Add ARC-1 model card: PRISM-based architecture search, Text/Image -> Text

README.md

@@ -1,3 +1,88 @@
 ---
 license: apache-2.0
+pipeline_tag: image-text-to-text
+tags:
+- prism
+- neural-architecture-search
+- multimodal
+- under-development
 ---
+
+<div align="center">
+
+![ARC-1 Banner](https://github.com/PlatformNetwork/prism/raw/main/assets/banner.png)
+
+# ARC-1
+
+**A multimodal model discovered through decentralized neural architecture search**
+
+[![PRISM](https://img.shields.io/badge/Built%20with-PRISM-6f42c1.svg)](https://github.com/PlatformNetwork/prism)
+[![Status](https://img.shields.io/badge/Status-In%20Development-orange.svg)]()
+[![Modality](https://img.shields.io/badge/Modality-Text%20%2F%20Image%20%E2%86%92%20Text-blue.svg)]()
+
+</div>
+
+---
+
+## ⚠️ Status: In Development
+
+**ARC-1 is currently under active development.** No weights are available yet. This repository will host the model once the architecture search and training phases are complete.
+
+## Overview
+
+ARC-1 is a model being developed through [**PRISM**](https://github.com/PlatformNetwork/prism), a decentralized neural architecture search (NAS) subnet on the Platform Network. Instead of hand-picking an architecture upfront, ARC-1's design is being **discovered competitively**: miners across the network submit novel architecture families and training recipes, which are evaluated in isolated benchmark environments for learning quality, training stability, and scaling behavior.
+
+The best-performing architecture that emerges from this search will be used to train ARC-1.
+
+## Modalities
+
+| Input | Output |
+|-------|--------|
+| Text 📝 | Text 📝 |
+| Image 🖼️ | Text 📝 |
+
+ARC-1 will support **Text/Image → Text**: it will accept both text and images as input and generate text as output.
+
+## Why is the model size unknown?
+
+The parameter count of ARC-1 is **intentionally undisclosed for now — because it is genuinely not decided yet.**
+
+In a conventional training pipeline, you fix an architecture and a parameter budget first, then train. ARC-1 inverts this process:
+
+1. **Architecture search comes first.** PRISM evaluates candidate architectures at compact proxy scales, measuring loss curves, gradient stability, activation behavior, and how performance scales across model size, depth, sequence length, and batch size.
+2. **Scaling laws are derived from the winner.** Each architecture family has its own scaling behavior. The optimal parameter count depends on the scaling-law signals of the architecture that wins the search — a number that simply cannot be known before the search concludes.
+3. **The final size is chosen from evidence, not convention.** Once the winning architecture's scaling characteristics are measured, the parameter budget will be set where the compute/performance trade-off is optimal for that specific design.
+
+The final model size will be announced **after the architecture search is complete**.
+
+## How PRISM discovers the architecture
+
+```mermaid
+flowchart LR
+    A[Miners submit<br/>architectures & recipes] --> B[Isolated evaluation<br/>environments]
+    B --> C[Scoring: learning quality,<br/>stability, scaling signals]
+    C --> D[Best architecture<br/>selected]
+    D --> E[ARC-1 training]
+    E --> F[Weights released<br/>here]
+```
+
+- **Decentralized search**: architecture and training ideas are sourced from a competitive network of miners rather than a single research team.
+- **Scaling-aware evaluation**: candidates are rewarded for smooth loss curves, stable gradients, and consistent improvements across scales — not just raw benchmark numbers.
+- **Separate ownership**: architecture discovery and training-recipe improvements are attributed and rewarded independently, so both the design and the training procedure are optimized.
+
+## Roadmap
+
+- [x] Repository created
+- [ ] Neural architecture search via PRISM *(in progress)*
+- [ ] Final architecture & model size announcement
+- [ ] Training
+- [ ] Weights release
+
+## Links
+
+- 🔬 **PRISM (architecture search)**: [github.com/PlatformNetwork/prism](https://github.com/PlatformNetwork/prism)
+- 📖 **PRISM documentation**: [Overview](https://github.com/PlatformNetwork/prism/blob/main/docs/overview.md) • [Scoring](https://github.com/PlatformNetwork/prism/blob/main/docs/scoring.md) • [Scaling evaluation](https://github.com/PlatformNetwork/prism/blob/main/docs/scaling.md)
+
+## License
+
+Apache 2.0