Adriano Koshiyama

Routing supervision falters precisely when it's most needed, as weaker agents yield fewer labels, limiting the potential for optimal mode selection.

Fourteen out of sixteen large language models exhibit a systematic optimism bias in their probability judgments, raising concerns about their reliability as decision aids.

Single-token SAE features can significantly impact model outputs, with their causal roles varying dramatically across different SAE families.