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Forget bigger models: massive gains in document parsing accuracy are still possible through smarter data engineering alone.
LLMs can now automatically evolve and optimize GPU kernels to beat hand-tuned and proprietary models like Gemini and Claude.
Foundation models trained on audio, general time series, and brain signals can be distilled into a single, powerful encoder for scientific time series, unlocking performance gains on par with task-specific training.
Decomposing GUI agent trajectories into verifiable milestones and auditing the evidence chain yields a 10% boost in RL training performance, outperforming single-judge reward systems.
