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Heavy-Tailed Memory Traces in Long-Horizon Language Agents
Not provided
memory managementlanguage agentstoken efficiencymachine learning
2610.00010
Builder Relevance
1h ago80%
Abstract
The paper discusses the importance of memory shape in long-horizon language agents and introduces a new memory controller that improves token efficiency while maintaining accuracy.
Reality Card
Core Claim
The proposed Core--Tail World Model (CTWM) reduces prompt tokens by 5.9% and lowers bottom-half tail prediction error by 13.6% compared to a graph-memory baseline.
Method / Result
CTWM achieves a 5.9% reduction in prompt tokens and a 13.6% decrease in prediction error.
Limitations
The concentration of memory use is policy-dependent, which may affect reproducibility across different agent policies.
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