Enhancing memory retrieval in generative agents through LLM-trained cross attention networks

Our findings not only introduce new perspectives and methodologies for memory retrieval in generative agents but also extend the utility of LLMs in memory management across varied AI agent applications.

Authors

Institutions

Publication Details

Journal
Frontiers in Psychology
Published
2025-05-07
DOI
https://doi.org/10.3389/fpsyg.2025.1591618
Citations
4
Primary Topic
Topic Modeling
Type
article
Field-Weighted Citation Impact
9.92
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Enhancing memory retrieval in generative agents through LLM-trained cross attention networks

Chul Un Hong, Qi He
4 citations
Frontiers in Psychology
Topic Modeling
9.92
article

Enhancing memory retrieval in generative agents through LLM-trained cross attention networks

Chul Un Hong, Qi He
article en
4 citations

Abstract

Our findings not only introduce new perspectives and methodologies for memory retrieval in generative agents but also extend the utility of LLMs in memory management across varied AI agent applications.

Frontiers in PsychologyVol. 16
Anhui University of Science and Technology (CN), Anhui University of Finance and Economics (CN), Southwestern University of Finance and Economics (CN), Anhui Science and Technology University (CN)
Quality Education
Openalex Percentile: Top 2%
Topic Modeling
9.92
DOIOpen Access PDFOpenAlex
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.