ARC has teamed up with AIcrowd to launch the ARC White-Box Estimation Challenge, a contest to improve upon our estimation algorithms for random MLPs. The warm-up round begins this week, and later rounds will have a total prize pool of at least $100,000.

We are very grateful to Sharada Mohanty, Sneha Nanavati, Dipam Chakraborty and everyone else at AIcrowd for working with us to host this contest, as well as to Paul Rosu for testing the contest and to Harshita Khera for operational support.

Introduction to the Challenge

Our challenge follows the same setup as our recent paper on wide random MLPs: we consider MLPs with weights , defined by

where the activation function is , applied coordinatewise.

To begin with, we are fixing the width and the number...

1.1 Tl;dr

Alignment is often conceptualized as AIs helping humans achieve their goals: AIs that increase people’s agency and empowerment; AIs that are helpful, corrigible, and/or obedient; AIs that avoid manipulating people. But that last one—manipulation—points to a challenge for all these desiderata: a human’s goals are themselves under-determined and manipulable, and it’s awfully hard to pin down a principled distinction between changing people’s goals in a good way (“providing counsel”, “providing information”, “sharing ideas”) versus a bad way (“manipulating”, “brainwashing”).

The manipulability of human desires is hardly a new observation in the alignment literature, but it remains unsolved (see lit review in §3 below).

In this post I will propose an explanation of how we humans intuitively conceptualize the distinction between guidance (good) vs manipulation (bad), in case it...

As AI models become increasingly capable and autonomous, keeping them safely aligned with human intentions is critical. Extending our previous work on evaluating scheming capabilities, we introduce complementary approaches to test whether AI models would sabotage their own safeguards, if given the opportunity.

Our new papers focus on propensity for scheming: when models are deployed as coding agents, will they take opportunities to undermine the safeguards designed to oversee them? We tackle this using two complementary testing methods: automated auditing using simulated agentic environments, and scheming honeypot evaluations based on real internal alignment research codebases.

Automated auditing for sabotage propensities

Our first paper introduces Gram (Gauging Realistic Agentic Misbehavior), an automated auditing framework that uses simulated agentic environments to evaluate sabotage risk.

Gram builds...

As various people have written about before, AIs that have long-term memory might pose additional risks (most notably, LLM AGI will have memory, and memory changes alignment by Seth Herd). Even if an AI is aligned or only occasionally scheming at the start of a deployment, the AI might become a consistent and coherent behavioral schemer via updates to its long-term memories. 

In this post, I’ll spell out the version of the threat model that I’m most concerned about, including some novel arguments for its plausibility, and describe some promising strategies for mitigating this risk. While I think some plausible mitigations are reasonably cheap and could be effective at reducing the risk from coherent scheming arising via this mechanism, research here will likely be substantially more productive in the future...

We’d like to develop training techniques that work when applied to future misaligned AI systems. One strategy for studying proposed techniques is to test them on model organisms. However, model organisms built with common techniques are often fragile: we (and other researchers like Roger et al. and Ryd et al.) have observed them to stop misbehaving after untargeted training—training that doesn't directly target the misbehavior. For example, we have observed that simple untargeted training methods like “train the model to talk like a pirate” is effective against many model organisms that we have created, including many replications of prior work like Hubinger et al., Greenblatt et al., and Ryd et al..

Fragile model organisms aren't very useful for technique development: when a sophisticated technique succeeds on one, you can't tell...