arXiv:2607. 14616v1 Announce Type: new Abstract: Vision--language models have become increasingly capable of interpreting visual scenes, but it remains unclear whether they can use information to make strategically effective decisions.
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arXiv:2607. 14616v3 Announce Type: replace Abstract: Vision-language models (VLMs) can describe a scene, but can they act well within one?
arXiv:2609.28049v1 Announce Type: cross Abstract: Video understanding is usually benchmarked on curated, single-actor, or professionally filmed clips, and a strong score there is routinely read as ev...
Video understanding is usually benchmarked on curated, single-actor, or professionally filmed clips, and a strong score there is routinely read as evidence a model is robust enough for deployment. Ama...
The paper investigates the reliability of Expected Goals (xG) as a measure of finishing skill in soccer, arguing that the common practice of comparing cumulative xG to actual goals is flawed. It presents three hypotheses: high variance and small sample sizes make the deviation metric inadequate, including all shot types can mask true finishing ability, and inherent biases in xG models reduce the apparent gap between expected and actual goals for top finishers. Using an AI‑fairness technique to calibrate xG across player subgroups, the authors demonstrate that standard models underestimate Messi’s goal‑adjusted xG (GAX) by 17% and that his GAX is 27% higher than that of typical elite high‑shot‑volume attackers, revealing him as an even more exceptional finisher than previously thought.
arXiv:2606. 11120v1 Announce Type: new Abstract: We recast pass evaluation in football (soccer) as a Monte Carlo Tree Search (MCTS)-like evaluation problem whose components mostly exist in the literature under different names: a value model (possession value), a world model (multi-agent trajectories with ball interactions), and a policy over counterfactual actions (sampling pass variants with noise).
arXiv:2606. 15032v1 Announce Type: new Abstract: World models have rapidly become one of the central abstractions in modern AI.