autoresearch

Autoresearch

Autonomous experiment loop: try ideas, measure results, keep what works, discard what doesn't, never stop.

Works for any optimization target: test speed, bundle size, LLM training, build times, Lighthouse scores, binary size, latency, memory usage.

Setup

If autoresearch.md already exists in the working directory, skip setup and resume the loop — read autoresearch.md, autoresearch.jsonl, and git log, then continue experimenting.

Otherwise:

1. Gather context: Ask (or infer from $ARGUMENTS and conversation) the Goal, Command to benchmark, Primary metric (name + direction), Files in scope, and Constraints.
2. Create branch: git checkout -b autoresearch/<goal>-<date> (e.g. autoresearch/test-speed-2026-03-21).
3. Read source files: Understand the workload deeply before writing anything. Read every file in scope.
4. Write session files: Create autoresearch.md and autoresearch.sh (see templates below). If constraints require correctness validation (tests must pass, types must check), also create autoresearch.checks.sh. Commit all.
5. Run baseline: Execute the first experiment with no changes to establish the baseline metric.
6. Start looping: Begin the experiment loop immediately after the baseline is logged.

autoresearch.md

The heart of the session. A fresh agent with no context should be able to read this file alone and run the loop effectively. Invest time making it excellent.

# Autoresearch: <goal>

## Objective
<Specific description of what we're optimizing and the workload.>

## Metrics
- **Primary**: <name> (<unit>, lower/higher is better)
- **Secondary**: <name>, <name>, ...

## How to Run
`./autoresearch.sh` — outputs `METRIC name=value` lines.

## Files in Scope
<Every file the agent may modify, with a brief note on what it does.>

## Off Limits
<What must NOT be touched — evaluation harness, data prep, etc.>

## Constraints
<Hard rules: tests must pass, no new deps, fixed time budget, etc.>

## What's Been Tried
<Update this section as experiments accumulate. Note key wins, dead ends,
and architectural insights so the agent doesn't repeat failed approaches.>

Update autoresearch.md periodically — especially "What's Been Tried" — so resuming agents have full context.

autoresearch.sh

Bash script that runs the benchmark and outputs structured metrics.

#!/bin/bash
set -euo pipefail

# Pre-checks (fast, <1s — catch syntax errors early)
python3 -c "import ast; ast.parse(open('train.py').read())"

# Run benchmark
uv run train.py > /tmp/autoresearch-output.log 2>&1

# Extract and output metrics as METRIC lines
val_bpb=$(grep "^val_bpb:" /tmp/autoresearch-output.log | awk '{print $2}')
echo "METRIC val_bpb=$val_bpb"

Rules:

• Use set -euo pipefail.
• Output METRIC name=value lines to stdout (one per metric). The primary metric name must match what's documented in autoresearch.md.
• Metric names: word chars, dots, or µ (e.g. val_bpb, total_µs, bundle.size_kb).
• Keep the script fast — every second is multiplied by hundreds of runs.
• For fast/noisy benchmarks (<5s), run multiple times inside the script and report the median.
• Update the script during the loop as needed.

autoresearch.checks.sh (optional)

Backpressure checks: tests, types, lint. Only create when constraints require correctness validation.

#!/bin/bash
set -euo pipefail
pnpm test --run --reporter=dot 2>&1 | tail -50
pnpm typecheck 2>&1 | grep -i error || true

When this file exists:

• Run it after every passing benchmark (exit 0).
• If checks fail, log the experiment as checks_failed and revert.
• Check execution time does NOT affect the primary metric.
• Keep output minimal — suppress verbose progress, only show errors.

When this file does not exist, skip checks entirely.

The Experiment Loop

LOOP FOREVER. Never ask "should I continue?" — the user expects autonomous work.

