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.
Advances in neural information processing systems , volume=
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DisAgg distributes secure aggregation to a client committee via secret sharing, eliminating local masking and homomorphic encryption while preserving privacy and delivering 4.6x speedup over OPA for 100k clients and 100k-dimensional updates.
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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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DisAgg: Distributed Aggregators for Efficient Secure Aggregation in Federated Learning
DisAgg distributes secure aggregation to a client committee via secret sharing, eliminating local masking and homomorphic encryption while preserving privacy and delivering 4.6x speedup over OPA for 100k clients and 100k-dimensional updates.