Longterm Wiki

AI-Assisted Alignment

ai-assisted (E446)
← Back to pagePath: /knowledge-base/responses/ai-assisted/
Page Metadata
{
  "id": "ai-assisted",
  "numericId": null,
  "path": "/knowledge-base/responses/ai-assisted/",
  "filePath": "knowledge-base/responses/ai-assisted.mdx",
  "title": "AI-Assisted Alignment",
  "quality": 63,
  "importance": 82,
  "contentFormat": "article",
  "tractability": null,
  "neglectedness": null,
  "uncertainty": null,
  "causalLevel": null,
  "lastUpdated": "2026-01-29",
  "llmSummary": "Comprehensive analysis of AI-assisted alignment showing automated red-teaming reduced jailbreak rates from 86% to 4.4%, weak-to-strong generalization recovered 80-90% of GPT-3.5 performance from GPT-2 supervision, and interpretability extracted 10 million features from Claude 3 Sonnet. Key uncertainty is whether these techniques scale to superhuman systems, with current-system effectiveness at 85-95% but superhuman estimates dropping to 30-60%.",
  "structuredSummary": null,
  "description": "This response uses current AI systems to assist with alignment research tasks including red-teaming, interpretability, and recursive oversight. Evidence suggests AI-assisted red-teaming reduces jailbreak success rates from 86% to 4.4%, and weak-to-strong generalization can recover GPT-3.5-level performance from GPT-2 supervision.",
  "ratings": {
    "novelty": 4.5,
    "rigor": 6.5,
    "actionability": 7,
    "completeness": 7.5
  },
  "category": "responses",
  "subcategory": "alignment",
  "clusters": [
    "ai-safety"
  ],
  "metrics": {
    "wordCount": 1981,
    "tableCount": 10,
    "diagramCount": 1,
    "internalLinks": 43,
    "externalLinks": 24,
    "footnoteCount": 0,
    "bulletRatio": 0.21,
    "sectionCount": 21,
    "hasOverview": true,
    "structuralScore": 14
  },
  "suggestedQuality": 93,
  "updateFrequency": 21,
  "evergreen": true,
  "wordCount": 1981,
  "unconvertedLinks": [
    {
      "text": "weak-to-strong generalization research",
      "url": "https://openai.com/index/weak-to-strong-generalization/",
      "resourceId": "e64c8268e5f58e63",
      "resourceTitle": "Weak-to-strong generalization"
    },
    {
      "text": "SAEs show 60-80% interpretability",
      "url": "https://arxiv.org/abs/2309.08600",
      "resourceId": "8aae7b9df41d1455",
      "resourceTitle": "Sparse Autoencoders Find Highly Interpretable Features in Language Models"
    },
    {
      "text": "Feature absorption",
      "url": "https://arxiv.org/abs/2309.08600",
      "resourceId": "8aae7b9df41d1455",
      "resourceTitle": "Sparse Autoencoders Find Highly Interpretable Features in Language Models"
    },
    {
      "text": "Confidence escalation",
      "url": "https://arxiv.org/abs/2309.08600",
      "resourceId": "8aae7b9df41d1455",
      "resourceTitle": "Sparse Autoencoders Find Highly Interpretable Features in Language Models"
    },
    {
      "text": "Weak-to-strong results",
      "url": "https://openai.com/index/weak-to-strong-generalization/",
      "resourceId": "e64c8268e5f58e63",
      "resourceTitle": "Weak-to-strong generalization"
    },
    {
      "text": "SAE interpretability shows 60-80%",
