Dave-he/LNN

Dave-he/LNN feels like a working research notebook that has grown into a real engineering project. What stood out to me is that it is not just collecting links about Liquid Neural Networks; it has an actual Python package under lnn/, with CfC, LTC, liquid neuron, graph, physics-informed, multimodal, and noise-adaptive model code. The repo also keeps a unusually detailed trail of experiments in analysis/ and docs/research/, which makes the benchmark claims easier to take seriously because you can see the iteration behind them rather than only a final number.

The strongest part is the practical orientation. LNN_QUICKSTART.md, LNN_TLDR.md, the EMMA rover benchmarks, Jetson notes, and the pytest suite give the project a clear “try this yourself” path. The tests cover more than basic imports: they check model shapes, masking, irregular time deltas, multimodal behavior, and regime-specific cases. That is a good sign for a fast-moving research repo.

The biggest thing holding it back is presentation for outside users. GitHub has no description, topics, or license, and the README is dense enough that a new visitor may not immediately know whether this is a library, research archive, benchmark suite, or Obsidian vault. I’d add a short English overview at the top, a license, a minimal API example, and a clearer split between stable code and experimental research logs. The project already has substance; it mainly needs a cleaner front door so other people can understand and reuse it faster.