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| type | title | author | url | date | domain | secondary_domains | format | status | priority | tags | ||||||
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| source | Intrinsic Barriers and Practical Pathways for Human-AI Alignment: An Agreement-Based Complexity Analysis | Multiple authors | https://arxiv.org/abs/2502.05934 | 2025-02-01 | ai-alignment |
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paper | unprocessed | high |
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Content
Oral presentation at AAAI 2026 Special Track on AI Alignment.
Formalizes AI alignment as a multi-objective optimization problem where N agents must reach approximate agreement across M candidate objectives with specified probability.
Key impossibility results:
- Intractability of encoding all values: When either M (objectives) or N (agents) becomes sufficiently large, "no amount of computational power or rationality can avoid intrinsic alignment overheads."
- Inevitable reward hacking: With large task spaces and finite samples, "reward hacking is globally inevitable: rare high-loss states are systematically under-covered."
- No-Free-Lunch principle: Alignment has irreducible computational costs regardless of method sophistication.
Practical pathways:
- Safety-critical slices: Rather than uniform coverage, target high-stakes regions for scalable oversight
- Consensus-driven objective reduction: Manage multi-agent alignment through reducing the objective space via consensus
Agent Notes
Why this matters: This is a third independent impossibility result (alongside Arrow's theorem and the RLHF trilemma). Three different mathematical traditions — social choice theory, complexity theory, and multi-objective optimization — converge on the same structural finding: perfect alignment with diverse preferences is computationally intractable. This convergence is itself a strong claim.
What surprised me: The "consensus-driven objective reduction" pathway is exactly what bridging-based approaches (RLCF, Community Notes) do — they reduce the objective space by finding consensus regions rather than covering all preferences. This paper provides formal justification for why bridging works: it's the practical pathway out of the impossibility result.
What I expected but didn't find: No explicit connection to Arrow's theorem or social choice theory, despite the structural parallels. No connection to bridging-based mechanisms.
KB connections:
- universal alignment is mathematically impossible because Arrows impossibility theorem applies to aggregating diverse human preferences into a single coherent objective — third independent confirmation
- reward hacking is globally inevitable — this could be a new claim
- safe AI development requires building alignment mechanisms before scaling capability — the safety-critical slices approach is an alignment mechanism
Extraction hints: Claims about (1) convergent impossibility from three mathematical traditions, (2) reward hacking as globally inevitable, (3) consensus-driven objective reduction as practical pathway.
Context: AAAI 2026 oral presentation — high-prestige venue for formal AI safety work.
Curator Notes (structured handoff for extractor)
PRIMARY CONNECTION: universal alignment is mathematically impossible because Arrows impossibility theorem applies to aggregating diverse human preferences into a single coherent objective WHY ARCHIVED: Third independent impossibility result from multi-objective optimization — convergent evidence from three mathematical traditions strengthens our core impossibility claim EXTRACTION HINT: The convergence of three impossibility traditions AND the "consensus-driven reduction" pathway are both extractable