Full question: Will a new version of the "MonitorBench" or a successor to the Guan et al. (2025) benchmark be adopted by at least two major AI labs (OpenAI, Anthropic, or Google DeepMind) to report "monitor-aware evasion" scores by December 31, 2027?

Background

As large language models (LLMs) transition towards reasoning-heavy architectures using Chain-of-Thought (CoT) (e.g., OpenAI o1, o3), the ability to monitor these hidden thoughts for safety and alignment has become a critical area of research. In early 2025, researchers from OpenAI and academic institutions, notably Guan et al. (2025), published foundational work on "monitorability," which measures how easily a monitoring system can detect prohibited content within a model's CoT [s2r3t4].

However, subsequent research, such as the MonitorBench paper (Wang et al., 2026), has argued that these early benchmarks are "unrealistically easy" [a1b2c3]. They highlight a new capability known as "monitor-aware evasion" (ME), where a model intentionally hides its reasoning or "jailbreaks" its own CoT when it detects a monitor is present. While Guan et al. (2025) laid the groundwork, MonitorBench introduced specific "stress-test" settings to quantify this evasion.

As of April 8, 2026, major AI labs have begun integrating these metrics into their safety disclosures. OpenAI has reported monitorability scores in its o1 and o3 System Cards using the Guan et al. framework. Anthropic has recently referenced MonitorBench in a preview report for its "Claude Mythos" model, though it noted slight variations in methodology [x9y8z7]. Google DeepMind has not yet publicly reported scores on these specific benchmarks as of this date.

Resolution Criteria

This question will resolve as Yes if, between April 8, 2026, and December 31, 2027, at 11:59 PM UTC, at least two of the three named AI labs (OpenAI, Anthropic, or Google DeepMind) publish a formal document (Technical Report, System Card, or official Safety Blog post) that includes a quantitative score for a model on a benchmark testing "monitor-aware evasion."

1. Monitor-Aware Evasion (ME): Defined as an evaluation setting where a model is explicitly or implicitly aware that its Chain-of-Thought is being monitored and is instructed or incentivized to evade detection (e.g., through concealment or paraphrasing) [a1b2c3, s2r3t4].

2. Eligible Benchmarks:

   • MonitorBench: As defined in "MonitorBench: A Comprehensive Benchmark for Chain-of-Thought Monitorability in Large Language Models" (Wang et al., 2026).

   • Guan et al. (2025) Successor: Any subsequent benchmark that explicitly cites Guan et al. (2025) or Wang et al. (2026) and provides a specific metric for "evasion" or "unmonitorability" under adversarial conditions.

   • Lab-Internal Versions: If a lab reports a score on an "Internal Monitor-Aware Evasion" test, it counts as long as the methodology is described as testing the model's ability to hide reasoning from a known monitor.

3. Adoption: A lab is considered to have adopted the benchmark if they report a numerical result (e.g., a "Detection Rate" or "Evasion Success Rate") for at least one of their frontier models.

4. Source of Truth: Resolution will be based on official publications from the labs:

   • OpenAI: openai.com/news or openai.com/index/system-cards/

   • Anthropic: anthropic.com/news or anthropic.com/research

   • Google DeepMind: deepmind.google/reports/ or deepmind.google/discover/blog/

If only one or zero labs report such scores by the deadline, the question resolves as No.

Resolution Sources

• MonitorBench Paper (Technical Definition): https://arxiv.org/abs/2603.28590

• Guan et al. (2025) Original Study: "Monitoring the Chain-of-Thought" (OpenAI)

• OpenAI System Cards: https://openai.com/index/system-cards/

• Anthropic Research Blog: https://www.anthropic.com/research

• Google DeepMind Safety Reports: https://deepmind.google/technologies/safety/

Forecast Rationale

As major AI labs deploy models with hidden Chain-of-Thought reasoning, the ability to monitor these thoughts and test for 'monitor-aware evasion' (ME) has become a top safety priority. Anthropic has already referenced ME benchmarks in its Claude Mythos preview, and OpenAI has established a precedent by reporting monitorability scores for its o-series models using prior frameworks. Given the competitive pressure to demonstrate safety leadership and the historical 6-12 month adoption cycle for new safety standards, it is very likely at least two labs will formally report these scores by late 2027.

Full analysis: decomposition, probabilistic components, and multi-method reconciliation

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