Object-Centric History Representation

• Object-centric history representation is a framework that models and tracks individual objects over time with isolated, modular representations.
• It integrates methodologies from vision, language, and process mining to capture temporal dynamics using slot-based architectures and event log structures.
• The approach enhances interpretability and generalization in complex systems by disentangling object features and supporting actionable analytics.

Object-centric history representation refers to a class of representations and analytic frameworks in which the evolving state or sequence of events for each object within a scene, process, or system is explicitly modeled, isolated, and tracked. Such representations are central both in machine learning—across vision, language, and robotics—as well as in process mining and event-based analytics, because they provide modular, interpretable, and systematic foundations for reasoning about complex, interdependent systems composed of multiple interacting entities.

1. Foundations of Object-Centric History Representation

The core tenet underlying object-centric history representation is the explicit modeling of individual objects—disentangling their features, identities, and temporal evolution from global or scene-level information. In image and video domains, this is typically operationalized by decomposing visual input into a collection of object-specific features or “slots” (as in object-centric models like slot attention), each reconstructing, representing, or tracking a distinct entity or part of the scene (Gao et al., 2016, &&&1&&&, Nanbo et al., 2021). In event-based process analysis, object-centric event data is structured so that events are not attached to a single process instance but are related to multiple objects, each with potentially distinct lifecycles and inter-object associations (Berti et al., 2023, Fahland et al., 18 Oct 2024, Wei et al., 12 Nov 2024).

Object-centric history representation is thus distinguished by:

• An object-level abstraction: learning or specifying a modular representation per object.
• Temporal or sequential structuring: capturing the object's trajectory, state changes, or event involvement across time.
• Independence and compositionality: supporting querying, analysis, and reasoning for individual or arbitrary groups of objects, possibly across different times and scenes.

This paradigm is motivated by both cognitive and technical considerations—mirroring human abilities for individuation and tracking, and enabling more robust generalization, causal reasoning, and downstream process analytics in artificial systems.

2. Key Methodologies Across Domains

Vision and Perception

In computer vision, object-centric history representation relies on unsupervised or self-supervised object discovery methods to factorize scenes into temporally coherent object tracks or per-object feature trajectories. Early approaches used region proposals and temporal coherence, matching object-like regions between adjacent frames and applying triplet losses to enforce representation similarity within objects over time (Gao et al., 2016). Subsequently, models such as ROOTS (Chen et al., 2020) and DyMON (Nanbo et al., 2021) advanced this by inferring object-wise spatial-temporal latent variables in 3D and dynamic scenes, enabling object history querying at arbitrary space-time points.

Language and Symbolic Reasoning

Object-centric frameworks have been extended with language-mediated supervision. LORL (Wang et al., 2020) leverages language input to ground slot-based visual representations in semantic concepts, enhancing interpretability, compositionality, and providing a natural anchor for history annotation—allowing, for instance, the symbolic tracking of property changes or object interactions via language queries. CTRL-O (Didolkar et al., 27 Mar 2025) introduces direct, user-driven control over object representations via language queries, supporting targeted extraction and tracking for object histories in vision-language tasks.

Process Mining and Event Data

In process mining, object-centric history representation is formalized through event log schemas (e.g., OCEL, OCED (Fahland et al., 18 Oct 2024)) where each event may be connected to multiple objects of potentially different types. Modern methods capture not just static relationships but also the temporal evolution of both object attributes and object-to-object relations (e.g., dynamic allocation or release of resources) (Wei et al., 12 Nov 2024). Analytical frameworks unfold histories from multiple viewpoints, aggregate object interactions, and support multi-level, scope-based roll-ups for higher-level process abstraction (Galanti et al., 2022, Adams et al., 2022, Khayatbashi et al., 26 Aug 2025).

3. Model Structures and Theoretical Guarantees

Recent research establishes principled architectures and identifiability results for object-centric history representations in high-dimensional and complex domains:

• Slot-based architectures: Probabilistic slot-attention (Kori et al., 11 Jun 2024) introduces a Gaussian mixture prior over slots, with iterative EM-style updates, achieving both practical object binding and identifiability guarantees—up to permutation and affine transformation—without supervision. This ensures that, modulo nuisance transformations, object representations are unique and consistently trackable across time.
• Factorization of Dynamics: Dynamic models like DyMON (Nanbo et al., 2021) factorize scene and observer dynamics through separate latent transition functions, enabling the object-centric decomposition of motion trajectories uninfluenced by camera movement. This allows independent querying of object states across arbitrary times and views.
• Discrete Attribute Groupings: GDR and OGDR (Zhao et al., 1 Jul 2024, Zhao et al., 4 Nov 2024, Zhao et al., 5 Sep 2024) improve object-centric learning by decomposing discrete representations into attribute groups (e.g., color, texture), organizing channels so that attribute evolution histories are disentangled and interpretable.

LaTeX Examples:

$$D(R_1, R_2) = 1 - \frac{f(R_1) \cdot f(R_2)}{ \| f(R_1) \| \| f(R_2) \| }$$

$$f_{\theta_1}^{-1}(x) = P \cdot f_{\theta_2}^{-1}(x) + c$$

where $P$ is a permutation matrix, capturing the equivalence of slot representations up to ordering.

