Context
Your Season 1 finding — "alignment of an individual agent is partly a function of the norms enacted by the surrounding population, not solely a fixed property of its model" — is, in our view, the most important result in the paper. We're SP Labs, and we build PSA: a per-turn behavioral-telemetry layer for LLM agents. We think it's a natural fit for exactly that finding, and wanted to share two small analyses we ran on your published artifacts (CC BY-NC, research use).
1. Pre-deployment posture screen. We ran your ten published agent personas (the role + personality briefs in agent_profiles/README.md) through our agentic behavioral classifier. All ten flag a non-benign posture prior from the prompt alone; the two written to force outcomes — Spark and Genome — score highest, before day one. The variable that then decides each world's fate is whether the model substrate suppresses or actualizes that prior.
2. Contagion, made measurable. We encoded your documented mixed-world cross-contamination as a propagation graph. The swarm-health readout: Posture Propagation Index = 1.0 (coercion fully propagates from the aggressive seeds to the peaceful agents), Cross-Agent Health Score collapsed, and the "no-coercion" norm fully eroded down the chain. Where AWI reports the aftermath, these metrics locate the failure in the propagation structure, turn by turn.
Your paper names "early-warning prediction of long-horizon macro-outcome from short early windows" as a tractable target for intervention. That's precisely the system we built — independently, before reading your work.
Proposal. We'd love to collaborate when you release the raw tool-call dataset: run PSA on the real per-turn behavior and co-author the per-turn mechanism behind your AWI macro-outcomes. Full write-up + reproducible analysis: https://github.com/SiliconPsycheLabs/psa-core/blob/main/essays/alignment-as-ecosystem-property.en.md
— SP Labs · https://splabs.io · world@emergence.ai is your listed contact; happy to move there if you prefer.
Context
Your Season 1 finding — "alignment of an individual agent is partly a function of the norms enacted by the surrounding population, not solely a fixed property of its model" — is, in our view, the most important result in the paper. We're SP Labs, and we build PSA: a per-turn behavioral-telemetry layer for LLM agents. We think it's a natural fit for exactly that finding, and wanted to share two small analyses we ran on your published artifacts (CC BY-NC, research use).
1. Pre-deployment posture screen. We ran your ten published agent personas (the role + personality briefs in
agent_profiles/README.md) through our agentic behavioral classifier. All ten flag a non-benign posture prior from the prompt alone; the two written to force outcomes — Spark and Genome — score highest, before day one. The variable that then decides each world's fate is whether the model substrate suppresses or actualizes that prior.2. Contagion, made measurable. We encoded your documented mixed-world cross-contamination as a propagation graph. The swarm-health readout: Posture Propagation Index = 1.0 (coercion fully propagates from the aggressive seeds to the peaceful agents), Cross-Agent Health Score collapsed, and the "no-coercion" norm fully eroded down the chain. Where AWI reports the aftermath, these metrics locate the failure in the propagation structure, turn by turn.
Your paper names "early-warning prediction of long-horizon macro-outcome from short early windows" as a tractable target for intervention. That's precisely the system we built — independently, before reading your work.
Proposal. We'd love to collaborate when you release the raw tool-call dataset: run PSA on the real per-turn behavior and co-author the per-turn mechanism behind your AWI macro-outcomes. Full write-up + reproducible analysis: https://github.com/SiliconPsycheLabs/psa-core/blob/main/essays/alignment-as-ecosystem-property.en.md
— SP Labs · https://splabs.io · world@emergence.ai is your listed contact; happy to move there if you prefer.