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Output-layer gradients alone are sufficient for effective post-training data selection, slashing feature-extraction costs by up to 10x.
This work investigates whether hybrid backbones can become effective DLMs by adapting Qwen3.5 at 0.8B, 2B, 4B, and 9B scales, and finds that they can be efficient starting points for adaptation.
AI can generate novel mathematical insights, as evidenced by tightening the Grothendieck constant bounds significantly.
Forget retraining or complex architectures: a simple linear head can effectively eliminate missingness bias in feature attribution, rivaling heavyweight methods.
