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Get Started Free →Check status of running autonomous experiment loops
| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-06 | ✗→✓ | ▲ Improved | -40% | 0% |
| case-07 | ✗→✓ | ▲ Improved | -66% | 0% |
| case-08 | ✗→✓ | ▲ Improved | -60% | 0% |
| case-09 | ✗→✓ | ▲ Improved | -57% | 0% |
| case-15 | ✗→✓ | ▲ Improved | -33% | 0% |
Check the current status of your autonomous experiment agent.
Claude Code: /experiment-status
Claude Code: /experiment-status --project /path/to/project
Codex: $experiment-statusPROJECT_BRIEF.md — show the research goalMEMORY_LOG.md — show key results and recent decisions .cycle_counter — show how many cycles completedHUMAN_DIRECTIVE.md exists (pending directive)If execution.mode=ssh, controller state still comes from the local project directory, but PID checks, training logs, and GPU status come from the configured remote host.
markdown# Experiment Status — my-project ## Goal Train ViT-B/16 on ImageNet to 78%+ accuracy ## Progress - Cycles completed: 4 - Current best: 78.3% (Exp004, ViT-B/16 + cosine + mixup) - Status: TRAINING (PID 12345, GPU 0, running 3.2h) ## Latest Training Log Epoch 45/90 | loss: 2.134 | acc: 77.1% | lr: 1.2e-4 ## Recent Decisions 1. [04-08 14:45] Target reached with mixup, trying stronger augmentation 2. [04-08 06:00] Cosine schedule helped, adding regularization ## Pending Directive None (drop a file at workspace/HUMAN_DIRECTIVE.md to intervene)
Other measured skills in the registry, with their headline benchmark lift.