Context Recycling for Long-Horizon LLM Inference

Short summary

ContextForge introduces context recycling for long-horizon LLM conversations, combining structured query generation, external memory retrieval, and controlled synthesis to reduce token overhead without sacrificing answer quality. Tested on a 15-turn healthcare benchmark, it demonstrates better consistency and significantly lower token consumption than baseline agents using identical models. Code is available on GitHub, offering a practical pattern for extending LLM capabilities without larger context windows.

• •Context recycling reduces token overhead in multi-turn LLM conversations while maintaining answer quality
• •Outperforms baselines on consistency and token efficiency in healthcare reasoning benchmarks
• •Code available; applicable pattern for LLM product teams building long-horizon agents

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