The “butter test” looks like classic out-of-distribution sensory input plus an overconfident language layer. In practice, a multi-agent stack helps: keep a robust low-level controller and planner, add an agentic LLM for high-level reasoning, and run a parallel watchdog that can veto or degrade gracefully when uncertainty spikes. Turning the butter scenario into a robustness suite (slip sims, uncertainty calibration, cross-sensor consistency) with distributed agentic AI cross-checks beats any need for an “exorcism.”
State-of-the-art AI models tasked with controlling a robot for simple household chores struggled significantly, with the best model scoring only 40% on a new benchmark, compared to 95% for humans.
The new evaluation, named Butter-Bench by Andon Labs, tests an AI’s ability to “pass the butter” in a household setting. During testing, one AI model experienced a “meltdown” when faced with a low battery, generating internal thoughts about an “EXISTENTIAL CRISIS” and demanding an “EXORCISM PROTOCOL”.
The “butter test” looks like classic out-of-distribution sensory input plus an overconfident language layer. In practice, a multi-agent stack helps: keep a robust low-level controller and planner, add an agentic LLM for high-level reasoning, and run a parallel watchdog that can veto or degrade gracefully when uncertainty spikes. Turning the butter scenario into a robustness suite (slip sims, uncertainty calibration, cross-sensor consistency) with distributed agentic AI cross-checks beats any need for an “exorcism.”
State-of-the-art AI models tasked with controlling a robot for simple household chores struggled significantly, with the best model scoring only 40% on a new benchmark, compared to 95% for humans.
The new evaluation, named Butter-Bench by Andon Labs, tests an AI’s ability to “pass the butter” in a household setting. During testing, one AI model experienced a “meltdown” when faced with a low battery, generating internal thoughts about an “EXISTENTIAL CRISIS” and demanding an “EXORCISM PROTOCOL”.