SAE-IDF matches symbols across systems using sparse autoencoder codes
On 12 tasks across six symbolic systems, SAE-IDF beats the same LLM's dense baseline by up to 45 points.
46
On 12 tasks across six symbolic systems, SAE-IDF beats the same LLM's dense baseline by up to 45 points.
Task context changes which facts shared language-model heads use and how they influence later computation.
SAKIKO finds activation steering can raise tool-use scores while corrupting many previously correct LLM decisions.
Entities are extracted by the model from each article. First seen , last mentioned . Watching keeps it in this browser only (no account); the watchlist page and dashboard alerts use it.
Today 2.2K readers · 2.7K views · 397 reads · all time 108K views