Waveformer

Waveformer / System Omega

This repository is the public Hugging Face surface for Nossair Bajddi's Waveformer/System Omega research line.

Current evidence-map technical note: https://doi.org/10.5281/zenodo.21044769

It is intentionally not a public trained-weights release. The core trained implementation, model weights, checkpoints, CUDA kernels, datasets, tokenizer files, and training recipes remain proprietary and are not released here.

Repository contents

This repository contains a public Hugging Face compatibility / architecture surface for the Waveformer line:

• README.md - public evidence map and validation-status summary
• config.json - public Waveformer configuration metadata
• configuration_waveformer.py - public custom Transformers config class
• modeling_waveformer.py - public architecture-surface wrapper

This repository does not contain:

• trained weights
• tokenizer files
• checkpoints
• CUDA kernels
• datasets
• exact data curation recipes
• training recipes
• the proprietary trained Waveformer/System Omega engine

Because trained weights are not included, this repository should not be read as a normal downloadable model checkpoint. It is a public proof/provenance and architecture landing surface for the Waveformer/System Omega research line.

Public evidence chain

How to read this repository

The public records above are different proof classes:

• The May 27 record is a timestamp/provenance anchor.
• The June 16 record is a phase-coherence method and empirical report.
• The June 18 record is the Waveformer architecture and training report.
• The June 23 record is the System Omega semantic-steering report.
• The June 29 record is a consolidation/status technical note that maps the public proof trail, private IP boundary, current validation status, and explicit non-claims.
• This Hugging Face repository is the AI-community landing surface tying those records together.

Public reproducibility would require releasing trained weights, source code, kernels, checkpoints, datasets, and recipes. That is intentionally outside the current public release boundary.

Current validation status

The current strongest result is not chatbot replacement or product-grade token generation. It is runtime-state event forecasting.

Private audit summary, 2026-06-29:

• System Omega runtime state predicts future internal error spikes on real telemetry with AUC around 0.95 to 0.99 across tested horizons.
• Fixed-state online operation was verified over a 1,048,576-token stream with flat allocated tensor memory.
• The load-bearing mechanism is saved phase/velocity plus bidirectional cross-coupling; ablation destroys the low-mode fossil when velocity or cross-coupling is removed.
• Predictive-maintenance/degradation streams are the strongest external use-path, especially when Omega/Waveformer state is combined with native features.

Current non-claims

This repository does not claim that:

• Waveformer currently beats frontier LLMs.
• The current public repository is a runnable trained model release.
• The current token-generation UX is product-grade.
• Markets, earthquakes, or Wikipedia attention streams are standalone product-grade wins.
• Current Fractal/Phi-4 LoRA bridge artifacts are crystallized.
• Public reproducibility is provided.

This repository also does not release proprietary source code, trained weights, checkpoints, CUDA kernels, datasets, tokenizer files, or training recipes.

Usage

This is not a public trained-weights release.

Do not expect the following to load a trained Waveformer checkpoint from this repository:

from transformers import AutoModelForCausalLM

model = AutoModelForCausalLM.from_pretrained(
    "wannaq/Waveformer",
    trust_remote_code=True,
)

The trained weights are intentionally not included. The repository is maintained as a public proof/provenance and architecture surface. See the Zenodo DOI chain above for the formal public records.

Research direction

Based on the strongest surviving evidence, the most credible current direction is stateful event forecasting and monitoring: detecting future instability, degradation, failure, or regime change in continuous streams.

Relevant private application areas include:

• local AI telemetry monitoring
• experiment-governor systems
• predictive maintenance and degradation monitoring
• GPU/training health monitoring
• autonomous verification pipelines
• closed-source research engineering
• private continuous-state event-prediction systems

Citation anchors

@misc{bajddi2026runtime,
  author = {Bajddi, Nossair},
  title = {Continuous Oscillator Intelligence Runtime - Timestamp Proof},
  year = {2026},
  publisher = {Zenodo},
  doi = {10.5281/zenodo.20412409},
  url = {https://doi.org/10.5281/zenodo.20412409}
}

@misc{bajddi2026phasecoherence,
  author = {Bajddi, Nossair},
  title = {Deterministic Layer Freezing in Autoregressive Language Models via Continuous Phase Coherence Analysis},
  year = {2026},
  publisher = {Zenodo},
  doi = {10.5281/zenodo.20720827},
  url = {https://doi.org/10.5281/zenodo.20720827}
}

@misc{bajddi2026waveformer,
  author = {Bajddi, Nossair},
  title = {Emergence Over Attention: Continuous-Time Phase Synchronization as a Computational Primitive},
  year = {2026},
  publisher = {Zenodo},
  doi = {10.5281/zenodo.20741536},
  url = {https://doi.org/10.5281/zenodo.20741536}
}

@misc{bajddi2026omega,
  author = {Bajddi, Nossair},
  title = {Non-Autoregressive Semantic Steering of Discrete Language Models via Continuous-Time Phase Manifolds},
  year = {2026},
  publisher = {Zenodo},
  doi = {10.5281/zenodo.20819119},
  url = {https://doi.org/10.5281/zenodo.20819119}
}

@misc{bajddi2026evidencemap,
  author = {Bajddi, Nossair},
  title = {Waveformer / System Omega: Public Evidence Map and Current Validation Status},
  year = {2026},
  publisher = {Zenodo},
  doi = {10.5281/zenodo.21044769},
  url = {https://doi.org/10.5281/zenodo.21044769}
}