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Long-range Modeling and Processing of Multimodal Event Sequences

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 7.0Download Report PDF
Temporal Point ProcessMultimodal LLM
Abstract:

Temporal point processes (TPPs) have emerged as powerful tools for modeling asynchronous event sequences. While recent advances have extended TPPs to handle textual information, existing approaches are limited in their ability to generate rich, multimodal content and reason about event dynamics. A key challenge is that incorporating multimodal data dramatically increases sequence length, hindering the ability of attention-based models to generate coherent, long-form textual descriptions that require long-range understanding. In this paper, we propose a novel framework that extends LLM-based TPPs to the visual modality, positioning text generation as a core capability alongside time and type prediction. Our approach addresses the long-context problem through an adaptive sequence compression mechanism based on temporal similarity, which reduces sequence length while preserving essential patterns. We employ a two-stage paradigm of pre-training on compressed sequences followed by supervised fine-tuning for downstream tasks. Extensive experiments, including on the challenging DanmakuTPP-QA benchmark, demonstrate that our method outperforms state-of-the-art baselines in both predictive accuracy and the quality of its generated textual analyses.

Disclaimer
This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
If you have any questions, please contact: mingzhang23@m.fudan.edu.cn

Overview

Overall Novelty Assessment

The paper proposes a multimodal temporal point process framework that extends LLM-based TPPs to visual modality and positions text generation as a core capability alongside time and type prediction. It resides in the 'Vision-Language-Action Models for Long-Horizon Manipulation' leaf, which contains four papers total including the original work. This leaf sits within the broader 'Long-Horizon and Sequential Task Modeling' branch, indicating a moderately populated research direction focused on extended temporal reasoning. The taxonomy reveals this is an active but not overcrowded area, with sibling papers addressing robotic manipulation and planning tasks.

The paper's position connects it to several neighboring research directions. Adjacent leaves include 'Memory-Driven and Chain-of-Thought Long-Horizon Planning' (2 papers) and 'Autoregressive and Phase-Aware Long-Horizon Generation' (2 papers), suggesting the broader branch emphasizes extended temporal horizons through diverse mechanisms. The parent branch excludes short-horizon prediction and non-sequential applications, clarifying that this work's focus on long-range dependencies distinguishes it from standard temporal point process architectures. Nearby branches like 'Multimodal Fusion and Integration Strategies' (9 papers across 4 leaves) and 'Temporal Point Process Architectures and Mechanisms' (7 papers across 3 leaves) provide complementary perspectives on fusion techniques and core modeling approaches.

Among 28 candidates examined across three contributions, no clearly refuting prior work was identified. The MM-TPP framework examined 10 candidates with 0 refutable matches, suggesting limited direct overlap in the specific combination of LLM-based TPPs with visual modality and text generation. The adaptive compression mechanism based on temporal similarity also examined 10 candidates with no refutations, indicating this particular approach to addressing long-context challenges may be relatively unexplored. The TAXI-PRO benchmark examined 8 candidates with no refutations, though benchmark novelty depends heavily on domain-specific requirements not fully captured in semantic search.

Based on the limited search scope of 28 top-K semantic matches, the work appears to occupy a relatively sparse intersection of multimodal TPPs, LLM integration, and long-horizon modeling. The taxonomy structure confirms this sits at a junction between temporal point processes, multimodal fusion, and long-horizon reasoning—areas that individually are well-studied but whose combination remains less densely explored. The analysis cannot assess exhaustive novelty but suggests the specific technical approach and application context may offer meaningful differentiation from existing work within the examined candidate set.