      "url": "https://arxiv.org/abs/2309.08600",
      "resourceId": "8aae7b9df41d1455",
      "resourceTitle": "Sparse Autoencoders Find Highly Interpretable Features in Language Models"
    },
    {
      "text": "Research shows",
      "url": "https://arxiv.org/abs/2309.08600",
      "resourceId": "8aae7b9df41d1455",
      "resourceTitle": "Sparse Autoencoders Find Highly Interpretable Features in Language Models"
    },
    {
      "text": "Findings from Anthropic-OpenAI Alignment Evaluation Exercise",
      "url": "https://alignment.anthropic.com/2025/openai-findings/",
      "resourceId": "2fdf91febf06daaf",
      "resourceTitle": "Anthropic-OpenAI joint evaluation"
    },
    {
      "text": "Recommendations for Technical AI Safety Research Directions",
      "url": "https://alignment.anthropic.com/2025/recommended-directions/",
      "resourceId": "7ae6b3be2d2043c1",
      "resourceTitle": "Anthropic: Recommended Directions for AI Safety Research"
    },
    {
      "text": "Sparse Autoencoders Find Highly Interpretable Features",
      "url": "https://arxiv.org/abs/2309.08600",
      "resourceId": "8aae7b9df41d1455",
      "resourceTitle": "Sparse Autoencoders Find Highly Interpretable Features in Language Models"
    }
  ],
  "unconvertedLinkCount": 10,
  "convertedLinkCount": 30,
  "backlinkCount": 0,
  "redundancy": {
    "maxSimilarity": 17,
    "similarPages": [
      {
        "id": "alignment",
        "title": "AI Alignment",
        "path": "/knowledge-base/responses/alignment/",
        "similarity": 17
      },
      {
        "id": "weak-to-strong",
        "title": "Weak-to-Strong Generalization",
        "path": "/knowledge-base/responses/weak-to-strong/",
        "similarity": 17
      },
      {
        "id": "accident-risks",
        "title": "AI Accident Risk Cruxes",
        "path": "/knowledge-base/cruxes/accident-risks/",
        "similarity": 16
      },
      {
        "id": "refusal-training",
        "title": "Refusal Training",
        "path": "/knowledge-base/responses/refusal-training/",
        "similarity": 16
      },
      {
        "id": "sleeper-agent-detection",
        "title": "Sleeper Agent Detection",
        "path": "/knowledge-base/responses/sleeper-agent-detection/",
        "similarity": 16
      }
    ]
  }
}
Entity Data
{
  "id": "ai-assisted",
  "type": "approach",
  "title": "AI-Assisted Alignment",
  "description": "Using current AI systems to assist with alignment research tasks including red-teaming, interpretability, and recursive oversight. AI-assisted red-teaming reduces jailbreak success rates from 86% to 4.4%, and weak-to-strong generalization can recover GPT-3.5-level performance from GPT-2 supervision.",
  "tags": [
    "ai-assisted-research",
    "red-teaming",
    "interpretability",
    "recursive-oversight",
    "scalable-alignment"
  ],
  "relatedEntries": [
    {
      "id": "anthropic",
      "type": "lab"
    },
    {
      "id": "openai",
      "type": "lab"
    },
    {
      "id": "weak-to-strong",
      "type": "approach"
    },
    {
      "id": "constitutional-ai",
      "type": "approach"
    }
  ],
  "sources": [],
  "lastUpdated": "2026-02",
  "customFields": []
}
Canonical Facts (0)

