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Automatically generated Multi-Agent Systems are not only outperformed by Single-Agent Systems but also exhibit architectural inefficiencies that challenge the very foundations of multi-agent design principles.
A single pair of boundary tokens transforms hidden-state reasoning into a trainable and interpretable framework, revealing causal insights that were previously obscured.
Certainty preservation in lab notes is the key to preventing AI agents from misinterpreting scientific uncertainty as actionable knowledge.
Get RL-level multi-turn LLM performance with SFT-level efficiency by decoupling trajectory generation and optimization via importance weighting.
Forget retraining: ACE-Merging unlocks state-of-the-art model merging by cleverly estimating task covariance from parameter differences alone.
