A Witnessing Duality for ReLU Network Depth
Organizations: Great Bay University, China
Abstract
A depth lower bound for ReLU networks asserts that every shallower network errs on some input; we ask when a weak theory can prove such a bound so that an error can be computed. For the exact computation of , recent work shows two hidden layers suffice with unrestricted rational weights. By contrast, Haase, Hertrich and Loho (ICLR 2023) proved that integer weights require depth logarithmic in , and Averkov, Hojny and Merkert (ICLR 2025) extended the bound to nonbias weights whose denominators are powers of a fixed integer not divisible by a prime . With integer weights the bound is sharp: the classical balanced construction has depth . For each fixed and depth cap , we prove this known bound in , the bounded-arithmetic theory of uniform constant-depth threshold computation, together with a refuter that is itself such a computation and whose correctness the theory proves. Given a network with at most hidden layers, nonbias weights whose reduced denominators avoid , arbitrary rational biases and inputs, the refuter returns a rational input of polynomial length on which the network errs. The proof makes the obstruction finite: an iterated intersection product of piecewise-linear functions is divisible by for the network but not for the maximum, so one of finitely many test points exposes the error. By witnessing, provable refutation corresponds to a provably correct refuter, and provable existence of a certified network to a provably correct constructor. On the construction side, we introduce certificates that label each maximum gadget by the inputs it aggregates; with them the theory verifies the balanced construction, which meets the refuter at the sharp threshold , and a rational family of polynomial description length and depth built from known depth-two blocks.
Figures & tables
| Result | Guarantee |
|---|---|
| Internal modular refutation | For each fixed prime and depth cap , a specified refuter has -provable correctness for when reduced nonbias denominators omit . Arbitrary rational biases and unreduced encodings are allowed ( Theorem 8.5 ). |
| Certified integer construction | A uniform depth- integer-weight family has polynomial total codes and full histories verified in . Combined with the prime-two refuter, it gives the sharp threshold for each fixed ( Theorems 9.4 and 9.7 ). |
| Certified rational construction | Polynomial total network and certificate length and full histories, with a uniform internal depth inequality giving hidden depth ( Theorem 9.8 and Theorem D.1 ). |
ReLU Neural Networks
Rectified linear unit (ReLU) neural networks use piecewise-linear activations to study approximation, learning dynamics, expressivity, and verification.
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