What Alignment Research Center does
Alignment Research Center (ARC) is a nonprofit research organization focused on scalable AI alignment methods, with an emphasis on mechanistic prediction of neural network behavior. ARC’s stated core technical direction is designing algorithms that estimate neural network behavior by analyzing a model’s weights (a “white-box” approach) rather than relying on large-scale input/output sampling. ARC frames this as part of a broader pipeline for aligning powerful models: detect structure during training, convert that structure into mechanistic estimation capabilities, estimate safety-relevant quantities (including rare/“tail” failures) more efficiently than sampling, and then optimize the model against those estimates. ARC also discusses research subprograms including “alignment robustness” (including deceptive alignment / scheming) and “eliciting latent knowledge” (ELK)—training systems to honestly report internal beliefs—using what it calls “heuristic explanations” for neural network behaviors as an enabling concept for scalable alignment work. ARC also operates at the boundary between research and evaluation tooling: it has publicly run a “White-Box Estimation Challenge” with AIcrowd to spur progress on the kind of estimation algorithms ARC is developing, and its broader page points readers toward related evaluation work under METR for those seeking “ARC Evals.”