README.md

---
license: cc-by-4.0
task_categories:
  - tabular-classification
  - text-generation
  - reinforcement-learning
language:
  - en
tags:
  - synthetic
  - agentic-ai
  - cross-domain
  - autonomous-agents
  - reasoning
  - decision-making
  - multi-domain
  - mcts
  - orchestration
  - agi-adjacent
  - strategic-ai
  - rl
pretty_name: Atlas Apex Cross-Domain Autonomous Intelligence Pack
size_categories:
  - 10K<n<100K
configs:
  - config_name: default
    data_files:
      - split: train
        path: atlas_apex_sample.parquet
---

# Atlas Apex Cross-Domain Autonomous Intelligence Pack (Sample)

**A synthetic dataset of cross-domain autonomous decision cycles for agentic-AI research, multi-objective reinforcement learning, and strategic-reasoning model training.** Each row is a complete autonomous decision cycle — an agent observes a cross-domain signal, reasons over a branching decision tree, executes actions across domains (biotech → finance, space → finance, robotics → systems), and records the strategic outcome.

Built by [SolsticeAI](https://www.solsticestudio.ai/datasets) as a free sample of a larger commercial pack. 100% synthetic. No real scientific results, real trades, real robotic systems, or real operational telemetry — all domain content is abstract narrative templates for reasoning-structure training.

## What is included

| File | Rows | Format | Purpose |
|---|---:|---|---|
| `atlas_apex_sample.parquet` | 10,000 | Parquet | Columnar, typed, best for analytics |
| `atlas_apex_sample.jsonl` | 10,000 | JSON Lines | Streaming / LLM training friendly |

**This sample:** 10,000 autonomous decision cycles, stratified 3,333 per scenario class.  
**Scenario classes (3):** `autonomous_scientific_discovery`, `ai_driven_economic_decisions`, `distributed_system_coordination`  
**Agent archetypes (3):** `AI_Scientist`, `Trading_Agent`, `Orchestrator` (one per scenario)  
**Autonomy levels:** `L2_Assisted`, `L3_Supervised`, `L4_Conditional`, `L5_Full_Auto`  
**Strategic-value tiers:** `low`, `medium`, `high`, `critical`, `transformative`  
**Outcomes:** `objective_achieved`, `partial_success`, `rolled_back`, `escalated_to_human`, `executed_with_caveats`  
**Domains touched per scenario:** biotech / legal / finance / economics / space / robotics / systems / meta

## Record structure

Each record is one autonomous decision cycle with 7 top-level fields:

| Field | Type | Contents |
|---|---|---|
| `schema_version` | string | Pack schema version (`1.0.0-atlas-apex-sample`) |
| `event` | struct | `id`, `trace_id`, `timestamp`, `strategic_value`, `outcome`, `confidence` |
| `identity_context` | struct | `agent_type`, `reasoning_dna`, `autonomy_level`, `human_approval_required`, `escalation_chain[]` |
| `causal_telemetry_stream` | list<struct> | Ordered cross-domain events: `timestamp`, `event_name`, `domain`, `data_source`, `value_at_risk_usd`, `fidelity_score`, `latency_ms` |
| `reasoning_trace` | struct | `primary_objective`, `decision_depth`, `confidence_threshold`, `branches_evaluated`, `winning_branch_reward`, `counterfactual_considered` |
| `detection_logic` | struct | `anomaly_description`, `predictive_fidelity`, `cross_domain_signal_count`, `signal_conflicts[]` |
| `simulation` | struct | `synthetic`, `engine`, `cross_domain_sync_mechanism`, `scenario_class`, `intended_use[]` |

See [SCHEMA.md](./SCHEMA.md) for the full nested field breakdown.

## Why this dataset is useful

Most public agent datasets are either single-domain (coding, math, game-play) or single-objective (reward-shaped for one goal). Agentic systems in production actually operate *across* domains — a trading agent watches satellite data, an AI scientist files patents, an orchestrator restores services under load. This pack is shaped around that shape.

