Pyramid MoA is a hierarchical Mixture-of-Agents system with a decision-theoretic router that achieves up to 42.9% compute savings while nearly matching oracle accuracy on MBPP, GSM8K, MMLU, HumanEval, and MATH.
Using anytime algorithms in intelligent systems.AI magazine, 17(3):73–73
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VECA learns effective visual representations using core-periphery attention where patches interact exclusively via a resolution-invariant set of learned core embeddings, achieving linear O(N) complexity while maintaining competitive performance.
Engagement Process (EP) decouples actions and observations as independent event streams over time within a POMDP structure to explicitly model temporal dynamics in agent interactions.
citing papers explorer
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Pyramid MoA: A Probabilistic Framework for Cost-Optimized Anytime Inference
Pyramid MoA is a hierarchical Mixture-of-Agents system with a decision-theoretic router that achieves up to 42.9% compute savings while nearly matching oracle accuracy on MBPP, GSM8K, MMLU, HumanEval, and MATH.
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Elastic Attention Cores for Scalable Vision Transformers
VECA learns effective visual representations using core-periphery attention where patches interact exclusively via a resolution-invariant set of learned core embeddings, achieving linear O(N) complexity while maintaining competitive performance.
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Engagement Process: Rethinking the Temporal Interface of Action and Observation
Engagement Process (EP) decouples actions and observations as independent event streams over time within a POMDP structure to explicitly model temporal dynamics in agent interactions.