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Across four agent benchmarks and two models, extensive experiments show that RASO consistently outperforms baselines without retrieval-augmented skill initialization and updating.
AgentGrad is proposed, a prompt optimization framework for multi-agent systems based on sequential intervention and semantic textual gradient abstraction that clusters semantically similar gradients to prevent mixing unrelated failure modes, and abstracts each cluster into a generalized gradient that captures the share...
Context-Calibrated DPO (C$^2$-DPO), which directly maximizes CPG while preserving the original preference ordering, is proposed, which substantially reduces hallucination without compromising general reasoning.
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