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• BACM-RL achieves over 1.6× performance gains over strong baselines in high-complexity settings.result 90%
• BACM-RL maintains strong performance advantages as context budgets shrink, while most methods exhibit a downward performance trend.result 85%
• BACM formulates context management as a sequential decision problem with a context budget constraint.method 90%
• BACM enables agents to assess available budget before incorporating new observations and decide when and how much interaction history to compress.method 85%
• BACM-RL uses an end-to-end curriculum-based reinforcement learning approach to learn compression strategies under varying context budgets.technical 90%
• LLM-based agents' context size is limited by deployment factors including memory, latency, and cost, creating a constrained context budget.limitation 80%
• BACM-RL consistently outperforms prior methods across model scales and task complexities.result 85%
• Experiments were conducted on compositional multi-objective QA and long-horizon web browsing benchmarks.method 90%

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