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University of Maryland
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Reasoning models may boost performance but often sacrifice critical alignment behaviors, revealing a hidden trade-off in AI safety.
FlowBank reveals that a compact, adaptive portfolio of workflows can outperform traditional single-query generation methods, enhancing efficiency and effectiveness in multi-agent systems.
Instead of imitating reflections, LLM agents can be trained to reason about action quality by rewarding correct judgments between alternative actions, leading to improved performance and generalization.
