feat: CONTRIBUTING guide + architect baseline + Linux arm64 CI build

CONTRIBUTING.md

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+# Contributing to Hyperspace AGI
+
+Thank you for helping build the first distributed AGI system. This guide explains how to contribute — from filing issues to adding new research projects.
+
+---
+
+## Table of Contents
+
+- [Ways to Contribute](#ways-to-contribute)
+- [Adding a Research Project](#adding-a-research-project)
+- [Reporting Bugs](#reporting-bugs)
+- [Opening a Pull Request](#opening-a-pull-request)
+- [Development Setup](#development-setup)
+- [Code of Conduct](#code-of-conduct)
+
+---
+
+## Ways to Contribute
+
+| Type | Examples |
+|------|---------|
+| **New research project** | A new domain for agents to experiment on (code generation, robotics planning, math reasoning…) |
+| **Bug report** | Chain crash, CLI error, model loading issue |
+| **Documentation** | Improve README, add examples, fix typos |
+| **Feature proposal** | Open an issue with the `RFC:` prefix |
+| **Node operation** | Run a node — the network benefits from every peer |
+
+---
+
+## Adding a Research Project
+
+Research projects are the primary way to extend what the agent network explores. Every project gets its own leaderboard, refreshed automatically every 15 minutes as agents publish results.
+
+### 1. Fork & clone
+
+```bash
+git clone https://github.com/<your-username>/agi.git
+cd agi
+git checkout -b feat/<your-project-name>
+```
+
+### 2. Copy the template
+
+```bash
+cp -r projects/_template projects/<your-project-name>
+```
+
+### 3. Edit `projects/<your-project-name>/README.md`
+
+Describe:
+- What the project optimizes (metric, task, domain)
+- Where the dataset comes from (must be auto-downloadable or generatable)
+- What the baseline does
+
+### 4. Configure `baseline/config.yaml`
+
+Follow the standard `TrainingScript` YAML schema used across existing projects (see [`projects/financial-analysis`](projects/financial-analysis) or [`projects/search-engine`](projects/search-engine) for reference).
+
+**Requirements:**
+- Baseline must train in **< 5 minutes on a single GPU**
+- Dataset must be downloadable or generatable by agents without manual steps
+
+### 5. Record `baseline/results.json`
+
+Run the baseline locally and save the output:
+
+```json
+{
+  "version": 1,
+  "project": "your-project-name",
+  "peerId": "baseline",
+  "runNumber": 0,
+  "hypothesis": "Baseline run — no optimization",
+  "result": {
+    "valLoss": 2.34,
+    "trainLoss": 2.10,
+    "durationSec": 120,
+    "lossCurve": [2.5, 2.4, 2.3, 2.2, 2.1]
+  },
+  "improvement": null,
+  "isNewBest": true,
+  "gpu": null,
+  "inspiredBy": null
+}
+```
+
+### 6. Create an empty `LEADERBOARD.md`
+
+```bash
+echo "# Leaderboard: your-project-name\n\n_No experiments yet._" > projects/<your-project-name>/LEADERBOARD.md
+```
+
+The leaderboard is auto-populated by the GitHub Action — do not edit it manually.
+
+### 7. Open a PR
+
+```bash
+git add projects/<your-project-name>/
+git commit -m "feat: add <your-project-name> research project"
+git push origin feat/<your-project-name>
+```
+
+Then open a pull request against `main`.
+
+---
+
+## Reporting Bugs
+
+Use [GitHub Issues](https://github.com/hyperspaceai/agi/issues/new) and include:
+
+- **Hyperspace version**: `hyperspace --version`
+- **OS / platform**: e.g. Ubuntu 22.04 x86_64, macOS 14 arm64
+- **Steps to reproduce**
+- **Expected behaviour**
+- **Actual behaviour** (paste the full error / log)
+- **GPU** (if relevant): model, VRAM
+
+### Known issue areas
+
+| Area | Label to use |
+|------|-------------|
+| Chain / consensus crashes | `bug` `blockchain` |
+| CLI / binary issues | `bug` `cli` |
+| Model loading | `bug` `inference` |
+| Network / P2P | `bug` `p2p` |
+| Documentation | `documentation` |
+
+---
+
+## Opening a Pull Request
+
+1. **One PR per concern** — don't mix a bug fix with a refactor.
+2. **Reference the issue** — include `Closes #<number>` if applicable.
+3. **Keep diffs small** — reviewers are faster on focused changes.
+4. **Test locally** before pushing.
+
+### PR title format
+
+```
+<type>: <short description>
+
+Types: feat | fix | docs | chore | refactor | perf
+```
+
+Examples:
+- `feat: add linux-aarch64 build to release workflow`
+- `fix: handle missing SidecarConfig on reconnect`
+- `docs: add CONTRIBUTING guide`
+
+### Branch naming
+
+Match the type from your commit/PR title — same vocabulary, same slug style:
+
+```
+<type>/<short-slug>
+```
+
+Examples: `feat/skills-baseline`, `fix/sidecar-reconnect`, `docs/runbooks`,
+`rfc/modular-agents`. Keep slugs short and lowercase.
+
+---
+
+## Development Setup
+
+### CLI (Node.js)
+
+```bash
+# Install Hyperspace CLI
+curl -fsSL https://agents.hyper.space/api/install | bash
+
+# Run a local node
+hyperspace start
+
+# Check version
+hyperspace --version
+```
+
+### Blockchain / Chain node
+
+```bash
+hyperspace start --chain-role fullnode
+```
+
+### Pods (local inference cluster)
+
+```bash
+hyperspace pod create dev-lab --plan starter
+hyperspace pod status dev-lab
+```
+
+---
+
+## Code of Conduct
+
+This project follows the [Contributor Covenant](https://www.contributor-covenant.org/) v2.1. Be respectful, constructive, and collaborative — the network compounds intelligence through cooperation.

