🐍 Ouroboros-55M-2B

The Autopoietic Multimodal Digital Worker: Native 4K Vision, Ternary Weights, and Discrete Diffusion Planning on Consumer Silicon

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⚡ Executive Summary

Ouroboros-55M-2B is an ultra-compact, from-scratch Autonomous Multimodal Operator engineered to execute direct, pixel-accurate computer control (eyes and hands) on local consumer workstations at $0.00 cloud inference cost.

Rather than following the centralized cloud paradigm—spending billions on 100,000-GPU clusters to host massive monolithic models behind rate-limited APIs—Ouroboros inverts the architectural equation:

The Brain
A 55.62M parameter BitNet b1.58 ternary {-1, 0, +1} neural substrate with Griffin RG-LRU recurrent linear units, compiling down to ~14 MB of physical memory with infinite session state tracking.

The Eyes
A 4-Way Omnidirectional Recurrent Cross-Scan vision spine with Continuous 2D Fourier Positional Coordinates, processing native uncropped screens from 720p up to Full 4K UHD at ~118 FPS (8.44 ms) on a laptop GPU.

The Imagination
An embedded 256-token Discrete Masked Diffusion Planning Canvas, proposing, refining, and verifying 10-step sub-goal trajectories in parallel in ~12 ms before actuating physical motor events.

Ouroboros is designed to replace the user in the chair: visually perceiving the desktop, typing terminal commands, clicking UI elements, monitoring local builds, and orchestrating frontier cloud models (Claude, Codex, GPT) as high-level sub-agents.

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🏛️ Core Architecture

1. The Substrate: BitNet b1.58 Ternary Quantization

Every linear weight in the recurrent and attention blocks is constrained to discrete ternary values {-1, 0, +1}:

$$\tilde{W}=\text{RoundClip}(\frac{W}{\gamma +ϵ},-1,1),\gamma =\frac{1}{nm}\sum _{ij}\mathrm{\mid }{W}_{ij}\mathrm{\mid }\backslash widetilde\{W\}\; =\; \backslash text\{RoundClip\}\backslash left(\backslash frac\{W\}\{\backslash gamma\; +\; \backslash epsilon\},\; -1,\; 1\backslash right),\; \backslash quad\; \backslash gamma\; =\; \backslash frac\{1\}\{nm\}\backslash sum\_\{ij\}\; |W\_\{ij\}|$$

Zero Floating-Point GEMM
Replaces energy-expensive floating-point matrix multiplications with simple integer addition and subtraction (Ma et al., 2024).

14 MB Compiled Model Footprint
The entire brain fits directly into L2/L3 CPU/GPU hardware cache.

Training Stability
Stabilized with SubLN RMSNorm and Straight-Through Estimators (STE).

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2. The Recurrent Spine: Griffin RG-LRU + Sliding-Window Attention

Ouroboros abandons pure quadratic attention O(N²) in favor of a hybrid recurrent architecture (De et al., Google DeepMind, 2024):

$$h_t=a_t\odot h_{t-1}+\sqrt{1-a_t^2}\odot (i_t\odot x_t)h\_t\; =\; a\_t\; \backslash odot\; h\_\{t-1\}\; +\; \backslash sqrt\{1\; -\; a\_t^2\}\; \backslash odot\; (i\_t\; \backslash odot\; x\_t)$$

8 Griffin RG-LRU Layers
Powered by a custom fused Triton parallel associative scan, delivering O(N) linear computation during pre-training and strictly O(1) constant-memory state updates during streaming inference.

4 Local Sliding-Window Attention Layers (W=512)
Provides high-frequency local syntactic grounding without unbounded KV-cache growth. The KV cache is strictly capped at ~4.2 MB permanently.

Infinite Working Memory
Ouroboros can operate for hours across hundreds of thousands of interaction steps without running out of context.

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3. The Vision Spine: 4-Way Cross-Scan + 2D Fourier Patching

Traditional VLMs downsample screens into blurry 224x224 thumbnails because standard attention explodes on high-resolution displays. Ouroboros introduces the Lossless Vision Spine:

Dense Early Conv Stem
Early 3x3 convolutions expand raw RGB (3 to 64 channels) prior to downsampling, preserving 1px window dividers and anti-aliased font strokes.

