When Less Latent Leads to Better Relay: Information-Preserving Compression for Latent Multi-Agent LLM Collaboration

This project addresses a highly specific bottleneck in the emerging 'Latent Multi-Agent' paradigm, where agents communicate by passing internal representations (KV caches) rather than text. While text-based communication is currently the standard (e.g., AutoGen, CrewAI), latent-space relay is theoretically more expressive but physically expensive. The introduction of Orthogonal Backfill (OBF) is a clever mathematical mitigation for the information loss inherent in KV eviction techniques like StreamingLLM or H2O. However, the project's defensibility is low (3) because it currently exists as a 3-day-old research implementation with minimal community signal. It is a 'feature' or an 'optimization' rather than a standalone platform. Frontier labs or inference engine developers (vLLM, TensorRT-LLM) could easily replicate or supersede this logic if latent-space relay gains mainstream traction. The risk is that while this specific technique is novel, the broader industry may move toward different context-transfer methods (e.g., cross-attention or specialized 'aggregator' models) that bypass the need for raw KV cache relay entirely.