Ahmadreza Jeddi

Using a fixed-context Pass@K analysis, this work shows that degradation after visual-token pruning is not only lost information but also unreliable use of surviving evidence (a representation-utilization gap), and introduces SCOPD and SCOPD+ sparse-context on-policy self-distillation, raising the normalized average over 13 benchmarks at 10% visual-token retention from 86.37% (Vanilla) to 90.49% and 92.43% without architectural changes, extra inference-time computation, or ground-truth answers.

Using a fixed-context Pass@K analysis, this work shows that degradation after visual-token pruning is not only lost information but also unreliable use of surviving evidence (a representation-utilization gap), and introduces SCOPD and SCOPD+ sparse-context on-policy self-distillation, raising the normalized average over 13 benchmarks at 10% visual-token retention from 86.37% (Vanilla) to 90.49% and 92.43% without architectural changes, extra inference-time computation, or ground-truth answers.