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Distilling LLM agents by supervising semantic actions, not token sequences, unlocks significant performance gains and bypasses tokenization constraints.
Refinement complexity in automotive requirements is driven more by architectural scope than by linguistic verbosity, revealing critical insights for improving product development efficiency.
SelPE achieves high-fidelity structured text synthesis under strict privacy constraints, outperforming traditional methods in low-data environments.
CLIP can now understand "no dog" without any fine-tuning, thanks to a plug-and-play module that disentangles negated semantics and penalizes false positive matches.