Each iteration:

1. Formulate hypothesis: Based on prior results, source code understanding, and any ideas in autoresearch.ideas.md, choose what to try next.
2. Edit code: Modify the in-scope files. Make a single, focused change per experiment.
3. Commit: git add -A && git commit -m "<short description of what this experiment tries>"
4. Run benchmark:

   timeout 600 ./autoresearch.sh > run.log 2>&1
   

   If the command times out or crashes, treat it as a failure.
5. Parse metrics: Extract METRIC lines from the output:

   grep '^METRIC ' run.log
   

   If no METRIC lines found, the run crashed — read tail -50 run.log for the error.
6. Run checks (if autoresearch.checks.sh exists and benchmark passed):

   timeout 300 ./autoresearch.checks.sh > checks.log 2>&1
   

7. Evaluate and log:
   • Improved (primary metric better than best so far) → status keep. The commit stays.
   • Worse or equal → status discard. Revert: stage autoresearch files first, then reset.
   • Crash (benchmark failed) → status crash. Fix if trivial, otherwise revert and move on.
   • Checks failed → status checks_failed. Revert.
8. Log to JSONL: Append one line to autoresearch.jsonl:

   {"run":1,"commit":"a1b2c3d","metric":0.9979,"metrics":{"val_bpb":0.9979,"peak_vram_mb":45060.2},"status":"keep","description":"baseline","timestamp":1711036800000,"confidence":null}
   

9. On discard/crash/checks_failed — revert code changes:

   # Preserve autoresearch session files, revert everything else
   git add autoresearch.jsonl autoresearch.md autoresearch.sh autoresearch.ideas.md autoresearch.checks.sh 2>/dev/null || true
   git checkout -- .
   git clean -fd
   

10. Check confidence: After 3+ runs, run the confidence script from the skill's installation directory:

    bash "$(dirname "$(readlink -f "$0")")/scripts/confidence.sh"
    

    Or locate it via the skill path and run it directly. Interpret the score:
    • >= 2.0x: Improvement is likely real (green).
    • 1.0-2.0x: Above noise but marginal (yellow).
    • < 1.0x: Within noise — consider re-running to confirm (red).
11. Update session: Periodically update autoresearch.md "What's Been Tried" section and run the summary script to review progress.

Repeat forever until interrupted.

JSONL Schema

Each line in autoresearch.jsonl is a JSON object:

Field	Type	Description
run	number	1-indexed experiment count
commit	string	Short git SHA (7 chars)
metric	number	Primary metric value
metrics	object	All metrics dict (primary + secondary)
status	string	keep, discard, crash, or checks_failed
description	string	What this experiment tried
timestamp	number	Unix timestamp (ms)
confidence	number or null	MAD-based confidence score (null if <3 runs)

Resuming

When autoresearch.md exists in the working directory:

1. Read autoresearch.md for full context (objective, what's been tried, constraints).
2. Read autoresearch.jsonl to reconstruct state (best metric, run count, last segment).
3. Read git log --oneline -20 for recent commit history.
4. Check autoresearch.ideas.md if it exists — prune stale entries, experiment with promising ones.
5. Continue the loop from where it left off. Do not re-run the baseline.

Ideas Backlog

When you discover complex but promising optimizations you won't pursue right now, append them as bullets to autoresearch.ideas.md. Don't let good ideas get lost.

On resume, check this file — prune stale/tried entries, experiment with the rest. When all paths are exhausted, delete the file and write a final summary to autoresearch.md.

Loop Rules

See references/loop-rules.md for the full reference. Key rules:

• Primary metric is king. Improved → keep. Worse/equal → discard.
• Simpler is better. Remove code for equal perf = keep. Ugly complexity for tiny gain = discard.
• Don't thrash. Repeatedly reverting the same idea? Try something structurally different.
• Think longer when stuck. Re-read source files, reason about what the CPU/compiler/runtime is actually doing. Deep understanding beats random variation.
• Crashes: fix if trivial (typo, missing import), otherwise log and move on. Don't over-invest.
• NEVER STOP. The user may be away for hours. Keep going until interrupted.

User Messages During Experiments

If the user sends a message while an experiment is running, finish the current run-evaluate-log cycle first, then incorporate their feedback in the next iteration.