OLSF-TRS is a generalized sequential decision framework using structured combinatorial optimization and multi-agent reinforcement learning for order-tote-robot coordination in tote-handling robotic systems, with near-optimal performance on small scales and 8-30%+ improvements over heuristics onlarge
Naval Research Logistics Quarterly , volume=
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
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Standard attention collapses on additively mixed signals because it is memoryless with respect to explained evidence, but adding multiplicative depletion with an attention bias prevents collapse and enables multi-source inference.
citing papers explorer
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Omni-scale Learning-based Sequential Decision Framework for Order Fulfillment of Tote-handling Robotic Systems
OLSF-TRS is a generalized sequential decision framework using structured combinatorial optimization and multi-agent reinforcement learning for order-tote-robot coordination in tote-handling robotic systems, with near-optimal performance on small scales and 8-30%+ improvements over heuristics onlarge
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When Attention Collapses: Residual Evidence Modeling for Compositional Inference
Standard attention collapses on additively mixed signals because it is memoryless with respect to explained evidence, but adding multiplicative depletion with an attention bias prevents collapse and enables multi-source inference.