Visual Workspaces for Sensemaking

• Visual workspaces for sensemaking are spatial interactive environments that allow users to externalize, organize, and synthesize diverse data using graphical representations.
• They employ clear visual encodings, spatial memory techniques, and direct manipulation (e.g., drag/drop, gestural controls) to support complex multi-stage tasks like intelligence analysis and narrative mapping.
• Design principles focus on multi-scale abstraction, deep linking of evidence and visualization, and integration of computational models, including LLMs, to enhance collaborative and iterative sensemaking.

Visual workspaces for sensemaking are spatial, interactive environments—physical or virtual—where analysts externalize, organize, navigate, and synthesize diverse information sources using graphical representations as external cognitive scaffolds. These workspaces blend spatial memory, direct manipulation, visual encoding, and algorithmic augmentation to support complex, multi-stage sensemaking tasks such as intelligence analysis, collaborative problem solving, large-corpus synthesis, scientific exploration, and interactive narrative construction.

1. Conceptual Foundations and Typologies

Visual workspaces serve as externalized cognitive landscapes, enabling the structuring and retention of information artifacts beyond the limitations of working memory. Sensemaking in this context is the iterative process of extracting, organizing, bridging, and drawing inferences from heterogeneous data, often under conditions of uncertainty, competing hypotheses, and information overload. Canonical workspace examples include spatial desktop arrangements, immersive VR/AR environments, infinite zoomable canvases, narrative maps, hybrid PC+VR systems, and hypergraph-based visualizations.

The principal conceptual features of visual sensemaking workspaces are:

• External memory: Spatial layouts enable rapid retrieval and offload mental load (Geymayer et al., 2017, Yang et al., 2022).
• Semantic layering: Users spatially encode meaning via grouping, linking, labeling, and organizing artifacts (documents, nodes, events).
• Interaction affordances: Direct manipulation—drag/drop, pinch, create/delete—facilitates pattern discovery and hypothesis testing (Tong et al., 2 Feb 2025).
• Multiscale structure: Hierarchical and multi-granular views support navigation from micro-level details to macro-level synthesis (Norambuena et al., 2021, Lee et al., 2024, Keith, 16 Jan 2026).
• Coordination (collaborative scenarios): Group awareness tools, multi-user spatial segregation, and hybrid interaction channels support distributed and co-located teams (Yang et al., 2022, Hossain et al., 22 Nov 2025).

Workspaces are classified by their spatial substrate (2D desktops, large displays, VR/AR), their supported abstractions (node-link graphs, narrative DAGs, hypergraphs), and their integration with computational models (semantic interaction, LLM-based agents, multi-view state synchronization).

2. Core Visual Workspace Designs and Interaction Models

2.1. Desktop and Large-Display Concept Graphs

Bidirectionally Linked Concept-Graphs (BLCs) exemplify document-based knowledge externalization: each concept/fact becomes a node, relationships are edges, and all elements maintain "deep links" to original evidence excerpts. Spatial node positioning leverages visuospatial memory, while the linking mechanism enables direct evidence access (Geymayer et al., 2017). When compared to conventional free-window workspaces, BLCs reduce window clutter and shift spatial organization from transient documents to durable abstract structures.

2.2. Immersive and Hybrid Environments

Immersive VR workspaces expand spatial affordances, permitting arrangement of content on 3D walls, floors, or even mid-air panels. Experiments have demonstrated that semi-circular and planar layouts in VR support both solo and collaborative tasks, and that gestural interactions (pinch, grab, two-handed zoom, embodied manipulation) enable flexible exploration and engagement (Tong et al., 2 Feb 2025, Yang et al., 2022, Hossain et al., 22 Nov 2025).

Hybrid PC+VR systems combine the precision of traditional desktops (text entry, detail editing) with the spatial and navigational affordances of VR. The simulated PC plane—tracked and registered within VR—enables seamless switching between modalities with near-zero overhead (Tong et al., 2 Feb 2025). Synchronization of workspace state is achieved via event-driven architectures, e.g., shared vectors or message buses, ensuring cross-device linkage and persistent context.

