Connect the field notes.
A manual names the chamber. A maintenance note names its control. Together, they point to a useful test.
A FIELD LAB FOR DATA & AGENT DEVELOPERS
Four simulated sensors are sending readings, but their labels are missing. Beam reads the notes, chooses an allowed test, and gives you a tested Python adapter: a small program that labels and converts the readings.
Each example allows one kind of test and a choice between two controls. Beam reads the notes, chooses a control and keeps the test within the stated limits.
For people testing AI agents or working with sensor data.
Two chambers. Unknowns stay explicit.
Same passive trace.
Two possible explanations.
FROM UNKNOWN TO UNDERSTOOD
A manual names the chamber. A maintenance note names its control. Together, they point to a useful test.
Beam chooses an allowed test. Simple calculations check which sensor responds and which explanation fits.
Download the tested Python program, sensor labels and evidence. Run new readings through it yourself. Anything still unknown stays unknown.
YOUR FIRST INVESTIGATION
Start with the jacket rig. Then try the coupled rig, where an honest answer is “we still don’t know.”
Try the downloads from two saved real Beam runs. These examples are separate from your own investigation and use no new AI requests.
Investigate. Inspect. Take it with you. Choose an example, let Beam test it, then explore the readings and download the tested Python program.
The rig is simulated. Live Beam selects its experiment; both explicit parser comparisons use the same public documents and tools without model inference.
Provider usage appears when supplied by the service.
THE RESULT TO TAKE WITH YOU
The exact tested adapter and fresh replay input, with this run’s evidence.
Run it yourself: python3 -I adapter.py < replay.jsonl > normalized.jsonl
The download includes the tested program, sensor labels, evidence, new input data and file checks.
Start a run to collect evidence.
THE DECIDING OBSERVATION
PASSIVE PREVIEW · NOT THIS RUN Simulated readings from a fixed preview. No investigation or AI request has started.
Start an investigation to compare actual passive and post-probe temperature samples.
Choose a time to keep it selected. Latest follows new recorded observations. These controls run no new experiment or inference.
Raw °F readings against event time. Missing or delayed values stay gaps.
THE APPARATUS / SIMULATED
A chamber gets a sensor label only when this run’s evidence supports it. You can view the original readings before the labels are known.
FROM BEFORE TO THE SELECTED MOMENT
Temperature samples appear after an investigation starts.
Both temperature channels will appear here.
No comparison yet.
Select a temperature channel and move the timeline. Only recorded values are compared; missing readings stay missing.
This comparison shows the difference between two readings in °F. The test also compares averages from before and after, converted to °C. Those are different measurements.
READ THE ACTUAL VALUES
Channel controls appear with the observations.
Choose a channel and a sample time to inspect its raw value and arrival.
Original readings appear after a run starts.
INVESTIGATION LOG
FOLLOW THE SUPPORT
The exact source passages appear when the investigator receives them.
RECORDED EVIDENCE / READ ONLY
You can use these saved examples even when a new AI run is unavailable. View what Beam observed, download its tested Python program, and run it yourself. These examples use simulated sensors and start no new AI requests.
The Jacket test found which sensor measures each chamber. It leaves the uncalibrated sensor readings unknown.
7d5c47df2d7d2801878e857e8df158742557802634192b4754df94fe08d9abfaBoth chambers reacted together, so either set of sensor labels could be right. The downloaded program keeps both possibilities and still converts the units it can verify.
18e1ff2cecb9e1a30b28e44d1bffeb6c614c4bae6a983d9bd49de1fd26395926Both saved adapters passed an independent 16-record replay. Extract a bundle and run python3 -I adapter.py < replay.jsonl > normalized.jsonl. Read the input-file format ↗ · Open the saved-example guide ↗
RECORDED WALKTHROUGH / 24 OCT 2026
94 seconds, silent, with captions. Edited pictures from real browser tests show one solved example and one where the answer stays unknown. Waiting and cursor movement are shortened. The Python results shown are from an actual run, presented as a results panel. This is a recording, separate from your own run. In the video, two notes use different names for the same part. The saved Jacket example above is a different run, with its own tested adapter.
Download recorded walkthrough · 17.2 MB ↓drain_cycle(valve=V2, cycles=30) response supports the channel mapping.python3 -I execution produces 16 records, preserves one injected fault and retains unknown calibration. This pane visualizes the saved CLI receipt.THE PROOF, INCLUDING THE LIMITS
These are saved study results, separate from your own run. Jacket and heater were used while building the app. The relay and valve examples were saved for the v2 test.
Beam can choose only from the allowed tests. The simple rules matched Beam on the standard v2 examples. Without a test, the starting readings cannot tell the labels apart, so keeping them unknown is correct.
Loading the published study report. The recorded v2 baseline remains below.
RECORDED / FROZEN V2
| Scenario | Passive Beam | Active Beam | Conventional |
|---|---|---|---|
| Relay / hold_ms | Unresolved | Resolved | Resolved |
| Valve / cycles | Unresolved | Resolved | Resolved |
| Coupled rig | Schema error | Unresolved | Unresolved |
4 supported resolutions · 4 unresolved · 1 error retained.
0 unsupported assignments · 8 adapters passed fresh replay.
The conventional parser had the same documents and probe access. This small study does not establish a general advantage over it. The retained passive schema error is separate from an expected unresolved result.