N. Lane

Flower Hub: A Reproducible Benchmarking Platform for Federated Learning in Simulation and Deployment

Flower Hub is presented, a platform for publishing, discovering, and executing decentralized and federated applications that enables reproducible benchmarking by packaging benchmarks as executable, versioned applications with standardized metadata, pinned dependencies, and explicit evaluation workflows.

Sparsity in Federated Learning: A Survey

Extracting Forgotten Prompts from Targeted Unlearned Models

It is shown that the forgotten prompts themselves can be extracted by using the retained data and black-box access to the model by TAS, which recovers the forgotten entity with 100% accuracy and reconstructs up to 95% of forgotten prompts while using up to $99.7\%$ fewer queries than naive probing.

Sparse Readout Prism: Explaining Logit-Lens Scores in Features Instead of Tokens

Sarse Readout Prism (SRP), which decomposes the readout using only its weights and expresses any token logit or logit difference as a sum of contributions from sparse readout features, reveals readout features as a new unit of analysis for lens readings, exposing structure that token identities can obscure and enabling...

How LLM Task-Adaptation Reshapes Alignment: A Multi-dimensional Study of Behavioral and Representational Drift

A systematic evaluation of representative task-adaptation methods shows that task adaptation is not merely a capability-improving step, but an alignment intervention in its own right, motivating multi-dimensional alignment evaluation as a standard component of post-training pipelines.