InnerPond: Fostering Inter-Self Dialogue with a Multi-Agent Approach for Introspection

Hayeon Jeon; Dakyeom Ahn; Sunyu Pang; Yunseo Choi; Suhwoo Yoon; Joonhwan Lee; Eun-mee Kim; Hajin Lim

cs.HCMar 29, 2026

InnerPond: Fostering Inter-Self Dialogue with a Multi-Agent Approach for Introspection

Authors: Hayeon Jeon, Dakyeom Ahn, Sunyu Pang, Yunseo Choi, Suhwoo Yoon, Joonhwan Lee, Eun-mee Kim, Hajin Lim

Organizations: hci+d lab. Seoul National University Seoul, Republic of Korea · Department of Communication Seoul National University Seoul, Republic of Korea

Abstract

Introspection is central to identity construction and future planning, yet most digital tools approach the self as a unified entity. In contrast, Dialogical Self Theory (DST) views the self as composed of multiple internal perspectives, such as values, concerns, and aspirations, that can come into tension or dialogue with one another. Building on this view, we designed InnerPond, a research probe in the form of a multi-agent system that represents these internal perspectives as distinct LLM-based agents for introspection. Its design was shaped through iterative explorations of spatial metaphors, interaction scaffolding, and conversational orchestration, culminating in a shared spatial environment for organizing and relating multiple inner perspectives. In a user study with 17 young adults navigating career choices, participants engaged with the probe by co-creating inner voices with AI, composing relational inner landscapes, and orchestrating dialogue as observers and mediators, offering insight into how such systems could support introspection. Overall, this work offers design implications for AI-supported introspection tools that enable exploration of the self's multiplicity.

Figures & tables

Figure 1. InnerPond fosters AI-mediated introspection through multi-agent dialogue. The system transforms users’ I-positions into conversing LLM agents (lotus leaves), supporting dialogue among multiple I-positions within the self. A conceptual visualization of the InnerPond system where users' I-positions become conversing LLM agents represented as lotus leaves on a pond. The image illustrates two example I-positions with contrasting perspectives on career priorities engaging in dialogue. Additional lotus leaves represent other I-positions, demonstrating the system's multi-agent approach to supporting dialogue among multiple I-positions within the self.

Figure 2. Core concepts of Dialogical Self Theory, illustrating the relationship between multiple I-positions in the dialogical space and a meta-position. This figure illustrates the core concepts of Dialogical Self Theory. The internal domain of the self contains multiple I-positions (e.g., `Myself, Seeking Recognition,' `Myself, Pursuing Stability'), each representing distinct perspectives within the society of mind. The meta-position, depicted as an observer, provides a higher-level vantage point for reflecting on and integrating these diverse internal voices. The dialogical space encompasses both internal and external domains where these I-positions engage in dynamic interactions. — Figure 2. Core concepts of Dialogical Self Theory, illustrating the relationship between multiple I-positions in the dialogical space and a meta-position. This figure illustrates the core concepts of Dialogical Self Theory. The internal domain of the self contains multiple I-positions (e.g., `Myself, Seeking Recognition,'` Myself, Pursuing Stability'), each representing distinct perspectives within the society of mind. The meta-position, depicted as an observer, provides a higher-level vantage point for reflecting on and integrating these diverse internal voices. The dialogical space encompasses both internal and external domains where these I-positions engage in dynamic interactions.

Figure 3. Three-phase metaphor evolution for the dialogical space: From group chat as battle ring (Phase 1), to stones highlighting individuality yet emphasizing separation (Phase 2), to lotus leaves appearing distinct yet sharing roots, enabling a meta-position perspective (Phase 3). Three-phase metaphor evolution showing: a group chat room resembling a battle ring (left), stones arranged on a landscape representing individual I-positions (center), and a pond with lotus leaves sharing roots underwater (right).

Figure 4. I-position extraction and story enrichment pipeline: user knowledge is transformed into distinct I-positions, iteratively refined through scaffolding questions and user responses. This figure illustrates the I-position extraction and story enrichment pipeline. In the extraction stage, user knowledge (career context and profile data) is transformed into distinct I-positions, each composed of a name, viewpoint, and narrative. In the enrichment stage, the system generates scaffolding questions to identify narrative gaps. The user's responses then iteratively refine each I-position's viewpoint and narrative.

