dhanesh-hf/Jarvis-Titan-M4-MoE-CSA3

🏛️ J.A.R.V.I.S. TITAN 14.8B MoE (Milestone M4 — CSA3)

Frontier High-Density Reasoning & Adaptive Memory Architecture

14.8B DeepSeekMoE Backbone + M4 Tri-Brid Neural Memory + CSA3 Differential Attention + Multi-Token Speculative Prediction

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⚡ Overview

J.A.R.V.I.S. Titan 14.8B MoE (CSA3) is a premier high-density reasoning model engineered for complex mathematical derivation, algorithmic synthesis, and extended-context cognitive tasks.

Built upon an upcycled 14.8B DeepSeekMoE backbone, the model incorporates the calibrated M4 Tri-Brid Neural Memory system, Continuous Sparse Attention 3 (CSA3), and Multi-Token Prediction (MTP), preserving full mathematical precision while enabling efficient long-context associative recall.

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🚀 Key Architectural Upgrades & Features

Jarvis-Titan-M4-MoE-CSA3 introduces five major capability enhancements over conventional dense and sparse transformers:

1. 🧠 M4 Tri-Brid Neural Memory Subsystem

• Multi-Tier Cognitive Storage: Integrates high-throughput local sliding window attention, an exact salient needle-in-a-haystack reservoir, and associative test-time neural memory matrices ($M_t$).
• Eliminates Associative Recall Decay: Maintains robust long-horizon retrieval across extended token contexts (up to 131,072 tokens) without suffering from the context fading typical of standard linear recurrent systems.

2. ⚡ Continuous Sparse Attention 3 (CSA3)

• Differential KV Caching: Uses quantum perturbation principles to compress intermediate attention states into continuous differential representations.
• Bounded Attention Footprint: Significantly minimizes key-value memory overhead during long multi-turn sessions and complex chain-of-thought derivations.

3. 🎯 Length-Adaptive Dynamic Memory Gating

• Zero-Distortion Short Prompt Reasoning: Gating dynamics smoothly adapt according to sequence length, maintaining 100% local attention fidelity with zero degradation on short queries, STEM problems, and interactive coding.
• Dynamic Recall Expansion: Automatically scales neural memory bandwidth as sequence depth grows, ensuring immediate access to critical needle tokens in long documents.

4. ⏩ Speculative Multi-Token Prediction (MTP)

• Parallel Target Projections: Features native multi-token prediction heads to forecast subsequent tokens in parallel.
• Accelerated Generation Throughput: Enables speculative decoding and verification speedups of up to $1.8\times$ to $2.2\times$ during inference serving.

5. 🛡️ 100% Preserved 120M High-Density Reasoning Core

• Fully preserves the verified STEM mathematics, Olympiad problem-solving, and executable code synthesis intellect developed during the 120M token high-density DeepSeekMoE training curriculum.

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📋 Model Architecture & Specifications

Attribute	Specification
Model Name	J.A.R.V.I.S. Titan 14.8B MoE — CSA3
Base Architecture	DeepSeekMoE Sparse Mixture-of-Experts
Total Parameters	14.8 Billion
Active Parameters	~3.2 Billion per token
Total Layers	28 Transformer Layers
Routing Topology	8 Routed Experts + 1 Isolated Shared Expert (Top-2 active)
Attention Mechanism	Grouped-Query Attention (GQA, 28 Q-Heads : 4 KV-Heads)
Memory Enhancement	M4 Tri-Brid Neural Memory (Sliding Window + Salient Reservoir + Neural Recurrence)
Differential Attention	Continuous Sparse Attention 3 (CSA3)
Speculative Decoding	Multi-Token Prediction (MTP) Head
Vocabulary Size	152,064 tokens
Max Context Length	Up to 131,072 tokens
Precision	Float16 / Bfloat16

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🛠️ Quickstart & Inference Guide

The model is compatible with Hugging Face transformers and can be loaded directly onto GPU/accelerator hardware:

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "dhanesh-hf/Jarvis-Titan-M4-MoE-CSA3"

tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map="auto",
    torch_dtype=torch.float16,
    trust_remote_code=True
)

prompt = "<|im_start|>system\nYou are J.A.R.V.I.S., an expert reasoning assistant engineered by Dhanesh. Before answering, think through the problem carefully inside <think>...</think> tags. Verify your reasoning. If you detect a flaw, correct it. Provide your final solution inside <answer>...</answer> tags. When the problem asks for a boxed answer, use \\boxed{your answer}. Be concise but complete.<|im_end|>\n<|im_start|>user\nSolve for x: 3x + 15 = 42.<|im_end|>\n<|im_start|>assistant\n<think>\n"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

with torch.inference_mode():
    outputs = model.generate(
        **inputs,
        max_new_tokens=1024,
        temperature=0.6,
        top_p=0.9,
        repetition_penalty=1.15
    )

print(tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))

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🛡️ License & Terms of Use

This model is governed by the J.A.R.V.I.S. Titan Proprietary Research License (JTRL-v1.0).

• Permitted: Non-commercial academic audit, independent scientific evaluation, and benchmark replication.
• Strictly Prohibited: Commercial exploitation, hosted inference services/APIs, unauthorized weights redistribution, and competitive model distillation.
• Please consult the complete LICENSE file for full terms and conditions.

Citation

@misc{jarvis_titan_m4_csa3_2026,
  author = {Dhanesh},
  title = {J.A.R.V.I.S. Titan 14.8B MoE: Unified Tri-Brid Neural Memory and Autonomous Reasoning Architecture},
  year = {2026},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/dhanesh-hf/Jarvis-Titan-M4-MoE-CSA3}}
}