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Delegation intelligence in LLMs can significantly enhance their ability to tackle complex, long-horizon tasks, as demonstrated by SearchSwarm's superior performance on challenging benchmarks.
Code agents are only eliminating 50% of code smells, revealing critical gaps in their understanding of maintainability and cross-file dependencies.
Current vision-language models falter in ultra-resolution reasoning, with errors primarily stemming from evidence grounding and local perception.
Bridging the perception-reasoning gap in visual planning, MGSD boosts model performance by over 19% while relying solely on visual inference during deployment.
Current AI agents falter in autonomous development, revealing critical gaps in robustness and alignment as they struggle against human-engineered solutions.
Forget memorizing manipulation artifacts: comparing against a reference library of authentic examples lets you spot forgeries and adapt to new domains without retraining.
Ditch SwiGLU's quadratic instability: PowLU offers a rational power function that stabilizes LLM pre-training without sacrificing performance.
