activities such as dishwashing and table setting, whereas the RVLSM, leveraging its recursive structure, reconstructs the full sequence of events in close alignment with the ground truth. This example illustrates how the framework scales with video duration, yielding greater improvements as content becomes longer and more complex.
Statistical Significance Tests
To rank the summarizers on each benchmark, we apply the Borda Count re-ranking strategy (van Erp and Schomaker 2000), a group consensus function that merges the per-video rankings (231 for VideoXum, 60 for MixTyVSum) into a single one by assigning decreasing integer scores to each ranking and summing them. The RVLSM approaches w/ STD dominate on both benchmarks: on MixTyVSum, Qwen3-Omni-30B and Qwen3-VL-30B rank first and second (242 and 221) ahead of the best w/o STD model (205), and Qwen3-VL-30B leads on VideoXum.
Conclusions and future work
We introduced the recursive Video Language Summarization Model (RVLSM), which extends the RLM paradigm to long-video abstractive summarization through Semantic Temporal Decomposition (STD) and Adaptive Multimodal Tool Calling (AMTC). Across multiple VideoLLMs and benchmarks, RVLSM yields consistent improvements that grow with video duration, while our ablation shows that the value of knowledge injection depends on the informativeness of the visual signal, motivating adaptive rather than indiscriminate augmentation. Future work will explore deeper recursion, aggregating summaries over multiple hierarchical levels, and Deep Reinforcement Learning to mitigate specific limitations of the RLM approach.
Limitations
Despite promising results, our approach has several limitations that point to directions for future work.
Routing Mechanism.
The current routing relies on heuristic keyword matching; more principled alternatives, such as learned or reinforcement-learning-based routing optimized for summarization quality, are a promising direction.
Dataset Availability.
The benefits of RVLSM would likely be more pronounced on longer videos, yet benchmarks with very long videos and high-quality reference summaries remain scarce due to the annotation effort required.
Chunking Quality.
Visual scene detection may yield semantically incoherent segments when transitions are subtle, as in egocentric videos; multimodal boundary detection combining visual, audio, and action signals could mitigate this.
Annotation Sensitivity.
Evaluation depends on the annotation style of the reference summaries: generic and event-driven ones respond very differently to knowledge injection, suggesting that standard metrics may not assess model quality in an annotation-agnostic way.
Ethics, risks, and Generative AI Statement
The video data and annotations used in this work were obtained from third-party sources and do not introduce ethical concerns, such as unethical or offensive content, related to the input data. Due to the non-deterministic nature of the generative process, we cannot ensure that the summarized outputs are free of hallucination. However, the output summaries have been reviewed by at least two PhD-level researchers prior to the final evaluation. Generative AI was used as a research engine and its outputs were carefully reviewed by the authors; minor AI support also includes writing suggestions such as grammar corrections. The project code is publicly available under the CC BY-NC license. Part of the project builds upon the original RLM architecture (Zhang et al. 2026); credits to the owner, changes, and specific settings are reported in the code repository.
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