Add ARC-T DeFi sample (10K lifecycles) with README, SCHEMA, parquet, JSONL

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README.md

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+---
+license: cc-by-4.0
+task_categories:
+  - tabular-classification
+  - tabular-regression
+  - reinforcement-learning
+language:
+  - en
+tags:
+  - synthetic
+  - defi
+  - decentralized-finance
+  - trading
+  - risk-detection
+  - on-chain
+  - mev
+  - liquidations
+  - autonomous-agents
+  - mcts
+  - reinforcement-learning
+  - blockchain
+  - agentic-ai
+pretty_name: ARC-T DeFi Decision Telemetry Pack
+size_categories:
+  - 10K<n<100K
+configs:
+  - config_name: default
+    data_files:
+      - split: train
+        path: arc_t_defi_sample.parquet
+---
+
+# ARC-T DeFi Decision Telemetry Pack (Sample)
+
+**A synthetic DeFi risk-to-execution decision-telemetry dataset for autonomous-trading research, on-chain anomaly detection, and reinforcement-learning environments.** Each row is a complete risk-to-execution lifecycle: a triggering market anomaly, market-state snapshot, adversarial-planner reasoning (MCTS branch count + winning strategy), step-by-step execution chain with per-step gas / slippage / latency telemetry, execution summary, and final decision outcome with PnL delta.
+
+Built by [SolsticeAI](https://www.solsticestudio.ai/datasets) as a free sample of a larger commercial pack. 100% synthetic. No real trades, wallets, addresses, or on-chain identities. Safe for model training, competitive evaluation, and public benchmark work.
+
+## What is included
+
+| File | Rows | Format | Purpose |
+|---|---:|---|---|
+| `arc_t_defi_sample.parquet` | 10,000 | Parquet | Columnar, typed, best for analytics |
+| `arc_t_defi_sample.jsonl` | 10,000 | JSON Lines | Streaming / LLM training friendly |
+
+**Source pack:** 100K-lifecycle corpus (production pack scales to 2.5M+)  
+**This sample:** 10,000 lifecycles, stratified 2,500 per decision outcome  
+**Outcome classes:** `alpha_captured`, `risk_mitigated`, `slippage_loss`, `execution_failed`  
+**Chains covered:** `ethereum`, `arbitrum`, `solana`, `optimism`  
+**Protocols covered:** Aave-V3, Uniswap-V3, Curve-Fi, GMX, MakerDAO, Drift, Lido, Kamino, Jupiter, Orca, Marinade, Synthetix, Meteora  
+**Risk triggers:** oracle staleness, TVL crash, whale dump, governance attack, bridge congestion, funding/borrow-rate dislocation, liquidity fragmentation, redemption run, liquid-staking dislocation
+
+## Record structure
+
+Each record is one risk-to-execution lifecycle with 7 top-level fields:
+
+| Field | Type | Contents |
+|---|---|---|
+| `schema_version` | string | Pack schema version (`1.0.0-arc-t-sample`) |
+| `event` | struct | `id`, `trace_id`, `timestamp`, `decision_outcome`, `pnl_delta_usd` |
+| `risk_context` | struct | `trigger`, `protocol`, `chain`, `impacted_asset`, `anomaly_signature`, nested `market_state` (severity, volatility_regime, liquidity_band, oracle_age, venue divergence, price impact, notional, 1h vol, etc.) |
+| `agent_reasoning` | struct | `engine`, `winning_strategy`, `confidence_score`, `mcts_branches` |
+| `correlated_telemetry` | list<struct> | Ordered component chain (`ARES`, `FRACTAL`, `ARGUS`, `SENTINEL`, …) with per-step latency, slippage, gas price, priority fee, route hops, node provider |
+| `execution_summary` | struct | `strategy`, total execution time, gas cost, average slippage |
+| `genetic_optimizer_feedback` | struct | `fitness_score_update`, `parameter_drift` |
+
+See [SCHEMA.md](./SCHEMA.md) for the full nested field breakdown.
+
+## Why this dataset is useful