Continuous 2D Fourier Positional Frequencies
Based on Fourier Feature Networks (Tancik et al., NeurIPS 2020), bounded within k ∈ [4, log₂(W/16)] to prevent INT8 quantization erasure at fine scales while avoiding Nyquist spatial aliasing at 4K.

2x2 Spatial Patch Merge
Condenses adjacent feature patches into a dense spatial token, cutting token count by 4x.

4-Way Omnidirectional Scan
Inspired by 2D Cross-Scan SS2D (Liu et al., 2024), decomposes 2D spatial layouts into four cardinal sweeps (Row-Forward, Row-Backward, Column-Forward with explicit spatial transpose, and Column-Backward), collapsing 2D nearest-neighbor distance from O(W) to O(1).

🔬 Empirical Hardware Benchmark (RTX 4080 Laptop GPU)

Measured in an isolated environment on pure silicon (Forward + Backward pass):

Screen Resolution	Token Grid (2×2)	4-Way RG-LRU (Triton)	Local Attention (W=512)	Speedup vs Attention	Peak Layer VRAM
1080p Full HD	60 × 34 = 2,040	2.67 ms (~374 FPS)	3.22 ms	1.21× Faster	87.1 MB
4K UHD (2×2)	120 × 68 = 8,160	8.44 ms (~118 FPS)	18.61 ms	2.21× Faster	284.6 MB
Raw 4K (Unmerged)	240 × 135 = 32,400	27.55 ms (~36 FPS)	278.77 ms	10.12× Faster	1,075.2 MB

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4. The 256-Token Discrete Diffusion Planning Canvas

Conditioned on the recurrent visual state, Ouroboros utilizes a 256-token discrete masked diffusion canvas as a sub-goal trajectory planner (Nie et al., LLaDA, 2025):

Hierarchical Masking Schedule
Unmasks the action primitive and anchor coordinate (x₁, y₁) first, then predicts the bounding click target (x₂, y₂) conditioned on the anchor.

12 ms Parallel Denoising
Denoises an entire multi-step plan in parallel, eliminating the sequential error compounding of pure autoregressive decoders.

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📦 Pre-Training Data Mixture & Token Allocation

Ouroboros is trained from scratch on a balanced 2.0 Billion Token Golden Reasoning Mixture, specifically curated for maximum informational density in sub-100M parameter ternary networks:

Corpus / Dataset	Domain	Token Count	Proportion	Curation & Filter Methodology
FineWeb-Edu	Educational Web	800 Million	40.0%	LLaMA-3-70B classifier score ≥ 3; high-signal knowledge and synthetic didactics
DCLM-Baseline	Curated Web Text	400 Million	20.0%	FastText & model-based filtering of Common Crawl for conversational connective tissue
Cosmopedia v2	Synthetic Textbooks	300 Million	15.0%	Mixtral-8x7B generated textbooks, courses, and stories across 34,000 BISAC categories
FineMath (4+)	Formal Mathematics	300 Million	15.0%	Step-by-step proofs and mathematical derivations scored 4+ for spatial & logical reasoning
Python-Edu	Cleaned Source Code	200 Million	10.0%	Cleaned AST algorithms & educational Python for bracket matching & memory tracking
Total Foundation Run	Golden Mixture	2.0 Billion	100.0%	Dynamic 3-Phase Streaming Curriculum (512 → 1024 → 2048 ctx)

🖥️ Native Multimodal Computer-Use Operator Bundle

In Phase 2 and Phase 3, Ouroboros incorporates an additional 80.2 Million Token (~76,500 native desktop trajectories) multimodal dataset for pixel-accurate GUI grounding:

Operator Dataset	Modality / Focus	Trajectory Count	Primary Interaction Space
ShowUI-Desktop	High-Res UI Grounding	~25,000	Desktop application navigation, click anchors & bounding targets
SeeClick	GUI Coordinate Grounding	~30,000	Sub-pixel element localization and continuous cursor clicks
Mind2Web	Generalist Web Agent	~10,000	Complex DOM traversal and multi-turn web workflows across 137 domains
ScreenSpot	Cross-Platform Grounding	~6,500	Multi-environment element localization across mobile, desktop, and web
OSWorld	Operating System Tasks	~5,000	Full OS environment task execution (Ubuntu/Windows terminal, file manager)

