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"My agent understands me better": Integrating Dynamic Human-like Memory Recall and Consolidation in LLM-Based Agents
Yuki Hou, Haruki Tamoto, Homei Miyashita
TL;DR
LLM dialogue agents have difficulty processing temporal information. The paper introduces cued memory recall and dynamic consolidation based on relevance, elapsed time, and recall frequency. It reports improved memory recall and response generation, including lower loss than Generative Agent.
Problem
LLMs have a stated limitation in processing temporal information, and existing recall models may assume one-trial learning with a constant decay rate.
Source: §1 · p. 1, §2.3 · p. 2Method
Results
The model demonstrated significantly lower loss than Generative Agent (t=-5.687, p=0.000299) and is reported to improve memory recall and response generation.
Source: §4.2.1 · p. 4, §5 · p. 6Takeaways & Limitations
The authors report that database-based memory supports context-aware, personalized responses that use memories' temporal context.
Source: §4.3.1 · pp. 5–6, §6 · p. 6Memory consolidation relies on users’ long-term behavioral patterns, which may limit adaptability when their behavior changes significantly.
Source: §5 · p. 6
Abstract
from arXiv · showhide
In this study, we propose a novel human-like memory architecture designed for enhancing the cognitive abilities of large language model based dialogue agents. Our proposed architecture enables agents to autonomously recall memories necessary for response generation, effectively addressing a limitation in the temporal cognition of LLMs. We adopt the human memory cue recall as a trigger for accurate and efficient memory recall. Moreover, we developed a mathematical model that dynamically quantifies memory consolidation, considering factors such as contextual relevance, elapsed time, and recall frequency. The agent stores memories retrieved from the user's interaction history in a database that encapsulates each memory's content and temporal context. Thus, this strategic storage allows agents to recall specific memories and understand their significance to the user in a temporal context, similar to how humans recognize and recall past experiences.
1 INTRODUCTION
LLM-based dialogue agents are limited in processing temporal information, motivating memory processes that better support nuanced interaction. The proposed approach uses cued recall and memory consolidation to support contextually relevant, coherent conversations.
1 INTRODUCTION
- LLMs have a stated limitation in processing temporal information, despite their capabilities in understanding and generating human-like text.The introduction frames human-like cognitive and memory processing as necessary for nuanced agent interaction.Source: §1 · p. 1
- The approach integrates human memory processes into LLM dialogue agents, using cued recall to retrieve memories needed during response generation.Repeated recalls over a long period are retained more strongly than recalls that are frequent but occur over a short period.Source: §1 · p. 2
- The authors seek to move beyond statistical imitation toward interactions integrating cognitive processes at cognitive and emotional levels.Source: §1 · p. 2
2 RELATED WORK
The related work connects LLMs and human memory, reviews memory systems in AI agents and mathematical recall models, and distinguishes the proposed temporal consolidation approach from event-scoring agents.
2.1 Similarities Between LLMs and Human Memory
- LLMs are described as having episodic-like recall of database events and semantic-like language understanding, paralleling two categories of human memory.Human episodic memory concerns re-experiencing the subjective past, while semantic memory supports language use and cognitive referents.Source: §2.1 · p. 2
2.2 Human-like Memory Processes in AI Agent
- Composite episodic-semantic agents outperformed single-memory variants [11], while MemoryBank strengthens a memory by 1 after each recall [28].The proposed architecture differs by avoiding complete forgetting, leaving old memories retrievable given the right trigger.Source: §2.2 · p. 2
2.3 Mathematical Models of Human Memory Processes
- The reviewed recall-probability model decays exponentially over time but assumes one-trial learning and a constant decay rate.The text argues that decay should adjust because consolidation differs between frequently recalled and unrecalled memories.Source: §2.3 · p. 2
2.4 LLM-based Autonomous Agents
- Generative Agents score recency, importance, and relevance to select actions [18], whereas this model uses elapsed time, relevance, and recall frequency to adjust consolidation.Figure 2 contrasts recall-probability decay rates and illustrates how a recalled event's temporal significance is updated.Source: §2.4 · pp. 2–3, Figure 2 · p. 3
3 ARCHITECTURE
The architecture models recall probability using relevance, elapsed time, and recall history, then stores episodic memories with temporal context for later retrieval.
