
| -------- | --------: |
| opus-4.7 |     30.0% |
| gpt-5.5  |     28.0% |

On the hardest tasks, frontier models land around **30%**. These tasks demand multi-step domain reasoning, file-format-compliant outputs, and multi-row trajectory handling that current models do not reliably deliver out of the box — which makes the corpus a strong training and evaluation signal.

## Trajectory length

APEX Agent tasks are genuinely long-horizon. The table below summarizes, for **IB+MC baseline rollouts**, the **assistant turns** (steps) and **tool calls** per rollout — shown as mean / median / p90, broken out by domain and difficulty tier:

| Tier       | IB steps       | MC steps       | IB tool calls  | MC tool calls  |
| ---------- | -------------- | -------------- | -------------- | -------------- |
| **Easy**   | 22.1 / 19 / 45 | 17.2 / 15 / 30 | 25.6 / 22 / 50 | 22.3 / 20 / 37 |
| **Medium** | 17.0 / 15 / 30 | 16.9 / 16 / 28 | 20.3 / 20 / 32 | 21.7 / 20 / 34 |
| **Hard**   | 17.1 / 16 / 30 | 19.1 / 18 / 30 | 19.6 / 18 / 32 | 24.4 / 24 / 38 |

* **IB easy tasks have the longest trajectories** (mean 22 steps, p90 45) — reading PDFs and 10-Ks involves many page reads.
* **MC trajectories are shorter and more uniform** (mean 17–19 steps) — spreadsheet navigation is more direct.
* **Tool calls exceed step counts** throughout, since a single assistant turn can issue several tool calls in parallel.

## Tool usage

The IB and MC streams exercise different tools, reflecting their different source material:

* **IB (PDF-heavy)** — rollouts are dominated by `pdfs_read_pdf_pages`, `filesystem_search_files`, and `pdfs_search_pdf`, with `code_execution_code_exec` used for numerical work. Code execution rises in prominence on harder tasks, which demand more quantitative analysis.
* **MC (spreadsheet-heavy)** — rollouts are dominated by `excel_read_tab`, `excel_list_tabs_in_spreadsheet`, and `filesystem_search_files`, navigating multi-tab financial models. The document tool `word_read_document_content` also appears on memo-writing tasks.

## Training utility

Supervised fine-tuning (SFT) a smaller open-weight model on successful APEX Agent trajectories yields substantial lifts on held-out tasks from the public [Mercor APEX-Agents benchmark](https://huggingface.co/datasets/mercor/apex-agents) — a third-party eval set distinct from the training corpus. The IB+MC and Law slices were trained separately because they exercise different agent capabilities — IB+MC emphasizes **data, formula, and numerical computation**, while Law emphasizes **information retrieval and text-based reasoning** over long documents.

**Training data.** The dataset provides **\~1,000 samples per domain** (task + trajectory + reward verifier + environment) for SFT or RL; the hard tier additionally supports RL via its environments and verifiers. The proof-of-concept SFT run below used a **500-per-domain subset** drawn from the easy and medium difficulty tiers — half the available SFT-compatible samples per slice. Base model: **Qwen3.6-27B**. Evaluation sampling: `pass@1`, max-steps=30, temperature=1.0.

**Metrics.** **Strict pass** = fraction of rollouts where every rubric criterion was passed (`reward = 1.0`). **Mean reward** = average fraction of rubric criteria passed per rollout (captures partial credit).

**IB+MC results (320 held-out tasks: 160 IB + 160 MC):**

| Model            |     IB strict pass |   IB mean |    MC strict pass |   MC mean | Overall strict pass | Overall mean |
| ---------------- | -----------------: | --------: | ----------------: | --------: | ------------------: | -----------: |
| Qwen3.6-27B base |      9.4% (15/160) |     0.127 |      4.4% (7/160) |     0.132 |       6.9% (22/320) |        0.130 |
| + SFT on IB+MC   | **12.5% (20/160)** | **0.168** | **8.8% (14/160)** | **0.234** |  **10.6% (34/320)** |    **0.201** |

Strict pass improves **+3.7 pp overall** (≈54% relative). MC sees the largest lift — strict pass doubles (4.4% → 8.8%) and mean reward rises +77% relative, reflecting that MC tasks (Excel-tab navigation, multi-row trajectories) benefit most from the cohort-trained protocol.

**Law results (160 held-out tasks):**

| Model            |        Strict pass | Mean reward |
| ---------------- | -----------------: | ----------: |
| Qwen3.6-27B base |      6.9% (11/160) |       0.207 |
| + SFT on Law     | **12.5% (20/160)** |   **0.314** |

Strict pass nearly doubles (6.9% → 12.5%, **+5.6 pp**, ≈82% relative), and mean reward rises +0.107. The baseline already produced partial credit on many Law tasks, and SFT concentrates on pushing partial-credit rollouts into fully-correct ones.

**Caveats.** Baseline rollouts were graded by `claude-haiku-4.5`; SFT rollouts by the stricter `claude-sonnet-4-5` (≈2–3 pp lower strict pass on shared spot-checks), so the SFT lifts above are a **lower bound**. All results are `pass@1`; the headline deltas of 3.7–5.6 pp are robust to sampling noise.

## Access & licensing

The full **APEX Agent** corpus — all environments, tasks, rubrics, and trajectories — is available for **commercial licensing, including model training**. For licensing, contact [support@eigenai.com](mailto:support@eigenai.com).

A free 10-task sample is available now under the [CC BY-NC-ND 4.0](https://creativecommons.org/licenses/by-nc-nd/4.0/) license — see [Demo Samples](/products/eigendata-cli/datasets/apex-agent/demo).