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.

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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

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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 │                                            │
│  └──────────────┘                                            │
└──────────────────────────────────────────────────────────────┘

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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.

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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+

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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

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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

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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

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Safety System

All input passes through a mandatory safety pipeline before any model computation:

1. Jailbreak Detection — regex + semantic scan
2. Rate Limiting — sliding-window per user/session
3. Multi-Vector Moderation — embedding-based classifier
4. EWC Knowledge Protection — prevents unsafe fine-tuning from corrupting core knowledge

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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	500T token pre-training run	Planned
15	RLHF alignment pipeline	Planned
16	Public API release	Planned

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Author

Rahul Sarkar — India GitHub: github.com/Sarkar-AGI

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Disclaimer: TitanCore Core-1 is an independent research project. NVIDIA GPU hardware is required. CPU execution is not supported.