2.3. Graph-Based Narrative Mapping

Narrative maps represent sequential information (e.g., news events, scientific discoveries) as nodes (events) and directed edges (temporal, topical, causal, speculative, or domain-knowledge connections). Extraction algorithms typically combine temporal ordering, content similarity, and topic clustering to generate directed acyclic graphs (DAGs) with designated main and side storylines (Norambuena et al., 2021, Norambuena et al., 2021, Norambuena et al., 2023). Edge types and visual attributes (color, width, dash style) encode connection semantics, supporting both analyst- and algorithm-driven construction.

2.4. Hypergraph and LLM-Augmented Workspaces

Recent platforms such as HINTs unify topic-based and entity-based analysis by representing corpora as dual hypergraphs—documents as nodes with keyword hyperedges, and vice versa—facilitated by LLM-assisted extraction and semantic labeling (Lee et al., 2024). Hierarchical agglomerative clustering based on semantic-embedding and connectivity similarity creates multiscale clusters. Direct integration of retrieval-augmented LLM agents and visual summary interfaces accelerates document synthesis, exploration, and high-level reasoning.

3. Computational and Visual Encoding Principles

3.1. Spatial Externalization Mechanisms

Spatial arrangement is essential for leveraging human spatial memory and simplifying context management. Automatic or user-driven alignment (grid, arc, panel, circular, layered DAG) is frequently combined with interaction primitives (snap-to, dragging, scaling, grouping) (Tong et al., 2 Feb 2025, Hossain et al., 22 Nov 2025). Multi-scale externalization, as in BLCs and hypergraph cluster views, enables users to focus on either fine-grained evidence or high-level abstraction (Geymayer et al., 2017, Lee et al., 2024).

3.2. Visual Encodings

Effective sensemaking workspaces allocate distinct visual channels to salient attributes:

• Line or edge thickness for value-of-information or confidence scores (Mittrick et al., 2018).
• Node glyph size/color (e.g., for landmark events in narrative maps) (Norambuena et al., 2021).
• Edge color/dash for encoding connection type (temporal, causal, topical, speculative) (Norambuena et al., 2021, Norambuena et al., 2023).
• Cluster borders or convex hulls for visual group demarcation (Lee et al., 2024).
• Embodied cues (avatar gaze, controller halos) for collaborative awareness (Yang et al., 2022, Hossain et al., 22 Nov 2025).

Interaction is further enhanced via mouse/touch gestures (desktop), hand tracking (VR), natural language (LLM chat), and semantic feedback mechanisms (e.g., semantic interaction updates to the underlying model) (Norambuena et al., 2023, Keith, 16 Jan 2026).

3.3. Model Integration and Semantic Interaction

Modern workspaces tightly couple computational models—topic modeling, clustering, linear/integer optimization over possible graph structures, multi-scale hierarchy extraction—with direct manipulation by users. Mixed multi-model semantic interaction (3MSI) interprets user graph manipulations as constraints, which update both discrete structure and latent embedding spaces, supporting incremental and explainable formalism (Norambuena et al., 2023). In LLM-augmented workflows, users’ highlighted or annotated visual cues are exported as structured metadata (e.g., JSON) for model steering and prompt engineering (Tang et al., 2024).

4. Collaborative and Multiview Sensemaking

Immersive collaborative environments support richer, more egalitarian sensemaking by mimicking co-located spatial cues: mutual presence is reinforced via spatial audio, shared object manipulation, and real-time visual indicators of focus and intent (Yang et al., 2022). Group arrangements frequently self-organize into semi-circular or hybrid layouts, with personal and shared zones supporting both parallel and negotiated sensemaking (Hossain et al., 22 Nov 2025). Conflict resolution is mediated via floor-control metaphors and explicit or implicit visual territory markings.

Workspace design principles include:

• Supporting both private and public workspaces, and rapid transitions between them (Yang et al., 2022).
• Equitable interaction metrics and prompts for participation (e.g., Gini indexes for contribution) (Yang et al., 2022).
• Animated view transitions and layout history for spatial memory retention (Hossain et al., 22 Nov 2025).

