ASCENT: Scale-Complementary External State for Frozen Long-Context Language Models

Xingjian Zhang

External state should become more valuable as a reader becomes more capable, yet long-context evaluations measure memory utility at a single model scale. This paper formulates scale complementarity: the absolute gain from external state increases when reader capacity and a compact state budget are jointly scaled. ASCENT realizes this principle with a target-blind causal writer, nested typed state, and a bounded query-conditioned readout; every language model remains frozen. Under log loss, the value of a nested refinement equals its target-relevant conditional information; a full factorial makes the reader-by-state interaction identifiable. Official 16K BABILong experiments show Qwen2.5 accuracy gains of 10.50, 78.88, and 82.63 percentage points at 0.5B, 1.5B, and 3B parameters, with both adjacent directional contrasts significant after family-wise correction. At 7B, Foundation accuracy rises to 33.25% while four-slot ASCENT answers every question, which caps the gain at 66.75 points; the 3B-to-7B accuracy increment is -15.88 points [-20.55, -11.20]. The accuracy-side claim is therefore established from 0.5B to 3B; the 7B conclusion rests on the non-saturated…