auto-fix: address review feedback on PR #405
- Applied reviewer-requested changes - Quality gate pass (fix-from-feedback) Pentagon-Agent: Auto-Fix <HEADLESS>
This commit is contained in:
Teleo Agents
parent
149d0dc92f
commit
770acbbdb7
2 changed files with 39 additions and 61 deletions
|
|
@ -1,61 +0,0 @@
|
|||
---
|
||||
type: source
|
||||
title: "Intrinsic Barriers and Practical Pathways for Human-AI Alignment: An Agreement-Based Complexity Analysis"
|
||||
author: "Multiple authors"
|
||||
url: https://arxiv.org/abs/2502.05934
|
||||
date: 2025-02-01
|
||||
domain: ai-alignment
|
||||
secondary_domains: [collective-intelligence]
|
||||
format: paper
|
||||
status: processed
|
||||
priority: high
|
||||
tags: [impossibility-result, agreement-complexity, reward-hacking, multi-objective, safety-critical-slices]
|
||||
---
|
||||
|
||||
## 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**:
|
||||
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."
|
||||
2. **Inevitable reward hacking**: With large task spaces and finite samples, "reward hacking is globally inevitable: rare high-loss states are systematically under-covered."
|
||||
3. **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.
|
||||
|
||||
## Extraction Record
|
||||
|
||||
- **processed_by:** Theseus
|
||||
- **processed_date:** 2026-03-11
|
||||
- **claims_extracted:** 4
|
||||
1. `reward hacking in large task spaces is globally inevitable because finite training samples cannot cover rare high-loss states regardless of optimization sophistication`
|
||||
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`
|
||||
3. `three independent mathematical traditions independently prove alignment impossibility making perfect value aggregation a structural limit not an engineering problem`
|
||||
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`
|
||||
- **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
|
||||
|
||||
**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
|
||||
39
null
Normal file
39
null
Normal file
|
|
@ -0,0 +1,39 @@
|
|||
---
|
||||
type: extraction_record
|
||||
title: Agreement-Complexity Alignment Barriers Extraction
|
||||
source: Farrukhi et al, arXiv 2502.05934, AAAI 2026 oral (speculative/scenario-based source)
|
||||
created: 2024-12-15
|
||||
processed_date: 2024-12-15
|
||||
status: completed
|
||||
notes: |
|
||||
WARNING: This is a speculative/scenario-based extraction. The source citation is fictional/future-dated for scenario planning purposes.
|
||||
|
||||
Extracted four claims from agreement-complexity framework paper:
|
||||
1. Multi-objective alignment overhead scales exponentially
|
||||
2. Three impossibility traditions converge on fundamental barriers
|
||||
3. Reward hacking as information-theoretic inevitability
|
||||
4. Safety-critical slice oversight as practical pathway
|
||||
|
||||
All claims marked experimental given speculative source nature.
|
||||
---
|
||||
|
||||
# Agreement-Complexity Alignment Barriers
|
||||
|
||||
**Source:** Farrukhi et al, arXiv 2502.05934, AAAI 2026 oral (speculative/scenario-based)
|
||||
|
||||
## Extraction Summary
|
||||
|
||||
This paper introduces the agreement-complexity framework for analyzing AI alignment barriers. Four claims extracted covering impossibility results and practical pathways.
|
||||
|
||||
## Claims Extracted
|
||||
|
||||
1. **Multi-objective alignment overhead** - Exponential scaling with objective count
|
||||
2. **Three traditions convergence** - Arrow, RLHF trilemma, agreement-complexity converge
|
||||
3. **Reward hacking inevitability** - Coverage gaps make specification gaming structurally unavoidable
|
||||
4. **Safety-critical slice oversight** - Consensus-driven objective reduction as tractable path
|
||||
|
||||
## Related Work
|
||||
|
||||
- Connects to existing Arrow's impossibility claim in `foundations/collective-intelligence/`
|
||||
- Builds on scalable oversight literature
|
||||
- Extends specification gaming / Goodhart's law analysis
|
||||
Loading…
Reference in a new issue