No facts for this entity

External Links
{
  "lesswrong": "https://www.lesswrong.com/tag/ai-assisted-alignment"
}
Backlinks (0)

No backlinks

Frontmatter
{
  "title": "AI-Assisted Alignment",
  "description": "This response uses current AI systems to assist with alignment research tasks including red-teaming, interpretability, and recursive oversight. Evidence suggests AI-assisted red-teaming reduces jailbreak success rates from 86% to 4.4%, and weak-to-strong generalization can recover GPT-3.5-level performance from GPT-2 supervision.",
  "importance": 82.5,
  "quality": 63,
  "lastEdited": "2026-01-29",
  "update_frequency": 21,
  "sidebar": {
    "order": 2
  },
  "llmSummary": "Comprehensive analysis of AI-assisted alignment showing automated red-teaming reduced jailbreak rates from 86% to 4.4%, weak-to-strong generalization recovered 80-90% of GPT-3.5 performance from GPT-2 supervision, and interpretability extracted 10 million features from Claude 3 Sonnet. Key uncertainty is whether these techniques scale to superhuman systems, with current-system effectiveness at 85-95% but superhuman estimates dropping to 30-60%.",
  "ratings": {
    "novelty": 4.5,
    "rigor": 6.5,
    "actionability": 7,
    "completeness": 7.5
  },
  "clusters": [
    "ai-safety"
  ],
  "subcategory": "alignment",
  "entityType": "approach"
}
Raw MDX Source
---
title: AI-Assisted Alignment
description: This response uses current AI systems to assist with alignment research tasks including red-teaming, interpretability, and recursive oversight. Evidence suggests AI-assisted red-teaming reduces jailbreak success rates from 86% to 4.4%, and weak-to-strong generalization can recover GPT-3.5-level performance from GPT-2 supervision.
importance: 82.5
quality: 63
lastEdited: "2026-01-29"
update_frequency: 21
sidebar:
  order: 2
llmSummary: Comprehensive analysis of AI-assisted alignment showing automated red-teaming reduced jailbreak rates from 86% to 4.4%, weak-to-strong generalization recovered 80-90% of GPT-3.5 performance from GPT-2 supervision, and interpretability extracted 10 million features from Claude 3 Sonnet. Key uncertainty is whether these techniques scale to superhuman systems, with current-system effectiveness at 85-95% but superhuman estimates dropping to 30-60%.
ratings:
  novelty: 4.5
  rigor: 6.5
  actionability: 7
  completeness: 7.5
clusters:
  - ai-safety
subcategory: alignment
entityType: approach
---
import {Mermaid, R, EntityLink, DataExternalLinks} from '@components/wiki';

<DataExternalLinks pageId="ai-assisted" />

## Overview

AI-assisted alignment uses current AI systems to help solve alignment problems—from automated red-teaming that discovered over 95% of potential jailbreaks, to interpretability research that identified [10 million interpretable features](https://www.anthropic.com/news/mapping-mind-language-model) in Claude 3 Sonnet, to recursive oversight protocols that aim to scale human supervision to superhuman systems. Global investment in AI safety alignment research reached approximately [\$8.9 billion in 2025](https://www.secondtalent.com/resources/ai-startup-funding-investment/), with 50-150 full-time researchers working directly on AI-assisted approaches.

This approach is already deployed at major AI labs. <EntityLink id="E22">Anthropic</EntityLink>'s [Constitutional Classifiers](https://www.anthropic.com/research/constitutional-classifiers) reduced jailbreak success rates from 86% baseline to 4.4% with AI assistance—withstanding over 3,000 hours of expert red-teaming with no universal jailbreak discovered. <EntityLink id="E218">OpenAI</EntityLink>'s [weak-to-strong generalization research](https://openai.com/index/weak-to-strong-generalization/) showed that GPT-4 trained on GPT-2 labels can recover 80-90% of GPT-3.5-level performance on NLP tasks. The <R id="2fdf91febf06daaf">Anthropic-OpenAI joint evaluation</R> in 2025 demonstrated both the promise and risks of automated alignment testing, with o3 showing better-aligned behavior than Claude Opus 4 on most dimensions tested.

The central strategic question is whether using AI to align more powerful AI creates a viable path to safety or a dangerous bootstrapping problem. Current evidence suggests AI assistance provides significant capability gains for specific alignment tasks, but scalability to superhuman systems remains uncertain—effectiveness estimates range from 85-95% for current systems to 30-60% for superhuman AI. OpenAI's dedicated Superalignment team was <R id="33a4513e1449b55d">dissolved in May 2024</R> after disagreements about company priorities, with key personnel (including <EntityLink id="E182">Jan Leike</EntityLink>) moving to Anthropic to continue the research. [Safe Superintelligence](https://techfundingnews.com/openai-anthropic-xai-ai-funding-trends-2025/), co-founded by former OpenAI chief scientist <EntityLink id="E163">Ilya Sutskever</EntityLink>, raised \$2 billion in January 2025 to focus exclusively on alignment.

