👁️ PyIntel Vantage: Spatial & Perspective Mixture (vantage-spatial-pov-mix)

PyIntel Vantage Spatial-POV Mix is a curated, multimodal-ready instruction dataset designed to teach compact edge language and vision models (specifically the Google Gemma family, including Gemma 4) advanced 3D spatial reasoning, camera perspective translations, and multi-agent Theory of Mind (POV of others).

Curated and published by PyIntel Research.

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📊 Dataset Overview

• Total Samples: 7,712 harmonized conversation pairs
• Train Split: 6,940 samples (90%)
• Validation Split: 772 samples (10%)
• Format: Parquet shards with native Gemma 4 conversational schema and step-by-step <|channel>thought reasoning blocks.

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🔬 Exactly What Was Done (Methodology & Transformation)

Raw open-source benchmarks in spatial reasoning and cognitive psychology typically exist in disjointed, non-conversational formats (flat JSON files, multiple-choice strings, or raw coordinate tuples) and lack explicit reasoning chains.

PyIntel transformed and synthesized this corpus through a 4-stage pipeline:

1. Schema Harmonization

All heterogeneous records were ingested and standardized into a unified OpenAI/Gemma conversational format:

{
  "id": "pyintel_vantage_viewspatial_000042",
  "source": "lidingm/ViewSpatial-Bench",
  "category": "camera_perspective_relative_direction",
  "conversations": [
    {"role": "system", "content": "..."},
    {"role": "user", "content": "..."},
    {"role": "assistant", "content": "<|channel>thought\n...\n<channel|>Answer"}
  ]
}

2. Situated Observer Prompt Engineering

Raw queries were re-framed from the perspective of an embodied, situated observer or external agent, compelling the model to understand where agents are positioned relative to objects in the scene.

3. Synthetic Chain-of-Thought (<|channel>thought) Deduction

Raw source labels contained only brief one-word or single-letter answers (e.g. "C. front-left" or "2"). For every sample, PyIntel generated a structured 4-step internal cognitive deduction chain within Gemma 4's native thought channel:

1. Task & Category Classification: Grounding the problem type (e.g., Person Perspective Relative Direction, Belief State Tracking).
2. Spatial / Agent Reference Frame: Grounding both the reference anchor and the target entity in the physical scene.
3. Perspective Translation: Calculating relative bearing, raycast visibility, or epistemic mental states.
4. Verification & Selection: Matching the computed vector or belief to candidate options.

4. Stratified Split & Parquet Compilation

The dataset was partitioned using a deterministic 90/10 split (seed=42) and compiled into high-performance Apache Parquet shards for zero-copy streaming into PyTorch and Hugging Face SFTTrainer.

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📚 Source Datasets & Attribution

PyIntel gratefully acknowledges the authors and creators of the foundational open-source datasets that made this harmonized mixture possible:

Dataset	Primary Focus	Original Creator / Repo	License
ViewSpatial-Bench	Camera perspective, 3D relative direction, and viewpoint transformations	lidingm/ViewSpatial-Bench	Research / Open
Tiny Theory of Mind	Multi-agent belief tracking, ignorance, visual perspective, and social reasoning	AxiomicLabs/Tiny_Theory_of_Mind	Apache 2.0
SQA3D (Benchmark Reference)	Situated question answering in 3D indoor environments	jeasinema/SQA3D	MIT
Open Spatial Reasoning	Spatial orderings, driving/robotics trajectories	ReasonCore/open-spatial-reasoning	Apache 2.0

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🚀 How to Use

from datasets import load_dataset

# Load the dataset directly from Hugging Face
dataset = load_dataset("pyintel/vantage-spatial-pov-mix")

print(dataset)
# Output:
# DatasetDict({
#     train: Dataset({ features: ['id', 'source', 'category', 'conversations'], num_rows: 6940 }),
#     validation: Dataset({ features: ['id', 'source', 'category', 'conversations'], num_rows: 772 })
# })

# Inspect a conversation turn
sample = dataset["train"][0]
print("Category:", sample["category"])
for msg in sample["conversations"]:
    print(f"[{msg['role'].upper()}]: {msg['content']}\n")

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📄 License & Terms

Released under the Apache 2.0 License. Free for research, commercial, and educational use with appropriate attribution to PyIntel Research and the constituent dataset authors.