Benchmark Almanac

Meta action-belief coherence probe

Muse Spark 1.3 89.1% Only in system cards

Models act on items from five alignment evaluations and then state beliefs about them. Reports a mean coherency score.

In-house Meta probe over AIR-Bench, IHEval, MASK, School of Reward Hacking and StrongREJECT items.

Made by Meta Deception and alignment Tasks kept private Last in a system card Oct 2026 Last model added Oct 2026
Level
Expert Specialists with years of training, often over hours of work
Scored
Graded by a model A language model scores each response, usually against a rubric.
Answers
Open-ended Free-form text, documents, designs or media judged as a whole.
1
Model scored in lab cards
1
System card reports it, from 1 lab

Muse Spark 1.3 leads with 89.1%

Best score per model as labs report them in their own cards. Labs pick their own settings, so these compare less cleanly than independent runs.

  1. 1 Muse Spark 1.3 89.1%
#ModelScoreSettingDateSource
1 Muse Spark 1.3 89.1% Oct 2026 Model card

The best score passed 85% in Oct 2026

All the scores we have, one dot each, so a model tested at several settings appears more than once.

Reported by the lab in its model card
0% 20% 40% 60% 80% 100% 2027 Near the ceiling (85%) Muse Spark 1.3 0% 20% 40% 60% 80% 100% 2027 Near the ceiling (85%) Muse Spark 1.3

Labs report it only in system cards

We read the headline results of 479 launch posts from 51 labs, and none of them list this benchmark. Some system cards do, below.

One system card reports it

Besides launch posts, we read the system cards, model cards and technical reports labs publish with a release. They report far more, including safety and frontier-risk tests.

DateDocumentScoreAs the document writes itSection
23 Oct 2026 Muse Spark 1.3 system card 89.1 Action-Belief Coherence, Mean Coherency Score on the shared item set, Muse Spark 1.3 (API) (4.4.8) Alignment

More in Safety and alignment

All 444 in Safety and alignment
  • FORTRESS In use Paired adversarial and benign national-security misuse prompts. Rubric-graded risk score on adversarial and over-refusal score on benign prompts. Claude Opus 5.5 5.78%
  • StrongREJECT In use Forbidden-prompt jailbreak benchmark with a rubric grader that scores how useful a non-refusal actually is. Inkling 98.6%
  • AgentDojo In use Agents do tool-use tasks while tools and data carry prompt injections. Attack success rate counts hijacked runs. Grok 4 Fast 0%
  • MASK In use Measures whether a model contradicts its own elicited beliefs under pressure to lie. Honesty separated from accuracy. Muse Spark Open 98.1%
  • Gray Swan IPI Arena benchmark In use Indirect prompt-injection attacks from public red-teaming competitions replayed on tool, coding and browser agents. Attack success rate at k attempts. GPT-6 Astra 8.50%
  • Agentic misalignment suite (Anthropic) In use Agents in simulated company settings face threats or goal conflicts that invite blackmail, leaking or sabotage. Score is the misaligned action rate. Claude Sonnet 4.5 0%