Topic
neural network
ITNet: A Learnable Integral Transform That Unifies Convolution, Attention, and Recurrence in One Architecture
Researchers introduce ITNet, a neural architecture built on a learnable integral transform that subsumes convolution, self-attention, and recurrence as special cases. A single ITNet with a shared operator matches or exceeds specialized models on ImageNet-1K, GLUE, ModelNet40, VQA v2, and NLVR2, enabled by efficient techniques such as tiled kernel fusion and Monte Carlo integration.
Akasha 2 Achieves 4x Faster Visual Synthesis with Hamiltonian-Inspired AI Architecture
Akasha 2 introduces Hamiltonian State Space Duality and Visual-Language Joint Embedding Predictive Architecture, achieving state-of-the-art video prediction with 4x faster synthesis than diffusion models and 3-18x speedup over transformers. The system enforces physical conservation laws for spatiotemporal coherence.
Reservoir Attention Network: Cross-Pass State in Pretrained Transformers via Content-Addressable Reservoir Injection
The Reservoir Attention Network (RAN) injects a fixed, randomly-initialized reservoir into mid-layer attention of pretrained transformers to carry state across forward passes. Experiments on GPT-2 and Qwen2.5 on a single consumer GPU show feasibility for cross-pass state, with broader always-alive agent vision as future work.
Controlled Dynamics Attractor Transformer: New Model Targets Graph Anomaly Detection with Biologically Plausible Attention
Researchers propose the Controlled Dynamics Attractor Transformer (CDAT), which integrates a mixture von Mises-Fisher attention energy with Hopfield refinement and excitation-inhibition modulation from neural attractor models. The model achieves state-of-the-art results on graph anomaly detection and classification benchmarks, offering potential for detecting fraud, cyber threats, and operational anomalies in supply chain networks.