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Results show that selective anti-interference is an effective objective-level signal for robust context use and identifies locally sufficient tokens and sharply reduces prefix sensitivity on gate-selected suffix tokens.
Dual Attention Residuals (DAR) is proposed, which brings multi-stream interaction into historical retrieval through reciprocal cross-stream addressing and consistently improves validation loss over standard residual Transformers and Attention Residuals.
ExACT, a supervision-allocation objective that assigns extra weight to long effective-context targets by inverse frequency within the long tail, supports a supervision-centric thesis: long-context adaptation depends on how strongly training supervises long-context predictions.
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