Ultra-Dense Context Compression
High-fidelity semantic context compression method outperforming Caveman for dense information retrieval and LLM context efficiency.
Context window constraints and attention dilution are major bottlenecks when processing voluminous documents or extensive conversation histories. This project develops an ultra-dense context compression pipeline engineered to outperform existing baselines such as Caveman in semantic retention and retrieval precision.
Key Highlights
- Outperforming Baselines: Demonstrates superior recall and answer accuracy compared to Caveman across multi-document QA and dense retrieval benchmarks.
- Hierarchical Information Distillation: Preserves fine-grained facts and relational structures while dramatically reducing token overhead.
- Low Latency & High Throughput: Enables cost-effective long-document reasoning for resource-constrained inference settings.