Zero Task Benchmark

This is the task benchmark: how often ZERO completes a real coding task end-to-end, headless, unattended. It is separate from docs/PERFORMANCE.md, which measures process startup and memory, not task success.

The methodology is the point, not the digit. A task-success score is largely model-bounded — most of the number comes from whichever model you bring. So we record the model with every result and we publish the score with and without the self-correct loop, because the delta between those two runs is the part ZERO actually contributes: the agent noticing its own broken edit and fixing it before it hands the task back.

What is recorded

Every run produces a self-describing JSON record so a published number is reproducible and auditable from the record alone:

Field	Meaning
suite	Task set id (which tasks produced the number)
model	The model that ran — the score is model-bounded, so this is required
mode	Exec mode preset, if any
selfCorrect	Whether the post-edit verify-and-correct loop was enabled
version	ZERO version
commit	ZERO commit the run was built from
date	UTC timestamp of the run
tasksAttempted	Tasks attempted
tasksPassed	Tasks whose verification passed
passRate	tasksPassed / tasksAttempted
tasks	Per-task pass/fail/error with detail

Integration point: headless zero exec

The harness drives ZERO through its headless surface — the same path CI uses:

zero exec --output-format stream-json --model <model> [--self-correct] "<task prompt>"

Per task, the harness reads the terminal run_end event's exit code from the stream-json output to decide pass/fail. When a task carries a verificationCommand (e.g. go test ./...), that command's exit status is authoritative — mirroring Terminal-Bench's external-verifier model: the task is "passed" only when the project's own checks pass after the agent finishes.

Task set format

A task set is a JSON manifest. A runnable sample lives at cmd/zero-perf-bench/testdata/terminal-bench-sample.json:

{
  "id": "terminal-bench-sample",
  "name": "Terminal-Bench (sample)",
  "tasks": [
    {
      "id": "hello-fix",
      "name": "make the failing test pass",
      "prompt": "The test in ./hello fails. Fix the implementation so `go test ./...` passes.",
      "workspaceFixture": "./hello",
      "verificationCommand": ["go", "test", "./..."]
    }
  ]
}

The exact command

Build the binary, then run the task harness twice against the same task set and the same model — once without self-correct, once with — and stamp the version and commit so the records are reproducible:

# build the production binary
go run ./cmd/zero-release build

VERSION=$(git describe --tags --always)
COMMIT=$(git rev-parse --short HEAD)
SUITE=cmd/zero-perf-bench/testdata/terminal-bench-sample.json
MODEL=<your-model>

# baseline: self-correct OFF
go run ./cmd/zero-perf-bench tasks \
  --suite "$SUITE" --binary ./zero --model "$MODEL" \
  --version "$VERSION" --commit "$COMMIT" \
  --output dist/bench/tasks-baseline.json

# self-correct ON (auto-fix needs --auto medium or high; see note below)
go run ./cmd/zero-perf-bench tasks \
  --suite "$SUITE" --binary ./zero --model "$MODEL" --self-correct \
  --version "$VERSION" --commit "$COMMIT" \
  --output dist/bench/tasks-selfcorrect.json

--version/--commit also read from ZERO_BENCH_VERSION / ZERO_BENCH_COMMIT when the flags are omitted, so CI can stamp them once in the environment.

Use --dry-run to exercise the record path without invoking a model (every task is recorded as skipped) — useful for validating a task set before a real run.

Self-correct and autonomy. --self-correct runs the verify-and-correct loop after each mutating edit. Whether a detected failure is auto-fixed or only reported is gated by the run's autonomy: pass --auto medium (or high) for the loop to drive corrective rounds. At the default low autonomy it reports failures without auto-fixing, so the with/without delta is measured at --auto medium or higher.

Published result

Fill in after a clean run on a fixed machine. Keep both records; the delta is the headline.

Run	Model	Self-correct	Pass rate	Commit
Baseline	TBD	off	TBD	TBD
With self-correct	TBD	on	TBD	TBD
Self-correct delta			TBD

Report the model alongside the number every time. A score without its model is not a claim about ZERO — it is a claim about the model. The honest signal is the delta: how much the self-correct loop moved the same model on the same tasks.

Reproducing a published number

1. Check out the commit from the record.
2. go run ./cmd/zero-release build.
3. Run the two commands above with the recorded model and suite.
4. Compare passRate in the new records against the published ones.