| --- |
| license: cc-by-nc-nd-4.0 |
| tags: |
| - waveformer |
| - Kuramoto |
| - phase-synchronization |
| - continuous-time |
| - attention-free |
| - oscillator |
| - system-omega |
| - event-prediction |
| - proprietary-implementation |
| language: |
| - en |
| pipeline_tag: text-generation |
| --- |
| |
| # 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 |
|
|
| | Date | Record | DOI | Role | |
| |---|---|---|---| |
| | 2026-05-27 | Continuous Oscillator Intelligence Runtime - Timestamp Proof | https://doi.org/10.5281/zenodo.20412409 | Timestamp and priority proof using SHA256 hashes of source/checkpoint artifacts. | |
| | 2026-06-16 | Deterministic Layer Freezing via Continuous Phase Coherence Analysis | https://doi.org/10.5281/zenodo.20720827 | Phase-coherence layer analysis across GPT-2, Qwen2.5, and Mistral scales. | |
| | 2026-06-18 | Emergence Over Attention: Continuous-Time Phase Synchronization as a Computational Primitive | https://doi.org/10.5281/zenodo.20741536 | Waveformer architecture and training report. | |
| | 2026-06-23 | Non-Autoregressive Semantic Steering of Discrete Language Models via Continuous-Time Phase Manifolds | https://doi.org/10.5281/zenodo.20819119 | System Omega semantic-steering report and raw validation metrics. | |
| | 2026-06-29 | Waveformer / System Omega: Public Evidence Map and Current Validation Status | https://doi.org/10.5281/zenodo.21044769 | Consolidates the public DOI trail, private IP boundary, current validation status, and explicit non-claims. | |
|
|
| ## 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: |
|
|
| ```python |
| 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 |
|
|
| ```bibtex |
| @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} |
| } |
| ``` |
|
|
