{
  "id": 15557449,
  "title": "Beyond SAEs: Consistent Concept Discovery with Sparse Coding",
  "url": "https://urgent.news/2026/10/16/beyond-saes-consistent-concept-discovery-with-sparse-coding",
  "topic": "ai",
  "section": "AI",
  "published": "2026-10-16T17:25:11.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2610.23801v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Sparse autoencoders (SAEs) explain foundation model representations by decomposing activations into sparse combinations of learned dictionary atoms. However, the learned dictionaries can vary across training runs, limiting the reliability and reproducibility of SAE-based analyses. We propose Consistent Sparse Dictionary Learning (ConSDL), which replaces the standard linear encoder with per-sample…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}