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LLM agents can now build lasting, reusable "competence" for specific knowledge sources, moving beyond simple retrieval to deep, adaptive understanding.
HEART achieves 84% task completion on real-world tasks, outperforming leading models while slashing API costs by up to 85%.
Modern LLMs rarely refuse to discuss restricted content, instead opting for nuanced warnings that reveal a significant shift in content moderation strategies.
Ditch the fixed interface: DiffMAS unlocks surprisingly large gains in multi-agent reasoning by jointly optimizing latent communication, outperforming text-based and prior latent methods by a wide margin.
