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This paper proposes SWRouter, a Similarity-Contractive Window Router for multi-turn large language model routing that combines a similarity-based context segmentation mechanism for prompt construction with a dual-metric evaluation framework that decouples construction accuracy from router performance.
STAMP is proposed, in which a reference-based verifier judges whether each cited document supports an entity or relation in a training-time evidence graph, and first-exposure attribution traces each supported citation back to the action that first surfaced it.
SAGE (Stability-Aware Gradient Efficiency), which maintains difficulty-stratified candidate pools refreshed during training and selects pairs within each pool by a forward-pass signal-to-curvature score, outperforms full-data and size-matched baselines while producing substantially smoother optimization trajectories.
SCOPE-RL improves average accuracy by up to 11.2 pp and reduces reasoning tokens by up to 27.1% over outcome-only GRPO, indicating that reward-signal densification is complementary to policy-update-level RLVR advances.
TurnSight is proposed, a turn-level hindsight self-distillation framework that derives supervision directly from execution-conditioned hindsight and selects reliable supervision through cross-horizon directional agreement.
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