Learning What Matters: Supervising Global Context Pruning with Causal Evidence Sets

תקציר מקורי באנגליתarXiv:2607.21692v2 Announce Type: replace Abstract: Sparse attention prunes a long context to the blocks a model needs, and the usual 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, and selectors inherit the disagreement. Teachers attend to outdated facts they have learned to ignore, and attend differently across training runs that use the same evidence. In a two-step reference task, attention at the answer position can skip the intermediate step, and how often it skips varies with the training run: selecting one block set per pass, a sel