SutroYaro

Research workspace for the Sutro Group -- energy-efficient AI training, meeting weekly at South Park Commons (SF).

Docs site: https://cybertronai.github.io/SutroYaro/
License: Unlicense (Public Domain)

Get Started

Clone the repo and open it with your coding agent. The workspace is structured so the agent can navigate it, run experiments, and report findings.

git clone https://github.com/cybertronai/SutroYaro.git
cd SutroYaro

# With Claude Code (recommended)
claude --dangerously-skip-permissions

# With Gemini CLI
gemini --yolo

# With Codex CLI
codex --full-auto

Then ask the agent anything: "What is this about?", "How do I run experiments?", "What are the latest findings?"

The agent reads CLAUDE.md (or equivalent) to understand the workspace, syncs Telegram and Google Docs for context, and can run experiments autonomously. See the one-hour walkthrough video for a demo.

Run experiments directly

# Solve 20-bit sparse parity in 0.12s (SGD)
PYTHONPATH=src python3 -m sparse_parity.fast

# Solve it in 509 microseconds (GF(2))
PYTHONPATH=src python3 src/sparse_parity/experiments/exp_gf2.py

# Run the locked evaluation harness
PYTHONPATH=src python3 src/harness.py --method gf2 --n_bits 20 --k_sparse 3

# Run the eval environment (Gymnasium)
PYTHONPATH=src python3 src/sparse_parity/eval/run_eval.py

What This Is

A structured workspace where multiple people use different AI tools (Claude Code, Gemini CLI, Codex, Antigravity) to run experiments on shared challenges. A locked evaluation harness ensures comparable results. Findings accumulate in DISCOVERIES.md.

Current challenge: sparse parity (learn XOR/parity from random {-1,+1} inputs), plus sparse sum and sparse AND.

Results So Far

36 experiments across 3 challenges. Energy metric: DMD (Data Movement Distance, Ding et al. arXiv:2312.14441), auto-measured via TrackedArray.

All 4 local learning rules (Hebbian, Predictive Coding, Equilibrium Propagation, Target Propagation) failed at chance level. Parity requires k-th order interaction detection.

Eval Environment

Gymnasium-compatible environment that tests whether an AI agent can do energy-efficient ML research. The agent picks methods, observes energy metrics, gets graded on research quality.

PYTHONPATH=src python3 -c "
import gymnasium as gym; import sparse_parity.eval
env = gym.make('SutroYaro/SparseParity-v0', metric='dmc', budget=10)
obs, _ = env.reset()
obs, r, _, _, info = env.step(5)  # try GF(2)
print(f'{info[\"method\"]}: DMC={info[\"dmc\"]}, reward={r:.2f}')
"

• 3 challenges, 16 methods, 72-point discovery grading rubric (12 categories)
• Registry-based: add challenges and methods without editing env code
• Compute backends: local (default), Modal (GPU), remote HTTP
• Adapters: Anthropic tool-use, UK AISI Inspect
• See AGENT_EVAL.md for the full guide

Recorded Sessions

Transcripts and chapters: https://cybertronai.github.io/SutroYaro/sessions/

Weekly Catch-Up

The agent syncs Telegram, Google Docs, and GitHub into a weekly summary with action items.

Latest: https://cybertronai.github.io/SutroYaro/catchups/2026-03-22/

Run Autonomous Experiments

# Single cycle with Claude Code
bin/run-agent --tool claude --max 10

# Single cycle with Gemini CLI
bin/run-agent --tool gemini --max 10

# Overnight: 10 cycles, 5 experiments each
bin/run-agent --loop 10 --max 5 --tool claude

# Any CLI via env var
AI_CMD="my-ai-tool -p" bin/run-agent --tool custom --max 5

Each cycle: fresh AI context, reads accumulated file state, runs experiments, logs results. If a cycle crashes, the next picks up from the files.

How It Works

AGENT.md                  # What the AI agent follows (the loop)
AGENT_EVAL.md             # Guide for running the eval environment
LAB.md                    # Human experiment protocol
DISCOVERIES.md            # Accumulated knowledge (91 proven facts)
TODO.md                   # Hypothesis queue

src/
  harness.py              # Locked evaluation harness (5 methods, CLI)
  sparse_parity/
    fast.py               # Numpy solver (0.12s, optional tracker)
    tracker.py            # Legacy MemTracker
    lru_tracker.py        # DMD measurement (LRUStackTracker)
    tracked_numpy.py      # Auto-instrumented numpy wrapper
    cache_tracker.py      # Cache-aware energy model
    experiments/          # All 36 experiment scripts
    eval/                 # Gymnasium RL environment
      env.py              # SutroYaro/SparseParity-v0
      baselines.py        # Random, Greedy, Oracle agents
      grader.py           # 72-point discovery grading (12 categories)
      registry.py         # Extensible challenge/method registry
      backends.py         # Local, Modal, Remote compute
      answer_key.json     # 36 experiments as ground truth
      adapters/           # Anthropic, Inspect platform adapters

research/
  search_space.yaml       # Bounded mutation space per challenge
  questions.yaml          # Dependency graph of open questions
  log.jsonl               # Machine-readable experiment log

results/
  scoreboard.tsv          # Leaderboard with DMD column
  plots/                  # DMD visualizations
  eval/                   # Baseline evaluation results

docs/
  catchups/               # Weekly catch-up summaries
  sessions/               # Recorded session transcripts and chapters
  findings/               # One markdown report per experiment
  research/               # Survey, eval docs, literature

Safety mechanisms:

• Harness integrity: SHA256 verified before and after each run
• Metric isolation: agents cannot modify tracker.py, harness.py, or data.py
• Circuit breaker: halts if 5+ INVALID in last 20 experiments

Multi-Researcher Workflow

Multiple people run independent experiments, then merge via PR:

# Merge a contributor's results
bin/merge-findings path/to/their-log.jsonl

# Regenerate the scoreboard
bin/merge-findings research/log.jsonl --scoreboard

See CONTRIBUTING.md for the full guide. Three levels of effort:

• Low: drop raw results in contributions/ (any format)
• Medium: write a findings doc using findings/_template.md
• High: code + results + findings following LAB.md

Links

• Docs site: https://cybertronai.github.io/SutroYaro/
• Eval environment: docs
• Sessions: transcripts and chapters
• Weekly catch-ups: latest
• Telegram: https://t.me/sutro_group
• Main code repo: https://github.com/cybertronai/sutro
• Meetings: Mondays 18:00 at South Park Commons (380 Brannan St, SF)