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SALR is proposed, a schema-anchored latent reasoning method for LF construction that performs multi-step reasoning by generating continuous thoughts in the model's hidden states, thereby delaying the explicit commitment to LF decisions.
Kimi K3's innovative architecture achieves a 2.5x scaling efficiency improvement, enabling robust performance across diverse long-horizon tasks.
Explicitly learning spatial priorities boosts infrared target detection performance, achieving up to 89.00 F1 score with real-gaze supervision.
Forget semantic similarity – now you can pinpoint *exactly* which object to retrieve in a composed image search with bounding box anchors, thanks to a new benchmark and model that blows away existing methods.
