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docs: remove Five Pillars section from README

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The architecture details are now in .claude/rules/python-dev.md.
Keep the README focused on installation, quickstart, and project info.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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Jon Saad-Falcon
2026-03-08 00:34:47 +00:00
co-authored by Claude Opus 4.6
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@@ -89,18 +89,6 @@ uv run pytest tests/ -v
See [Contributing](docs/development/contributing.md) for more.
## The Five Pillars
| Pillar | What it does | Key abstractions |
|--------|-------------|-----------------|
| **Intelligence** | Model management and routing | `RouterPolicy`, `QueryAnalyzer`, `ModelCatalog` |
| **Engine** | Inference runtime abstraction | `InferenceEngine` ABC — Ollama, vLLM, SGLang, llama.cpp, MLX |
| **Agents** | Pluggable reasoning strategies | `BaseAgent` ABC — Simple, Orchestrator, ReAct, OpenHands, OpenClaw |
| **Tools** | Capabilities via MCP | `BaseTool` ABC — calculator, code interpreter, web search, memory; external MCP servers auto-discovered |
| **Learning** | Trace-driven adaptation | `LearningPolicy` ABC — SFT (model routing), AgentAdvisor (restructuring), ICL (tool usage) |
Every interaction produces a **Trace** — a structured record of the full reasoning chain. Learning policies consume traces to improve model selection, agent behavior, and tool usage over time.
## About
OpenJarvis is part of [Intelligence Per Watt](https://www.intelligence-per-watt.ai/), a research initiative studying the efficiency of on-device AI systems. The project is developed at [Hazy Research](https://hazyresearch.stanford.edu/) and the [Scaling Intelligence Lab](https://scalingintelligence.stanford.edu/) at [Stanford SAIL](https://ai.stanford.edu/).