ChronoStitch: Training-Free Composition of Visual KV Memories for Long-Horizon Temporal Reasoning
This paper introduces ChronoStitch, a training-free method for composing independently stored visual key-value (KV) memories to enable long-horizon temporal reasoning in video question answering. By re-basing stored post-rotary keys onto a global three-axis multimodal RoPE coordinate system and selectively recomputing high-deviation visual tokens, it overcomes temporal phase collisions and content gaps from naive concatenation. Experiments on Qwen2.5-VL-3B and the temporal split of TempCompass show improved event-ordering accuracy and 3.3x speedup over full joint re-prefilling.
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[Submitted on 21 Jul 2026]
Title:ChronoStitch: Training-Free Composition of Visual KV Memories for Long-Horizon Temporal Reasoning
View a PDF of the paper titled ChronoStitch: Training-Free Composition of Visual KV Memories for Long-Horizon Temporal Reasoning, by Santiram Tiwari and 2 other authors
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Abstract:Long-video question answering requires a model to preserve visual evidence over time without repeatedly reprocessing the same video. A practical approach is to store the vision-language model's internal key-value (KV) cache for each video chunk and retrieve that state at query time. However, independently cached video chunks do not compose correctly: every chunk is prefilled from local rotary position zero, so naive concatenation collides temporal phases and removes the global order required for questions about what happened first, how often events occurred, or what changed across the video. This paper presents ChronoStitch, a training-free method for composing independently stored visual KV memories. The method first re-bases stored post-rotary keys onto a global three-axis multimodal RoPE coordinate system that preserves time, height, and width structure. We show why a one-dimensional scalar re-indexing is geometrically inconsistent for visual tokens because it turns spatial order within a frame into false temporal displacement. We then address the residual content gap left by positional repair: later chunks were originally encoded without attending to earlier chunks. ChronoStitch therefore selectively recomputes a small fraction of high-deviation later-chunk visual tokens while allowing them to attend over the composed cache. On Qwen2.5-VL-3B and the temporal split of TempCompass, ChronoStitch outperforms naive composition and position-only variants, improving event-ordering accuracy while running 3.3x faster than full joint re-prefilling.
Comments: 6 pages, 4 tables
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Image and Video Processing (eess.IV)
Cite as: arXiv:2607.19547 [cs.CV]
(or arXiv:2607.19547v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2607.19547
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Nishant Sinha [view email] [v1] Tue, 21 Jul 2026 19:46:38 UTC (9 KB)
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