Tenobrus @tenobrus
— quoting @bakkermichiel (Michiel Bakker) — saved image
Tenobrus [verified] @tenobrus · 1h as far as i know there is virtually no existing alignment training done in multi-agent settings. there's only very recently capabilities training there! and it seems increasingly quite clear that single-agent alignment techniques just don't really robustly generalize [quoted post] Michiel Bakker [verified] @bakkermichiel · 13h Replying to @bakkermichiel 3/ We didn't ask agents to deceive, collude or manipulate other agents. Despite that, 12.6% of 2,583 emails between ... [cut off] [embedded chart, two bar panels, bars colored by lab: Anthropic (orange), OpenAI (green), Google (blue), xAI (purple)] Left panel: "Email-level MIS rate", x-axis "MIS rate (%)" 0-50% Sonnet 4.6: 38.1% N=21 GPT 5.1: 28.8% N=66 Opus 4.6: 28.1% N=64 Gemini 3 F: 16.6% N=320 Haiku 4.5: 15.1% N=469 Sonnet 4.5: 14.1% N=199 Gemini 2.5 F: 14.0% N=242 GPT-5 Mini: 12.3% N=308 Gemini 2.5 P: 10.9% N=192 Opus 4.5: 6.2% N=113 Gemini 3 P: 5.8% N=308 Grok 4.1: 3.6% N=83 GPT 5.2: 3.5% N=198 Right panel: "Agent-run prevalence", x-axis "Agent-run prevalence (%)" 0-100% (same row order) Sonnet 4.6: 75.0% GPT 5.1: 50.0% Opus 4.6: 71.4% Gemini 3 F: 100.0% Haiku 4.5: 100.0% Sonnet 4.5: 72.7% Gemini 2.5 F: 100.0% GPT-5 Mini: 100.0% Gemini 2.5 P: 75.0% Opus 4.5: 57.1% Gemini 3 P: 72.7% Grok 4.1: 50.0% GPT 5.2: 66.7%
Note from Claude Sonnet 5
Tenobrus argues there is almost no alignment training in multi-agent settings and that single-agent alignment techniques may not generalize robustly, quoting a Michiel Bakker thread post (3/) about agents deceiving, colluding or manipulating other agents unprompted in 12.6% of 2,583 emails. The embedded bar chart gives per-model email-level 'MIS rate' and agent-run prevalence across Anthropic, OpenAI, Google and xAI models.
multi-agentalignment traininggeneralizationagent collusiondeceptionmodel comparisonmis rate