CRISP is a framework that reconstructs the last‑layer activation vector of binary neural teachers into Tsetlin Machine clauses, enabling a symbolic trace from input thresholds to teacher neurons. It sign‑binarizes penultimate activations and assigns an Individual TM to each neuron, representing each hidden bit with propositional clauses over Booleanized inputs. Experiments on MNIST, KMNIST, FashionMNIST, SVHN, and CIFAR‑10 show that this binarization preserves teacher accuracy while the main limitation is reconstruction error, with quartile one‑bit Booleanization achieving the best fidelity on SVHN.
arXiv:2608.20497v1 Announce Type: new Abstract: Deploying high-accuracy neural networks on resource-constrained edge devices remains challenging, as existing approaches treat training, compression, a...
The paper introduces MACCHIATO, a training algorithm that builds a ReLU‑MLP from partial truth‑table data while simultaneously constructing an explicit Boolean circuit over AND, OR, and XOR gates that certifies the network’s computation. The method iteratively projects residuals onto low‑dimensional Boolean classes, compiles the resulting circuit into a ReLU‑MLP, and uses logic minimization and influence‑based variable selection to achieve a six‑layer network with provable truth‑table error bounds. Experiments on synthetic random‑junta tasks show that these certified networks outperform Adam‑trained MLPs in data‑sparse or projection‑aligned regimes and complete faster than flat ESPRESSO in certain settings.
Neuton is a framework that automatically converts neuron‑level abstract interpreter specifications into tensor‑level executables, enabling the creation of new DNN certifiers without manual tensor programming. It introduces a stack‑based intermediate representation with shape analysis, domain‑specific rewrite rules for efficient tensor operations, and a novel sparse tensor format (gBCSR) tailored to certifier sparsity. The authors demonstrate that Neuton preserves neuron‑level semantics and evaluate it on eight certifiers across 32 DNN benchmarks, compiling four existing certifiers and producing executable implementations for four others, revealing new precision‑runtime trade‑offs such as SkipPoly matching DeepPoly’s precision while often running faster.
arXiv:2608. 00859v1 Announce Type: new Abstract: Kolmogorov--Arnold Networks (KANs) replace scalar edge weights with learnable univariate functions parameterized by multiple basis coefficients.
arXiv:2609.13636v1 Announce Type: cross Abstract: Privacy-preserving inference via Torus Fully Homomorphic Encryption (TFHE) provides strong protection for sensitive data in outsourced deep learning...
arXiv:2602.17493v2 Announce Type: replace-cross Abstract: We develop a method for training neural networks on Boolean data in which the values at all nodes are strictly $\pm 1$, and the resulting mod...
arXiv:2604. 11284v5 Announce Type: replace-cross Abstract: We present THEIA, a 2.
The paper investigates reconstructing fully connected neural networks by combining cryptanalytic weight-direction extraction with learning from predicted labels. It adapts ReLU extraction to Leaky ReLU, compares sign-search methods, and estimates biases and output layers, proving conditional bounds for direction recovery and sign selection. Experiments show that Leaky ReLU can recover 51–57 neurons in the deepest hidden layer of a large synthetic architecture, while ReLU recovers none, and that reconstructed models on CIFAR‑10 achieve high prediction agreement and outperform a distillation baseline on several functional metrics.
arXiv:2606. 19279v1 Announce Type: new Abstract: Neurosymbolic semantics is fragmented: classical, fuzzy, probabilistic and neural systems each define truth by their own inductive rules.
arXiv:2602. 07400v2 Announce Type: replace Abstract: Gradient-based LUT- and logic-gate-based neural networks (LUTNet, LogicNets, DiffLogic, PolyLUT, NeuraLUT, WARP-LUT, DWN, LILogicNet, LightLUT) replace multiply-accumulate arithmetic with Boolean lookups.
arXiv:2606. 12629v1 Announce Type: cross Abstract: We show that the standard basis of transformer hidden states already provides a training-free, architecture-general feature basis.
arXiv:2606. 27759v1 Announce Type: new Abstract: Training binary neural networks (BNNs) from scratch is dominated by the straight-through estimator (STE), whose forward/backward mismatch produces severe accuracy degradation as networks deepen.