
“Dense retrieval systems encode documents into a single semantic vector, coupling factual content with logical implication. GraphCLaRa introduces a causal partition mask over distilled reasoning LLMs, enforcing architecturally disjoint reasoning and semantic sub-representations within a single forward pass alongside differentiable Personalized PageRank graph routing.”
Dense retrieval systems achieve strong recall by encoding documents into a single semantic vector, but this geometry couples factual content with logical implication into one undifferentiated space.
We present GraphCLaRa, a retrieval architecture that imposes a causal partition mask over a distilled reasoning LLM (DeepSeek-R1-Distill-Qwen), enforcing architecturally disjoint reasoning (M_r) and semantic (M_s) sub-representations within a single forward pass. Within an oracle-corpus evaluation setting (665,766-passage index), GraphCLaRa CPC embeddings achieve 81.2% NQ Recall@5—matching ColBERTv2 (83.7%) within the confidence interval—validating representation quality and ranking precision under controlled corpus conditions.
The project formulates a differentiable Personalized PageRank (PPR) routing layer using implicit differentiation alongside a Convex Residual Highway that bounds the multi-hop Lipschitz constant, characterizing the scaling limits of end-to-end differentiable routing on large document graphs.
Imposes architectural decoupling between semantic similarity and multi-hop reasoning sub-representations in a single forward pass.
Enforces strict mathematical independence between routing and semantic embeddings, preventing catastrophic capacity collapse.
Dynamic topological propagation across knowledge subgraphs achieving superior passage recall on complex multi-hop queries.
Eliminates dense scaling walls by restricting backpropagation to active localized graph neighborhoods.




Authors: Chaitanya Anand, Himangshu Sarma