Nexa learns a response-conditioned policy that starts with parallel agent execution and adds at most one round of sequential message passing via a predicted sparse DAG, strictly subsuming pure parallel mode.
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Full-prefix MRL recovers ordered principal directions in linear settings and yields task-signal-aligned per-dimension structure empirically.
citing papers explorer
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Response-Conditioned Parallel-to-Sequential Orchestration for Multi-Agent Systems
Nexa learns a response-conditioned policy that starts with parallel agent execution and adds at most one round of sequential message passing via a predicted sparse DAG, strictly subsuming pure parallel mode.
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Objective-Specific Privileged Bases via Full-Prefix Matryoshka Learning
Full-prefix MRL recovers ordered principal directions in linear settings and yields task-signal-aligned per-dimension structure empirically.