Learning What Matters: Causal Evidence Sets for Global Context Pruning

Pruning a long context means committing to the blocks a model will keep, and a common selector is distilled from a dense teacher’s attention. That assumes attention shows which context the answer depends on. We test the assumption on retrieval tasks where the evidence is known exactly, by masking context and measuring whether the answer changes. Attention and causal dependence disagree. Teachers attend to outdated facts the answer does not use, and a selector distilled from that attention routes a multi-hop task at 36% to 98% across runs. The same selector trained on causal evidence sets reaches 99% or better on every run, and known evidence labels beat attention-imitation and accumulated-attention eviction through 32B when the context must be pruned before the question arrives. A reconstruction compressor, KVzip, is stronger on natural-text QA with native per-head KV eviction. Under our paragraph-level adaptation, its scores do no better than labels recovered by intervention.