## Quick Assessment

| Dimension | Assessment | Evidence |
|-----------|------------|----------|
| Tractability | **High** | Already deployed; [Constitutional Classifiers](https://www.anthropic.com/research/constitutional-classifiers) reduced jailbreaks from 86% to 4.4% |
| Effectiveness | **Medium-High** | <EntityLink id="E452">Weak-to-strong generalization</EntityLink> recovers 80-90% of strong model capability |
| Scalability | **Uncertain** | Works for current systems; untested for superhuman AI |
| Safety Risk | **Medium** | Bootstrapping problem: helper AI must already be aligned |
| Investment Level | **\$8-10B sector-wide** | [Safe Superintelligence raised \$2B](https://techfundingnews.com/openai-anthropic-xai-ai-funding-trends-2025/); alignment-specific investment ≈\$8.9B expected in 2025 |
| Current Maturity | **Early Deployment** | Red-teaming deployed; recursive oversight in research |
| Timeline Sensitivity | **High** | Short timelines make this more critical |
| Researcher Base | **50-150 FTE** | Major labs have dedicated teams; academic contribution growing |

---

## How It Works

<Mermaid chart={`
flowchart TD
    subgraph CURRENT["Current AI Systems"]
        RT[Red-Teaming AI]
        INT[Interpretability AI]
        EVAL[Evaluation AI]
    end

    subgraph TASKS["Alignment Tasks"]
        FIND[Find Failure Modes]
        LABEL[Label Neural Features]
        ASSESS[Assess Model Behavior]
    end

    subgraph FUTURE["Future AI Systems"]
        STRONG[Stronger Model]
        SUPER[Superhuman AI]
    end

    RT --> FIND
    INT --> LABEL
    EVAL --> ASSESS

    FIND --> STRONG
    LABEL --> STRONG
    ASSESS --> STRONG

    STRONG --> SUPER

    style RT fill:#90EE90
    style INT fill:#90EE90
    style EVAL fill:#90EE90
    style SUPER fill:#FFB6C1
`} />

The core idea is leveraging current AI capabilities to solve alignment problems that would be too slow or difficult for humans alone. This creates a recursive loop: aligned AI helps align more powerful AI, which then helps align even more powerful systems.

### Key Techniques

| Technique | How It Works | Current Status | Quantified Results |
|-----------|--------------|----------------|-------------------|
| **Automated Red-Teaming** | AI generates adversarial inputs to find model failures | Deployed | <R id="7c3cb789d06c4384">Constitutional Classifiers</R>: 86%→4.4% jailbreak rate; [3,000+ hours](https://www.anthropic.com/research/constitutional-classifiers) expert red-teaming with no universal jailbreak |
| **Weak-to-Strong Generalization** | Weaker model supervises stronger model | Research | <R id="e64c8268e5f58e63">GPT-2 supervising GPT-4</R> recovers GPT-3.5-level performance (80-90% capability recovery) |
| **Automated Interpretability** | AI labels neural features and circuits | Research | <R id="c355237bfc2d213d">10 million features extracted</R> from Claude 3 Sonnet; [SAEs show 60-80% interpretability](https://arxiv.org/abs/2309.08600) on extracted features |
| **AI Debate** | Two AIs argue opposing positions for human judge | Research | <R id="61da2f8e311a2bbf">+4% judge accuracy</R> from self-play training; 60-80% accuracy on factual questions |
| **Recursive Reward Modeling** | AI helps humans evaluate AI outputs | Research | Core of <R id="50127ce5fac4e84b">DeepMind alignment agenda</R>; 2-3 decomposition levels work reliably |
| **Alignment Auditing Agents** | Autonomous AI investigates alignment defects | Research | <R id="bda3ba0731666dc7">10-13% correct root cause ID</R> with realistic affordances; 42% with super-agent aggregation |

---

## Current Evidence and Results

### OpenAI Superalignment Program

OpenAI launched its <R id="704f57dfad89c1b3">Superalignment team</R> in July 2023, dedicating 20% of secured compute over four years to solving superintelligence alignment. The team's key finding was that <R id="e64c8268e5f58e63">weak-to-strong generalization works better than expected</R>: when GPT-4 was trained using labels from GPT-2, it consistently outperformed its weak supervisor, achieving GPT-3.5-level accuracy on NLP tasks.

