arXiv subjects

Ana Trišović

Publications and source records attributed to Ana Trišović.

4 recordsLinked to original sources

arXiv2026

Science Is Falling Behind the Frontier: Foundation Model Adoption Across Half a Million Papers

We present the first large-scale analysis of AI foundation model usage in science -- not just citations or keywords. We find that adoption has grown rapidly, at nearly-exponential rates, with the highest uptake in Linguistics, Computer Science, and Engineering. Vision models are the most used foundation models in science, although language models' share is growing. Open-weight models dominate. As AI builders increase the parameter counts of their models, scientists have followed suit but at a much slower rate: in 2015, the mean foundation model adopted in science was 5.4x larger than the mean model being built; by 2024 that relationship had reversed, with the mean model built 6.9x larger than the mean model adopted. We also present suggestive evidence that scientists' use of these smaller models may be limiting them from getting the full benefits of AI-enabled science, as papers that use larger models appear in higher-impact journals and accrue more citations.

cs.DL↗

arXiv2026

Open Science, Closed Models: How Funding Shapes AI in Science

How funding shapes AI engagement in science is poorly understood despite its structural importance. We analyze 104,226 papers (2018-2025), linking funding acknowledgments to how each paper engages with foundation models: whether it extends a model (fine-tunes or builds on it), uses one without modification, or references models only peripherally. We report three findings. First, both private and public funding are associated with greater AI engagement. Public funding is associated with more off-the-shelf and open-weight use, while private funding is associated with greater model extension. Industry cloud credits are associated with less open-weight use, consistent with greater reliance on closed models. Second, industry involvement is associated with model extension, suggesting that corporate resources reach science through coauthorship, though this channel is concentrated in Computer Science and Global North collaborations. Third, the gap across research systems is not one of AI adoption but of control. Off-the-shelf use is rising across fields and country groups, and the least publicly funded group, the Global South, now has the highest share of API-only engagement (0.4% to 10.0% of engaged papers). China shows the opposite profile: the highest public-funding share and lowest API-only engagement, with extension remaining stable as it declines across the Global North. Standard capacity indicators show convergence in AI adoption even as dependence on proprietary infrastructure diverges.

cs.DL↗

arXiv2026

The Shrinking Lifespan of LLMs in Science

Scaling laws describe how language model capabilities grow with compute and data, but say nothing about how long a model matters once released. We introduce time-to-peak and lifespan as measures of model obsolescence and use them to characterize the scientific adoption trajectories of 62 LLMs across more than 108k citing papers (2019-2025), separating active adoption from background citation to recover per-model trajectories that citation counts cannot resolve. We find that a model's longevity is shaped more by when it was released than by its characteristics: release year predicts time-to-peak and lifespan more strongly than architecture, openness, or scale. LLM adoption follows an inverted-U curve (rising after release, peaking, and then declining), but this pattern is rapidly compressing. Each successive release year is associated with a 27% shorter time-to-peak and a 23% shorter lifespan ($p < 0.001$), robust to minimum-age thresholds and controls for model size. These adoption-side dynamics are invisible to scaling laws and suggest that specialization on any single model may be a depreciating investment, with costs falling on reproducibility and migration.

cs.DL↗

arXiv2026

Rapid drone-based wildfire detection at a fraction of current prevention spending

Early wildfire detection is critical to prevent small ignitions from escalating into large-scale disasters, yet current monitoring systems lack a quantitative framework for allocating detection infrastructure at scale. We jointly optimize the placement of monitoring infrastructure and the routing of autonomous drones under realistic operational constraints to quantify the investment required for rapid, large-scale wildfire detection. Evaluated out-of-sample on 3,693 California ignitions from 2021-2024, an optimized drone network operating at a $100 million five-year budget detects 97.3% of fires, including 74% within the first hour. Amortized over five years, that budget is about $20 million per year, roughly 5% of California's annual wildfire-prevention expenditure. Under current technology costs, drone-based monitoring is substantially more cost-effective than static ground sensors. Detection is governed primarily by spatial coverage, while routing strategy mainly determines detection speed.

math.OC↗