Cognitively-Inspired Episodic Memory Architectures for Accurate and Efficient Character AI

• URL: http://arxiv.org/abs/2511.10652v1
• Date: Sat, 01 Nov 2025 02:26:16 GMT
• Title: Cognitively-Inspired Episodic Memory Architectures for Accurate and Efficient Character AI
• Authors: Rafael Arias Gonzalez, Steve DiPaola,
• Abstract summary: Large language models show promise for embodying historical characters in dialogue systems, but existing approaches face a critical trade-off.<n>We present an architecture that resolves this tension through offline data augmentation and efficient parallel retrieval from structured episodic memory.<n>Our system transforms biographical data into 1,774 enriched first-person memories with affective-semantic metadata, then employs two-stage retrieval achieving 0.52s prompt generation.
• Score: 1.0742675209112622
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language models show promise for embodying historical characters in dialogue systems, but existing approaches face a critical trade-off: simple retrieval-augmented generation produces shallow responses, while multi-stage reflection achieves depth at prohibitive latency. We present an architecture that resolves this tension through offline data augmentation and efficient parallel retrieval from structured episodic memory. Our system transforms biographical data into 1,774 enriched first-person memories with affective-semantic metadata, then employs two-stage retrieval achieving 0.52s prompt generation. Evaluation using LLM-as-judge and RAGAs metrics shows our approach achieves parity with traditional RAG on GPT-4 while significantly outperforming it on smaller models (GPT-3.5, GPT-3), suggesting particular value for resource-constrained deployments. Beyond dialogue, the structured memory enables novel visualization tools: spatiotemporal heatmaps, emotional trajectory analysis, and interactive path tracking, positioning the system as both a dialogue interface and research tool for biographical analysis. We use Van Gogh as a test case, but the architecture is generalizable to any historical figure with substantial textual records, offering a practical framework for educational, museum, and research applications requiring both accuracy and efficiency

