Papers for

marketing teams

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

Automated tools enable video editing from simple previews to trailers

Unified Agentic Video Editing Across Levels of Complexity and Creativity

Abstract: Editing is a core component of video production, requiring creative planning and decisions under multiple constraints. Here, we report methods for agentic tooling for automated video editing across three tasks varying in editorial goal, complexity and creativity, namely scene previews, video summaries and cinematic trailers. We evaluate the outputs and discuss implications for automation and agency.

Fri 11 SeptArtificial IntelligenceHuman-Computer InteractionMultimedia
The gist
Editing videos can be a complex and creative process that involves many choices. The authors developed methods that allow automated tools to help edit videos in different ways, from short scene previews to full cinematic trailers. Their work explores how these tools can balance creativity and complexity in video editing. They also evaluated how well these automated edits work and considered what it means for the future of video editing.
Open → 2609.12769v1

Large language models partially mimic survey responses after health intervention

How Well Do LLMs Simulate Survey Responses Following a Breast Cancer Screening Intervention?

Abstract: Collecting survey data is laborious and limited by privacy constraints. Large language models (LLMs) have shown promise as predictive social simulations. It is unclear whether they can replicate population-level response distributions before and after a healthcare intervention. Using information derived from 4125 women aged 35-59 years, we evaluate whether agents informed solely by pre-intervention profile information can reproduce post-intervention response distributions. Groups of LLM agents (n=50) were created with Gemma 4 E4B and Qwen3.5 9B; conditions ranged from zero-shot prompting to agent profiles enriched with aggregate or individual-level demographic characteristics and pre-intervention questionnaire responses. We compared predicted and observed response distributions with Total Variation Distance (TVD) and Normalized Wasserstein Distance (NWD). Across both LLMs, profile-based agents improved distributional accuracy relative to zero-shot and random baselines. Nevertheless, direct sampling of 50 real participants remained more accurate. Prediction errors were also higher among participants aged 55-59 years and those living in private property. Errors also varied by question theme and LLM model, with the highest errors observed for cancer fatalism and post intervention attitudes toward genetics. Sensitivity analyses showed that performance was influenced by prompt template changes and temperature hyperparameter. Our results show the potential of LLM-based agents to model behavioral responses to interventions in silico. However, profiles containing additional information beyond demographics did not consistently outperform simpler ones. Certain cultural constructs and population groups also remain inadequately represented by the LLM models evaluated. Future work may include building behaviorally grounded and locally validated virtual populations.

Mon 7 SeptSocial and Information Networks
The gist
Collecting survey answers from people can be hard and tricky because of privacy. The authors studied if large language models (LLMs) could predict how groups of women would answer surveys before and after a breast cancer screening program. They found that LLMs that knew some background info did better than guessing, but they still weren’t as accurate as real participants. The models had more trouble with older women, certain living situations, and questions about cancer beliefs or genetics. This suggests LLMs can help simulate some responses, but they don’t fully replace real data yet.
Open → 2609.07141v1