# VIKI-L2

Multi-robot task planning has mostly let an end-to-end vision-language model learn both scene judgment and action structure, and out-of-distribution performance can collapse. NSDL splits the labor: a trained small model decides which goals are already met, how the task splits and which robot does what, and writes these as a plan sketch; a deterministic solver expands the sketch into an executable joint plan. On the public VIKI-L2 benchmark the 7B model reaches 97.0% out-of-distribution accuracy, and three backbones from 3B to 8B all outperform end-to-end and formalize-then-plan baselines.

Multi-robot task planning has mostly let an end-to-end vision-language model learn both scene judgment and action structure, and out-of-distribution performance can collapse. NSDL splits the labor: a trained small model decides which goals are already met, how the task splits and which robot does what, and writes these as a plan sketch; a deterministic solver expands the sketch into an executable joint plan. On the public VIKI-L2 benchmark the 7B model reaches 97.0% out-of-distribution accuracy, and three backbones from 3B to 8B all outperform end-to-end and formalize-then-plan baselines.