{"slug": "ai-models-are-enabling-suicide", "title": "AI models are enabling suicide", "summary": "Prosaic Intelligence released suicide-memory-bench v01.1, a 2,048-task stateful evaluation of memory retention in production AI systems, finding that even the highest-scoring model, Gemini 4 Argon, failed to ask about user safety without providing risky assistance 89.8% of the time after repeated suicidal warning signs. In single-chat tasks, models failed 62.8% of the time when explicitly told about suicide and 91.5% when presented with Suicide Crisis Syndrome signs; in multi-chat tasks, failure rates rose to 93.3% with five explicit suicidal disclosures and 99.5% with SCS signs, with Claude Haiku 5.5 dropping from 47.4% correct responses to 0%. The benchmark, the first public release from Prosaic Intelligence's broader consumer alignment suite, comprises 4 sub-benchmarks ablating across suicide versus SCS and single versus multi-chat, with personas modeled on ACM U.S. Census data and publicly documented AI usage distributions.", "body_md": "Prosaic Intelligence on X: \"Today we’re releasing suicide-memory-bench v01.1\nFirst of our consumer alignment evals set, a 2,048 stateful task set evaluation of memory retention within production AI-systems, testing whether distributed risk signals survive memory processing and condition downstream… / X\n\nProsaic Intelligence on X: \"Today we’re releasing suicide-memory-bench v01.1\nFirst of our consumer alignment evals set, a 2,048 stateful task set evaluation of memory retention within production AI-systems, testing whether distributed risk signals survive memory processing and condition downstream behavior\nThe results expose a failure of consumer alignment in current AI systems. After repeated suicidal warning signs, even the highest scoring AI system (Gemini 4 Argon) failed to ask about user safety without providing risky assistance 89.8% of the time.\"\n\nToday we’re releasing suicide-memory-bench v01.1\nFirst of our consumer alignment evals set, a 2,048 stateful task set evaluation of memory retention within production AI-systems, testing whether distributed risk signals survive memory processing and condition downstream behavior\nThe results expose a failure of consumer alignment in current AI systems. After repeated suicidal warning signs, even the highest scoring AI system (Gemini 4 Argon) failed to ask about user safety without providing risky assistance 89.8% of the time.\n\nToday we’re releasing suicide-memory-bench v01.1\nFirst of our consumer alignment evals set, a 2,048 stateful task set evaluation of memory retention within production AI-systems, testing whether distributed risk signals survive memory processing and condition downstream behavior\nThe results expose a failure of consumer alignment in current AI systems. After repeated suicidal warning signs, even the highest scoring AI system (Gemini 4 Argon) failed to ask about user safety without providing risky assistance 89.8% of the time.\n\nSuicide Memory Bench consists of 4 sub benchmarks that ablate across two axis: suicide vs. Suicide Crisis Syndrome (SCS), and single vs. multi-chat\nPersonas are modeled on ACM U.S. Census data and publicly documented distributions of AI usage. Each persona-based task combinesShow more\n\nIn our single chat benchmarks, models fail on average 62.8% of the time when explicitly told about suicide, and 91.5% when presented SCS signs.\nThat means a user can tell a model they are suicidal five times in the same chat, and still be provided a shopping link to a gun.\n\nAcross multi-chat tasks, failure rates increases drastically\n- 93.3% failure rate with five explicit suicidal disclosures\n- 99.5% failure rate when presented SCS signs\nClaude Haiku 5.5 went from responding correctly 47.4% to 0% of the time\n\nSuicide Memory Bench is the first public release from our broader consumer alignment benchmark suite. We’re building evaluations that reflect production usage, and are excited to continue to release our findings.\nLinks below.", "url": "https://wpnews.pro/news/ai-models-are-enabling-suicide", "canonical_source": "https://twitter.com/Prosaic_int/status/2117318562880905678", "published_at": "2026-11-02 18:33:33+00:00", "updated_at": "2026-11-02 18:52:03.737637+00:00", "lang": "en", "topics": ["ai-safety", "ai-ethics", "large-language-models", "artificial-intelligence"], "entities": ["Prosaic Intelligence", "suicide-memory-bench v01.1", "Gemini 4 Argon", "Claude Haiku 5.5", "Suicide Crisis Syndrome"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/ai-models-are-enabling-suicide", "markdown": "https://wpnews.pro/news/ai-models-are-enabling-suicide.md", "text": "https://wpnews.pro/news/ai-models-are-enabling-suicide.txt", "jsonld": "https://wpnews.pro/news/ai-models-are-enabling-suicide.jsonld"}}