Datasets:

pCwOrM
/

werr_open_decisions

license: other
license_name: bsl-1.1
license_link: https://github.com/pCwOrM/werr/blob/main/LICENSE
task_categories:
  - text-classification
  - zero-shot-classification
  - feature-extraction
language:
  - en
  - tr
tags:
  - edge-ai
  - zero-vram
  - fractal-neural-synthesis
  - reflex-ai
  - system-1
  - mandelbrot
  - decision-making
size_categories:
  - 1K<n<10K
dataset_info:
  features:
    - name: id
      dtype: int64
    - name: timestamp
      dtype: string
    - name: received_at
      dtype: string
    - name: source
      dtype: string
    - name: version
      dtype: string
    - name: seed
      struct:
        - name: cx
          dtype: float64
        - name: cy
          dtype: float64
        - name: zoom
          dtype: float64
    - name: state_summary
      dtype: string
    - name: questions
      list:
        - name: name
          dtype: string
        - name: type
          dtype: string
        - name: instruction
          dtype: string
        - name: decision
          dtype: string
        - name: probability
          dtype: float64
        - name: confidence
          dtype: float64
        - name: probabilities
          dtype: string
    - name: latency_ms
      dtype: float64
  splits:
    - name: train
      num_bytes: 1462486
      num_examples: 1205

WERR Open Decisions Benchmark Dataset

Official benchmark dataset accompanying the research paper:
"Universal Fractal Natural Language Decision Map: Real-Time Edge Triage Across Heterogeneous Domains"
arXiv:2609.25498

📌 Dataset Summary

werr_open_decisions contains 1,205 verified decision trajectories and over 3,200 evaluation questions evaluated using the WERR (Waves & Errors) machine-native zero-VRAM reflex engine and the production answerr.me platform.

Unlike conventional neural networks storing millions of static tensor parameters, each decision is procedurally synthesized from a 24-byte Mandelbrot coordinate seed $\Theta = (c_x, c_y, \text{zoom})$ evaluating non-linear escape dynamics along the chaotic boundary of the Mandelbrot set $\mathbb{M}$.

Decision Primitives:

noul (Boolean / Binary): Instant binary triage (e.g. permit / reject, anomaly detection, panic trip).
choice (Categorical): Multi-class routing across discrete destination microservices.
score (Ordinal / Continuous): Multi-tier risk severity scoring (0–3 scale).

🚀 Usage

from datasets import load_dataset

# Load directly from Hugging Face Hub
dataset = load_dataset("pCwOrM/werr_open_decisions")

# Inspect the first decision
print(dataset["train"][0])

📊 Dataset Structure

Each JSONL record represents an operational decision event:

id: Monotonic event identifier.
seed: 24-byte coordinate seed (cx, cy, zoom) on the complex plane $\mathbb{C}$.
state_summary: Operational state features (user role, error rates, frequency, telemetry).
questions: Array of strongly-typed decision probes (noul, choice, score) with computed confidence and probability distributions.
latency_ms: Measured end-to-end evaluation latency on CPU hardware (sub-2ms reflex execution).

🏛️ Citation

@article{dagli2026universal,
  title={Universal Fractal Natural Language Decision Map: Real-Time Edge Triage Across Heterogeneous Domains},
  author={Da{\u{g}}l{\i}, Volkan and Da{\u{g}}l{\i}, Zerrin and Da{\u{g}}l{\i}, Da{\u{g}}han},
  journal={arXiv preprint arXiv:2609.25498},
  year={2026},
  url={https://arxiv.org/abs/2609.25498}
}

📜 License

Licensed under the Business Source License 1.1 (BSL 1.1). Converts to Apache 2.0 on 2030-01-01. Free for academic, educational, and research benchmarking. Corporate Licensor: ITouch Systems (ITouch Bilişim Sistemleri Ltd. Şti., Çukurova Teknokent). Contact: ask@answerr.me / info@itouch.com.tr.