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
28
Contribution Candidate Papers Compared
0
Refutable Paper

Research Landscape Overview

Core task: Multimodal temporal point process modeling with long-range dependencies. This field addresses the challenge of capturing event sequences that unfold over time and involve multiple modalities—such as vision, language, and sensor data—while maintaining sensitivity to dependencies that span extended temporal horizons. The taxonomy reflects a diverse landscape organized into six main branches. Temporal Point Process Architectures and Mechanisms focuses on foundational modeling techniques, including transformer-based approaches for mixed event types (Transformers Mixed Events[1]) and neural architectures for event sequences (Event Sequences Networks[32]). Multimodal Fusion and Integration Strategies examines how to combine heterogeneous data streams, with applications ranging from ICU prediction (Multimodal ICU Prediction[3]) to fault diagnosis (Multimodal Fault Diagnosis[4]). Long-Horizon and Sequential Task Modeling emphasizes extended temporal reasoning, particularly in vision-language-action settings for robotic manipulation and planning. Spatiotemporal and Convolutional Temporal Modeling addresses spatial dynamics alongside temporal patterns, as seen in trajectory prediction (UAV Trajectory Prediction[5], Trajectory Prediction Dependencies[41]) and urban analytics. Domain-Specific Multimodal Temporal Applications showcases specialized use cases in healthcare, energy forecasting, and affective computing, while Temporal Modeling Enhancements and Specialized Mechanisms explores refinements such as attention mechanisms and memory structures (Multimodal Agent Memory[2]). A particularly active line of work centers on long-horizon sequential tasks, where models must integrate multimodal observations over extended episodes to guide decision-making or manipulation. Within this branch, vision-language-action models for robotic manipulation represent a dense cluster, with papers like Long-VLA[36] and Robotic Stacking Preferences[29] exploring how to ground language instructions in visual perception and action sequences. The original paper, Multimodal Event Sequences[0], sits naturally within this cluster, emphasizing the modeling of event sequences that span long temporal ranges and multiple modalities. Compared to Long-VLA[36], which focuses on robotic manipulation tasks, Multimodal Event Sequences[0] appears to take a broader view of temporal point processes, potentially addressing a wider variety of event-driven scenarios beyond embodied agents. Meanwhile, works like Watch to Imagine[9] highlight the role of predictive modeling in long-horizon settings, contrasting with the more direct event-sequence framing of Multimodal Event Sequences[0]. Open questions remain around scalability, the trade-offs between specialized domain models and general-purpose architectures, and the effective integration of symbolic event representations with continuous multimodal streams.

Claimed Contributions

MM-TPP: Multimodal Temporal Point Process Framework

The authors introduce MM-TPP, a unified framework that extends temporal point processes to handle multimodal data (visual, textual, and temporal information). Unlike prior work limited to text, MM-TPP jointly models and generates content across multiple modalities, positioning text generation as a core capability alongside traditional time and type prediction.

10 retrieved papers
Adaptive Compression Mechanism Based on Temporal Similarity

The authors propose a novel sequence compression strategy that exploits temporal similarity between events. When consecutive events have similar inter-event intervals, they are compressed using special tokens, enabling the model to fit longer event histories within fixed context windows and capture long-range dependencies more effectively.

10 retrieved papers
TAXI-PRO: Multimodal TPP Benchmark Dataset

The authors create TAXI-PRO, a new benchmark dataset that enriches the classic NYC Taxi data with multimodal content including map image patches and natural language descriptions. This dataset provides a complementary evaluation scenario with shorter sequences compared to existing benchmarks.

8 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

MM-TPP: Multimodal Temporal Point Process Framework

The authors introduce MM-TPP, a unified framework that extends temporal point processes to handle multimodal data (visual, textual, and temporal information). Unlike prior work limited to text, MM-TPP jointly models and generates content across multiple modalities, positioning text generation as a core capability alongside traditional time and type prediction.

Contribution

Adaptive Compression Mechanism Based on Temporal Similarity

The authors propose a novel sequence compression strategy that exploits temporal similarity between events. When consecutive events have similar inter-event intervals, they are compressed using special tokens, enabling the model to fit longer event histories within fixed context windows and capture long-range dependencies more effectively.

Contribution

TAXI-PRO: Multimodal TPP Benchmark Dataset

The authors create TAXI-PRO, a new benchmark dataset that enriches the classic NYC Taxi data with multimodal content including map image patches and natural language descriptions. This dataset provides a complementary evaluation scenario with shorter sequences compared to existing benchmarks.

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