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Existing models miss critical visual evidence in metaphor understanding, but M$^3$R-Reasoner closes the gap, outperforming larger models in both accuracy and justification metrics.
Orthogonal Representation Editing achieves unprecedented precision in knowledge updates by decoupling semantic entanglement, outperforming traditional methods in both efficiency and accuracy.
Aligning rewards with sub-goals and emphasizing key trajectory segments with hindsight information significantly improves multi-turn agentic RL, outperforming existing methods on complex tasks.
