Future-L1-50K: 50,000 Video-Language Pairs for Event Prediction

Future-L1-50K comprises 50,000 samples used to train the Future-L1 model for video event prediction. The dataset is associated with a research framework for interleaved language and latent visual reasoning, with a project repository and paper available. It was uploaded by author Eurayka to Hugging Face in June 2026.

Use Cases

• Training video-language models for future event prediction based on the described interleaved reasoning framework.
• Benchmarking multimodal reasoning systems on tasks involving latent visual features and language instructions.
• Fine-tuning models for instruction-following tasks in video domains, as suggested by its use for SFT training.

Strengths

• 50,000 samples specifically created for supervised fine-tuning of a published research model.
• Directly linked to a peer-reviewed paper (arXiv:2606.05769) and a public GitHub repository, providing research context.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• Data may reflect temporal or source bias inherent to the referenced video sources (Panda-70M, TwiFF-2.7M).

Provenance

Source

Eurayka on Hugging Face, referencing the OpenGVLab GitHub repository.

Collection Method

Likely assembled for supervised fine-tuning (SFT) of the Future-L1 model, with video sources from Panda-70M and TwiFF-2.7M datasets.

Freshness

Last updated 2026-06-12 08:40:56; freshness should be verified.