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A neural scorer fine-tuned on NASA's Earth Observation data outperforms traditional methods, while a zero-shot reranking stage boosts retrieval effectiveness by 28%.
A transparent probe-success rule boosts robot policy selection success rates by over 14 percentage points, revealing the hidden power of pre-deployment evaluations.
Agon reveals that machine-driven research can scale effectively while exposing critical failure modes that still require human oversight.
Multi-agent orchestration prompting is critically under-evaluated, with only 14.9% of models passing the new PerspectiveGap benchmark.
Forget hand-tuning PDE solvers – a new multi-agent framework designs, implements, debugs, and verifies them directly from natural language, outperforming neural and LLM baselines.
