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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 unlocking reliable AI-driven scientific exploration without misinterpretation of uncertainty as certainty.
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.
