CTEPM: Continuous-Time Event Process Memory for Long-Video Language Models

Long-video language models (Video-LMMs) face a funda-mental bottleneck: temporal reasoning is often performed implicitly overdiscretized frames/clips and limited-context tokens. As a result, queriesthat require computable temporal structure—such as counting repeatedevents, comparing rates across time ranges, estimating intervals, or rea-soning about order—are handled by heuristic sampling, retrieval, or free-form summarization. We propose CTEPM, a continuous-time eventprocess memory that represents a video as a marked temporal pointprocess with learnable intensities and semantic marks. CTEPM first con-verts dense video features into a sparse set of latent events with contin-uous timestamps and mark embeddings, then fits an interaction-awaremarked point process that models global time trends and cross-eventtriggering/inhibition. Given a question, CTEPM executes a small libraryof temporal operators on the learned process to produce compact struc-tured evidence, which conditions the final answer generation. This designmakes temporal reasoning explicit, lightweight, and inspectable withoutperforming video generation. Experiments on long-video understandingbenchmarks demonstrate consistent improvements on temporally compo-sitional queries under tight context budgets, while retaining competitiveaccuracy on standard video QA.