MemArbiter: Arbitrating Memory at the Moment of Decision
Y. Dong et al. · 2026
What this means for traders
A constraint the system knows about but does not put in front of the model at decision time is, in practice, a constraint that does not exist.
Abstract
Identifies the Memory-Action Gap: in long-horizon agents the bottleneck is neither storing information nor retrieving it, but arbitrating which of it surfaces at the moment a decision is made. MemArbiter organises memory into functional banks (goal, task state, constraint, episodic, reference), scores decision relevance per bank and per item, and applies a temporal gate that deliberately shields goals and constraints from time decay. On unseen ALFWorld tasks it reaches 82.8% success at a fixed 500-token memory budget against 61.9% for flat retrieval, and halves the rate at which an agent repeats an action that has already failed.