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Update README.md #34

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Expand Up @@ -11,7 +11,7 @@
[![Python](https://img.shields.io/badge/Python-3.13+-3776AB.svg)](core/pyproject.toml)
[![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg)](CONTRIBUTING.md)

[Overview](docs/overview.md) · [Quick Start](docs/quickstart.md) · [Technical Report](docs/technical_report.md) · [Agentic Evolution](docs/agentic-evolution.pdf) · [Development](docs/development.md) · [Contributing](CONTRIBUTING.md) · [Roadmap](docs/roadmap.md)
[Overview](docs/overview.md) · [Quick Start](docs/quickstart.md) · [Technical Report](docs/technical_report.md) · [Agentic Evolution](https://github.com/agentic-evolution/awesome-agentic-evolution) · [Development](docs/development.md) · [Contributing](CONTRIBUTING.md) · [Roadmap](docs/roadmap.md)

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On top of this runtime, Sico organizes work into **three loops** that together form the co-evolution cycle between Operators and Digital Workers:

- **Execution Loop**: turns an Operator goal into a traced agent run. The Cortex–Action–Memory stack executes inside an observable Sandbox and emits structured trajectories: actions, intermediate states, tool outputs, and environmental feedback.
- **Evolution Loop**: converts those trajectories into reusable capability. A Reflector → Curator pipeline distills successful strategies and recurring failure patterns into a per-(project, agent) **Playbook** that is injected into the next run's workspace (training-free), while the same signals can also be fed back into base-model training (training-based).
- **Evolution Loop**: converts those trajectories into reusable capability. A Reflector → Curator pipeline distills successful strategies and recurring failure patterns into a per-(project, agent) **Playbook** that is injected into the next run's workspace (training-free), while the same signals can also be fed back into base-model training (training-based). Beyond any single model, Sico provides a general-purpose model evolution pipeline that turns real execution traces into round-after-round improvement for any agent. Our first open release, [Sico-Evolution GUI agent](https://huggingface.co/microsoft/GELab-Zero-4B-preview-Sico-Evolution), applies this pipeline to a GUI agent and already reaches 82.9% task success, competitive with leading proprietary models, with more evolved models to follow.
- **Evaluation Loop** *(planned)*: analyzes failed task trajectories and attributes the root cause using an L1–L4 failure taxonomy, from high-level ownership to concrete failure mode. The results help the Operator provide targeted corrections and feed failure insights back into Experience Learning and future training.

## Features
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