Research Archive

We formulate machine unlearning for online L-BFGS as a counterfactual state-alignment problem. Given an actual event stream and a deletion-edited counterfactual stream, the target of unlearning is the optimizer state that would have arisen had the deleted samples never been processed. We introduce state-aware metrics that separately measure parameter error, memory-operator error, combined state error, and update-direction error. The memory metric compares the inverse-Hessian actions induced by the o-L-BFGS memory, rather than treating curvature pairs as of finite influence. Under convexity assumptions, we derive a recursive bound on counterfactual state deviation. We then evaluate a state-aware benchmark of deletion interventions, including memory-only and parameter-only corrections, against an counterfactual oracle model. These results show that unlearning for online L-BFGS is not merely a parameter-correction problem: it requires alignment with a realizable counterfactual optimizer state.

Individuals have the right to be forgotten, but does AI has the right to forget?

In this work, we introduce a localized, MDL-based divergence measure that quantifies the structural complexity of induced subgraphs relative to a global reference model. The measure compares the compressibility of local neighborhoods under globally fitted and locally optimized comparator models while penalizing model flexibility, yielding a statistically grounded notion of local structural surprise. The results show that the measure converges under controlled ablations, is robust to sampling choices, and detects meaningful structural irregularities that are invariant to geometric embedding. This framework generalizes MDL-based network analysis to arbitrary information graphs and provides a principled bridge between global structure and agent-level experience.

The Lab investigates the math behind a machine's right to forget, and the human's right to be forgotten as presented by the seminal Sekhari paper.

We propose an MDL-based measure of network structural complexity. The method utilizes the two-phase MDL approach for describing random and nonrandom variation.

Unlearning is more than performance. It's a matter of model state.

We review Sinha and Mahajan's take on the sufficiency of an information state. We're such fans, we decide to take it even further.

A primer on entropy, mutual information, and variational inference.

Continuing our Foundations series, we explore sigma-algebras—the mathematical scaffolding that makes probability rigorous and connects abstract theory to real-world randomness in our models.