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Expanding linear RNN state-transition matrix eigenvalues to include negatives improves state-tracking tasks and language modeling without added training or inference costs.
DeltaProduct is a new linear recurrent neural network architecture that uses products of generalized Householder matrices as state-transition matrices enabling better expressivity, state-tracking, and language modeling performance than DeltaNet.
Rust-native MoE inference runtime with custom CUDA kernels for Blackwell GPUs. Includes DFlash speculative decoding, multi-tier Engram memory, and entropy-adaptive routing. Targets Qwen3.5-35B-A3B on a single RTX 5060 Ti 16GB.
CPU inference engine in Rust for Qwen3.8-27B: run the full Q4_K_M model on a 16 GB laptop by streaming what does not fit, instead of dropping to a worse quant. Hand-written AVX2 K-quant kernels, gated DeltaNet + GQA, identical token ids at every memory budget and thread count.
Distributed Engrammics — gradient-free transfer of a persistent fast-weight associative state between model instances (DeltaNet, RWKV-7). Paper + reproducible code.
Two fast-weight experiments (FENRIR and FREKI) on whether a cheap recurrent layer can chain multi-hop lookups inside one forward pass, without attention. Honest results, builds on prior art.
Continuous latent reasoning (Coconut) ported to Qwen3.5's hybrid gated-DeltaNet + full-attention architecture. At 500-example LoRA scale on Kaggle T4s, latent tokens gave no measurable benefit over a same-curriculum no-thought control; ablations and raw logs included.
The Delta Rule Lab — error-correcting memory in linear attention (DeltaNet / Gated DeltaNet). Real in-browser autodiff, live MQAR training, gradient-checked.
minGRU (Feng et al.) in PyTorch, plus a measured ladder of state-tracking recoveries — signed, rotation, Givens, and delta (DeltaNet-style Householder products) mixers with time decay, all under one parallel scan — with fused Triton GPU kernels as an optional backend