Related papers

• From Verbatim to Gist: Distilling Pyramidal Multimodal Memory via Semantic Information Bottleneck for Long-Horizon Video Agents [78.30630000529133]
  We propose MM-Mem, a pyramidal multimodal memory architecture grounded in Fuzzy-Trace Theory.<n> MM-Mem memory structures hierarchically into a Sensory Buffer, Episodic Stream, and Symbolic.<n>Experiments confirm the effectiveness of MM-Mem on both offline and streaming tasks.
  arXiv  Detail & Related papers  (2026-03-02T05:12:45Z)
• VimRAG: Navigating Massive Visual Context in Retrieval-Augmented Generation via Multimodal Memory Graph [42.348770377488094]
  VimRAG is a framework tailored for multimodal Retrieval-augmented Reasoning across text, images, and videos.<n>We propose a Graph-Guided Policy Optimization strategy to disentangle step-wise validity from trajectory-level rewards.<n>Experiments demonstrate that VimRAG consistently achieves state-of-the-art performance on diverse multimodal RAG benchmarks.
  arXiv  Detail & Related papers  (2026-02-13T09:05:09Z)
• IGMiRAG: Intuition-Guided Retrieval-Augmented Generation with Adaptive Mining of In-Depth Memory [33.00642870872058]
  Retrieval-augmented generation (RAG) equips large language models with reliable knowledge memory.<n>Recent research integrates graphs and hypergraphs into RAG to capture pairwise and multi-entity relations as structured links.<n>We propose IGMiRAG, a framework inspired by human intuition-guided reasoning.
  arXiv  Detail & Related papers  (2026-02-07T12:42:31Z)
• Grounding Agent Memory in Contextual Intent [22.299598216046103]
  STITCH is a memory system that indexes each trajectory step with a structured retrieval cue, contextual intent, and retrieves history by matching the current step's intent.<n>For evaluation, we introduce CAME-Bench, a benchmark for context-aware retrieval in realistic, dynamic, goal-oriented trajectories.<n>Our analysis shows that intent indexing substantially reduces retrieval noise, supporting intent-aware memory for robust long-horizon reasoning.
  arXiv  Detail & Related papers  (2026-01-15T18:55:13Z)
• Memory-T1: Reinforcement Learning for Temporal Reasoning in Multi-session Agents [80.33280979339123]
  We introduce Memory-T1, a framework that learns a time-aware memory selection policy using reinforcement learning (RL)<n>On the Time-Dialog benchmark, Memory-T1 boosts a 7B model to an overall score of 67.0%, establishing a new state-of-the-art performance for open-source models.
  arXiv  Detail & Related papers  (2025-12-23T06:37:29Z)
• KnowMT-Bench: Benchmarking Knowledge-Intensive Long-Form Question Answering in Multi-Turn Dialogues [58.305425399644086]
  Multi-Turn Long-Form Question Answering (MT-LFQA) is a key application paradigm of Large Language Models (LLMs) in knowledge-intensive domains.<n>We introduce textbfKnowMT-Bench, the textitfirst-ever benchmark designed to systematically evaluate MT-LFQA for LLMs across knowledge-intensive fields.
  arXiv  Detail & Related papers  (2025-09-26T04:32:29Z)
• OST-Bench: Evaluating the Capabilities of MLLMs in Online Spatio-temporal Scene Understanding [50.72259772580637]
  We introduce OST-Bench, a benchmark designed to evaluate Online Spatio-Temporal understanding from the perspective of an agent.<n>Built on an efficient data collection pipeline, OST-Bench consists of 1.4k scenes and 10k question-answer pairs collected from ScanNet, Matterport3D, and ARKitScenes.<n>We find that both complex-based spatial reasoning demands and long-term memory retrieval requirements significantly drop model performance along two separate axes.
  arXiv  Detail & Related papers  (2025-07-10T17:56:07Z)
• PersonalAI: A Systematic Comparison of Knowledge Graph Storage and Retrieval Approaches for Personalized LLM agents [15.524189150821147]
  Large language models (LLMs) combined with Retrieval-Augmented Generation (RAG) fail to scale in complex, long-term interactions.<n>We propose a flexible external memory framework based on knowledge graphs, automatically constructed and updated by the LLM itself.<n>Building upon the AriGraph architecture, we introduce a novel hybrid graph design that supports both standard edges and two types of hyperedges.<n>We evaluate our system on three benchmarks-TriviaQA, HotpotQA, and DiaASQ-demonstrating that different memory and retrieval configurations yield optimal performance depending on the task.
  arXiv  Detail & Related papers  (2025-06-20T13:52:15Z)
• SEAL: Speech Embedding Alignment Learning for Speech Large Language Model with Retrieval-Augmented Generation [10.828717295018123]
  We propose a unified embedding framework that eliminates the need for intermediate text representations.<n>Our model reduces pipeline latency by 50% while achieving higher retrieval accuracy compared to traditional two-stage methods.
  arXiv  Detail & Related papers  (2025-01-26T15:04:02Z)
• InstructERC: Reforming Emotion Recognition in Conversation with Multi-task Retrieval-Augmented Large Language Models [9.611864685207056]
  We propose a novel approach, InstructERC, to reformulate the emotion recognition task from a discriminative framework to a generative framework based on Large Language Models (LLMs) InstructERC makes three significant contributions: (1) it introduces a simple yet effective retrieval template module, which helps the model explicitly integrate multi-granularity dialogue supervision information; (2) we introduce two additional emotion alignment tasks, namely speaker identification and emotion prediction tasks, to implicitly model the dialogue role relationships and future emotional tendencies in conversations; and (3) Pioneeringly, we unify emotion labels across benchmarks through the feeling wheel to fit real application scenarios.
  arXiv  Detail & Related papers  (2023-09-21T09:22:07Z)
• Multi-grained Hypergraph Interest Modeling for Conversational Recommendation [75.65483522949857]
  We propose a novel multi-grained hypergraph interest modeling approach to capture user interest beneath intricate historical data. In our approach, we first employ the hypergraph structure to model users' historical dialogue sessions and form a session-based hypergraph, which captures coarse-grained, session-level relations. We further conduct multi-grained hypergraph convolution on the two kinds of hypergraphs, and utilize the enhanced representations to develop interest-aware CRS.
  arXiv  Detail & Related papers  (2023-05-04T13:13:44Z)
• Ranking Enhanced Dialogue Generation [77.8321855074999]
  How to effectively utilize the dialogue history is a crucial problem in multi-turn dialogue generation. Previous works usually employ various neural network architectures to model the history. This paper proposes a Ranking Enhanced Dialogue generation framework.
  arXiv  Detail & Related papers  (2020-08-13T01:49:56Z)
• ORD: Object Relationship Discovery for Visual Dialogue Generation [60.471670447176656]
  We propose an object relationship discovery (ORD) framework to preserve the object interactions for visual dialogue generation. A hierarchical graph convolutional network (HierGCN) is proposed to retain the object nodes and neighbour relationships locally, and then refines the object-object connections globally. Experiments have proved that the proposed method can significantly improve the quality of dialogue by utilising the contextual information of visual relationships.
  arXiv  Detail & Related papers  (2020-06-15T12:25:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.