[Re] BiRT: Bio-inspired Replay in Vision Transformers for Continual Learning

At a fixed replay-memory budget, how sensitive are retention measurements to task order?

Fix task orders, memory accounting and held-out examples independently; calculate the complete task-by-time performance matrix from predictions.

What you could produce

• Versioned protocol, input and environment manifest, and independent per-case comparison table including uncertainty and incomplete cases.

Before you use it

• Exact article-specific dependencies, data and build requirements remain uninspected.

Limits to keep in view

• No research code was executed; no independent scientific verification has been performed.
• The current qualified pilot is self-contained Python 3.13 with a 90-second author deadline. This article's environment has not been qualified for that path.
• Published source metadata and a historical review do not establish compatibility, successful reproduction, operator independence or current scientific correctness.

Source and permission context

Maheshwari, Disha. [Re] BiRT: Bio-inspired Replay in Vision Transformers for Continual Learning. ReScience C 10(1), #2; 10.5281/zenodo.14964875.

Rights need review. Review the scope and upstream conditions before reuse.

Still unresolved

• The associated code's exact license and version-specific third-party notices have not been independently checked.
• Data licenses, permissions, consent restrictions and redistribution conditions have not been independently checked.
• Artifacts are referenced only; PDF/archive contents, dependency locks and bytes have not been inspected or executed.