Figure 5. The interface for [Stage 1: I-position Construction]: (a) I-positions visualized as lotus leaves on the main pond view, with a profile modal showing name, viewpoint, and narrative; (b) “Story Enrichment” modal with scaffolding questions to refine the narrative; (c) “1:1 Dialogue” modal for direct conversation with a leaf agent. This figure illustrates the Stage 1 (I-position Construction) interface. (a) The main pond view displays I-positions as lotus leaves; clicking a leaf opens a profile modal showing the I-position's name, core viewpoint, and narrative. (b) The Story Enrichment'' modal presents three scaffolding questions to help users refine the I-position's narrative. (c) The 1:1 Dialogue'' modal shows a conversation between the user and a leaf agent, allowing users to explore the I-position's motivations.

Figure 6. The interface for [Stage 2: Relational Positioning]: Users can freely customize their inner landscape by (a) adjusting leaf size, (b) repositioning leaves via drag-and-drop, and (c) selecting colors through the dewdrop-shaped button. This figure illustrates the Stage 2 (Relational Positioning) interface. (a) Users can adjust a leaf's size by hovering over its edge. (b) Leaf positions can be modified via drag-and-drop to arrange the inner landscape. (c) Clicking the dewdrop-shaped button opens a color palette for personal expression. These manipulations allow users to visually compose their inner structure in personally meaningful ways.

Figure 7. The interface for [Stage 3: Dialogical Exchange]: (Left) After selecting two leaf agents, the system suggests three discussion topics based on their relationship; (Right) the group chat where users can participate via “Send” or observe via “Skip.” This figure illustrates the Stage 3 (Dialogical Exchange) interface. (Left) After selecting two leaf agents, the user chooses one of three tailored discussion topics to initiate the conversation. (Right) The group chat shows two leaf agents responding to the selected topic from their distinct perspectives; the user can participate by sending a message or skip to observe the dialogue between agents.

Figure 8. Example of a one-on-one dialogue with [Myself, Diving Deep] (P15). This figure provides an example of a one-on-one dialogue between a user (P15) and their I-position, `Myself, Diving Deep.' In the conversation, the user asks for advice on sustaining motivation. The `Myself, Diving Deep' persona responds with insights that reflect its core character, suggesting a flexible, curiosity-driven approach over a rigid plan. This exchange demonstrates how users can engage with specific facets of their personality as distinct conversational partners to explore personal challenges and gain new perspectives. — Figure 8. Example of a one-on-one dialogue with [Myself, Diving Deep] (P15). This figure provides an example of a one-on-one dialogue between a user (P15) and their I-position, `Myself, Diving Deep.' In the conversation, the user asks for advice on sustaining motivation. The` Myself, Diving Deep' persona responds with insights that reflect its core character, suggesting a flexible, curiosity-driven approach over a rigid plan. This exchange demonstrates how users can engage with specific facets of their personality as distinct conversational partners to explore personal challenges and gain new perspectives.

Panel of Figure 9 — Figure 9. Examples of holistic inner landscapes composed by participants. (a) P4 created a scene of self-compassion, describing one self whispering encouragement to another. (b) P6 arranged the leaves to express gradual personal growth, likened to a musical crescendo. This figure presents two examples of inner landscapes, demonstrating how participants used the size and spatial arrangement of I-position leaves to convey personal narratives. Participant P4 (a) depicted a scene of self-compassion by positioning a large `Myself, Believing in Myself' leaf behind` Myself, Wanting to Make Meaningful Contribution,' creating a visual impression of one self whispering encouragement to another. Participant P6 (b) arranged numerous leaves in a progressive spread of varying sizes to represent gradual personal growth, likening the composition to a musical crescendo.

Figure 10. Example of a group conversation where P3 pairs [Myself, Yearning for Creative Freedom] and [Myself, Desiring Financial Abundance]. This figure captures a group conversation where a user mediates between two conflicting I-positions: `Myself, Yearning for Creative Freedom' and `Myself, Desiring Financial Abundance.' The dialogue begins with the user posing a dilemma about balancing creative work with financial stability. Each I-position initially presents its perspective, creating a clear tension. The user then acts as a facilitator, synthesizing their points into a potential compromise. In the final exchange, the I-positions refine this compromise, ultimately agreeing that a stable job can serve as a foundational step towards creative goals, rather than an obstacle. This demonstrates the process of negotiating internal conflicts and reaching a unified, actionable strategy. — Figure 10. Example of a group conversation where P3 pairs [Myself, Yearning for Creative Freedom] and [Myself, Desiring Financial Abundance]. This figure captures a group conversation where a user mediates between two conflicting I-positions: `Myself, Yearning for Creative Freedom' and` Myself, Desiring Financial Abundance.' The dialogue begins with the user posing a dilemma about balancing creative work with financial stability. Each I-position initially presents its perspective, creating a clear tension. The user then acts as a facilitator, synthesizing their points into a potential compromise. In the final exchange, the I-positions refine this compromise, ultimately agreeing that a stable job can serve as a foundational step towards creative goals, rather than an obstacle. This demonstrates the process of negotiating internal conflicts and reaching a unified, actionable strategy.

