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Existing large language model (LLM) based multi-agent systems have been shown to scale poorly, and we identify three problems: reading burden that grows with the group, useful information diluted by shared work of unknown quality, and fragility to failed agents. We argue that the problems are largely due to agent-centr...
H-Spec is a hybrid Mamba-attention parallel drafter that consumes the two target-context sources through complementary modules, and consistently achieves higher throughput while maintaining lower KV cache utilization than baselines across evaluated concurrency levels.
MatrAIx is introduced, a population-scale simulated-user evaluation infrastructure for testing AI systems and digital products with heterogeneous users and provides an end-to-end infrastructure for evaluating AI systems and digital products with diverse simulated human users.
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