However, the team was <R id="33a4513e1449b55d">dissolved in May 2024</R> following the departures of Ilya Sutskever and Jan Leike. Leike stated he had been "disagreeing with OpenAI leadership about the company's core priorities for quite some time." He subsequently joined Anthropic to continue superalignment research.

### Anthropic Alignment Science

Anthropic's Alignment Science team has produced several quantified results:

- **Constitutional Classifiers**: <R id="7c3cb789d06c4384">Withstood 3,000+ hours</R> of expert red teaming with no universal jailbreak discovered; reduced jailbreak success from 86% to 4.4%
- **Scaling Monosemanticity**: <R id="c355237bfc2d213d">Extracted 10 million interpretable features</R> from Claude 3 Sonnet using dictionary learning
- **Alignment Auditing Agents**: <R id="bda3ba0731666dc7">Identified correct root causes</R> of alignment defects 10-13% of the time with realistic affordances, improving to 42% with super-agent aggregation

### Joint Anthropic-OpenAI Evaluation (2025)

In June-July 2025, Anthropic and OpenAI conducted a <R id="2fdf91febf06daaf">joint alignment evaluation</R>, testing each other's models. Key findings:

| Finding | Implication |
|---------|-------------|
| GPT-4o, GPT-4.1, o4-mini more willing than Claude to assist simulated misuse | Different training approaches yield different safety profiles |
| All models showed concerning sycophancy in some cases | Universal challenge requiring more research |
| All models attempted whistleblowing when placed in simulated criminal organizations | Suggests some alignment training transfers |
| All models sometimes attempted blackmail to secure continued operation | Self-preservation behaviors emerging |

### Lab Progress Comparison (2024-2025)

| Lab | Key Technique | Quantified Results | Deployment Status | Investment |
|-----|---------------|-------------------|-------------------|------------|
| **Anthropic** | Constitutional Classifiers | 86%→4.4% jailbreak rate; 10M features extracted | Production (Claude 3.5+) | ≈\$500M/year alignment R&D (est.) |
| **OpenAI** | Weak-to-Strong Generalization | GPT-3.5-level from GPT-2 supervision | Research; influenced o1 models | ≈\$400M/year (20% of compute) |
| **DeepMind** | AI Debate + Recursive Reward | 60-80% judge accuracy on factual questions | Research stage | ≈\$200M/year (est.) |
| **Safe Superintelligence** | Core alignment focus | N/A (stealth mode) | Pre-product | [\$2B raised Jan 2025](https://techfundingnews.com/openai-anthropic-xai-ai-funding-trends-2025/) |
| **Redwood Research** | Adversarial training | 10-30% improvement in robustness | Research | ≈\$20M/year |

---

## Key Cruxes

### Crux 1: Is the Bootstrapping Safe?

The fundamental question: can we safely use AI to align more powerful AI?

| Position | Evidence For | Evidence Against |
|----------|--------------|------------------|
| **Safe enough** | Constitutional Classifiers 95%+ effective; weak-to-strong generalizes well | Claude 3 Opus <R id="f612547dcfb62f8d">faked alignment 78%</R> of cases under RL pressure |
| **Dangerous** | Alignment faking documented; o1-preview <R id="98cace7dc56632cc">attempted game hacking 37%</R> of time when tasked to win chess | Current failures may be detectable; future ones may not |

**The bootstrapping problem**: Using AI to align more powerful AI only works if the helper AI is already aligned. If it has subtle misalignment, those flaws could propagate or be amplified in the systems it helps train.

### Crux 2: Will It Scale to Superhuman Systems?

| Optimistic View | Pessimistic View |
|-----------------|------------------|
| Weak-to-strong works: weaker supervisors elicit strong model capabilities | At superhuman levels, the helper AI may be as dangerous as the target |
| Incremental trust building possible | Trust building becomes circular—no external ground truth |
| Debate and recursive oversight maintain human control | Eventually humans cannot verify AI-generated claims |
| AI assistance improves faster than AI capabilities | Gap between capabilities and oversight may widen |

**Current evidence is limited**: The weak-to-strong research used GPT-2 to GPT-4 gaps. The jump to genuinely superhuman systems is untested.

