u/Present_Brilliant

Hi all — I'm an independent researcher (Mendel Infolabs) about to put my first paper on arXiv, and as a first-time submitter to cs.LG I need an endorsement from someone already established in that category. If you've published in cs.LG and would be open to endorsing, I'd really appreciate it.

An honest summary so you can decide whether it's something you'd feel comfortable vouching for:

"FeedFlipNets: Feedback-Driven Bit-Flips for Ternary Networks, Activation-Routed DFA, and the Per-Weight Sign Barrier to Transport-Free Learning"

It trains ternary ({-1, 0, +1}) neural networks by flipping weight bits directly from a cheap feedback signal — no float shadow weights. The headline result is a negative one I think is worth putting on the record: transport-free feedback (Direct Feedback Alignment) doesn't actually help discrete/ternary training, because the binding constraint is per-weight sign correctness, not the aggregate cosine-alignment angle that prior work optimizes. Everything is pre-registered and reproducible.

Endorsing only confirms you think I'm a bona fide researcher submitting work appropriate to the category — it is not a review of the paper's correctness, and it takes about a minute:

• Link: https://arxiv.org/auth/endorse?x=WHWXBC
• Or go to https://arxiv.org/auth/endorse and enter code WHWXBC

Happy to share the full PDF with anyone who wants to read it before deciding — just comment or DM. Thanks a lot for considering it.

Hi all — I'm an independent researcher (Mendel Infolabs) about to put my first paper on arXiv, and as a first-time submitter to cs.LG I need an endorsement from someone already established in that category. If you've published in cs.LG and would be open to endorsing, I'd really appreciate it.

An honest summary so you can decide whether it's something you'd feel comfortable vouching for:

"FeedFlipNets: Feedback-Driven Bit-Flips for Ternary Networks, Activation-Routed DFA, and the Per-Weight Sign Barrier to Transport-Free Learning"

It trains ternary ({-1, 0, +1}) neural networks by flipping weight bits directly from a cheap feedback signal — no float shadow weights. The headline result is a negative one I think is worth putting on the record: transport-free feedback (Direct Feedback Alignment) doesn't actually help discrete/ternary training, because the binding constraint is per-weight sign correctness, not the aggregate cosine-alignment angle that prior work optimizes. Everything is pre-registered and reproducible.

Endorsing only confirms you think I'm a bona fide researcher submitting work appropriate to the category — it is not a review of the paper's correctness, and it takes about a minute:

• Link: https://arxiv.org/auth/endorse?x=WHWXBC
• Or go to https://arxiv.org/auth/endorse and enter code WHWXBC

Happy to share the full PDF with anyone who wants to read it before deciding — just comment or DM. Thanks a lot for considering it.

Hi all — I'm an independent researcher (Mendel Infolabs) about to put my first paper on arXiv, and as a first-time submitter to cs.LG I need an endorsement from someone already established in that category. If you've published in cs.LG and would be open to endorsing, I'd really appreciate it.

An honest summary so you can decide whether it's something you'd feel comfortable vouching for:

"FeedFlipNets: Feedback-Driven Bit-Flips for Ternary Networks, Activation-Routed DFA, and the Per-Weight Sign Barrier to Transport-Free Learning"

It trains ternary ({-1, 0, +1}) neural networks by flipping weight bits directly from a cheap feedback signal — no float shadow weights. The headline result is a negative one I think is worth putting on the record: transport-free feedback (Direct Feedback Alignment) doesn't actually help discrete/ternary training, because the binding constraint is per-weight sign correctness, not the aggregate cosine-alignment angle that prior work optimizes. Everything is pre-registered and reproducible.

Endorsing only confirms you think I'm a bona fide researcher submitting work appropriate to the category — it is not a review of the paper's correctness, and it takes about a minute:

• Link: https://arxiv.org/auth/endorse?x=WHWXBC
• Or go to https://arxiv.org/auth/endorse and enter code WHWXBC

Happy to share the full PDF with anyone who wants to read it before deciding — just comment or DM. Thanks a lot for considering it.

Edit: The PDF was publicly requested so here it is: https://doi.org/10.5281/zenodo.21152011 

Hi all — I'm an independent researcher (Mendel Infolabs) about to put my first paper on arXiv, and as a first-time submitter to cs.LG I need an endorsement from someone already established in that category. If you've published in cs.LG and would be open to endorsing, I'd really appreciate it.

An honest summary so you can decide whether it's something you'd feel comfortable vouching for:

"FeedFlipNets: Feedback-Driven Bit-Flips for Ternary Networks, Activation-Routed DFA, and the Per-Weight Sign Barrier to Transport-Free Learning"

It trains ternary ({-1, 0, +1}) neural networks by flipping weight bits directly from a cheap feedback signal — no float shadow weights. The headline result is a negative one I think is worth putting on the record: transport-free feedback (Direct Feedback Alignment) doesn't actually help discrete/ternary training, because the binding constraint is per-weight sign correctness, not the aggregate cosine-alignment angle that prior work optimizes. Everything is pre-registered and reproducible.

Endorsing only confirms you think I'm a bona fide researcher submitting work appropriate to the category — it is not a review of the paper's correctness, and it takes about a minute:

• Link: https://arxiv.org/auth/endorse?x=WHWXBC
• Or go to https://arxiv.org/auth/endorse and enter code WHWXBC

Happy to share the full PDF with anyone who wants to read it before deciding — just comment or DM. Thanks a lot for considering it.