The "Central Nervous System" of the VaultAI ecosystem. Corpus-Callosum is not a chatbot; it is a high-speed intent classifier and traffic controller.
Engineered for millisecond latency, Corpus-Callosum is a 1.5B parameter Slerp Merge. It bridges the generalist comprehension of Qwen 2.5 Instruct with the syntax-sensitivity of Qwen 2.5 Coder. Its sole job is to analyze your prompt and decide which expert in your fleet is best equipped to handle it.
🧠 Architecture & Identity: The Traffic Cop
Corpus-Callosum is designed to run silently in the background of system RAM. By utilizing a 50/50 Spherical Linear Interpolation (Slerp), VaultAI has created a tiny but hyper-intelligent router that understands the difference between a request for lore and a request for code.
Key Routing Commands:
[1] Creative/Abstract: Routes to Ouroboros-level creative reasoning.
[2] Logic/Code: Routes to Sovereign-level execution engines.
[3] Hybrid Relay: Triggers a multi-stage collaborative workflow between experts.
⚡ Performance & Efficiency
Corpus-Callosum is optimized to live entirely in CPU RAM, leaving 100% of your GPU VRAM available for the primary experts.
Metric
Speed (Prompt Processing)
Hardware Requirement
System Footprint
Latency
< 50ms
0% GPU VRAM
~1.1 GB (Q4_K_M)
Model Size
1.5B Parameters
CPU/RAM Only
Lightweight Background Process
Standardized Accuracy Benchmarks
Benchmarks are currently queued to test classification accuracy.
Benchmark
Focus Area
Accuracy
Status
Intent Classification
Logic vs Creative
TBD
⏳ Pending Eval
MMLU (Micro)
Knowledge Retention
TBD
⏳ Pending Eval
Model Details
Type: Classification Language Model (Slerp Merge)
Base Architecture: Qwen 2.5 (1.5B)
Merge Method: SLERP (Spherical Linear Interpolation)
