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AREX achieves superior performance in deep research tasks by recursively refining answers through a novel self-improvement mechanism that outpaces traditional search methods.
Arbor's innovative approach to autonomous research enables a cumulative learning process that outperforms existing models by over 2.5 times in real-world tasks.
Scaling out peer agents with a shared reasoning hub, AgentFugue, unlocks a new dimension of capability gains in long-horizon tasks, proving that collective reasoning is more than just parallel compute.
LLMs can now recall relevant information from long interaction histories without retraining, thanks to a state-adaptive memory framework that uses lightweight cues to reconstruct distant information on demand.
LLMs that ace general web browsing still fail miserably at autonomous scientific literature discovery, revealing a critical gap in research-oriented AI agent capabilities.
