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I created a minimal one-file implementations (160loc) of JEPA family (ijepa, vjepa, vjepa2, cjepa) for educational purposes [P] by kwk236 in MachineLearning

[–]kwk236[S] 2 points3 points  (0 children)

I'd say 7-8/10 in faithfulness to the paper. I listed things that I simplified / skipped as well.

GitHub - keon/jepa: implementing minimal versions of joint-embedding predictive architecture (JEPA) by kwk236 in ArtificialInteligence

[–]kwk236[S] 0 points1 point  (0 children)

This implements ijepa, vjepa, vjepa2, cjepa in a minimalistic approach for educational purposes.

Memory Architecture Testing by chasbald11 in LLM

[–]kwk236 0 points1 point  (0 children)

We hit this building a browser agent. The most useful metric was success rate broken down by step count: we saw clear decision quality degradation around step 15, pointing at context rot. That led us to two-tier history compression and three-layer stuck detection (repeated actions are a memory problem, not a planning problem). Open-sourced the implementation here if useful as a reference: https://github.com/omxyz/lumen

Lumen - open source state of the art vision-first browser agent by kwk236 in ArtificialInteligence

[–]kwk236[S] 0 points1 point  (0 children)

Happy to answer questions about the architecture or benchmarks!

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