AQUI: Asymmetric Query Embedding for Implicit Reasoning-aware Memory Retrieval

Memory systems are a core component of large language model (LLM) agents for retrieving multi-session conversations stretches beyond the context window. A particularly challenging setting is implicit reasoning, where the user's query is only indirectly connected to the relevant memory fact needed for a correct response. Existing implicit-reasoning memory systems build and traverse per-user trees or graphs, requiring costly memory construction, many LLM calls at inference time, or heavy dependence on the LLM's own reasoning during retrieval. We propose AQUI (Asymmetric QUery embedding for Implicit reasoning-aware memory retrieval), an embedding-based approach that moves the query representation toward implicitly related memory facts while keeping fact embeddings fixed. This asymmetric design lets the retriever internalize implicit connections without distorting the stored memory space, enabling relevant user facts to be retrieved in a single first-stage search. To train AQUI, we introduce a three-stage data generation pipeline that constructs user facts, implicit bridge facts, and query-negative pairs for reasoning-aware retrieval; the resulting $2,414$ synthetic training rows are generated without any access to the evaluation benchmarks. On ImplexConv-Opposed and ActMemEval-hard, AQUI's single-stage retrieval (no LLM call) already surpasses the state-of-the-art baselines' in session recall; a controlled ablation that holds the training data and hard negatives fixed attributes the gain to the asymmetric objective itself. We then pair AQUI with a lightweight LLM reranker in a simple end-to-end answer-generation pipeline, which yields higher LLM-as-a-judge answer accuracy than baselines. We also show that ActMemEval contains strong lexical and semantic shortcuts, and propose ActMemEval-hard, a strengthened supplementary dataset that removes these shortcuts. On this harder setting as well, AQUI achieves stronger retrieval and answer-generation performance while requiring no per-user memory construction, unlike graph- or tree-based memory systems