+
+Most public on-chain datasets are either raw block-level trace data or narrow single-protocol slices. This pack is shaped around what autonomous-trading and on-chain-risk teams actually need to train decision models:
+
+- Complete risk-to-execution lifecycles rather than isolated transactions
+- Balanced outcome classes across winning and losing execution states
+- Adversarial reasoning trace (strategy + MCTS branch count + confidence) alongside the telemetry
+- Per-step gas, slippage, and latency signals to train execution-aware policies
+- Multi-chain, multi-protocol coverage for generalization across venues
+- PnL-linked outcomes for reward shaping in RL environments
+- Stable schema suitable for backtesting, RL gym integration, and dashboarding
+
+## Typical use cases
+
+- Autonomous DeFi agent training (RL and supervised)
+- On-chain anomaly detection and risk-trigger classification
+- Execution-strategy optimization (gas / slippage / route)
+- Arbitrage and cross-venue routing model development
+- Market-microstructure and liquidity-state modeling
+- LLM fine-tuning on execution narratives and decision rationale
+- Benchmarking MCTS strategy selection under uncertainty
+- Gym environment authoring for multi-chain trading
+
+## Quick start
+
+```python
+import pandas as pd
+import pyarrow.parquet as pq
+
+df = pq.read_table("arc_t_defi_sample.parquet").to_pandas()
+
+# Outcome distribution (stratified balanced)
+print(df["event"].apply(lambda e: e["decision_outcome"]).value_counts())
+
+# Average PnL by strategy
+df["strategy"] = df["agent_reasoning"].apply(lambda r: r["winning_strategy"])
+df["pnl"] = df["event"].apply(lambda e: e["pnl_delta_usd"])
+print(df.groupby("strategy")["pnl"].mean().round(2))
+
+# Chain vs outcome cross-tab
+df["chain"] = df["risk_context"].apply(lambda r: r["chain"])
+df["outcome"] = df["event"].apply(lambda e: e["decision_outcome"])
+print(pd.crosstab(df["chain"], df["outcome"]))
+```
+
+Streaming form:
+
+```python
+import json
+
+with open("arc_t_defi_sample.jsonl") as f:
+    for line in f:
+        lifecycle = json.loads(line)
+        # one risk-to-execution lifecycle per line
+```
+
+## Responsible use
+
+This dataset is intended for **research, prototyping, and defensive / monitoring** use cases around autonomous DeFi systems: anomaly detection, strategy evaluation, execution-policy training, and RL environments. It contains synthesized market states, execution traces, and decision outcomes — it does **not** contain real wallet addresses, real transaction hashes, real pool reserves, or any live on-chain state. Do not deploy policies trained solely on this synthetic data to real capital without independent validation against live data.
+
+## License
+
+Released under **CC BY 4.0**. Use freely for research, RL experiments, education, and commercial prototyping with attribution.
+
+## Get the full pack
+
+This Hugging Face repo is a **10K-lifecycle sample**. The production pack scales to 2.5M+ lifecycles with expanded chain and protocol coverage, finer-grained market-state regimes, campaign-linked multi-step attack sequences, MEV-aware telemetry, richer step-level component traces, parquet + JSONL + gym-compatible formats, and buyer-specific variants.
+
+**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_arc_t_defi_pack_2026,
+  title        = {ARC-T DeFi Decision Telemetry Pack (Sample)},
+  author       = {SolsticeAI},
+  year         = {2026},
+  publisher    = {Hugging Face},
+  url          = {https://huggingface.co/datasets/solsticestudioai/arc-t-defi-pack}
+}
+```

