The Shape of Time: Video-Token Contrast for Temporal Understanding in VideoLMs

• URL: http://arxiv.org/abs/2609.04110v1
• Date: Thu, 03 Sep 2026 17:14:55 GMT
• Title: The Shape of Time: Video-Token Contrast for Temporal Understanding in VideoLMs
• Abstract summary: We propose VT-Contrast, a representation-level temporal counterfactual objective for VideoLMs.<n>Its design asks where temporal supervision should act and what temporal differences it should expose.<n> VT-Contrast supervises selected late-layer last-frame video tokens, where temporal information is expected to be integrated before language generation.
• Score: 21.75029303199712
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Seeing frames in order does not mean representing time. Modern VideoLMs receive ordered video streams, yet their main supervision acts on generated text rather than video-token representations where event dynamics should first emerge. This mismatch allows models to learn temporal answers from shortcuts such as objects, scenes, and language priors, without requiring internal video representations to capture event progression. To address this, we propose VT-Contrast, a representation-level temporal counterfactual objective for VideoLMs. Its design asks where temporal supervision should act and what temporal differences it should expose. VT-Contrast supervises selected late-layer last-frame video tokens, where temporal information is expected to be integrated before language generation, and contrasts order-preserving views with same-video reordered counterfactuals graded by Kendall tau distance. It requires no architectural changes, is compatible with diverse VideoLM training tasks, and improves overall performance across temporal understanding benchmarks. Our code is available at https://github.com/ANDgate99/VT-Contrast.

Related papers

• Kairos: A Dataset for Fine-Grained Video-Language Modeling over Space, Time, and Dynamics [72.11325009788095]
  We introduce Kairos, a video dataset for video-language modeling with time-resolved annotations.<n>Kairos consists of long-duration videos, ranging from ten minutes to half an hour, annotated with fine-grained temporal alignment.
  arXiv  Detail & Related papers  (2026-09-08T13:50:30Z)
• STORM: Internalized Modeling for Spatial-Temporal Reasoning in Video-Language Models [81.32710031596591]
  Video reasoning tasks require tracking motion, temporal order, and evolving visual states across frames.<n>Existing methods built on vision-language models (LVLMs) often address this challenge by externalizing reasoning through chain-of-thought (CoT)<n>We propose STORMS, a two-stage framework that teaches LVLMs to reason through bounded continuous latent trajectories instead of explicit textual CoT.
  arXiv  Detail & Related papers  (2026-05-25T16:33:00Z)
• TV-RAG: A Temporal-aware and Semantic Entropy-Weighted Framework for Long Video Retrieval and Understanding [14.570869250170139]
  TV-RAG is a training-free architecture that couples temporal alignment with entropy-guided semantics to improve long-video reasoning.<n>By weaving these temporal and semantic signals together, TV-RAG realises a dual-level reasoning routine that can be grafted onto any LVLM without re-training or fine-tuning.
  arXiv  Detail & Related papers  (2025-12-29T14:10:22Z)
• AlcheMinT: Fine-grained Temporal Control for Multi-Reference Consistent Video Generation [58.844504598618094]
  We propose AlcheMinT, a unified framework that introduces explicit timestamps conditioning for subject-driven video generation.<n>Our approach introduces a novel positional encoding mechanism that unlocks the encoding of temporal intervals, associated in our case with subject identities.<n>We incorporate subject-descriptive text tokens to strengthen binding between visual identity and video captions, mitigating ambiguity during generation.
  arXiv  Detail & Related papers  (2025-12-11T18:59:34Z)
• Seeing the Forest and the Trees: Query-Aware Tokenizer for Long-Video Multimodal Language Models [24.875526594002434]
  We present QTSplus, a visual token selection module for long video understanding scenarios.<n>It is integrated into Qwen2.5-VL and compresses the vision stream by up to textbf89% and reduces end-to-end latency by textbf28% on long videos.<n>Results show that QTSplus is an effective, general mechanism for scaling MLLMs to realworld long-video scenarios.
  arXiv  Detail & Related papers  (2025-11-14T22:41:27Z)
• Harnessing Synthetic Preference Data for Enhancing Temporal Understanding of Video-LLMs [54.502280390499756]
  We propose TimeWarp to create a targeted synthetic temporal dataset to fine-tune the model's responses to encourage it to focus on the given input video.<n>We demonstrate that when our method is applied to existing models, it significantly improves performance on temporal understanding benchmarks.
  arXiv  Detail & Related papers  (2025-10-04T21:48:40Z)
• When and What: Diffusion-Grounded VideoLLM with Entity Aware Segmentation for Long Video Understanding [12.410012029024342]
  We present Grounded VideoDiT, a Video LLM designed to overcome limitations by introducing three key innovations.<n>First, a Diffusion Temporal Latent (DTL) encoder enhances boundary sensitivity and maintains temporal consistency.<n>Second, object grounded representations explicitly bind query entities to localized visual evidence, strengthening alignment.<n>Third, a mixed token scheme with discrete temporal timestamp tokens provides explicit modeling, enabling fine grained temporal reasoning.
  arXiv  Detail & Related papers  (2025-08-21T15:12:14Z)
• Multimodal Long Video Modeling Based on Temporal Dynamic Context [13.979661295432964]
  We propose a dynamic long video encoding method utilizing the temporal relationship between frames, named Temporal Dynamic Context (TDC)<n>We segment the video into semantically consistent scenes based on inter-frame similarities, then encode each frame into tokens using visual-audio encoders.<n>To handle extremely long videos, we propose a training-free chain-of-thought strategy that progressively extracts answers from multiple video segments.
  arXiv  Detail & Related papers  (2025-04-14T17:34:06Z)
• STORM: Token-Efficient Long Video Understanding for Multimodal LLMs [116.4479155699528]
  STORM is a novel architecture incorporating a dedicated temporal encoder between the image encoder and the Video-LLMs.<n>We show that STORM achieves state-of-the-art results across various long video understanding benchmarks.
  arXiv  Detail & Related papers  (2025-03-06T06:17:38Z)
• VIMPAC: Video Pre-Training via Masked Token Prediction and Contrastive Learning [82.09856883441044]
  Video understanding relies on perceiving the global content modeling its internal connections. We propose a block-wise strategy where we mask neighboring video tokens in both spatial and temporal domains. We also add an augmentation-free contrastive learning method to further capture global content.
  arXiv  Detail & Related papers  (2021-06-21T16:48:19Z)

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.