v1.0.0 — Context Spine (ReaderLM + Qwopus + Jina v5)

RISC Thought Engine — Context Spine v1.0

2.4 MB replaces 54 GB. 277 reasoning queries/sec. Zero GPU.

What's Inside

Component	Purpose	Size	Speed
ReaderLM-v2 codebook	Fast lexical routing + HTML detection	425 KB	5,676 q/s
Jina v5 codebook	Embedding anchor (forward pass ground truth)	401 KB	5,676 q/s
Qwopus 27B gates	Deep semantic context (8 of 64 layers)	2 MB	277 ctx/s

Architecture

spider-rs → raw HTML → ReaderLM-v2 (candle, BF16) → clean markdown
  → Qwen tokenizer (151936 vocab) → codebook → centroids
  → F32ThinkingEngine (softmax T=0.01) → peaks
  → Qwopus gate EKG (8 layers × 4 roles) → deep context
  → AriGraph triplet_graph → SPO knowledge
  → NARS truth revision → confidence
  → ContrastiveLearner → table improves over time

Proven Results

• HighHeelBGZ i16 encoding: 100% top-5 fidelity (7,127× compression)
• Softmax T=0.01: 100% agreement with plain cosine (attractor collapse SOLVED)
• Gate EKG: perfect discrimination (0/8 agreement between different topics)
• False triplets: correctly detected as LOW confidence
• Garbage detection: entropy < 1.0 = bad ReaderLM output

Railway Deployment

# Clone + deploy
git clone https://github.com/AdaWorldAPI/lance-graph.git
cd lance-graph
railway up

Or use the Dockerfile directly:

docker build -f crates/thinking-engine/Dockerfile.railway -t thinking-engine .
docker run -p 8080:8080 thinking-engine

Download

curl -LO https://github.com/AdaWorldAPI/lance-graph/releases/download/v1.0.0-context-spine/context-spine-v1.0.tar.gz
tar xzf context-spine-v1.0.tar.gz
# 4.2 MB extracted: codebooks + manifest.json