SCHEMA.md

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+# ARC-T DeFi Decision Telemetry Pack — Schema
+
+One row = one complete risk-to-execution lifecycle. All records share the same seven top-level fields.
+
+Schema version: `1.0.0-arc-t-sample`
+
+## Top-level fields
+
+### `schema_version` — string
+Schema identifier. Constant within a sample release. Used to detect pack version drift.
+
+### `event` — struct
+Identifier fields, decision outcome, and the PnL delta for the lifecycle.
+
+| Field | Type | Notes |
+|---|---|---|
+| `id` | string | Stable event identifier, e.g., `ARCT-100000`. |
+| `trace_id` | string (UUID) | Cross-links telemetry steps within the lifecycle. |
+| `timestamp` | string (ISO-8601) | Lifecycle start time. |
+| `decision_outcome` | string | One of: `alpha_captured`, `risk_mitigated`, `slippage_loss`, `execution_failed`. |
+| `pnl_delta_usd` | double | Realized PnL delta attributable to this lifecycle. Can be negative. |
+
+### `risk_context` — struct
+Environmental and market context plus the triggering anomaly.
+
+| Field | Type | Notes |
+|---|---|---|
+| `trigger` | string | Triggering risk event. One of: `oracle_staleness`, `tvl_crash`, `whale_dump`, `governance_attack`, `bridge_congestion`, `funding_dislocation`, `borrow_rate_spike`, `liquidity_fragmentation`, `redemption_run`, `liquid_staking_dislocation`. |
+| `protocol` | string | DeFi protocol (e.g., `Aave-V3`, `Uniswap-V3`, `Curve-Fi`, `GMX`, `MakerDAO`, `Lido`, `Drift`, `Kamino`, `Jupiter`, `Orca`, `Marinade`, `Synthetix`, `Meteora`). |
+| `chain` | string | L1/L2 chain: `ethereum`, `arbitrum`, `solana`, `optimism`. |
+| `impacted_asset` | string | Ticker of the affected asset (e.g., `WETH`, `USDC`, `stETH`, `SOL`). |
+| `anomaly_signature` | string | Human-readable signature summarizing the anomaly. |
+| `market_state` | struct | See below. |
+
+`market_state` struct:
+
+| Field | Type | Notes |
+|---|---|---|
+| `severity` | double | 0–1 severity score. |
+| `volatility_regime` | string | `calm`, `normal`, `stress`, etc. |
+| `liquidity_band` | string | `thin`, `normal`, `deep`. |
+| `oracle_age_sec` | int | Age of the oracle feed in seconds. |
+| `venue_divergence_bps` | int | Cross-venue price divergence, in basis points. |
+| `estimated_price_impact_bps` | int | Expected price impact of a trade, in basis points. |
+| `order_notional_usd` | double | Order size in USD. |
+| `liquidity_usd` | double | Available pool liquidity in USD. |
+| `volatility_1h_bps` | int | 1-hour realized volatility, in basis points. |
+| `bridge_delay_sec` | int | Cross-chain bridge delay, in seconds. |
+| `open_interest_delta_pct` | double | Change in open interest, in percent. |
+
+### `agent_reasoning` — struct
+Planner-side metadata for the autonomous agent selecting the strategy.
+
+| Field | Type | Notes |
+|---|---|---|
+| `engine` | string | Planner engine label. In this sample: `arct_planner`. |
+| `winning_strategy` | string | Selected top-level strategy. One of: `Immediate_Hedge`, `TWAP_Exit`, `Cross_Venue_Sweep`, `Arb_Rebalance`, `Priority_Fee_Exit`, `Collateral_Rotation`, `Delta_Neutral_Reprice`, `Liquidity_Provision`. |
+| `confidence_score` | double | Planner confidence at selection time. |
+| `mcts_branches` | int | Number of MCTS branches considered. |
+
+### `correlated_telemetry` — list<struct>
+Ordered sequence of internal component actions during execution. One struct per step.
+
+Step struct:
+
+| Field | Type | Notes |
+|---|---|---|
+| `timestamp` | string (ISO-8601) | Step timestamp. |
+| `component` | string | Which internal component emitted the step (e.g., `ARES`, `FRACTAL`, `ARGUS`, `SENTINEL`, `EXECUTION_ENGINE`). |
+| `action` | string | Action code (e.g., `ARES_WHALE_ALARM`, `FRACTAL_MARKET_IMPACT_REESTIMATE`, `ARGUS_ANOMALY_CONFIRMED`, `SENTINEL_ORDERBOOK_SCAN`). |
+| `telemetry.latency_ms` | int | Step execution latency in milliseconds. |
+| `telemetry.slippage_bps` | int | Observed slippage in basis points. |
+| `telemetry.gas_price_gwei` | int | Gas price observed during the step (gwei). |
+| `telemetry.priority_fee_gwei` | int | Priority fee (gwei). |
+| `telemetry.route_hops` | int | Number of hops in the selected route. |
+| `telemetry.estimated_price_impact_bps` | int | Step-level price impact estimate (bps). |
+| `telemetry.venue_divergence_bps` | int | Venue-vs-venue price divergence observed at the step (bps). |
+| `telemetry.node_provider` | string | RPC provider observed (e.g., `Alchemy`, `Local_Geth`, `Triton`, `Solana-RPC`). |
+
+### `execution_summary` — struct
+Roll-up metrics for the full lifecycle.
+
+| Field | Type | Notes |
+|---|---|---|
+| `strategy` | string | Final strategy label executed. |
+| `total_execution_ms` | int | End-to-end lifecycle duration. |
+| `gas_cost_usd` | double | Realized gas cost in USD. |
+| `avg_slippage_bps` | double | Average observed slippage across steps, in basis points. |
+
+### `genetic_optimizer_feedback` — struct
+Outer-loop optimizer feedback used to tune future planner iterations.
+
+| Field | Type | Notes |
+|---|---|---|
+| `fitness_score_update` | double | Delta applied to the strategy's fitness score. |
+| `parameter_drift` | string | Coarse drift descriptor (`none`, `minor`, `major`, …). |
+
+## Distribution of this sample
+
+- 10,000 lifecycles, stratified 2,500 per decision_outcome across all four outcomes.
+- Balanced (within shuffled sampling) across 10 risk triggers (~1,000 each).
+- Balanced across 4 chains (weighted toward `ethereum`).
+- Broad protocol coverage (13 protocols).
+- 8 winning strategies observed.
+- Planner engine label constant (`arct_planner`) after sanitization of internal method identifiers.
+
+## Sanitization notes
+
+- Internal identifier prefix (`SIMA-V4-ARC-*`) has been normalized to `ARCT-*`.
+- Internal planner engine code name (`QuantumImagination_V4`) has been normalized to `arct_planner`.
+- No real wallet addresses, transaction hashes, pool reserves, or on-chain identifiers are present.
+
+## Relationship to the full pack
+
+The production pack scales to 2.5M+ lifecycles with expanded chain and protocol coverage, finer-grained market-state regimes, MEV-aware telemetry, campaign-linked multi-step attack sequences, richer step-level component traces, and gym-compatible delivery. See the pack card for commercial access.

arc_t_defi_sample.jsonl

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