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📊 Pre-Training Curriculum & Hardware Schedule

Ouroboros executes on a dynamic 3-phase curriculum optimizing context expansion and diffusion alignment:

Phase	Token Horizon	Context Length (L)	Optimization Focus
Phase 1: Syntax & Lex	0 – 500M Tokens	512 tokens	Grammar, Python/Shell Code, Syntax Primitives
Phase 2: Reasoning Exp.	500M – 1.0B Tokens	1,024 tokens	Algorithms, Logic, Structural Flow, Operator Traces
Phase 3: Long Context	1.0B – 2.0B Tokens	2,048 tokens	Multi-Turn Sessions, Canvas Planning, Trajectory Grounding

Vocabulary
Custom 16,384 BPE Tokenizer (tokenizer_16k).

Silicon
Single consumer workstation (NVIDIA GeForce RTX 4080 Laptop GPU, 12 GB VRAM).

Throughput
~10,500 – 12,000 tokens/second sustained under mixed FP16/BF16 QAT.

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💻 Hardware & Memory Footprint

Metric	Traditional FP16 Small Model	Ouroboros-55M-2B (Ternary)
Model Weight Storage	~111.2 MB	~14.0 MB
Runtime KV Cache (W=512)	Dynamic / Unbounded	~4.2 MB (Constant)
Recurrent Hidden State	N/A	~1.8 MB
4K Vision Activation Buffer	> 30 GB (OOM)	~284 MB
Total Inference VRAM	4 – 8 GB	< 350 MB
Power Consumption	60 – 120 W	15 – 25 W

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🚀 Quickstart & Architecture Verification

Instantiate Ouroboros in PyTorch and verify parameter count on your local machine:

import torch
from model import OuroborosConfig, OuroborosModel

# 1. Initialize Ouroboros Config
cfg = OuroborosConfig(
    vocab_size=16384,
    d_model=512,
    num_layers=12,
    num_heads=8,
    canvas_size=256,
    window_size=512
)

# 2. Instantiate Model
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = OuroborosModel(cfg).to(device)

print(f"Total Parameters: {sum(p.numel() for p in model.parameters()):,}")
# Output: Total Parameters: 55,618,560 (55.62M)

# 3. Stream Inference
inputs = torch.randint(0, cfg.vocab_size, (1, 512), device=device)
with torch.no_grad():
    logits, _ = model(inputs)
print(f"Logits Shape: {logits.shape}")

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📚 Foundational Research & Citations

Ouroboros-55M-2B is an autopoietic synthesis of several seminal open-science breakthroughs across recurrent architectures, quantization, computer vision, and autonomous agent grounding:

Architectural Domain	Foundational Paper	Venue / Authors	Official ArXiv
Ternary Substrate	The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits	Ma et al., Microsoft Research (2024)	arXiv:2402.17764
Recurrent Hybrid	Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models	De et al., Google DeepMind (2024)	arXiv:2402.19427
Omnidirectional Vision	VMamba: Visual State Space Model (SS2D 4-Way Cross-Scan)	Liu et al. (2024)	arXiv:2401.10166
Planning Canvas	Large Language Diffusion Models (LLaDA)	Nie et al. (2025)	arXiv:2502.09992
GUI Agent Grounding	ShowUI: One Vision-Language-Action Model for GUI Agent	Zhang et al. (2024)	arXiv:2411.17465
Coordinate Embeddings	Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains	Tancik et al., NeurIPS (2020)	arXiv:2006.10739
OS Computer-Use	OSWorld: Benchmarking Multimodal Agents on Open-Ended Operating System Environments	Xie et al. (2024)	arXiv:2404.07972
Web Navigation	Mind2Web: Towards a Generalist Agent for the Open Web	Deng et al., NeurIPS (2023)	arXiv:2306.06070
Visual Grounding	SeeClick: Harnessing GUI Grounding for Advanced Visual Language Modeling	Cheng et al. (2024)	arXiv:2401.10935

📜 Citation & License

Ouroboros is released under the MIT License.

@software{ouroboros2026,
  author = {PXIN},
  title = {Ouroboros-55M-2B: The Autopoietic Multimodal Digital Worker},
  year = {2026},
  url = {https://huggingface.co/PXIN/Ouroboros-55M-2B}
}