On the Depth Scalability of Logic Gate Networks
Logic Gate Networks (LGNs) compute through compositions of Boolean operations, yet existing LGNs do not reliably benefit from increased depth. We identify two causes: optimization collapse and topology-induced degradation of output-specific credit that persists even after skip-biased initialization and straight-through estimation stabilize training. We introduce Input-Anchored Logic Gate Networks (IALGNs), in which each gate combines a private hidden spine with a direct input anchor. This topology prevents output-path merging while retaining input access at every layer. Credit diagnostics show that random wiring dilutes or conflicts output-specific gradients, whereas IALGN maintains usabl...
| Authors | Taegun An, Dohun kim, Haebeom Lee et al. |
|---|---|
| Primary category | cs.LG |
| Published | 19 July 2026 |
| Last updated at source | 28 August 2026 |
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