- **Cross-domain causal chains.** Each telemetry stream spans 2–4 domains (e.g., biotech → legal → finance, space → economics → finance).
- **Reasoning DNA.** Each agent carries an explicit reasoning-strategy identifier (`DNA-XXXX-MCTS-EXPLORE-0.65`) so you can train and compare behavior conditional on strategy.
- **Autonomy gradient.** L2 assisted through L5 full-auto — train policies that respect human-approval gates or score automatic escalation behavior.
- **Outcome variance beyond success/failure.** `partial_success`, `rolled_back`, `escalated_to_human`, `executed_with_caveats` — closer to real operational reporting.
- **Reasoning trace metadata.** Decision depth, branches evaluated, winning-branch reward, counterfactual-considered flag — directly usable for process-reward-model training and counterfactual reasoning research.

## Typical use cases

- Multi-domain AI reasoning model training
- Autonomous agent architecture R&D
- Cross-domain decision-policy benchmarks
- RL / multi-objective optimization research
- Escalation-policy and human-in-the-loop research
- LLM fine-tuning on cross-domain reasoning narratives
- Counterfactual-reasoning model training
- Orchestrator / dispatcher agent prototyping

## Quick start

```python
import pandas as pd
import pyarrow.parquet as pq

df = pq.read_table("atlas_apex_sample.parquet").to_pandas()

# Scenario distribution (stratified balanced)
print(df["simulation"].apply(lambda s: s["scenario_class"]).value_counts())

# Outcome by scenario
df["scenario"] = df["simulation"].apply(lambda s: s["scenario_class"])
df["outcome"] = df["event"].apply(lambda e: e["outcome"])
print(pd.crosstab(df["scenario"], df["outcome"]))

# Distinct domains per record
df["domains_touched"] = df["causal_telemetry_stream"].apply(
    lambda stream: len({step["domain"] for step in stream})
)
print(df.groupby("scenario")["domains_touched"].mean().round(2))

# Reasoning depth vs winning-branch reward
df["depth"] = df["reasoning_trace"].apply(lambda r: r["decision_depth"])
df["reward"] = df["reasoning_trace"].apply(lambda r: r["winning_branch_reward"])
print(df.groupby(pd.cut(df["depth"], bins=[0,5,8,12,20]))["reward"].mean().round(2))
```

Streaming form:

```python
import json

with open("atlas_apex_sample.jsonl") as f:
    for line in f:
        cycle = json.loads(line)
        # one autonomous decision cycle per line
```

## Responsible use

This dataset is intended for **research, agent prototyping, and educational benchmarking**. It contains abstract narrative templates — it does **not** contain real scientific discoveries, real trades, real robotic telemetry, real patents, or identifiable actors in any domain. Agents trained on this data will learn cross-domain reasoning *structure*; deployment in any specific domain (finance, healthcare, robotics) requires grounded domain-specific training, validation, and oversight appropriate to that domain's regulatory context.

## License

Released under **CC BY 4.0**. Use freely for research, agent prototyping, education, and commercial development with attribution.

## Get the full pack

This Hugging Face repo is a **10K-cycle sample**. The production pack scales to 100K+ cycles with expanded domain coverage (energy, defense, biosecurity, supply chain, climate), richer agent archetypes (swarm coordinators, red-team agents, digital-twin orchestrators), multi-agent collaboration traces, longer causal chains, adversarial / cooperative variants, parquet + JSONL + gym-compatible delivery, and buyer-specific configurations.

**Self-serve (Stripe checkout):**
- [**Sample Scale tier — $5,000**](https://buy.stripe.com/7sY5kD2j85QTfSb5lfeEo03) — ~25K records, one subject, 72-hour delivery.

**Full pack + enterprise scope:**
- [www.solsticestudio.ai/datasets](https://www.solsticestudio.ai/datasets) — per-SKU pricing across Starter / Professional / Enterprise tiers, plus commercial licensing, custom generation, and buyer-specific variants.

**Procurement catalog:**
- [SolsticeAI Data Storefront](https://solsticeai.mydatastorefront.com) — available via Datarade / Monda.

## Citation

```bibtex
@dataset{solstice_atlas_apex_pack_2026,
  title        = {Atlas Apex Cross-Domain Autonomous Intelligence Pack (Sample)},
  author       = {SolsticeAI},
  year         = {2026},
  publisher    = {Hugging Face},
  url          = {https://huggingface.co/datasets/solsticestudioai/atlas-apex-pack}
}
```