APWA is a distributed multi-agent architecture that decomposes parallelizable agentic workflows into non-interfering subproblems for scalable execution on heterogeneous resources.
Reflex- ion: language agents with verbal reinforcement learning
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VPR converts symbolic, constraint, or posterior oracles into dense turn-level rewards for RL, improving credit assignment in agentic reasoning and transferring to general benchmarks.
LLM agents improve adaptability by first using an interaction budget for systematic exploration measured via Exploration Checkpoint Coverage before executing tasks.
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