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This paper introduces The Efficiency Frontier, a unified framework for cost–performance optimization in LLM context management that models context strategy selection as a deployment-aware optimization problem, achieving 25% reduction in token usage and over 50% lower token cost with amortized memory compression compared to full-context prompting.
A controlled study of compound LLM agent design in an adversarial POMDP (CybORG CAGE-2), systematically varying context, reasoning, and hierarchy across five model families. Key findings: programmatic state abstraction yields large returns per token, hierarchy without deliberation tools achieves best absolute performance, and context engineering is more cost-effective than deeper reasoning.
Benchmarks seven foundation models on Ukrainian legal text, finding tokenizer fertility varies 1.6×, few-shot prompting degrades performance, and cost-performance analysis shows NVIDIA Nemotron Super 3 outperforms larger models.