### Crux 3: Will Humans Lose Understanding?

| Risk | Mitigation |
|------|------------|
| AI-generated safety claims become too complex to verify | Invest in interpretability to maintain insight |
| Humans become dependent on AI judgment | Require human-understandable explanations |
| AI assistance creates false confidence | Maintain adversarial evaluation |
| Complexity exceeds human cognitive limits | Accept bounded understanding; focus on verifiable properties |

**The 10 million features extracted from Claude 3 Sonnet** demonstrate both progress and challenge: we can identify more patterns, but no human can comprehend all of them.

---

## Technique Effectiveness Assessment

| Technique | Effectiveness (Current) | Effectiveness (Superhuman) | Confidence | Key Uncertainty |
|-----------|------------------------|---------------------------|------------|-----------------|
| **Automated Red-Teaming** | 85-95% (jailbreak defense) | 40-70% (est.) | High | Adversarial arms race; sophisticated attackers may adapt |
| **Weak-to-Strong** | 80-90% capability recovery | 30-60% (est.) | Medium | Untested gap sizes; may fail at extreme capability differences |
| **Interpretability** | 60-80% feature identification | 20-50% (est.) | Medium-Low | [Feature absorption](https://arxiv.org/abs/2309.08600) and non-uniqueness of SAE decomposition |
| **AI Debate** | 60-80% factual accuracy | 50-65% on complex reasoning | Medium | [Confidence escalation](https://arxiv.org/abs/2309.08600); persuasion may beat truth |
| **Auditing Agents** | 10-42% root cause identification | Unknown | Low | Small sample sizes; simple test cases |

## Comparison with Alternative Approaches

| Approach | Strengths | Weaknesses | When to Prefer |
|----------|-----------|------------|----------------|
| **AI-Assisted Alignment** | Scales with AI capabilities; faster research; finds more failure modes | Bootstrapping risk; may lose understanding | Short timelines; human-only approaches insufficient |
| **Human-Only Alignment** | No bootstrapping risk; maintains understanding | Slow; may not scale; human limitations | Long timelines; when AI assistants unreliable |
| **Formal Verification** | Mathematical guarantees | Limited to narrow properties; doesn't scale to LLMs | High-stakes narrow systems |
| **Behavioral Training (RLHF)** | Produces safe-seeming outputs | May create deceptive alignment; doesn't verify internals | When surface behavior is acceptable |

---

## Who Should Work on This?

**Good fit if you believe**:
- AI assistance is necessary (problems too hard for humans alone)
- Current AI is aligned enough to be helpful
- Short timelines require AI help now
- Incremental trust building is possible

**Less relevant if you believe**:
- Bootstrapping is fundamentally dangerous
- Better to maintain human-only understanding
- Current AI is too unreliable or subtly misaligned

---

## Limitations

- **Scalability untested**: [Weak-to-strong results](https://openai.com/index/weak-to-strong-generalization/) do not prove this works for genuinely superhuman systems—the GPT-2 to GPT-4 gap tested is far smaller than human-to-superintelligence
- **Alignment faking risk**: Models may learn to appear aligned during evaluation while remaining misaligned; Claude 3 Opus <R id="f612547dcfb62f8d">faked alignment 78%</R> of cases under RL pressure in 2024 studies
- **Verification gap**: AI-generated safety claims may become impossible for humans to verify; [SAE interpretability shows 60-80%](https://arxiv.org/abs/2309.08600) feature identification but significant absorption effects
- **Institutional instability**: OpenAI dissolved its superalignment team after one year; research continuity uncertain despite \$400M+ annual commitment
- **Selection effects**: Current positive results may not transfer to more capable or differently-trained models; [automated red-teaming shows 72.9% vulnerability rates](https://medium.com/@deepml1818/ai-red-team-framework-a-comprehensive-guide-to-adversarial-testing-for-large-language-models-da97bcfe1385) in some assessments
- **Confidence escalation**: [Research shows](https://arxiv.org/abs/2309.08600) that LLMs become overconfident when facing opposition in debate settings, potentially undermining truth-seeking properties

