Update README.md

README.md

@@ -1,66 +1,51 @@
-license: unknown
 ---
-# 🛑 COMMERCIAL LICENSE & FULL DATASET AVAILABLE
-The files uploaded here serve as a **25,000-frame sample (Proof of Concept)** for verification and testing. 
-
-To purchase the unrestricted commercial license or order high-volume production datasets (scalable from $10^5$ to $10^7+$ deterministic frames generated on-demand), contact our lab directly:
-
-* **Instant Commercial Access:** `Deterministic-Data@proton.me`
-* **Enterprise & Custom Volume Inquiries:** `Deterministic-Data@proton.me`
-
+license: other
+license_name: ase-sample-license
+license_link: https://huggingface.co/datasets/Deterministic-Data/ase-trajectories/blob/main/LICENSE
+tags:
+- rl
+- reinforcement-learning
+- trajectory
+- synthetic-data
+- offline-rl
+- imitation-learning
+- behavioral-cloning
+- deterministic
+- sequential-decision
+task_categories:
+- reinforcement-learning
+- tabular-regression
+- time-series-forecasting
 ---
 
-# DATASET SPECIFICATION: 32-bit Anonymized Deterministic Trajectories for Sequential Decision Models
+# ASE Syntax Extractions
 
-## 1. Overview
-This dataset contains high-density, **100% deterministic trajectory frames** designed for training sequential decision-making models, Reinforcement Learning (RL), and behavioral cloning. Every frame contains structural contextual states compressed into anonymous, low-collision 32-bit identifiers.
+*The machine does not dream. It computes — and in that computation, structure emerges. This is not simulated data; it is an extraction of axiomatic necessity.*
 
-* **Format:** Standardized `.jsonl` (optimized 5-core-feature format) and `.csv` (full 90-feature tabular format).
-* **Compliance:** 100% GDPR-compliant / Zero PII. All data consists of synthetic environment interactions.
-* **IP Isolation:** Data is fully obfuscated via one-way cryptographic 32-bit CRC hashing and generic sequence indexing ($v0$ to $v89$) to guarantee complete IP isolation. Underlying simulation rules, ontologies, and environment logic are completely enclosed and non-recoverable.
+## Overview
+This dataset contains deterministic trajectory extractions from a closed, axiomatic system. Every frame is the output of a syntax engine where `(seed, tick, entity)` tuples are resolved through fixed transformations.
 
----
+*   **Deterministic:** The same `(run_id, batch_index)` generates identical output, bit-for-bit, regardless of platform.
+*   **Pure:** No human data, no scraping. Every byte is synthetic, produced by pure operation.
 
-## 2. Technical Validation Metrics
+## Formats
+*   **`.jsonl` (6 fields):** Minimal state transitions.
+    *   `frame_id`: Sequence identifier.
+    *   `context_hash_crc32`, `state_hash_crc32`, `action_hash_crc32`, `next_state_hash_crc32`: Deterministic fingerprints.
+    *   `reward`: Scalar feedback signal.
+*   **`.csv` (90 dimensions):** Full expansion (`v0–v89`) including topology, behavioral indices, and system ecology metrics.
 
-The generating environment operates as a closed, fault-tolerant system. The mathematical integrity of the state-space transmission has been verified through standardized testing:
-
-| Parameter | Status / Value | Verification Notes |
-| :--- | :--- | :--- |
-| **Determinism** | **VERIFIED** | The exact mapping of `(agent, tick)` → consistently produces identical state fingerprints. |
-| **Collision Rate** | **0.0001%** | Validated mathematical bounds for 32-bit CRC state-space distribution. |
-| **System Stability** | **Fault-Tolerant** | Closed-loop execution ensures zero invalid states or undefined transitions. |
-
----
+## Determinism Logic
+The engine seeds each batch from a compound key:
+$$seed = (run\_id \times 7919 + batch\_index \times 104729) \mod 2^{32}$$
 
-## 3. Schema & Data Frame Structure
+| Property | Value |
+| :--- | :--- |
+| **Throughput** | 200+ ticks/sec |
+| **Collision rate** | ~2.3×10⁻⁸ % per pair |
+| **Stability** | Closed-loop, zero invalid states |
 
-To offer maximum flexibility for different machine learning pipelines, the dataset is delivered in two distinct, anonymized structural formats:
-
-### A. High-Efficiency JSONL Layout (5 Core Primitives)
-Optimized for direct policy network training and fast I/O throughput. Each line contains exactly 5 core mathematical primitives:
+## Sample (JSONL)
 ```json
-{
-  "frame_id": 0,
-  "context_hash_crc32": 3786707923,
-  "state_hash_crc32": 162848530,
-  "action_hash_crc32": 1782081112,
-  "next_state_hash_crc32": 1816890948,
-  "reward": 0.2396
-}
-```
-
-### B. High-Density CSV Layout (Full 90-Feature Matrix)
-Optimized for deep causal inference and structural representation learning. The table contains exactly 90 anonymized, sequential features labeled from **`v0` to `v89`**:
-* **`v0`:** `frame_id` (Sequentially incrementing time-step index).
-* **`v5`:** `reward` (Continuous performance scalar bound within $[-1.0, 1.0]$).
-* **`v6` to `v9`:** Core state-space and transaction CRC32 hashes.
-* **`v10` to `v89`:** Fully masked environmental, structural, and behavioral agent traits tracking relational topology without exposing domain semantics.
-
----
-
-## 4. Use Cases
-
-* **Imitation Learning & Offline RL:** Provides high-fidelity expert demonstrations to bootstrap agent behavior without live environment exploration costs.
-* **Causal Representation Learning:** The explicit separation of *State* and *Context* hashes allows models to isolate environmental noise from core logical transitions.
-* **Robustness & Validation Tests:** Ideal for testing the limits of policy network generalization against complex, highly structured state spaces.
+{"frame_id":104,"context_hash_crc32":2210934871,"state_hash_crc32":991823410,"action_hash_crc32":4022881193,"next_state_hash_crc32":88213410,"reward":-0.1832}
+{"frame_id":105,"context_hash_crc32":2210934871,"state_hash_crc32":88213410,"action_hash_crc32":129384710,"next_state_hash_crc32":340281993,"reward":0.5011}