Ji Xia

LOCI introduces a hybrid spatial-memory architecture in which 15 of 30 transformer blocks keep a key-value cache of past observations while the other 15 are restricted to the current chunk and complemented by a recurrent linear-attention memory whose reads and writes are conditioned on projective camera geometry, with recurrent readouts flowing into subsequent cache-backed blocks to supply their queries with accumulated scene context; on the public MIND memory benchmark and on held-out recorded trajectories it reproduces revisited content more faithfully than representative world models and a same-recipe full-softmax model, lowers peak memory at equal length by about 30% with full history, and streams long videos at constant memory with a bounded bank of retained observations.

LOCI introduces a hybrid spatial-memory architecture in which 15 of 30 transformer blocks keep a key-value cache of past observations while the other 15 are restricted to the current chunk and complemented by a recurrent linear-attention memory whose reads and writes are conditioned on projective camera geometry, with recurrent readouts flowing into subsequent cache-backed blocks to supply their queries with accumulated scene context; on the public MIND memory benchmark and on held-out recorded trajectories it reproduces revisited content more faithfully than representative world models and a same-recipe full-softmax model, lowers peak memory at equal length by about 30% with full history, and streams long videos at constant memory with a bounded bank of retained observations.