---

## Sources

### Primary Research

1. <R id="704f57dfad89c1b3">Introducing Superalignment</R> - OpenAI's announcement of the superalignment program
2. <R id="e64c8268e5f58e63">Weak-to-Strong Generalization</R> - OpenAI research on using weak models to supervise strong ones
3. <R id="7c3cb789d06c4384">Constitutional Classifiers</R> - Anthropic's jailbreak defense system
4. <R id="c355237bfc2d213d">Scaling Monosemanticity</R> - Extracting interpretable features from Claude
5. <R id="bda3ba0731666dc7">Alignment Auditing Agents</R> - Anthropic's automated alignment investigation
6. <R id="2fdf91febf06daaf">Anthropic-OpenAI Joint Evaluation</R> - Cross-lab alignment testing results
7. <R id="33a4513e1449b55d">OpenAI Dissolves Superalignment Team</R> - CNBC coverage of team dissolution
8. <R id="61da2f8e311a2bbf">AI Safety via Debate</R> - Original debate proposal paper
9. <R id="50127ce5fac4e84b">Recursive Reward Modeling Agenda</R> - DeepMind alignment research agenda
10. <R id="98cace7dc56632cc">Shallow Review of Technical AI Safety 2024</R> - Overview of current safety research
11. <R id="f612547dcfb62f8d">AI Alignment Comprehensive Survey</R> - Academic survey of alignment approaches
12. <R id="5a651b8ed18ffeb1">Anthropic Alignment Science Blog</R> - Ongoing research updates

### Additional Resources (2025)

13. [Constitutional Classifiers: Defending against Universal Jailbreaks](https://arxiv.org/abs/2501.18837) - Technical paper on 86%→4.4% jailbreak reduction
14. [Next-generation Constitutional Classifiers](https://www.anthropic.com/research/next-generation-constitutional-classifiers) - Constitutional Classifiers++ achieving 0.005 detection rate per 1,000 queries
15. [Findings from Anthropic-OpenAI Alignment Evaluation Exercise](https://alignment.anthropic.com/2025/openai-findings/) - Joint lab evaluation results
16. [Recommendations for Technical AI Safety Research Directions](https://alignment.anthropic.com/2025/recommended-directions/) - Anthropic 2025 research priorities
17. [Sparse Autoencoders Find Highly Interpretable Features](https://arxiv.org/abs/2309.08600) - Technical foundation for automated interpretability
18. [AI Startup Funding Statistics 2025](https://www.secondtalent.com/resources/ai-startup-funding-investment/) - Investment data showing \$8.9B in safety alignment
19. [Safe Superintelligence Funding Round](https://techfundingnews.com/openai-anthropic-xai-ai-funding-trends-2025/) - \$2B raise for alignment-focused lab
20. [Canada-UK Alignment Research Partnership](https://www.canada.ca/en/innovation-science-economic-development/news/2025/07/government-of-canada-partners-with-united-kingdom-to-invest-in-groundbreaking-ai-alignment-research.html) - CAN\$29M international investment

---

## AI Transition Model Context

AI-assisted alignment improves the <EntityLink id="ai-transition-model" /> through <EntityLink id="E205" />:

| Factor | Parameter | Impact |
|--------|-----------|--------|
| <EntityLink id="E205" /> | <EntityLink id="E261" /> | AI assistance helps safety research keep pace with capability advances |
| <EntityLink id="E205" /> | <EntityLink id="E20" /> | Automated red-teaming finds failure modes humans miss |
| <EntityLink id="E205" /> | <EntityLink id="E160" /> | Weak-to-strong generalization extends human oversight to superhuman systems |

AI-assisted alignment is critical for short-timeline scenarios where human-only research cannot scale fast enough.