PRAM: Partial-Cue Retrieval with Associative Memory over Frozen LLM Representations

Long-term language agents need to recover related experiences from partial cues, compose them across relations, and selectively retain information as new experiences arrive. We investigate whether fixed-size linear associative maps over frozen language-model representations can support these functions. Our framework, Partial-cue Retrieval with Associative Memory (PRAM), binds whitened entity representations with relation and time codes and organizes facts into complementary maps for target, set, and temporal access. Successive association reads construct a query-specific tree. A learned bridge converts its target representations into soft tokens, which are interleaved with textual relation structure and supplied to a frozen language model for answering. We further study a dynamic extension in which fast acquisition, decay, and error-driven consolidation regulate the persistence of associations. Through controlled retrieval, neural readout, conversational question answering, and streaming-memory experiments, we characterize the roles of representation geometry, structured access, and selective retention. The framework connects graph-like memory operations with a continuous language interface and provides a basis for studying both how associations are recalled and how they are maintained.