BIRC: Bridging Information Retrieval and Compression through Latent Memory

Latent-memory retrieval-augmented generation (RAG) compresses retrieved passages into continuous states, but selecting evidence within a fixed candidate pool restricts access to the rest of the corpus. We introduce BIRC (Bridging Information Retrieval and Compression), a fixed-depth interface in which compressed evidence guides a second corpus search. BIRC uses retrieval relevance to gate passage memories, projects their pooled representation into a feedback query, and orders the final evidence using relevance and source-reconstruction fidelity. Training passes answer-loss gradients through selected-document scores and reconstruction fidelity while keeping passage selection and forward ordering discrete. Across four open-domain QA benchmarks, BIRC with Mistral-7B achieves 44.50% average F1 at 16× compression, exceeding CLaRa by 5.55 percentage points in a complete-system comparison. When both systems are independently trained and evaluated on identical five-passage inputs, BIRC with gating and fidelity-based ordering achieves a 2.90-point advantage with feedback retrieval disabled. BIRC also achieves higher average F1 than CLaRa across all five tested Mistral-7B compression ratios and all four reader configurations at 16×.