synthetic-pot-o-challenges-ch7-v1

Synthetic PoT-O challenges that prove tensor dimensioning and networking effects from the manuscript Part IV — Information-Theoretic Foundation of Spacetime (§3, §4, §7).

Format (JSONL)

Same schema as synthetic-pot-o-challenges-v1: challenge, optimal_path, difficulty, source.

Ch7 challenge strings extend the classic format with optional:

• nodes=N;edges=a-b,c-d; — graph G=(V,E) for networking effects
• bond_dims=d or bond_dims=d1,d2,... — bond dimension(s) for S = |γ| log d
• target_entropy=k — target entropy (bits)

See CHALLENGE_FORMAT.md in the repo for the full spec.

Example (network):

{"challenge": "tensor:shape=[16,32];dtype=float32;nodes=3;edges=1-2,2-3;bond_dims=8;target_entropy=3.0;ops:contract,cut,...", "optimal_path": "contract:bond_8 -> cut:1 -> quant:4bit -> I:0.25 -> score:0.25", "difficulty": 3.9, "source": "PoT-O/TribeWarez-Ch7"}

Dataset details

• Size: 100 examples (v1)
• Train/Val split: 90/10
• Generation: generate_synthetic_pot_o_challenges_ch7.py — rule-based graphs (chain, star, mesh), bond dims 2/4/8/16, target_entropy from S = |γ| log₂(d). ~80% network challenges, ~20% dimensioning-only (multi_shape).
• Intended use: Training pathfinder models to predict paths that satisfy tensor dimensioning and minimal-cut / information-action constraints (Part IV §3, §4, §7).

Cluster / scaling

This v1 dataset is a 100-example starter. For larger, cluster-generated data (1,024–8k total records split across Raspberry Pi and ESP32 workers), see:

• Cluster repo: pot-o-ch7-cluster (or Tribewarez/pot-o-ch7-cluster) — generator script for Pi + ESP with same considerations (lightweight, deterministic, partitionable); each worker produces a shard and can push to Tribewarez/synthetic-pot-o-challenges-ch7-cluster-XXXX.
• Model: These datasets train pot-o-pathfinder-tiny-v1 for path prediction on PoT-O challenges (ESP32, mobile, edge).

Superposition-style tensor data

Challenges align with Part IV (entanglement, graph geometries). The cluster workflow produces shards that can be combined into a larger “superposition” of network states (many geometries/configurations across shards), per manuscript §11 (superposition of geometries).

Reference

Manuscript Part IV — Information-Theoretic Foundation of Spacetime:

• §3 Information network structure
• §4 Tensor network model (S = |γ| log d)
• §7 Spacetime dynamics and information Lagrangian

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MIT licensed • Tribewarez guild • Live beta • 2026