Wang Chen陈旺

Visual experience in the real world rarely arrives as a tidy stack of independent images. An experiment, a journey, or a match can last for hours, while the evidence that changes a judgment may appear only briefly. A model has to notice when an event changes and remember how those changes connect. This motivates my first research thread: organizing semantic boundaries, event anchors, and query-relevant information so that a finite input still retains a coherent account of what happened.

Generation adds another possibility. A model can propose an event description, a missing state, or a possible future, then return to its observations to look for support and contradiction. These generations are candidates rather than evidence. Their value lies in turning an ambiguous internal state into something that can be inspected. If a candidate cannot be grounded in time, entities, or causal order, it should be revised or rejected.

The system I ultimately want to build should maintain a working account of events while video keeps arriving. It should know what has been confirmed, what remains hypothetical, and when earlier evidence needs to be revisited. An answer is only one output of this process. More important is an event representation that can keep changing with new information while preserving where its evidence came from.