Pre-Storage Reasoning for Episodic Memory: Shifting Inference Burden to Memory for Personalized Dialogue (2025)

Abstract: Effective long-term memory in conversational AI requires synthesizing information across multiple sessions. However, current systems place excessive reasoning burden on response generation, making performance significantly dependent on model sizes. We introduce PREMem (Pre-storage Reasoning for Episodic Memory), a novel approach that shifts complex reasoning processes from inference to memory construction. PREMem extracts fine-grained memory fragments categorized into factual, experiential, and subjective information; it then establishes explicit relationships between memory items across sessions, capturing evolution patterns like extensions, transformations, and implications. By performing this reasoning during pre-storage rather than when generating a response, PREMem creates enriched representations while reducing computational demands during interactions. Experiments show significant performance improvements across all model sizes, with smaller models achieving results comparable to much larger baselines while maintaining effectiveness even with constrained token budgets. Code and dataset are available at https://github.com/sangyeop-kim/PREMem.

## introduction human cognition seamlessly synthesizes past experiences into coherent episodic memories that support personalized interactions @xcite @xcite . when engaging with familiar people, individuals effortlessly perform relevant interactions, track evolving preferences, and maintain consistent mental models without explicitly reviewing conversation histories. this natural memory process enables meaningful relationships through contextualized understanding. in conversational ai, well-designed memory structures are essential for maintaining personalized interactions across multiple sessions @xcite @xcite . effective memory mechanisms allow ai assistants to track user preferences, recall shared experiences, and sustain consistent understanding over time-capabilities that form the foundation of truly personalized dialogue systems @xcite . current memory approaches in conversational ai systems rely on three core mechanisms @xcite : indexing and storing, retrieval, and memory-based generation. recent advances have explored various structural granularities-from turn-level and session-level segmentation to compressed summaries @xcite and knowledge graphs @xcite . these approaches primarily investigate how different memory structures affect retrieval efficiency and accuracy, yet struggle with cross-session challenges that require understanding continuity, causality, and state changes. recent works @xcite have attempted to address multi-session reasoning through metadata annotations and conceptlinking knowledge graphs. however, these methods typically define cross-session relationships as simple clusters without modeling the nature of relationships or temporal evolution. beyond these limitations of retrieval-focused approaches, a more critical challenge emerges even when retrieval succeeds. even with optimal retrieval systems that can provide relevant context, models frequently struggle with complex reasoning tasks that require synthesis and inference-particularly temporal relationships and cross-session information integration @xcite . unlike simple information retrieval, these tasks demand sophisticated cognitive processes including pattern recognition, causal reasoning, and contextual synthesis. this computational burden during response generation creates significant inefficiency and amplifies performance disparities between large and small models. to address these challenges, we present premem (pre-storage reasoning for episodic memory), a cognitive science-grounded approach that shifts complex reasoning processes from response generation to memory construction. our approach draws inspiration from human cognitive processes: rather than exhaustively reviewing conversation histories during interactions, humans rely on pre-consolidated memories that have undergone sophisticated synthesis during offline periods @xcite . based on schema theory @xcite , human memory actively transforms information during storage through assimilation and accommodation processes, enabling efficient retrieval and coherent understanding across temporal contexts. premem implements this cognitive principle by extracting memory fragments into three theoretically-grounded categories-factual, experiential, and subjective information-and establishing explicit cross-session relationships through five evolution patterns derived from schema modification mechanisms, as shown in figure 1 . by performing complex reasoning during pre-storage rather than at response time, our approach creates enriched memory representations while reducing computational demands during interactions-offering both performance gains and practical deployment advantages. experimental results on longmemeval @xcite and locomo @xcite benchmarks demonstrate significant improvements across all model sizes. premem shows particularly strong results on cross-session reasoning tasks, with even small language models (≤4b) achieving competitive performance compared to much larger baseline models. additional experiments confirm its practical applicability in resource-constrained environments through efficient token utilization. our contributions include: (1) a cognitive science-grounded memory framework based on established schema theory that extracts structured episodic fragments and models information evolution through five theoretically-validated patterns; ## methodology we present premem, a novel approach that shifts complex memory synthesis and analysis from response generation to the memory construction phase. by performing pre-storage reasoning across conversations, our approach reduces the computational burden during dialogue while creating more cognitive-inspired memory representations. figure 1 illustrates the overall architecture of our approach, which consists of a memory construction phase (with two steps detailed in the following sections) and an inference phase. this method improves personalized conversation performance across all model sizes, with smaller models (≤4b) achieving results comparable to baselines using much larger models. all prompts and pseudo code can be found in appendix a and f, respectively. ## practical applications memory systems are foundational for personalized conversational agents, with resource efficiency critical for real-world deployment. to demonstrate the value of premem under resource constraints, we evaluate three key dimensions: (1) storage efficiency through alternative retrieval methods (section 5.1), (2) computational cost reduction using smaller reasoning models (section 5.2), and (3) token budget for context efficiency (section 5.3). vector embeddings for semantic search demand substantial storage for personalized assistants that must maintain separate indexes for each user. while keyword-based retrieval methods like bm25 typically underperform semantic search methods @xcite , experimental results shown in figure 2 (left) demonstrate bm25 remain surprisingly competitive with premem. this provides an efficient option for resource-constrained deployments with minimal performance tradeoffs. ## conclusion we present premem, a novel episodic memory system that shifts complex reasoning processes from response generation to the memory construction phase. this method transforms conversations into structured memories with categorized information types and cross-session reasoning patterns. our approach significantly improves performance on longmemeval and locomo benchmarks, with particularly strong results for temporal reasoning and multi-session tasks. notably, even modest-sized models using premem achieve competitive results compared to larger state-of-the-art systems. additionally, our focus on token budget, retrieval efficiency, and streamlined memory construction makes premem effective for real-world conversational ai systems that require long-term personalization under resource constraints.