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The proposed Uncertainty-Calibrated MOPD (UCMOPD), which addresses limitations of standard MOPD through two complementary mechanisms, improves the general-capability average over standard MOPD by $4.48\% and $7.86\% while maintaining vertical-domain performance.
Counteraction-Aware Multi-Teacher On-Policy Distillation is proposed, which addresses issues with decoupled alternating training and gap-based sample selection and outperforms all baselines in general capability recovery while maintaining domain-specific behavior.
This work presents Iris-mini and Iris-pro, two search agents trained at the 35B-A3B and 397B-A17B scales, together with the data pipeline and training recipe behind them, and plans to release the model weights together with the complete recipe for data construction, training, and evaluation.
MoNO is proposed, a training-free method that performs Manifold-constrained Noise Optimization on a low-dimensional, quality-stabilizing noise manifold that combines Riemannian updates on an affine low-frequency sphere to preserve prior likelihood and fix unstable high-frequency components by construction.
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