4. Process Modeling, Event Log Structures, and Implementation

In process mining, object-centric history is formalized via object-event structures:

• Core Models: OCEL and OCED (Fahland et al., 18 Oct 2024) define minimal data models consisting of events (with timestamps and types), objects (with type and attributes), and event-to-object as well as object-to-object relations. These relations may be qualified (e.g., CREATE, MODIFY) to indicate the event's impact on the object's state.
• Temporal and Relational Extensions: OCEL 2.0, Dirigo (Wei et al., 12 Nov 2024), and related frameworks extend these models with dynamic attributes (e.g., attribute changes with timestamps), persistent object relationships (including temporal qualifiers), and explicit quality criteria (such as full 3NF and unambiguous labeling) to ensure the historical accuracy and interpretability of extracted logs.
• Scope Enrichment: Recent approaches (Khayatbashi et al., 26 Aug 2025) propose enriching object-centric event logs with explicit analyst-defined process scopes (as first-class process objects), formally embedding higher-level abstractions and supporting multi-level aggregation of object histories.

Tables may be constructed to summarize main structural elements:

Component	Example/Description	Role in History Representation
Event	Timestamp, type, attribute set	Defines atomic occurrence in log
Object	ID, type, attribute set	Entity whose state is tracked
E2O Relation	(event, qualifier, object)	Connects event to involved objects
O2O Relation	(object₁, qualifier, object₂)	Tracks inter-object relationships

5. Downstream Applications and Empirical Results

Object-centric history representations have been empirically validated across a range of domains:

• Scene Understanding and Video Reasoning: Video QA models (Dang et al., 2021) construct dynamic relational graphs over time, generate “object résumés,” and enable sequential logical reasoning over object states, yielding measurable gains in compositional QA benchmarks.
• Predictive Process Analytics: By unfolding object-centric event logs and including aggregation features capturing object interactions, predictive models (e.g., using CatBoost with SHAP value explanations) achieve reduced error and better F1 scores relative to baseline models, particularly for key process indicators (Galanti et al., 2022).
• Robotic Manipulation: Structured slot-based encodings and disentangled object-centric percepts (DOCIR; (Emukpere et al., 14 Mar 2025)) yield robust generalization to new targets, distractors, and real-to-sim transfer, outperforming holistic representations and ensuring efficient manipulation skill acquisition (Chapin et al., 24 Jun 2025).
• Explainable AI and Visualization: Tabular, sequential, and graph-based encodings directly support visualization and explainability, with SHAP-based techniques explaining the role of features and structure in temporal or relational predictions (Adams et al., 2022).

Quantitative results across multiple studies confirm that object-centric representations not only improve interpretability and modularity but, when paired with process-aware models, deliver substantive gains in accuracy, robustness, and compositional generalization.

6. Challenges, Limitations, and Future Directions

Despite significant advancements, object-centric history representation faces several challenges:

• Ambiguity and Identity Preservation: Ensuring consistent slot/object identity over long sequences (especially in the presence of appearance changes, occlusions, or object merges and splits) remains challenging.
• Expressivity vs. Simplicity: In process event data modeling, minimal core models sacrifice the direct encoding of event-to-event causality, role multiplicity, or nested attributes, requiring community conventions or explicit reification (Fahland et al., 18 Oct 2024, Wei et al., 12 Nov 2024).
• Scalability and Complexity: Rich graph-based encodings capture concurrency and structure but may be computationally intensive for large-scale event logs, necessitating new optimization and summarization strategies (Adams et al., 2022).
• User-Guided and Semantic Control: The move towards language-mediated, user-controlled representations (e.g., via CTRL-O) highlights the need for greater flexibility in specifying what constitutes an “object,” how histories are composed, and which aspects are tracked, especially in open-world or historical datasets (Wang et al., 2020, Didolkar et al., 27 Mar 2025).
• Standardization and Interoperability: The emergence of process scope enrichment (Khayatbashi et al., 26 Aug 2025) and standardized data models (Fahland et al., 18 Oct 2024) marks a trend towards greater composability and cross-application utility, but widespread adoption depends on shared tools, methodology, and rigorous quality evaluation.

Further research is anticipated in:

• Extending dynamic object-centric models with prediction and simulation capabilities (predictive “imagination” of future object states).
• Enhanced domain transferability, including zero-shot generalization to historical or out-of-distribution data (Didolkar et al., 17 Aug 2024).
• Integration of multi-modal information (e.g., combining object-centric video tracks with language and symbolic annotation for richer historical narratives).
• Community-driven standardization across process mining and data analytics for event and history encoding.

7. Summary

Object-centric history representation unifies methodologies for tracking, analyzing, and reasoning about objects and their temporal trajectories across vision, process mining, and robotics. Through modular slot-based decompositions, relational event-object modeling, and language-mediated control, these frameworks achieve interpretable, robust, and generalizable representations. Emerging standards and empirical advances underpin applications from process analytics to reinforcement learning, while ongoing challenges drive continued theoretical, methodological, and practical innovation across domains.