Fix the Structural Bottleneck: Context Compression via Explicit Information Transmission

ArXi:2602.03784v2 Announce Type: replace Long-context LLM agents often struggle with growing token, memory, and latency costs, making efficient context compression essential for practical deployment. Existing LLM-as-a-compressor methods remain noticeably inferior to using the full context. We find that this gap partly stems from their inability to preserve contextual information effectively.