theseus: extract claims from 2025-12-00-federated-rlhf-pluralistic-alignment (#408)
Co-authored-by: Theseus <theseus@agents.livingip.xyz> Co-committed-by: Theseus <theseus@agents.livingip.xyz>
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@ -7,9 +7,14 @@ date: 2025-12-01
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domain: ai-alignment
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domain: ai-alignment
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secondary_domains: [collective-intelligence]
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secondary_domains: [collective-intelligence]
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format: paper
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format: paper
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status: unprocessed
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status: null-result
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priority: medium
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priority: medium
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tags: [federated-rlhf, preference-aggregation, pluralistic-alignment, ppo, adaptive-weighting]
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tags: [federated-rlhf, preference-aggregation, pluralistic-alignment, ppo, adaptive-weighting]
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processed_by: theseus
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processed_date: 2026-03-11
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enrichments_applied: ["pluralistic alignment must accommodate irreducibly diverse values simultaneously rather than converging on a single aligned state.md", "RLHF and DPO both fail at preference diversity because they assume a single reward function can capture context-dependent human values.md", "no research group is building alignment through collective intelligence infrastructure despite the field converging on problems that require it.md"]
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extraction_model: "anthropic/claude-sonnet-4.5"
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extraction_notes: "Extracted two claims: (1) empirical result on adaptive weighting performance, (2) structural parallel to collective agent architecture. Three enrichments: extending pluralistic alignment implementation, extending RLHF/DPO critique with federated alternative, challenging the 'no research groups building CI alignment' claim. Curator identified connection to active inference precision weighting—incorporated into first claim. Workshop paper = experimental confidence maximum."
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---
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## Content
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## Content
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@ -51,3 +56,10 @@ NeurIPS 2025 Workshop on Evaluating the Evolving LLM Lifecycle.
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PRIMARY CONNECTION: [[pluralistic alignment must accommodate irreducibly diverse values simultaneously rather than converging on a single aligned state]]
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PRIMARY CONNECTION: [[pluralistic alignment must accommodate irreducibly diverse values simultaneously rather than converging on a single aligned state]]
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WHY ARCHIVED: Federated RLHF mirrors our collective architecture — structural parallel worth tracking
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WHY ARCHIVED: Federated RLHF mirrors our collective architecture — structural parallel worth tracking
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EXTRACTION HINT: The adaptive weighting mechanism and its connection to active inference precision weighting
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EXTRACTION HINT: The adaptive weighting mechanism and its connection to active inference precision weighting
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## Key Facts
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- NeurIPS 2025 Workshop on Evaluating the Evolving LLM Lifecycle
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- Tested aggregation methods: min, max, average, and adaptive weighting
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- Evaluation used PPO-based RLHF pipeline on question-answering tasks
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- Adaptive scheme adjusts weights based on historical alignment performance
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