AINeutralarXiv – CS AI · 6h ago6/10
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Learning What to Remember: Observability-Safe Memory Retention via Constrained Optimization for Long-Horizon Language Agents
Researchers introduce OSL-MR, a framework that optimizes memory retention for long-horizon language agents by treating it as a constrained optimization problem rather than local decisions. The approach combines learned evidence valuation with heuristic scoring while respecting real-world observability constraints, demonstrating superior performance over existing methods on benchmark datasets.