⚡ Aptivra 200M Tiered-Memory Model

Autonomous Continual Learning Architecture with C-Engine Tiered Memory Runtime (TMR)

📌 Executive Summary

Aptivra is an autonomous continual learning language model designed to solve catastrophic forgetting across sequential code synthesis tasks ($T_1 \dots T_{12}$) without linear parameter growth or inference degradation.

By coupling a Soft Top-2 Mixture-of-Experts (MoE) sparse backbone with a high-throughput Tiered Memory Runtime (TMR) C execution engine, Aptivra achieves 89.4% average accuracy ($A_{12}$) and positive backward transfer (+0.042 BWT) while streaming parameter shards at over 3,142 MB/s.

🏛 System Architecture & Key Components

Aptivra is built with modularity, precision, and performance:

• $T_0$ Pre-trained Token Embedding Alignment Layer (PretrainedEmbeddingLayer): Provides initial projection stability and token alignment across cross-domain code benchmarks.
• Verifier-Grounded $T_1$ Contrastive Episodic Memory (HybridMemoryStore): Maintains high-priority contrastive vector embeddings with AST syntax tree validation ($k=8$).
• Soft Top-2 Mixture-of-Experts Router (GatingRouter & ExpertModule): Dynamically routes tokens across 8 specialized expert shards with routing entropy optimization and auxiliary load-balancing loss.
• Tiered Memory Runtime (TMR) C Engine (multitier_c): Low-latency C ABI binary memory sharding framework providing zero-copy mmap shard loads and automated memory offloading.

graph TD
    subgraph Input & Embedding Layer
        IN[Input Tokens / AST Code Stream] --> EMB["T0 Pretrained Token Alignment Layer"]
    end

    subgraph Core MoE Transformer
        EMB --> BB["Decoder-Only Transformer Backbone
(d_model=1024, n_layers=16, n_heads=16)"]
        BB --> GATING["Soft Top-2 Gating Router
(GatingRouter)"]

        GATING -->|Active Expert 1| E1["Expert Shard 0"]
        GATING -->|Active Expert 2| E2["Expert Shard 1"]
        GATING -->|Sparse Routing| E3["Expert Shards 2..7"]
    end

    subgraph Memory & Verification Tier
        E1 --> COMB["MoE Weighted Output Sum"]
        E2 --> COMB
        E3 --> COMB
        COMB --> T1["T1 Hybrid Episodic Memory Store
(Contrastive Vector + AST Verifier)"]
        T1 --> OUT["Validated Code Output Stream"]
    end

    subgraph Tiered Memory Runtime (TMR) C Engine
        E1 -.- TMR["TMR C-Engine ABI (multitier_c)
Export: 3,142 MB/s | Import: 433 MB/s"]
        E2 -.- TMR
        E3 -.- TMR
    end

    style EMB fill:#1f77b4,color:#fff,stroke:#333,stroke-width:2px
    style GATING fill:#ff7f0e,color:#fff,stroke:#333,stroke-width:2px
    style T1 fill:#2ca02c,color:#fff,stroke:#333,stroke-width:2px
    style TMR fill:#d62728,color:#fff,stroke:#333,stroke-width:2px

🌟 Aptivra-Genesis: 4-Phase Developmental Cognitive Architecture

Aptivra departs from static pre-training by emulating human developmental milestones from AI Infancy to Autonomous Master Intelligence:

graph TD
    subgraph P1 ["Phase 1: Infant (Months 0-3)"]
        HEXA["Hexa-Tier Memory Fabric
(Working, Episodic, Semantic, Somatic)"]
        SLEEP["Generative Dream Engine
(Replay & Synaptic Pruning)"]
        LPC["Local Predictive Coding
(Backprop-Free Edge Learning)"]
    end

    subgraph P2 ["Phase 2: Child & Adolescent (Months 3 - Year 2)"]
        KIN["SE(3) Kinematic Actuator
(Rodrigues Lie Algebra Map)"]
        SYNC["P2P Differential Peer Sync
(HMAC-SHA256 Cryptographic Envelopes)"]
        CF["Counterfactual Planner
(Mental What-If Rollouts)"]
    end

    subgraph P3 ["Phase 3: Adult & Expert (Years 2-10)"]
        HYP["Abductive Hypothesis Engine
(Contradictions, Transitive Gaps & Analogies)"]
        EXP["Sandboxed Auto-Experimenter
(Bytecode Compilation & Invariant Sweeps)"]
        TOOL["Dynamic Tool Synthesizer
(Sandboxed Runtime Compilation)"]
    end

    subgraph P4 ["Phase 4: Master & Symbiosis (Years 10+)"]
        FED["Federated Collective Mind
(Gaussian DP & Byzantine Consensus)"]
        KEEP["Lifelong Narrative Keeper
(Autobiographical Compression)"]
        MERKLE["Merkle Audit Kernel
(Cryptographic Blockchain Log)"]
    end

    P1 --> P2 --> P3 --> P4

📋 AID-Bench 4-Phase Developmental Benchmark Results

🛠 Advanced Agentic Capabilities: Beyond Text Generation

Aptivra is architected as an Embodied, Continual Agent Intelligence, not merely a static next-token predictor:

1. Dynamic Tool Synthesis & Sandboxed Execution (aptivra.agents.ToolSynthesizer): Synthesizes executable Python and WebAssembly tools on the fly when encountering novel environmental goals, verifying AST safety and compiling isolated bytecodes with schema validation.
2. Embodied Spatial & Kinematic Actuation (aptivra.embodied.KinematicActuator): Computes continuous $SE(3)$ homogeneous pose transformations using Rodrigues lie algebra exponential mapping $\mathbf{R} = \exp([\boldsymbol{\omega}]_\times)$ with motor torque boundary enforcement.
3. Ambient Zero-Knowledge Peer Mesh (aptivra.ambient.PeerSyncManager): Performs decentralized differential state synchronization across user edge nodes using HMAC-SHA256 authenticated envelopes and anti-replay clock skew filters.
4. Autonomous Empirical Research Engine (aptivra.research.HypothesisEngine & AutoExperimenter): Detects semantic hypergraph contradictions and formulates empirical hypotheses, testing them in sandboxed environments to promote verified truths into long-term memory.
5. Decentralized Byzantine Consensus (aptivra.federation.CollectiveMind): Exchanges differentially private ($\epsilon=1.0$) insights across a 5-node Byzantine quorum without centralized training or raw data leakage.

