Conventional Retrieval-Augmented Generation (RAG) architectures exhibit a fundamental asymmetry because they operate as read-only systems. In these standard designs, the retrieval corpus is populated once during document ingestion, after which the model solely consumes from that fixed knowledge base.
This approach overlooks a critical opportunity: over extended periods of deployment, language models generate numerous high-quality responses, such as summaries and syntheses, that often surpass the informativeness of the original data chunks. These valuable knowledge artifacts are currently discarded rather than being preserved for future use.
To address this, we introduce Bidirectional RAG, a novel architecture that enables the safe expansion of a corpus through the validated write-back of model-generated responses. This enables the system to learn from its own deployment and accumulate knowledge over time without the need for constant model retraining. The central challenge of such a system is safety: naively storing every output would rapidly pollute the knowledge base with hallucinations, creating a self-reinforcing cycle of degradation. Bidirectional RAG solves this through a multi-stage acceptance layer that validates responses against strict criteria before they are inserted into the corpus.
Furthermore, Bidirectional RAG uses an experience store to capture critique logs explaining why certain responses were rejected. These logs are retrieved at query time to guide future generation away from past failure modes, providing a form of meta-cognitive learning.
The acceptance layer implements three sequential checks: grounding verification, attribution checking, and novelty detection. Grounding verification uses Natural Language Inference to ensure that a response is strictly supported by existing documents. Attribution checking then verifies that every generated citation references an actual document from the retrieved set. Finally, novelty detection prevents the insertion of near-duplicate information by measuring semantic similarity against the existing corpus. This conservative approach ensures that only high-quality, verified content is added, which maintains the integrity of the knowledge base.
Experimental results across multiple datasets show that Bidirectional RAG can nearly double the retrieval coverage of a standard, static system. This is achieved while adding 72 per cent fewer documents than a naive write-back system, demonstrating that rigorous validation is effective at filtering out bad content. Furthermore, Bidirectional RAG uses an experience store to capture critique logs explaining why certain responses were rejected. These logs are retrieved at query time to guide future generation away from past failure modes, providing a form of meta-cognitive learning. As AI systems become more integrated into production, the ability to safely learn from interaction will be vital for maintaining relevance and accuracy.
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