MemArbiter: Decision-Time Memory Arbitration for Long-Horizon LLM Agents
J. 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, information can be stored and retrievable yet still fail to guide the action, because of how it is organised, prioritised and presented 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 134 unseen ALFWorld tasks it reaches 82.8% success at a fixed 500-token memory budget against 61.9% for flat retrieval, and cuts the rate at which the agent repeats an action that has just failed from 47.1% to 26.7%.