A new arXiv preprint describes an automated system that turns textbook PDFs into interactive video explanations tailored to individual questions. After a user uploads a PDF and submits a query, the pipeline produces a video answer, with the reported focus on NCERT educational materials. The work targets personalized, question-driven learning content.
A new arXiv paper introduces BEACON, a method for generating videos of a specific person that retains both their visual identity and their individual expressive behavior. The authors argue that beyond matching appearance, such models must also capture the facial mannerisms that distinguish how a given subject acts on camera. The work targets human-centric video synthesis where subject-specific fidelity is the main challenge.
A new arXiv paper describes an approach for building and steering causal virtual worlds using large language and video model agents. It relies on feedback fuzzy cognitive maps to capture the detailed causal structure of the simulated environment. The technique converts sequenced FCMs into worlds that video generators can render.
A new arXiv paper argues that coherent, cinematic output from multi-shot audio-video generators does not mean those systems can actually perform professional editing techniques. The authors introduce a benchmark that measures how well such models follow shot structure, transition grammar, and audio-video editing conventions rather than just producing smooth sequences. It aims to separate raw generative quality from genuine editing competence.
A new arXiv paper argues that adding inference-time compute helps video world models only when the extra samples are actually better and can be reliably picked out. The authors separate the benefit of a larger candidate pool from the ability to select the best one, an effect they call sampling headroom versus selection gain. They propose auditing test-time scaling along this distinction to judge whether the added compute is worth its cost.
A new arXiv preprint addresses the difficulty of generating coherent minute-long videos, noting that while short clips are plentiful and high quality, long-form training data is scarce and confined to a few domains. The authors propose a training approach that combines mode-seeking and mean-seeking objectives to speed up long video generation. The work is positioned as a way to overcome the data bottleneck that limits scaling from seconds to minutes.
A new arXiv preprint introduces AgenticGen, a framework that treats advertising video creation as an agentic reasoning task shaped by reward signals rather than simple clip synthesis. The method conditions generation on a specific product and aims to optimize for online business outcomes, building on video foundation models that can produce realistic footage from multimodal inputs. The paper appears as a cross-listing in arXiv's AI and computational linguistics categories.
Researchers have introduced PRISM-Bench, a diagnostic benchmark for text-to-audio-video generation models that places the audio modality at the center of evaluation. The paper argues that prior benchmarks tend to treat sound as a minor add-on to video quality metrics or test it separately from the combined audiovisual output. The new benchmark is intended to give a fuller picture of how generative systems handle audio together with visuals.
A new arXiv paper argues that while video generation models now produce convincing and temporally consistent output, it is unclear whether they can reason through video by following symbolic rules, obeying physics, and working toward defined goals. The authors introduce a benchmark for measuring this think-with-video ability and propose an approach for improving it. The work is listed under the cs.AI cross-submission category.