arXiv Machine Learning
Topographic Training Concentrates Causal Circuits Without Improving Neuron Monosemanticity
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
Related stories
Hugging Face Trending Papers
Sep 21
Topographic Training Concentrates Causal Circuits Without Improving Neuron Monosemanticity
Mechanistic interpretability of vision transformers seeks to decompose model computation into human-readable units, but learned representations entangle many concepts in each neuron. Feature superposi...
arXiv Machine Learning
Sep 11
Breaking the Central Bias: Spatially Partitioned Experts for Coordinate-Based Neuroevolution
The paper investigates a spatial-concentration bias in Evolvable-Substrate HyperNEAT (ES‑HyperNEAT) when applied to MNIST, where evolved networks focus on a central cluster of input pixels. By partitioning the input image into 13 non‑overlapping spatial segments and evolving a separate expert network for each, the authors achieve a 43% mean accuracy—an 106% relative improvement over the baseline—without relying on data‑driven weighting. The study also introduces a receptive‑field diagnostic to detect silent input‑coverage collapse and a spatial‑partitioning remedy to restore full image coverage.
arXiv AI
Jul 21
Emergent Hierarchical Monosemantic Neurons from the Group-Contrastive Forward-Forward Algorithm
arXiv:2607. 16295v1 Announce Type: cross Abstract: Mechanistic interpretability has made significant strides in understanding neural network representations, with sparse dictionary learning (SDL) methods, most prominently sparse autoencoders, as a central paradigm.
arXiv Computer Vision
Sep 14
DRDN: Decoupled Representation Dynamic Network for From-Scratch ViT Class-Incremental Learning
arXiv:2607.01630v2 Announce Type: replace Abstract: Dynamic expansion methods for class-incremental learning (CIL) protect task-specific knowledge by growing dedicated tokens or subnetworks, yet our...
arXiv AI
1d ago
NeuronEye: Query-Guided Visual Concept Activation for Vision-Language Reasoning
NeuronEye is a plug‑in framework that builds a sparse, concept‑level neuron vocabulary from intermediate vision‑language model (VLM) representations and selectively activates query‑relevant visual concepts during inference. It decomposes vision‑token states into an overcomplete sparse basis organized by concept clusters, uses the language query to activate relevant clusters, localizes the corresponding image patches, and injects the focused evidence back into the vision tokens, while a suppression mechanism attenuates dominant perceptual directions. Experiments on Qwen2.5‑VL‑7B and LLaVA‑1.6‑7B show that NeuronEye improves CV‑Bench overall accuracy by +3.1, boosts Distance by +9.5, and raises BLINK Multi‑view by +8.3, indicating that sparse neuron vocabularies can act as active interfaces for concept‑level visual reasoning.
arXiv AI
Jul 10
When Structured Sparse Autoencoders Learn Consistent Concepts Across Modalities
arXiv:2607. 08605v1 Announce Type: cross Abstract: Sparse autoencoders (SAEs) have emerged as a promising technique for mechanistic interpretability by learning a set of sparse latent features in large models, each of which encodes a distinct concept.