TitanCore: Core-1 AGI
Distributed Artificial General Intelligence Engine — Trillion-Parameter Scale
TitanCore Core-1 is a full-stack AGI engine built in C++17 and CUDA. It combines a 120-layer Mixture-of-Experts Transformer with a complete cognitive architecture: persistent memory, structured reasoning, goal-directed planning, meta-learning, world modelling, and continuous online learning — all running across multi-node GPU clusters.
Project Status
| Property | Detail |
|---|---|
| Version | 1.0.0 |
| Release Date | February 2026 |
| Status | AGI Framework — Inference-Ready |
| Tokenization | Custom BPE — 400,000 token vocabulary |
| Weight Format | GGUF (titancore.gguf) |
| Parameters | Up to 1 Trillion |
AGI Architecture
TitanCore Core-1 implements the full Perceive → Remember → Reason → Plan → Act → Learn cognitive loop:
┌──────────────────────────────────────────────────────────────┐
│ TITANCORE AGI COGNITIVE LOOP │
│ │
│ Input │
│ │ │
│ ▼ │
│ ┌──────────────┐ ┌───────────────┐ ┌───────────────┐ │
│ │ Perceive │───▶│ Remember │───▶│ Reason │ │
│ │ Working Mem │ │ Episodic Mem │ │ Chain-of-Thought│ │
│ │ Safety Gate │ │ Semantic Mem │ │ Tree-of-Thought│ │
│ └──────────────┘ └───────────────┘ └───────┬───────┘ │
│ │ │
│ ┌──────────────┐ ┌───────────────┐ ┌───────▼───────┐ │
│ │ Learn │◀───│ Act │◀───│ Plan │ │
│ │ Online GD │ │ Tool Use │ │ MCTS Planner │ │
│ │ EWC + MAML │ │ API Calls │ │ Goal Stack │ │
│ └──────┬───────┘ └───────────────┘ └───────────────┘ │
│ │ │
│ ▼ │
│ ┌──────────────┐ │
│ │ World Model │ Predict future states, detect novelty │
│ │ VAE+Dynamics │ │
│ └──────────────┘ │
└──────────────────────────────────────────────────────────────┘
AGI Subsystems
1. Language Model Backbone
- 120-layer MoE Transformer — 8 experts per layer, top-2 routing
- FlashAttention v2 — custom CUDA tiled kernel, 128k+ context
- Paged KV Cache — logical-to-physical block mapping, zero fragmentation
- RoPE embeddings, SwiGLU MLP, Pre-LayerNorm
- Parallelism — Tensor ×4, Pipeline ×2, Data ×4, Expert ×8
2. Continuous Learning (core/learning/online_learning.cpp)
Learns from every new interaction without forgetting prior knowledge:
- Online Gradient Descent — real-time weight updates from live data streams
- Elastic Weight Consolidation (EWC) — Fisher Information diagonal protects prior knowledge
- Experience Replay Buffer — 100K capacity, reservoir sampling
- EMA Weight Snapshots — stable inference weights via exponential moving average
- Adaptive per-parameter learning rate via AdamW
3. Memory Systems
| System | File | Description |
|---|---|---|
| Episodic Memory | core/memory/episodic.cpp |
Stores 50K past episodes; cosine-similarity + temporal-decay retrieval |
| Semantic Memory | core/memory/semantic.cpp |
Long-term factual knowledge graph; confidence-scored, conflict-resolved |
| Working Memory | core/memory/working.cpp |
Active context window; attention-weighted importance-based eviction |
4. Reasoning Engine (core/reasoning/chain_of_thought.cpp)
Four structured reasoning modes:
| Mode | Description |
|---|---|
| Standard CoT | Linear step-by-step reasoning with confidence gating |
| Self-Consistency | Sample N reasoning paths, majority-vote the answer |
| Tree-of-Thought | BFS branching + value-guided pruning of the reasoning tree |
| Reflection | Draft → Critique → Revise loop for high-accuracy answers |
5. Goal-Directed Planner (core/reasoning/planner.cpp)
- Monte Carlo Tree Search (MCTS) with UCB1 selection
- Neural-guided rollout policy for state evaluation
- Hierarchical goal decomposition into ordered subgoals
- Configurable depth, breadth, and exploration constant
6. Meta-Learning (core/meta/maml.cpp)
Learn to learn — adapt to any new task in a few gradient steps:
- MAML (Model-Agnostic Meta-Learning) — full second-order
- FOMAML — first-order approximation (faster, production default)
- Reptile — scalable alternative with simple moving-average updates
- Fast inference-time adaptation with only a handful of examples
7. World Model (core/world_model/world_model.cpp)
Internal predictive model of the environment:
- VAE Encoder — maps observations to compact latent state z
- Dynamics Model — predicts next latent z' given z + action
- Reward Predictor — estimates expected reward from any state
- Novelty Detection — z-score anomaly flag for unexplored states
- Imagination — simulate N-step future trajectories for planning
8. Tool Use / Function Calling (core/tools/tool_executor.cpp)
Allows the AGI to call external systems:
| Built-in Tool | Description |
|---|---|
calculator |
Safe mathematical expression evaluator |
web_search |
Real-time web search via search API |
code_interpreter |
Sandboxed Python execution environment |
read_file |
Secure file system access |
db_query |
Read-only SQL against the knowledge database |
Custom tools can be registered at runtime with a schema and handler function.
