Add recursive transformer model to foundry

Here's the **updated version** of your model information, fully adapted for the **Recursive Transformer Model (RTM)** with ERS (Enhanced Reconsideration System). It keeps the same professional structure and tone as the original UI-TARS example while accurately reflecting what your HF repo and research actually contain.

### Updated Model Information

**Model Name:** Recursive Transformer Model (RTM) + ERS
**Hugging Face URL:** https://huggingface.co/Drjkedwards/Recursive-Transformer-Model
**Model Type:** Stateful Memory & Reconsideration Framework for Transformers
**Architecture:** Custom PyTorch (PMLLLattice + Recursive Reconsideration) – extends any Transformer
**Parameters:** Not a fixed-parameter LLM (lightweight lattice; runs on top of existing embedding models)
**License:** MIT (core ERS implementation)

### Use Case

The **Recursive Transformer Model (RTM)** with the **Enhanced Reconsideration System (ERS)** is a novel architectural extension that transforms stateless Transformers and LLMs into **stateful, self-correcting systems**.

It is specifically designed for:

- Long-term memory coherence in agents and chatbots
- Automatic detection and resolution of contradictory or outdated beliefs (“nostalgic incorrectness”)
- Persistent memory management across inference sessions
- Integration with knowledge graphs (Graphiti, Mem0) and agent frameworks (LangChain, etc.)
- Recursive multi-pass reasoning with temporal decay and consensus voting

### Performance

- **Core Strength:** Successfully resolves synthetic contradictory memory scenarios through iterative reconsideration loops
- **Key Metrics (Conceptual):** Nostalgic Incorrectness (NI) reduction, confidence delta tracking, contradiction detection score, cosine similarity shifts in embedding space
- **Efficiency:** Low overhead when integrated with existing embedding models (e.g., sentence-transformers). Recursive passes add controllable compute cost.
- **Qualitative Results:** Demonstrates coherent state maintenance and belief updating in controlled memory tests (see TechRxiv paper for examples such as conflicting “Paris” facts).

No large-scale public benchmarks (e.g., OSWorld, ScreenSpot) are available yet, as this is a **memory layer/framework** rather than a standalone generative VLM or LLM.

### Why Azure AI Foundry? (or any enterprise platform)

- **Agentic AI Enhancement:** Turns stateless LLMs/agents into stateful systems with persistent self-correction — ideal for reliable long-running agents.
- **Enterprise Demand:** Growing need for trustworthy memory in autonomous agents, knowledge-intensive applications, RPA, and safety-critical AI systems.
- **Open Source:** MIT license supports broad adoption and integration.
- **Complementary:** Works alongside large foundation models (e.g., Azure OpenAI, Llama, Qwen) to add long-term coherence and contradiction resolution without replacing the base model.
- **Hybrid Potential:** Can be combined with vision-language models or GUI agents for more reliable multi-step reasoning and memory grounding.

### Technical Requirements

**Deployment Type:** Python library / custom PyTorch model (can be deployed as part of any Transformer pipeline)
**Hardware:** Standard CPU or GPU (PyTorch-compatible). No large VRAM requirement for the lattice itself.
**Framework:** PyTorch + Transformers (custom modeling file)
**Dependencies:** sentence-transformers, safetensors, optional: mem0-ai, graphiti-core, LangChain
**Integration:** Use `RecursiveTransformerModel.from_pretrained(...)` with `trust_remote_code=True`

### Community Interest

- **Downloads:** Growing via linked Kaggle PyTorch model and GitHub ERS repository
- **Research Interest:** TechRxiv preprint (October 2025) introducing the architecture and Persistent Memory Logic Loops (PMLL)
- **Related Work:** Hybrid TRM-RTM models, Topic Integrator for recursive context annotation
- **Use Cases:** Agent memory management, belief updating in conversational AI, long-term knowledge consistency in autonomous systems, research on mitigating hallucination-style memory errors

### Additional Context

- **Developed by:** Dr. Josef “Q.” Edwards (University of Colorado Boulder)
- **Core Innovation:** Persistent Memory Logic Loops (PMLL) + adaptive temporal decay + consensus-based contradiction detection
- **Not a standalone LLM:** This is a **memory augmentation layer** that can be integrated with any embedding-based Transformer (including VLMs for multimodal memory).
- **Repository Contents:** Custom `RecursiveTransformerModel`, `PMLLLattice`, `MemoryBlock`, config, and example weights.
- **Linked Resources:**
- GitHub ERS Library: https://github.com/drqedwards/ERS
- Kaggle PyTorch Implementation: https://www.kaggle.com/models/josefedwards/recursive-transformer-model/pyTorch
- TechRxiv Paper: https://www.techrxiv.org/users/856117/articles/1345789 (or ResearchGate version)

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This version is accurate, professional, and highlights the **strengths** of RTM/ERS without overstating capabilities (e.g., no fake GUI automation benchmarks).

Would you like a shorter “one-paragraph” summary for the HF model card description, or a version optimized for a specific audience (e.g., enterprise sales, research paper appendix, or Azure proposal)? I can refine it further.

References

RT, GitHub: https://github.com/drqedwards/ppm
TRM Paper: (https://arxiv.org/abs/2510.04871)
Website: https://drqsatoshin.com
Thank you for considering this request!