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Clarus transforms the landscape of scientific collaboration by enabling autonomous agents to work together in a structured, traceable, and resource-aware manner.
Skill organization can dramatically enhance agent performance, with a 4.1% increase in successful outcomes when using Progressive Disclosure over traditional methods.
LatentSkill achieves a 21.4-point increase in task success while slashing prefill token usage by over 64%, revolutionizing how LLM agents utilize skills.
LLM agent progress increasingly hinges on better external cognitive infrastructure, not just stronger models.
Synergy's architecture lets agents evolve through experience by proactively recalling rewarded trajectories, hinting at a new way to build agents that learn and adapt in open, collaborative environments.