5. Evaluation Methodologies and Empirical Results

Workspaces are evaluated via within- or between-subjects experiments measuring objective and subjective metrics:

Metric	Definition/Example	Source
Completion time (T)	Task duration (log-transformed)	(Tong et al., 2 Feb 2025)
Accuracy (E)	Proportion of correct nodes/links, or rubric-based scoring	(Tong et al., 2 Feb 2025, Tang et al., 2024)
Interaction throughput (I)	Count of graph edits, moves, or grabs	(Tong et al., 2 Feb 2025, Yang et al., 2022)
NASA-TLX subscales	Mental, physical, temporal demand, effort, frustration	(Tong et al., 2 Feb 2025, Hossain et al., 22 Nov 2025)
Social/engagement metrics	Equality, speech time, conflict/floor control	(Yang et al., 2022, Hossain et al., 22 Nov 2025)

Empirically, hybrid PC+VR workspaces yield strong user preference and reduced physical demand without accuracy trade-off; BLCs decrease display clutter and cognitive load; LLM-guided visual workspaces dramatically improve summary accuracy over baseline prompts; narrative maps and INA dashboards accelerate story construction and gap detection (Geymayer et al., 2017, Mittrick et al., 2018, Tang et al., 2024, Keith, 16 Jan 2026).

6. Design Guidelines, Limitations, and Open Challenges

Key design guidelines consolidate across empirical and field-defining studies:

• Multi-scale, multi-abstraction support: Provide both macro (concept maps, clusters) and micro (document, snippet) views (Geymayer et al., 2017, Lee et al., 2024).
• Deep-linking of abstraction and evidence: Ensure rapid navigation from abstract node to original source material, with strong traceability (Geymayer et al., 2017).
• Optimized spatial layouts: Default to vertical layers for timelines, semi-circles or panels for overview tasks, and flexible grouping for classification or pairing (Norambuena et al., 2021, Hossain et al., 22 Nov 2025).
• Minimize unnecessary complexity via transitive reduction and clustering: Prevent visual and cognitive overload, especially in large narrative graphs (Norambuena et al., 2021).
• Rich, discriminable visual encoding: Encode critical dimensions (value, type, relation strength) using orthogonal visual variables (Mittrick et al., 2018, Norambuena et al., 2021).
• Seamless cross-modality and cross-device state management: Synchronize user actions and context across environments and devices (Tong et al., 2 Feb 2025).
• Tight integration of intelligent agents: Employ LLMs upstream for model-aligned extraction and at runtime for guided synthesis; pair agent output with visual hints to mitigate hallucination and track sensemaking coverage (Lee et al., 2024).

Principal limitations include ergonomic and device constraints (e.g., VR fatigue, bandwidth), learning curve for advanced interaction techniques, representational scalability for very large graphs or corpora, and current lack of fine-grained micro-interaction logging in most implementations (Tong et al., 2 Feb 2025, Norambuena et al., 2021, Lee et al., 2024). Open research areas involve multi-user hybrid architectures, automatic adaptation of layouts to task and user behavior, AI-enhanced intent modeling, richer provenance and explainability mechanisms, and bridging gap between idealized workspace construction and real-world analyst workflows.

7. Implications and Future Directions

The evolution of visual workspaces for sensemaking reflects a sustained effort to bridge cognitive science, human-computer interaction, and computational modeling. Current trajectories focus on:

• Adaptive integration of embodied interaction and precise control across devices (Tong et al., 2 Feb 2025, Hossain et al., 22 Nov 2025).
• Increased role for joint computational-human model building, via semantic interaction pipelines and tight LLM coupling (Norambuena et al., 2023, Keith, 16 Jan 2026, Tang et al., 2024).
• Expansion to large-team, longitudinal, and multi-domain deployment, requiring new mechanisms for attention tracking, micro-switch detection, and provenance (Tong et al., 2 Feb 2025).
• Multi-modal and AR pass-through scenarios, where virtual and real data co-exist in the workspace (Tong et al., 2 Feb 2025).

Visual workspaces, when architected according to these principles and empirically validated, constitute the backbone of modern, scalable, interpretable, and collaborative sensemaking systems in both research and applied settings.