Add Atlas Apex sample (10K cross-domain decision cycles) with README, SCHEMA, parquet, JSONL

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README.md

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+---
+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}
+}
+```

SCHEMA.md

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+# Atlas Apex Cross-Domain Autonomous Intelligence Pack — Schema
+
+One row = one complete autonomous decision cycle. All records share the same seven top-level fields.
+
+Schema version: `1.0.0-atlas-apex-sample`
+
+## Top-level fields
+
+### `schema_version` — string
+Schema identifier. Constant within a sample release.
+
+### `event` — struct
+Identifier fields and the overall strategic classification for the cycle.
+
+| Field | Type | Notes |
+|---|---|---|
+| `id` | string | Stable event identifier, e.g., `ATLAS-100000`. |
+| `trace_id` | string (UUID) | Cross-links telemetry within the cycle. |
+| `timestamp` | string (ISO-8601) | Cycle anchor time. |
+| `strategic_value` | string | `low`, `medium`, `high`, `critical`, `transformative`. |
+| `outcome` | string | `objective_achieved`, `partial_success`, `rolled_back`, `escalated_to_human`, `executed_with_caveats`. |
+| `confidence` | double | 0–1 engine confidence in the outcome. |
+
+### `identity_context` — struct
+Agent archetype and autonomy posture.
+
+| Field | Type | Notes |
+|---|---|---|
+| `agent_type` | string | `AI_Scientist`, `Trading_Agent`, `Orchestrator`. |
+| `reasoning_dna` | string | Explicit reasoning-strategy identifier (e.g., `DNA-A7F3-MCTS-EXPLORE-0.65`). Encodes a branch preference (`EXPLORE`/`EXPLOIT`/`HYBRID`/`CONSERVATIVE`/`AGGRESSIVE`) and a scalar exploration parameter. |
+| `autonomy_level` | string | `L2_Assisted`, `L3_Supervised`, `L4_Conditional`, `L5_Full_Auto`. |
+| `human_approval_required` | bool | `true` when autonomy is L2 or L3. |
+| `escalation_chain` | list<string> | Ordered escalation path (e.g., `agent_runtime` → `domain_expert` → `governance_board`). |
+
+### `causal_telemetry_stream` — list<struct>
+Ordered cross-domain events in the cycle. One struct per step.
+
+Step struct:
+
+| Field | Type | Notes |
+|---|---|---|
+| `timestamp` | string (ISO-8601) | Step time. |
+| `event_name` | string | Scenario-specific action label (e.g., `HYPOTHESIS_GENERATED`, `SATELLITE_SIGNAL_DETECTED`, `SWARM_NODE_FAILURE_DETECTED`). |
+| `domain` | string | `biotech`, `legal`, `finance`, `economics`, `space`, `robotics`, `systems`, `meta`. |
+| `data_source` | string | Abstract upstream source name (e.g., `literature_corpus`, `earth_observation_feed`, `task_scheduler`). |
+| `value_at_risk_usd` | double | Scenario-scaled USD value at stake at the step. |
+| `fidelity_score` | double | 0–1 data-fidelity score for the source. |
+| `latency_ms` | int | Observed latency for the step. |
+
+### `reasoning_trace` — struct
+Agent-reasoning metadata for the cycle.
+
+| Field | Type | Notes |
+|---|---|---|
+| `primary_objective` | string | Short objective label (scenario-appropriate). |
+| `decision_depth` | int | Depth of the reasoning tree (MCTS-style). |
+| `confidence_threshold` | double | 0–1 engine confidence gate. |
+| `branches_evaluated` | int | Number of reasoning branches considered. |
+| `winning_branch_reward` | double | Reward attributed to the selected branch. |
+| `counterfactual_considered` | bool | Whether an alternative was explicitly scored. |
+
+### `detection_logic` — struct
+Cross-domain anomaly / conflict metadata.
+
+| Field | Type | Notes |
+|---|---|---|
+| `anomaly_description` | string | Natural-language description of the cross-domain pattern observed. |
+| `predictive_fidelity` | double | 0–1 predictive fidelity of the detection logic. |
+| `cross_domain_signal_count` | int | Number of distinct `domain` values in the telemetry. |
+| `signal_conflicts` | list<string> | Conflicts observed (e.g., `fidelity_mismatch`, `temporal_inversion`, `value_at_risk_divergence`). Often empty. |
+
+### `simulation` — struct
+Simulation engine provenance and scenario class.
+
+| Field | Type | Notes |
+|---|---|---|
+| `synthetic` | bool | Always `true`. |
+| `engine` | string | Simulation engine label (`atlas_apex_sim_v1`). |
+| `cross_domain_sync_mechanism` | string | `event_sourced_bus`, `shared_knowledge_graph`, `temporal_lockstep`, `cross_domain_oracle`, `digital_twin_state_sync`. |
+| `scenario_class` | string | `autonomous_scientific_discovery`, `ai_driven_economic_decisions`, `distributed_system_coordination`. |
+| `intended_use` | list<string> | ML use-case tags. |
+
+## Distribution of this sample
+
+- 10,000 cycles total.
+- Scenario class: balanced 3,333 per class.
+- Agent type: balanced 3,333 per archetype (one archetype per scenario).
+- Strategic value: scenario-weighted (science discovery carries more `transformative` tail; system coordination skews lower value).
+- Autonomy level: weighted toward L4 `Conditional` with meaningful L5 `Full_Auto` and L3 `Supervised` shares.
+- Outcomes: scenario-weighted; ~45% `objective_achieved`, ~28% `partial_success`, remainder split across rolled-back, escalated-to-human, and executed-with-caveats.
+
+## Sanitization notes
+
+- Event IDs are synthetic (`ATLAS-*`).
+- Trace IDs are random UUIDs.
+- All domain content is abstract narrative templates — no real scientific results, trades, robotic telemetry, or patents are present.
+- `data_source` values (e.g., `earth_observation_feed`, `legal_llm`, `lims_feed`) are generic type labels, not references to specific products or vendors.
+
+## Relationship to the full pack
+
+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, and gym-compatible delivery. See the pack card for commercial access.

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