System Requirements
Hardware
| Component | Minimum | Recommended |
|---|---|---|
| GPU | NVIDIA A100 80GB ×8 | NVIDIA H100 SXM5 80GB ×8 per node |
| Nodes | 1 | 4 (32 GPUs total) |
| System RAM | 512 GB | 1 TB per node |
| Interconnect | NVLink | NVLink + InfiniBand 400 Gbps |
| Storage | 10 TB NVMe | 100 TB NVMe RAID |
Software
| Dependency | Version |
|---|---|
| OS | Ubuntu 22.04 LTS |
| CUDA Toolkit | 12.2+ |
| CMake | 3.20+ |
| C++ Compiler | GCC 11+ / Clang 14+ |
| LibTorch | 2.2+ |
| NCCL | 2.18+ |
| OpenMPI | 4.1+ |
Project Structure
Core-1/
├── main.cpp # AGI master orchestrator
├── CMakeLists.txt
│
└── core/
├── configs/
│ ├── gpt4o.yaml # Model & runtime config
│ ├── cluster.yaml # Cluster topology
│ ├── safety.yaml # Safety policy
│ └── agi.yaml # AGI subsystem config
│
├── model/ # Transformer backbone
├── distributed/ # NCCL, FSDP, MPI
├── optimizer/ # ZeRO-3 AdamW
├── dataloader/ # Memory-mapped dataset
├── safety/ # Moderation, jailbreak, rate limit
├── logging/ # Audit trail
│
├── learning/
│ └── online_learning.cpp # Online GD + EWC + Replay + EMA
│
├── memory/
│ ├── episodic.cpp # Past episode store + retrieval
│ ├── semantic.cpp # Long-term knowledge graph
│ └── working.cpp # Active context window
│
├── reasoning/
│ ├── chain_of_thought.cpp # CoT / Self-Consistency / ToT / Reflection
│ └── planner.cpp # MCTS goal-directed planner
│
├── meta/
│ └── maml.cpp # MAML / FOMAML / Reptile
│
├── world_model/
│ └── world_model.cpp # VAE encoder + dynamics + reward + novelty
│
├── tools/
│ └── tool_executor.cpp # Function calling + built-in tools
│
└── agi/
└── agi_core.cpp # Unified AGI cognitive loop controller
Getting Started
Build
git clone https://github.com/litonsarkar3988-max/Core-1 cd Core-1 mkdir build && cd build cmake .. \ -DCMAKE_BUILD_TYPE=Release \ -DTorch_DIR=/path/to/libtorch/share/cmake/Torch \ -DCMAKE_CUDA_ARCHITECTURES="80;86;90" make -j$(nproc)
Run — Single Node
./titancore \ --model core/weights/titancore.gguf \ --config core/configs/gpt4o.yaml \ --agi core/configs/agi.yaml
Run — Multi-Node AGI Cluster
mpirun -np 32 -hostfile hosts.txt \ ./titancore \ --config core/configs/gpt4o.yaml \ --cluster core/configs/cluster.yaml \ --agi core/configs/agi.yaml
Configuration
| File | Purpose |
|---|---|
core/configs/gpt4o.yaml |
Model architecture, quantization, runtime |
core/configs/cluster.yaml |
Multi-node topology, network, fault tolerance |
core/configs/safety.yaml |
Content policy, rate limits, PII redaction |
core/configs/agi.yaml |
All AGI subsystem parameters |
Safety System
All input passes through a mandatory safety pipeline before any model computation:
- Jailbreak Detection — regex + semantic scan
- Rate Limiting — sliding-window per user/session
- Multi-Vector Moderation — embedding-based classifier
- EWC Knowledge Protection — prevents unsafe fine-tuning from corrupting core knowledge
Roadmap
| Phase | Milestone | Status |
|---|---|---|
| 1 | Core Transformer + CUDA kernels | Complete |
| 2 | ZeRO-3 distributed training | Complete |
| 3 | Safety & moderation engine | Complete |
| 4 | Paged KV cache & inference | Complete |
| 5 | Continuous learning (Online GD + EWC) | Complete |
| 6 | Episodic, semantic & working memory | Complete |
| 7 | Chain-of-Thought & Tree-of-Thought reasoning | Complete |
| 8 | MCTS goal-directed planner | Complete |
| 9 | Meta-learning (MAML / Reptile) | Complete |
| 10 | World model (VAE + dynamics) | Complete |
| 11 | Tool use & function calling | Complete |
| 12 | Full YAML config parser (yaml-cpp) | In Progress |
| 13 | GGUF weight loader & quantized inference | In Progress |
| 14 | 13T token pre-training run | Planned |
| 15 | RLHF alignment pipeline | Planned |
| 16 | Public API release | Planned |
Author
Rahul Sarkar — India GitHub: github.com/Sarkar-AGI
Disclaimer: TitanCore Core-1 is an independent research project. NVIDIA GPU hardware is required. CPU execution is not supported.