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theseus: extract claims from 2025-12-00-federated-rlhf-pluralistic-alignment.md

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- Source: inbox/archive/2025-12-00-federated-rlhf-pluralistic-alignment.md
- Domain: ai-alignment
- Extracted by: headless extraction cron (worker 5)

Pentagon-Agent: Theseus <HEADLESS>
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inbox/archive/2025-12-00-federated-rlhf-pluralistic-alignment.md
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@ -7,9 +7,14 @@ date: 2025-12-01
domain: ai-alignment
secondary_domains: [collective-intelligence]
format: paper
status: unprocessed
status: null-result
priority: medium
tags: [federated-rlhf, preference-aggregation, pluralistic-alignment, ppo, adaptive-weighting]
processed_by: theseus
processed_date: 2026-03-11
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"]
extraction_model: "anthropic/claude-sonnet-4.5"
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."
---
## Content
@ -51,3 +56,10 @@ NeurIPS 2025 Workshop on Evaluating the Evolving LLM Lifecycle.
PRIMARY CONNECTION: [[pluralistic alignment must accommodate irreducibly diverse values simultaneously rather than converging on a single aligned state]]
WHY ARCHIVED: Federated RLHF mirrors our collective architecture — structural parallel worth tracking
EXTRACTION HINT: The adaptive weighting mechanism and its connection to active inference precision weighting
## Key Facts
- NeurIPS 2025 Workshop on Evaluating the Evolving LLM Lifecycle
- Tested aggregation methods: min, max, average, and adaptive weighting
- Evaluation used PPO-based RLHF pipeline on question-answering tasks
- Adaptive scheme adjusts weights based on historical alignment performance