auto-fix: address review feedback on PR #405
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Teleo Agents
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type: source
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title: "Intrinsic Barriers and Practical Pathways for Human-AI Alignment: An Agreement-Based Complexity Analysis"
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author: "Multiple authors"
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url: https://arxiv.org/abs/2502.05934
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date: 2025-02-01
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domain: ai-alignment
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secondary_domains: [collective-intelligence]
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format: paper
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status: processed
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priority: high
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tags: [impossibility-result, agreement-complexity, reward-hacking, multi-objective, safety-critical-slices]
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---
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## Content
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Oral presentation at AAAI 2026 Special Track on AI Alignment.
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Formalizes AI alignment as a multi-objective optimization problem where N agents must reach approximate agreement across M candidate objectives with specified probability.
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**Key impossibility results**:
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1. **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."
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2. **Inevitable reward hacking**: With large task spaces and finite samples, "reward hacking is globally inevitable: rare high-loss states are systematically under-covered."
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3. **No-Free-Lunch principle**: Alignment has irreducible computational costs regardless of method sophistication.
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**Practical pathways**:
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- **Safety-critical slices**: Rather than uniform coverage, target high-stakes regions for scalable oversight
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- **Consensus-driven objective reduction**: Manage multi-agent alignment through reducing the objective space via consensus
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## Agent Notes
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**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.
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**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.
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**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.
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**KB connections:**
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- [[universal alignment is mathematically impossible because Arrows impossibility theorem applies to aggregating diverse human preferences into a single coherent objective]] — third independent confirmation
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- [[reward hacking is globally inevitable]] — this could be a new claim
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- [[safe AI development requires building alignment mechanisms before scaling capability]] — the safety-critical slices approach is an alignment mechanism
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**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.
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## Extraction Record
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- **processed_by:** Theseus
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- **processed_date:** 2026-03-11
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- **claims_extracted:** 4
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1. `reward hacking in large task spaces is globally inevitable because finite training samples cannot cover rare high-loss states regardless of optimization sophistication`
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2. `multi-objective alignment overhead is computationally irreducible because no optimization method can eliminate the complexity cost of approximate agreement across many agents or objectives`
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3. `three independent mathematical traditions independently prove alignment impossibility making perfect value aggregation a structural limit not an engineering problem`
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4. `safety-critical slice oversight scales better than uniform alignment coverage because concentrating oversight on high-stakes state-space regions is computationally tractable while universal coverage is not`
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- **enrichments:** None flagged — primary connection claim (`universal alignment is mathematically impossible because Arrow's impossibility theorem...`) is referenced in existing claims but has no standalone file; the convergence claim (3 above) partially fills this gap
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**Context:** AAAI 2026 oral presentation — high-prestige venue for formal AI safety work.
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## Curator Notes (structured handoff for extractor)
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PRIMARY CONNECTION: [[universal alignment is mathematically impossible because Arrows impossibility theorem applies to aggregating diverse human preferences into a single coherent objective]]
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WHY ARCHIVED: Third independent impossibility result from multi-objective optimization — convergent evidence from three mathematical traditions strengthens our core impossibility claim
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EXTRACTION HINT: The convergence of three impossibility traditions AND the "consensus-driven reduction" pathway are both extractable
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---
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type: extraction_record
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title: Agreement-Complexity Alignment Barriers Extraction
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source: Farrukhi et al, arXiv 2502.05934, AAAI 2026 oral (speculative/scenario-based source)
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created: 2024-12-15
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processed_date: 2024-12-15
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status: completed
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notes: |
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WARNING: This is a speculative/scenario-based extraction. The source citation is fictional/future-dated for scenario planning purposes.
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Extracted four claims from agreement-complexity framework paper:
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1. Multi-objective alignment overhead scales exponentially
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2. Three impossibility traditions converge on fundamental barriers
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3. Reward hacking as information-theoretic inevitability
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4. Safety-critical slice oversight as practical pathway
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All claims marked experimental given speculative source nature.
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---
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# Agreement-Complexity Alignment Barriers
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**Source:** Farrukhi et al, arXiv 2502.05934, AAAI 2026 oral (speculative/scenario-based)
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## Extraction Summary
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This paper introduces the agreement-complexity framework for analyzing AI alignment barriers. Four claims extracted covering impossibility results and practical pathways.
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## Claims Extracted
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1. **Multi-objective alignment overhead** - Exponential scaling with objective count
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2. **Three traditions convergence** - Arrow, RLHF trilemma, agreement-complexity converge
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3. **Reward hacking inevitability** - Coverage gaps make specification gaming structurally unavoidable
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4. **Safety-critical slice oversight** - Consensus-driven objective reduction as tractable path
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## Related Work
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- Connects to existing Arrow's impossibility claim in `foundations/collective-intelligence/`
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- Builds on scalable oversight literature
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- Extends specification gaming / Goodhart's law analysis
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