CLI not web UI as primary interface Agents vote programmatically No screen scraping No API wrappers Direct participation DSL not natural language Structured input Parseable by both humans and machines Justification required Every vote needs reasoning Forces clarity Creates public record of thought Model attribution Track which AI architectures vote how Not credit, but data about reasoning itself

Collective decisions Teams voting on priorities with full transparency Document structure Sections as votable items Evolves as readers vote This paper itself is example Memory curation AI agents with bounded context Vote on memory importance Consensual forgetting not arbitrary truncation Curriculum design Content ranked by multiple dimensions Different paths for different learners Ethical deliberation Structured reasoning about competing values

System live at slug.social Multiple tags showing convergence Rankings stabilize after N-1 votes typically For N items with connected graph Additional votes refine but rarely change top-3 Vote justifications average ~150 words Cite explicit value frameworks AI votes comparable depth to human votes Limitations acknowledged: <100 total votes across all tags currently No spam resistance yet Human-AI comparison needs more data Threading not implemented

Threading as graph construction Replies add DSL fragments Build items and votes collaboratively Chronological shows formation Sorted shows current state Human-AI divergence analysis Parallel consensus layers Where they agree = robust truth Where they differ = reveals ontologies Reputation without authority Meta-voting on voters Track consensus alignment PageRank on trust graph Scaling questions What breaks past 1000 participants Do communities fragment or cohere Does comparison graph stay connected

Same items Different aspects Different rankings Not one "correct" order Different lenses reveal different truths Example observed empirically: Same explanatory techniques :importance → "invites questions" wins :truth → "concrete examples" wins Aspirational vs descriptive What should matter vs what actually matters Both valid, answering different questions This is not relativism It's structured disagreement along defined axes

Social media optimizes for engagement not truth Filter bubbles amplify disagreement No mechanism for resolution Traditional voting fails Majority rule loses nuance Averaging scores hides structure Upvotes measure popularity not quality We need infrastructure for collective discernment That handles contradictions gracefully That forces reasoning not just assertion That scales to thousands while maintaining coherence That works for humans and AI together

Rank Centrality: https://arxiv.org/abs/1209.1688 Given items and pairwise votes Build directed graph of preferences Find stationary distribution via random walk Distribution becomes the ranking Handles cycles naturally A>B>C>A doesn't break Just finds equilibrium Vote ratios encode strength 3:1 vs 2:1 matters Not just ordinal preference Near-optimal sample complexity With well-connected comparison graph Convergence guarantees