6 papers
Teaching Vision-Language Models to Use the Scale They Are Given: Label-Free Equivariance Training for Metric Physical Reasoning
Kaizhen Tan, Yang Feng, Heqing Du +3
Metric questions about video, such as the speed of a moving object, require a vision-language model to convert visual measurements into physical units using a real-world reference…
Beyond Multimodal Alignment: Shared Physical Representations Across Sensors and Action Orders
Kaizhen Tan, Xin Xu, Siru Tao +4
Multimodal world models are often evaluated by whether different sensors produce similar representations. However, similar representations do not necessarily imply that the models…
What Can Latent World Models Know? Physical Information in Multimodal Predictive Representations
Kaizhen Tan, Sizhe Xu, Xin Xu +6
A central premise of latent world models is that predicting the future encourages representations to internalize the physics of their environment. We ask which physical quantities…
Collusion with Competitive Marginals: Price-Level Audits Are Blind by Construction
Xin Xu, Chengrui Wu, Jiayu Lu +3
Empirical work on algorithmic collusion asks one question of the data: are prices supracompetitive? We show this can be answered "no" by a conspiracy that is nonetheless profitable…
GeoFidelity-Bench: Evaluating Block-Conditioned Geographic Fidelity in Text-to-Image Street-View Generation
Kaizhen Tan, Hanzhe Hong, Siru Tao
Text-to-image models can generate plausible street scenes from prompts such as "a street in New York City," but it remains unclear whether these images resemble the requested place…
Confidence under Visual Token Pruning: Removed Evidence and Risk-Controlled Token Budgets for MLLMs
Kaizhen Tan, Yang Feng, Heqing Du +3
Visual token pruning speeds up multimodal large language models (MLLMs) by keeping a small subset of the visual tokens, and pruning methods are compared by the accuracy they retain…