Hyounghun Kim

Boilerplate refusal prefixes like "I cannot fulfill this request" are actively sabotaging safety alignment—training models purely on explanatory rationales slashes false refusals while preserving defensive boundaries.

VLMs that reliably refuse entirely invalid prompts systematically fall apart when unanswerable, infeasible, or unsafe sub-claims are embedded inside otherwise benign compound queries.

Seemingly benign user requests frequently clash with unstated personal constraints, yet current LLM assistants consistently fail to retrieve the implicit knowledge-base evidence required to trigger appropriate refusals.

Users can now dictate the focus of image captions with unprecedented precision, steering outputs toward specific attributes or relations through simple prompts.

Selectively repeating only the most informative tokens can dramatically enhance reasoning in LLMs while slashing computational costs.