Event-Causal RAG: A Retrieval-Augmented Generation Framework for Long Video Reasoning in Complex Scenarios

• URL: http://arxiv.org/abs/2605.06185v1
• Date: Thu, 07 May 2026 13:01:28 GMT
• Title: Event-Causal RAG: A Retrieval-Augmented Generation Framework for Long Video Reasoning in Complex Scenarios
• Abstract summary: Event-Causal RAG is a lightweight retrieval-augmented framework for infinite long-video reasoning.<n>Method segments streaming videos into semantically coherent events and represents each event as a structured State-Event-State graph.<n>On top of this memory, we design a bidirectional retrieval strategy to efficiently identify the most relevant event causal chains.
• Score: 9.729442664774988
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Recent large vision-language models have achieved strong performance on short- and medium-length video understanding, yet they remain inadequate for ultra-long or even infinite video reasoning, where models must preserve coherent memory over extended durations and infer causal dependencies across temporally distant events. Existing end-to-end video understanding methods are fundamentally limited by the $O(n^2)$ complexity of self-attention, while recent retrieval-augmented generation (RAG) approaches still suffer from fragmented clip-level memory, weak modeling of temporal and causal structure, and high storage and online inference costs. We present Event-Causal RAG, a lightweight retrieval-augmented framework for infinite long-video reasoning. Instead of indexing fixed-length clips, our method segments streaming videos into semantically coherent events and represents each event as a structured State-Event-State (SES) graph, capturing the event together with its surrounding state transitions. These graphs are merged into a global Event Knowledge Graph and stored in a dual-store memory that supports both semantic matching and causal-topological retrieval. On top of this memory, we design a bidirectional retrieval strategy to efficiently identify the most relevant event causal chains and provide them, together with the associated video evidence, to a backbone video foundation model for answer generation. Experiments on long-video understanding benchmarks demonstrate that Event-Causal RAG consistently outperforms strong clip-based retrieval baselines and long-context video models, particularly on questions requiring multi-event integration and causal inference across long temporal gaps, while also achieving improved memory efficiency and robust streaming performance.

Related papers

• EventMemAgent: Hierarchical Event-Centric Memory for Online Video Understanding with Adaptive Tool Use [9.106278853322008]
  EventMemAgent is an active online video agent framework based on a hierarchical memory module.<n>Our framework employs a dual-layer strategy for online videos: short-term memory detects event boundaries and utilizes event-granular reservoir sampling to process streaming video frames.<n>Experiments show that EventMemAgent internalizes competitive results on online video benchmarks.
  arXiv  Detail & Related papers  (2026-02-17T03:17:24Z)
• VideoMem: Enhancing Ultra-Long Video Understanding via Adaptive Memory Management [17.645183933549458]
  VideoMem is a novel framework that pioneers models long video understanding as a sequential generation task via adaptive memory management.<n>We show that VideoMem significantly outperforms existing open-source models across diverse benchmarks for ultra-long video understanding tasks.
  arXiv  Detail & Related papers  (2025-12-04T07:42:13Z)
• WorldMM: Dynamic Multimodal Memory Agent for Long Video Reasoning [66.24870234484668]
  We introduce WorldMM, a novel multimodal memory agent that constructs and retrieves from multiple complementary memories.<n>WorldMM significantly outperforms existing baselines across five long video question-answering benchmarks.
  arXiv  Detail & Related papers  (2025-12-02T05:14:52Z)
• GCAgent: Long-Video Understanding via Schematic and Narrative Episodic Memory [59.869552603264076]
  We introduce GCAgent, a novel Global-Context-Aware Agent framework that achieves comprehensive long-video understanding.<n>Our core innovation is the Schematic and Narrative Episodic Memory, which structurally models events and their causal and temporal relations into a concise, organized context.<n>Experiments confirm that GCAgent significantly enhances long-video understanding, achieving up to 23.5% accuracy improvement on the Video-MME Long split over a strong MLLM baseline.
  arXiv  Detail & Related papers  (2025-11-15T04:29:00Z)
• Vgent: Graph-based Retrieval-Reasoning-Augmented Generation For Long Video Understanding [56.45689495743107]
  Vgent is a graph-based retrieval-reasoning-augmented generation framework to enhance LVLMs for long video understanding.<n>We evaluate our framework with various open-source LVLMs on three long-video understanding benchmarks.
  arXiv  Detail & Related papers  (2025-10-15T19:14:58Z)
• Mixture of Contexts for Long Video Generation [72.96361488755986]
  We recast long-context video generation as an internal information retrieval task.<n>We propose a simple, learnable sparse attention routing module, Mixture of Contexts (MoC), as an effective long-term memory retrieval engine.<n>As we scale the data and gradually sparsify the routing, the model allocates compute to salient history, preserving identities, actions, and scenes over minutes of content.
  arXiv  Detail & Related papers  (2025-08-28T17:57:55Z)
• InfLVG: Reinforce Inference-Time Consistent Long Video Generation with GRPO [73.33751812982342]
  InfLVG is an inference-time framework that enables coherent long video generation without requiring additional long-form video data.<n>We show that InfLVG can extend video length by up to 9$times$, achieving strong consistency and semantic fidelity across scenes.
  arXiv  Detail & Related papers  (2025-05-23T07:33:25Z)
• SALOVA: Segment-Augmented Long Video Assistant for Targeted Retrieval and Routing in Long-Form Video Analysis [52.050036778325094]
  We introduce SALOVA: Segment-Augmented Video Assistant, a novel video-LLM framework designed to enhance the comprehension of lengthy video content.<n>We present a high-quality collection of 87.8K long videos, each densely captioned at the segment level to enable models to capture scene continuity and maintain rich context.<n>Our framework mitigates the limitations of current video-LMMs by allowing for precise identification and retrieval of relevant video segments in response to queries.
  arXiv  Detail & Related papers  (2024-11-25T08:04:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.