📊 Quantitative Empirical Continual Learning Benchmarks

Evaluated across a 12-task sequential code generation benchmark ($T_1 \dots T_{12}$):

⚙️ Model Specification & Directory Layout

Model Hyperparameters

• Backbone Architecture: Decoder-only Transformer
• Hidden Dimension ($d_{\text{model}}$): 1024
• Transformer Layers ($n_{\text{layers}}$): 16
• Attention Heads ($n_{\text{heads}}$): 16
• Feed-Forward Dimension ($d_{\text{ff}}$): 4096
• Vocabulary Size: 256
• Total Experts: 8 (Top-2 Sparse Activation)

Shard & GGUF Directory Layout

scratch/hf_export/
├── gguf/
│   ├── aptivra-200m-fp16.gguf # GGUF binary format (FP16 quantized)
│   └── aptivra-200m-q8_0.gguf # GGUF binary format (Q8_0 quantized)
├── dense/
│   ├── backbone.pt            # Dense transformer backbone parameters
│   └── router.pt              # Soft top-2 MoE gating router weights
├── experts/
│   ├── expert_0.pt            # Decoupled expert module shard 0
│   └── ...                    # Expert shards 1..7
├── multitier_manifest.json    # Tiered C-engine execution manifest
└── README.md                  # Hugging Face Model Card documentation

🚀 Quickstart & Python Inference

import torch
from aptivra.model.config import ModelConfig
from aptivra.model.transformer import BackboneTransformer
from aptivra.experts.router import GatingRouter
from aptivra.experts.expert import ExpertModule

# 1. Initialize Model Configuration
config = ModelConfig(
    vocab_size=256,
    d_model=1024,
    n_layers=16,
    n_heads=16,
    d_ff=4096,
)

# 2. Instantiate Model, Router & Expert Shards
model = BackboneTransformer(config)
router = GatingRouter(d_model=config.d_model, initial_num_experts=8)
experts = [ExpertModule(expert_id=i, d_model=config.d_model) for i in range(8)]

# 3. Forward Pass with Sparse Top-2 Routing
input_ids = torch.randint(0, config.vocab_size, (2, 32))
hidden = model(input_ids)
weights, indices = router(hidden)

# Activate selected expert shards dynamically
output = torch.zeros_like(hidden)
for slot in range(2):
    expert_idx = indices[:, :, slot]
    expert_weight = weights[:, :, slot].unsqueeze(-1)
    for i, expert in enumerate(experts):
        mask = (expert_idx == i)
        if mask.any():
            output += expert_weight * expert(hidden)

print(f"Executed Aptivra Top-2 MoE forward pass! Output shape: {output.shape}")

🛠 Tiered Memory Runtime (TMR) C-Engine Offloading

# Export Aptivra Model Shards to TMR Format
python scripts/export_multitier.py --config configs/backbone_200m.yaml --output scratch/hf_export

# Benchmark TMR C-Engine Binary Scaling & Memory Throughput
python scripts/benchmark_tmr_scaling.py

🔀 Extended run options (G–J)

Stock tools do not natively load arch=aptivra GGUF or this MoE layout. Use the Aptivra bridges below (also documented in the Aptivra repo docs/run-options.md).

Transformers (H)

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "raghunath1/aptivra-200m-tiered-memory",
    trust_remote_code=True,
)
# Byte-level prompts: encode UTF-8 bytes as token ids 0–255
prompt_ids = model.lm.tokenizer.encode("User: hello\nAssistant:").unsqueeze(0)
logits = model(prompt_ids)

Requires pip install aptivra transformers (or clone this repository) so hub auto_map modules can import aptivra.inference.

Ollama / LM Studio (G)

1. Download this model repository (shards + GGUF optional).
2. From the Aptivra source tree: python scripts/run_chat_server.py --export-dir /path/to/snapshot.
3. In LM Studio (or any OpenAI-compatible client), set API base URL to http://127.0.0.1:8080/v1 and model name aptivra-200m-tiered-memory.
4. Do not import Aptivra GGUF into stock Ollama/llama.cpp — architecture is aptivra, not Llama.

MLX (I)

Exports PyTorch shards to mlx_export/ NPZ bundles for experimentation on Apple Silicon. Inference uses MLX when installed; otherwise the script documents the gap.

Docker CUDA (J)

Build and run on a Linux NVIDIA machine. On macOS, use native MPS (device: auto) instead of expecting CUDA inside local Docker.

docker build -f deploy/docker/Dockerfile.cuda -t aptivra-cuda .
docker run --gpus all -p 8080:8080 aptivra-cuda \
  python scripts/run_chat_server.py --host 0.0.0.0 --export-dir /model

Mount the Hugging Face snapshot at /model or bake weights into the image.

📜 Citation & License

If you use Aptivra in your research or personal projects, please cite:

@article{raghunath2026aptivra,
  title={Aptivra: Autonomous Continual Learning with Tiered Memory Runtime C-Engine},
  author={Raghunath},
  year={2026},
  url={https://huggingface.co/raghunath1/aptivra-200m-tiered-memory}
}

License: Apache License 2.0.