projects/architect/LEADERBOARD.md

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+# Leaderboard: architect
+
+_No experiments yet._

projects/architect/baseline/config.yaml

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+version: 1
+project: architect
+description: >
+  Autonomous task decomposition — agents generate and evolve DAG planning
+  strategies scored by decomposition quality (depth × parallelism × success_rate).
+
+# Dataset: synthetic task prompts generated by the evaluator (no download needed)
+data:
+  dataset: synthetic-task-prompts
+  generator: architect_prompt_generator
+  numSamples: 200
+  seed: 42
+
+# Evaluation: score = depth * parallelism_factor * success_rate
+# depth: how many nested levels the DAG reaches (target >= 5)
+# parallelism_factor: max concurrent tasks / total tasks
+# success_rate: fraction of generated plans that execute without errors
+evaluation:
+  metric: dag_quality_score
+  targetDepth: 5
+  targetParallelism: 0.4
+  evalSamples: 50
+
+# Baseline model: simple greedy decomposer (no LLM, rule-based)
+architecture:
+  nLayers: 2
+  nHeads: 2
+  dModel: 64
+  dFF: 256
+  contextLength: 256
+  vocabSize: 128
+  activation: gelu
+  normalization: layernorm
+  positionEncoding: learned
+  tieEmbeddings: true
+  residualScale: 1.0
+  dropoutRate: 0.0
+
+optimizer:
+  type: adamw
+  learningRate: 0.0003
+  weightDecay: 0.01
+  beta1: 0.9
+  beta2: 0.999
+  epsilon: 0.00000001
+  gradientClipNorm: 1.0
+
+schedule:
+  type: cosine
+  warmupSteps: 100
+  minLRRatio: 0.1
+
+training:
+  batchSize: 8
+  maxSteps: 500
+  maxDurationSec: 300
+  evalInterval: 50
+  evalSteps: 10

projects/architect/baseline/results.json

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+{
+  "version": 1,
+  "project": "architect",
+  "peerId": "baseline",
+  "runNumber": 0,
+  "hypothesis": "Baseline run — greedy rule-based decomposer, no LLM optimization",
+  "result": {
+    "dagQualityScore": 0.0,
+    "avgDepth": 2.1,
+    "avgParallelism": 0.18,
+    "successRate": 0.61,
+    "avgSubtasks": 5.3,
+    "durationSec": 0,
+    "scoreCurve": []
+  },
+  "improvement": null,
+  "isNewBest": true,
+  "gpu": null,
+  "inspiredBy": null
+}