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télos (τέλος)

Research Portal: telos.research.wingit.tech
Note: The research portal is currently under active construction and is not finished yet.

About Télos

“Whatever is new — we wing it.”

Télos is an open research project by Wing It Research focused on AI research, benchmarks, and new architectures.

We are working on:

1. COROSred (COnfidence ROuted Selective ReDiffusion)
2. Inference Optimal Scaling Laws for Masked Diffusion and Uniform Noise Diffusion models
3. Benchmarks for new models

Latest Findings

Benchmarks (AFM-3 vs Granite vs LFM 8B)

We tested three edge models: Apple AFM-3 Core Advanced, IBM Granite 4.2 3B, and LiquidAI LFM 8B across 7 evaluation suites.

Runaway Loops and Hardware Efficiency

Edge models sometimes get caught repeating words over and over, or they use too much memory.

• Runaway Loops: IBM Granite 4.2 3B had the highest runaway loop rate at 36.8% (1,129 tasks caught in repeat loops), followed by LiquidAI LFM 8B at 28.5%, and Apple AFM-3 at 23.4%.
• Speed and Memory: Apple AFM-3 runs natively via Swift IPC at 58.7 tok/s using 2.4 GB RAM. IBM Granite 4.2 3B runs at 41.2 tok/s using 3.2 GB RAM. LiquidAI LFM 8B runs at 22.4 tok/s using 7.8 GB RAM.

See benchmarks/README.md for full benchmark reports and details.

Research (COROSred)

COROSred combines fast autoregressive token drafting with bidirectional diffusion fixing. High-confidence tokens are accepted in one step. Low-confidence tokens are fixed with bidirectional re-diffusion.

+-------------------------------------------------------+
|                 TÉLOS MODEL BACKBONE                  |
|       (RoPE, SwiGLU, RMSNorm, Weight Tying, GQA)      |
+---------------------------+---------------------------+
                            |
             +--------------+--------------+
             |                             |
             v                             v
+-------------------------+   +-------------------------+
|  Causal AR Next-Token   |   |   Learned Reliability   |
|  Loss: L_causal (alpha) |   |   Head Gate             |
+------------+------------+   +------------+------------+
             |                             |
             +--------------+--------------+
                            |
                     Routing Decision
                            |
             +--------------+--------------+
             |                             |
             v                             v
+---------------------------+ +---------------------------+
| High Confidence (>= 0.65) | | Low Confidence (< 0.65)   |
| Accept Draft Token        | | Route to Bidirectional    |
| (Fast 1-Step AR)          | | Masked Re-Diffusion       |
+---------------------------+ +---------------------------+

• Perplexity: Achieves 5.08 validation PPL (1.29x lower than pure AR).
• Bidirectional Infilling: Achieves 63.0% Top-1 accuracy on fill-in-the-blank code tasks (compared to 7.6% for pure AR).
• Anti-Cheat Span Masking: Achieves 52.0% exact match with a low 2.0% copy rate when multiple tokens are masked.

See research/README.md for research papers and scaling details.

Documentation Directory Routing

Basic CLI Usage

Installation

# Standard install
pip install telos-ml

# With Apple Silicon Metal support (MLX)
pip install "telos-ml[mlx]"

Core CLI Commands

# 1. Prepare token data
telos dataprep --input raw_data/ --output data/python.bin --vocab-size 8192

# 2. Train a 50M COROSred model on Apple Silicon
telos train --paradigm corosred --params 50M --tokens 2.5B --hardware mlx

# 3. Run evaluation
telos eval --checkpoint checkpoints/corosred/model.safetensors --type all

# 4. Hardware benchmark (capped at 5 minutes)
telos bench --paradigm corosred --params 50M --hardware mlx

# 5. Check Apple Foundation Models on Apple Silicon
telos afm status
telos afm generate "Explain binary search in two sentences."

# 6. Verification suite
telos test

Python API

import telos

# Use on-device Apple Foundation Models
if telos.afm.probe().available:
    response = telos.afm.generate("Write a python function for quicksort.")
    print(response)

See cli/README.md for all CLI flags and options.

Citations & References

Primary Citation

@article{samuel2026telos,
  title   = {télos: Exploring Scaling Laws, Hardware Optimizations, and Paradigm Trade-offs in Discrete Diffusion and Autoregressive Language Models},
  author  = {Ivan Samuel},
  journal = {Wing It Research},
  year    = {2026},
  url     = {https://telos.research.wingit.tech}
}

Framework & Related Research

• Apple MLX: Hannun et al., MLX: Efficient machine learning on Apple silicon, 2023.
• Masked Diffusion Language Models (MDLM): Sahoo et al., Masked Diffusion Language Models, 2024.
• Compute-Optimal Scaling (Chinchilla): Hoffmann et al., Training Compute-Optimal Large Language Models, 2022.
• Large Language Diffusion Models (LLaDA): Nie et al., LLaDA: Large Language Diffusion Models, 2025.
• Discrete Diffusion Language Modeling (DiffusionGemma): Google DeepMind, DiffusionGemma: An experimental discrete diffusion model based on Gemma, 2026.

License

Apache-2.0 License. See LICENSE for details.