3.1 Model
- The model adapts an exponential recall-probability function using event relevance and elapsed time, with relevance measured as cosine similarity between vectorized texts.Source: §3.1 · p. 3
- Recall frequency modifies the decay constant through a sigmoid consolidation function whose per-recall reduction is capped.As recall count increases, the decay rate falls more slowly; the recall-probability threshold for triggering retrieval was set at 0.86 in trials.Source: §3.1 · p. 3
- The system triggers recall when recall probability exceeds a threshold, with 0.86 identified as appropriate in trials pending further threshold research.Source: §3.1 · p. 3
3.2 Memory Recall and Consolidation in Database Architecture
- The database stores episodic memories with content and temporal context, allowing the agent to retrieve information and interpret its temporal significance.Figure 2-B depicts memory reinforcement after recall; the text contrasts an event recalled less often across years with one recalled repeatedly over a shorter period.Source: §3.2 · p. 3
4 EXPERIMENT
The experiments compare memory recall with Generative Agents using task-based loss and participant dialogues, finding significantly lower loss while also illustrating personalized responses and difficulty with novel contexts.
4.1 Setup
- The system used GPT-4-0613 with Qdrant-triggered retrieval and Firestore chat-history management, evaluated on 10 tasks and six participants.The participant dialogues lasted from one week to three months; analysis relied on non-textual output logs.Source: §4.1 · p. 4
4.2 Analysis
- t=-5.687, p=0.000299, and 95% CI [-0.27, -0.12] indicate significantly lower loss than Generative Agents across tasks.Figure 3 compares loss values across tasks and verifies significance.Source: Figure 3 · p. 4, §4.2.1 · p. 4
4.3 Calculation of the Loss Function
- Loss evaluation converted normalized scores to probabilities with Softmax and compared them with one-hot labels using mean squared error.The scores were normalized to a [0, 1] range before probability conversion.Source: §4.3 · pp. 4–5
- For the novel concert plan, both models missed; the proposed model favored long-term patterns, while Generative Agents favored recent, relevant events.Although Event B was recalled most frequently, its gradient was smaller than those of Events A and C; the authors call for refinement on novel contexts.Source: Table 1 · p. 5, §4.3 · p. 5
4.3.1 Response Generation Performance.
- The agent retrieved a post-work ice-cream reward and connected a Hawaii vacation wish to the paper deadline, illustrating personalized responses from long-term memories.A sarcastic personality prompt shaped the second dialogue; future research will examine personality customization and its effects on recall and interaction.Source: §4.3.1 · pp. 5–6
5 CONCLUSION
The proposed model improves memory recall and response generation while adding only one retrieved dialogue history to the prompt. Its reliance on long-term behavioral patterns limits adaptability to changed behavior, and resource requirements remain unexamined.
5 CONCLUSION
- The model adds only one retrieved dialogue history to the prompt, avoiding the prompt-length impact seen in systems like ChatGPT.The conclusion also reports significant improvements in memory recall and response generation.Source: §5 · p. 6
- Reliance on users’ long-term behavioral patterns may limit adaptation when behavior changes, such as after a new job, school, or lifestyle change.The authors suggest detecting behavioral shifts and adjusting consolidation calculations.Source: §5 · p. 6
- Storage-resource and computational-overhead implications remain unexplored, as detailed resource analysis and optimization were outside this study’s scope.Source: §5 · p. 6
6 INTERACTION WITH LLMS
The system uses LLM prompts to incorporate relevant dialogue history retrieved from a database. Runtime placeholders supply the username and current timestamp, while the prompts direct the agent’s persona and response style.
6 INTERACTION WITH LLMS
- The agent prompt specifies a caring, charming persona and directs the agent to ask contextual questions to continue conversation.The system prompt also asks for a simple, short response.Source: §6 · p. 6
- The runtime placeholders self.username and current.time supply the actual username and real-time timestamp for personalized, time-sensitive prompts.Source: §6 · p. 6
- On user input, the LLM searches the database for relevant past dialogue and incorporates search results into a prompt for response generation.This interaction is described as supporting context-aware and personalized responses.Source: §6 · p. 7
7 FUTURE WORK
Future work identifies two refinement needs: selecting the consolidation parameters and assessing the method across domains and dialogue tasks.
7 FUTURE WORK
- The optimal combination of relevance, elapsed time, and recall frequency for memory consolidation remains to be determined.The authors suggest emotional significance as a possible additional factor.Source: §7 · p. 7
- The method’s generalizability remains to be assessed because the current evaluation focused on specific domains and tasks.Future work should identify any domain-specific adaptations that may be needed.Source: §7 · p. 7