Chain Lens: training-free technique extends single-token interpretability lenses to multi-token via MTP heads
Sauers_ · x · 2026-11-12
A WIP post introduces Chain Lens, a new interpretability technique for verbalizing model hidden states. It extends single-token lenses like the J-lens into multi-token ones without any training, reusing the model's existing multi-token prediction (MTP) head — a novel training-free approach to peering into hidden states.
Related event: Chain Lens: Training-Free Multi-Token Readout from Model Activations(6 posts)→
More from Research
- As models get stronger, the next trainable tasks look increasingly like alignment itself — herbiebradley · 2026-11-12
- RoboCap-10k: 10,000 hours of open egocentric robot data across 21 industries released — micoolcho · 2026-11-12
- Talent vs. luck model reproduced: equal funding is ~9x more efficient than funding winners — ChenhaoTan · 2026-11-12
- Stanford HAI dashboards show employment in AI-exposed jobs down over 4% this year — StanfordHAI · 2026-11-12
- Shun-ichi Amari on how 'do as you like' freedom birthed the math behind AI's 60 years — FrnkNlsn · 2026-11-12
- Formal verification proves a 3,000-step Sokoban optimum, with a bounty on beating it — banteg · 2026-11-12