Appendix figures & tables1 asset

Supplementary material from the paper’s appendix.

Appendix

Stage	Data Type	Description	Example Metrics
Stage 1: I-Position Construction	I-position profiles	Initial AI-generated and user-modified leaf profiles	Name, core viewpoint, narrative
	Profile modifications	User edits to leaf profile	Edit count per leaf
	Leaf additions and deletions	User-initiated creation or removal of leaves	Additions ( $M=1.67$ ), Deletions ( $M=1.17$ )
	1:1 dialogue logs	Chat messages between user and individual leaves	Leaves engaged ( $M=5.29$ ), Turns ( $M=4.92$ )
Stage 2: Relational Positioning	Spatial configurations	Position coordinates of each leaf	X, Y coordinates
	Visual attributes	Size and color assignments for each leaf	Size value, color

Appendix

Table 1. Overview of System Log Data Collected Across InnerPond Stages

Language Model Introspection

Language model introspection studies whether models can detect, interpret, and report their own internal states and behavior.

See all 36 papers

Intelligent Co-Design: An Interactive LLM Framework for Interior Spatial Design via Multi-Modal Agents
Mar 16, 2026Ren Jian Lim, Rushi Dai3D Scene Layout Generation Human-AI Co-Creation
Hong Kong Center for Construction Robotics, The Hong Kong University of Science and Technology
Me, Myself, and $π$ : Evaluating and Explaining LLM Introspection
Mar 17, 2026Atharv Naphade, Samarth Bhargav, Sean Lim +1Language Model Introspection Self-Reflection in Language Model Agents
Carnegie Mellon University
Hybrid LLM-Embedded Dialogue Agents for Learner Reflection: Designing Responsive and Theory-Driven Interactions
Feb 24, 2026Paras Sharma, YuePing Sha, Janet Shufor Bih Epse Fofang +6Self-Regulated Learning Educational Dialogue Systems
University of Pittsburgh Pittsburgh, Pennsylvania, USA · Carnegie Mellon University Pittsburgh, Pennsylvania, USA · Bowie State University Bowie, Maryland, USA

Mar 16, 2026J/K move · Enter open · S save

Intelligent Co-Design: An Interactive LLM Framework for Interior Spatial Design via Multi-Modal Agents

Ren Jian Lim, Rushi Dai

Hong Kong Center for Construction Robotics, The Hong Kong University of Science and Technology

3D Scene Layout Generation Human-AI Co-Creation Indoor Scene Generation Participatory Design Multimodal Agents

Open paper AskPDF arXiv

In architectural interior design, miscommunication frequently arises as clients lack design knowledge, while designers struggle to explain complex spatial relationships, leading to delayed timelines and financial losses. Recent advancements in generative layout tools narrow the gap by automating 3D visualizations. However, prevailing methodologies exhibit limitations: rule-based systems implement hard-coded spatial constraints that restrict participatory engagement, while data-driven models rely on extensive training datasets. Recent large language models (LLMs) bridge this gap by enabling intuitive reasoning about spatial relationships through natural language. This research presents an LLM-based, multimodal, multi-agent framework that dynamically converts natural language descriptions and imagery into 3D designs. Specialized agents (Reference, Spatial, Interactive, Grader), operating via prompt guidelines, collaboratively address core challenges: the agent system enables real-time user interaction for iterative spatial refinement, while Retrieval-Augmented Generation (RAG) reduces data dependency without requiring task-specific model training. This framework accurately interprets spatial intent and generates optimized 3D indoor design, improving productivity, and encouraging nondesigner participation. Evaluations across diverse floor plans and user questionnaires demonstrate effectiveness. An independent LLM evaluator consistently rated participatory layouts higher in user intent alignment, aesthetic coherence, functionality, and circulation. Questionnaire results indicated 77% satisfaction and a clear preference over traditional design software. These findings suggest the framework enhances user-centric communication and fosters more inclusive, effective, and resilient design processes. Project page: https://rsigktyper.github.io/AICodesign/

Abstract

Language Model Introspection

Explore similar work

Intelligent Co-Design: An Interactive LLM Framework for Interior Spatial Design via Multi-Modal Agents

Me, Myself, and πππ : Evaluating and Explaining LLM Introspection

Hybrid LLM-Embedded Dialogue Agents for Learner Reflection: Designing Responsive and Theory-Driven Interactions

Me, Myself, and $π$ : Evaluating and Explaining LLM Introspection