Abstract
Human understanding of video dynamics is typically grounded in a structured mental representation of entities, actions, and temporal relations, rather than relying solely on immediate deductive reasoning. In contrast, existing Video-LLMs largely depend on unstructured video reasoning, where critical visual evidence is embedded in verbose textual descriptions and temporal causality is often weakly modeled. This leads to inefficient processes and fragile causal inference. To bridge this cognitive gap, we propose constructing a compact representation of salient events and their causal relationships, which we name Structured Event Facts, prior to the reasoning stage. This structured prior serves as an explicit constraint to promote concise and causally grounded reasoning, while also making intermediate evidence easier to verify. To effectively train models on such structured facts, we introduce CausalFact-60K and a four-stage training pipeline comprising facts alignment, format warm-start, thinking warm-start, and reinforcement learning-based post-training. During RL stage, we find that this framework introduces competing objectives, as structural completeness and causal fidelity must be balanced against reasoning length, making it difficult to optimize. We address this challenge by formulating the optimization as a Multi-Objective Reinforcement Learning (MORL) problem and explicitly optimizing toward the Pareto-Frontier to balance these trade-offs. As a result, we introduce Factum-4B, which yields more reliable reasoning and delivers stronger performance on challenging video understanding tasks requiring fine-grained temporal inference.
Abstract (translated)
人类对视频动态的理解通常基于实体、动作及时序关系的结构化心智表征，而非单纯依赖即时演绎推理。相比之下，现有视频大语言模型主要依赖非结构化视频推理——关键视觉证据嵌入冗长文本描述中，时序因果关系往往建模薄弱，导致推理过程低效且因果推断脆弱。为弥合这一认知差距，我们提出在推理阶段前构建显著事件及其因果关系的紧凑表示，命名为“结构化事件事实”。该结构化先验作为显式约束，可促进简洁且具因果基础的推理，同时使中间证据更易验证。为有效训练模型掌握此类结构化事实，我们引入CausalFact-60K数据集及四阶段训练流程：事实对齐、格式预热、思维预热及基于强化学习的后训练。在强化学习阶段，我们发现该框架引入竞争性目标——结构完整性与因果保真度需与推理长度权衡，导致优化困难。我们通过将优化问题表述为多目标强化学习（MORL）并显式优化帕累托前沿来解决此挑战，以平衡这些权衡。最终推出的Factum-4B模型实现了更可靠的推理，在需要细粒度时序推断的挑战性视频理解任务上展现出更强性能。
URL
https://arxiv.org/abs/2604.04415
