Reader--Writer Coupling in Transformer MLPs
A transformer MLP gives each hidden channel two roles: a reader that decides when the channel responds, and a writer that decides what it adds back to the residual stream.
ProofPaper ↗
Key points
- Read as a key--value memory, the layer can be measured one factor set at a time: the pairing of readers to writers is carried only by their shared index, invisible to any quantity computed from one factor set alone.
- The block supplies the joint instrument: the MLP writes back into the very stream it reads, so the exact Jacobian of its update is square, and its agreement with its own transpose resolves into reader--writer terms whose inner products give each channel, read as a couple, a signed pairing coordinate.
- Ranked by this coordinate, each block we measure has a small core of strongly coupled channels; where initialization gives either sign equally, the core is nearly all opposed: each writer points against the direction its reader responds to.
- This opposed core recurs from the smallest models trained from scratch to released checkpoints; its sign is in place within a few percent of training and holds while the coupling grows.
Sources (1)
- [1]Reader--Writer Coupling in Transformer MLPsarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 21, 07:35 AM
A transformer MLP gives each hidden channel two roles: a reader that decides when the channel responds, and a writer that decides what it adds back to the residual stream.
Read as a key--value memory, the layer can be measured one factor set at a time: the pairing of readers to writers is carried only by their shared index, invisible to any quantity computed from one factor set alone.
Extractive summary: sentences quoted from the sources.
Before this
- Oct 20, 2026Beyond the KV cache: Training and serving i14’s Laplace 3B linear attention model on AWS Trainium2 with native PyTorch
- Oct 20, 2026Gradium Open-Sources Phonon, a 31M-Parameter On-Device TTS Model Under Apache 2.0
- Oct 20, 2026SPAR: Search-trained Push Action Ranker for Efficient Navigation Among Movable Obstacles
- Oct 20, 2026MPS in PyTorch 2.15
- Oct 19, 2026LEAF: LUT-aware Efficient Attention on FPGAs
- Oct 19, 2026NVIDIA Cosmos3-H-Dreams: Bringing Clinical Surgery to Life Through Generative Simulation