acon-context-compression-agents

Reduce memory overhead of long-horizon LLM agents by learning task-specific context compression strategies. A learnable compressor adapts by analyzing failure cases, achieving 26-54% memory reduction while preserving 95%+ accuracy, enabling smaller models to act as efficient long-context agents.

Independent GitHub snapshot · Automated classification · Original documentation governs