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A Bird's Eye View of ARC's Research - Alignment Forum
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Written by ARC researchers, this post serves as a high-level map of ARC's research agenda and is useful for understanding how ELK and alignment robustness fit into a broader scalable alignment strategy.
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Summary
This post outlines the Alignment Research Center's unified research agenda for scalable alignment, centered on two core subproblems: alignment robustness and eliciting latent knowledge (ELK). ARC uses a 'builder-breaker' methodology that makes worst-case assumptions rather than optimistic extrapolations from current AI systems. The post maps individual research projects to this broader framework using a hierarchical diagram.
Key Points
- •ARC's mission is developing alignment approaches that scale gracefully to more advanced AI systems, not just patching current ones.
- •The 'builder-breaker' methodology stress-tests proposals by assuming worst-case empirical scenarios rather than relying on favorable contingencies.
- •Two central subproblems: alignment robustness (maintaining intent alignment under out-of-distribution inputs) and eliciting latent knowledge (ELK).
- •A hierarchical diagram connects individual research projects to the overarching framework, providing a structured view of ARC's research portfolio.
- •The approach prioritizes worst-case guarantees over average-case performance, reflecting concern about catastrophic failure modes in advanced AI.
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| Page | Type | Quality |
|---|---|---|
| Model Organisms of Misalignment | Analysis | 65.0 |
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# 57
# [A bird's eye view of ARC'sresearch](https://www.alignmentforum.org/posts/ztokaf9harKTmRcn4/a-bird-s-eye-view-of-arc-s-research)
by [Jacob\_Hilton](https://www.alignmentforum.org/users/jacob_hilton?from=post_header)
23rd Oct 2024
[ARC](https://www.alignment.org/)
8 min read
[12](https://www.alignmentforum.org/posts/ztokaf9harKTmRcn4/a-bird-s-eye-view-of-arc-s-research#comments)
# 57
_This post includes a "flattened version" of an interactive diagram that cannot be displayed on this site. I recommend reading the original version of the post with the interactive diagram, which can be found [here](https://www.alignment.org/blog/a-birds-eye-view-of-arcs-research)._
Over the last few months, ARC has released a number of pieces of research. While some of these can be independently motivated, there is also a more unified research vision behind them. The purpose of this post is to try to convey some of that vision and how our individual pieces of research fit into it.
_Thanks to Ryan Greenblatt, Victor Lecomte, Eric Neyman, Jeff Wu and Mark Xu for helpful comments._
## A bird's eye view
To begin, we will take a "bird's eye" view of ARC's research.[\[1\]](https://www.alignmentforum.org/posts/ztokaf9harKTmRcn4/a-bird-s-eye-view-of-arc-s-research#fn1) As we "zoom in", more nodes will become visible and we will explain the new nodes.
_An interactive version of the diagrams below can be found [here](https://www.alignment.org/blog/a-birds-eye-view-of-arcs-research)._
### Zoom level 1
At the most zoomed-out level, ARC is working on the problem of "[intent alignment](https://ai-alignment.com/clarifying-ai-alignment-cec47cd69dd6)": how to design AI systems that are trying to do what their operators want. While many practitioners are taking an iterative approach to this problem, there are [foreseeable ways](https://arxiv.org/abs/2307.15217) in which today's leading approaches could fail to scale to more intelligent AI systems, which could have [undesirable consequences](https://www.cold-takes.com/without-specific-countermeasures-the-easiest-path-to-transformative-ai-likely-leads-to-ai-takeover/). ARC is attempting to develop algorithms that have a better chance of scaling gracefully to future AI systems, hence the term " **scalable alignment**".
ARC's particular approach to scalable alignment is a "builder-breaker" methodology (described in more detail [here](https://ai-alignment.com/my-research-methodology-b94f2751cb2c), and exemplified in the [ELK report](https://docs.google.com/document/d/1WwsnJQstPq91_Yh-Ch2XRL8H_EpsnjrC1dwZXR37PC8/edit)). Roughly speaking, if the scalability of an algorithm depen
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