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Kaizhen Tan

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Preprint Aug 2026

Vision Models Predict Urban Scene Appraisal with Limited Neural Alignment

Pretrained vision embeddings are increasingly used as general-purpose representations for modelling how people appraise urban scenes, and are validated almost entirely by how well they predict human ratings. High predictive accuracy does not establish that these embeddings organise scenes as human perception does. We t...

Kai-Zhen Tan, Yuan-Tao Deng · 0 citations
Preprint Aug 2026

Renaming or Tightness: Enforcing Disjunctive Information Flow Policies

A disjunctive policy allows a value to depend on at most one of two secrets and never on both: an analyst may consult one client's file or the other's, a share of a split secret may be released but not its sibling. Such policies are not lattice-shaped, and Hunt and Sands introduced the quantale of information to give t...

Xin Xu, Si-Ru Tao, Kai-Zhen Tan · 0 citations
Preprint Sep 2026

Teaching Vision-Language Models to Use the Scale They Are Given: Label-Free Equivariance Training for Metric Physical Reasoning

Metric questions about video require vision-language models to use supplied real-world references to convert visual measurements into physical units. Yet we find that current models use this scale information only partially. When every world-space quantity in a prompt is rescaled by a common factor, the video remains e...

Kai-Zhen Tan, Yang Feng, Heqing Du et al. · 0 citations
#small language model Preprint Sep 2026

You Cannot Photograph the Same Street Twice: Reliability Limits in Vision-Language Measurement of Urban Change

Vision-language models are increasingly used to measure urban change from repeated street-level imagery, but their longitudinal reliability is not well understood. We test how much a perception score can change when the street itself does not undergo substantial redevelopment. Using 4,648 consecutive-epoch image pairs...

Kaizhen Tan · 0 citations
Jul 2026

How Many Bits Can an Adapter Write? Measuring the Capacity and Memorization of Parameter-Efficient Fine-Tuning

Extending compression-based memorization analysis to the frozen-base setting, this work measures directly, in bits, how much a low-rank adapter writes into a model it never changes, finding that the answer is both smaller than full fine-tuning and less lawful than parameter counting would predict.

Kaizhen Tan, Heqing Du, Yang Feng · 0 citations
#robotics Jul 2026

What Can Latent World Models Know? Physical Parameter Identifiability in Multimodal Predictive Representations

A central premise of latent world models is that predicting the future forces a representation to internalize the physics of its environment. Which physical quantities does a trained latent actually contain, and what decides this? We answer with controlled interventions in POKEWORLD, an interactive environment whose vi...

Kai-Zhen Tan, Xin Xu, Siru Tao et al. · 2 citations
Jul 2026

Collusion with Competitive Marginals: Price-Level Audits Are Blind by Construction

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. Consider bidding agents that couple only through the joint distribution of their unexplained bid components, leaving every agent's own bid l...

Xin Xu, Cheng-Rui Wu, Jiayu Lu et al. · 1 citation

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