Theory of Mind (ToM) is crucial for understanding social dialogue, where characters’ mental states are rarely stated explicitly but evolve through interaction and shape later behavior. Existing ToM benchmarks focus mainly on selected dialogue turns or state changes across separate scenarios, leaving within-dialogue mental state trajectories underexplored. We introduce TrajToM, a benchmark for evaluating whether LLMs can reason over evolving emotions, beliefs, intentions, and desires in continuous social dialogue. Built with a Sketch-first construction paradigm, TrajToM contains 700 dialogues and 18,165 multiple-choice questions across mental state reasoning, behavioral reasoning, and trajectory reasoning. Experiments reveal a substantial human-model gap, with GPT-5.2 reaching 67.4% overall accuracy compared with 88.2% for humans, and show persistent bottlenecks in second-order reasoning, behavior explanation, and change direction assessment. We further examine MARTA as an exploratory trajectory-aligned process-supervision framework, showing that explicit supervision over mental state trajectories can improve ToM reasoning.

Recently, question answering over temporal knowledge graphs (i.e., TKGQA) has been introduced and investigated, in quest of reasoning about dynamic factual knowledge. To foster research on TKGQA, a few datasets have been curated (e.g., CronQuestions and Complex-CronQuestions), and various models have been proposed based on these datasets. Nevertheless, existing efforts overlook the fact that real-life applications of TKGQA also tend to be complex in temporal granularity, i.e., the questions may concern mixed temporal granularities (e.g., both day and month). To overcome the limitation, in this paper, we motivate the notion of multi-granularity temporal question answering over knowledge graphs and present a large scale dataset for multi-granularity TKGQA, namely MultiTQ. To the best of our knowledge, MultiTQis among the first of its kind, and compared with existing datasets on TKGQA, MultiTQfeatures at least two desirable aspects—ample relevant facts and multiple temporal granularities. It is expected to better reflect real-world challenges, and serve as a test bed for TKGQA models. In addition, we propose a competing baseline MultiQA over MultiTQ, which is experimentally demonstrated to be effective in dealing with TKGQA. The data and code are released at https://github.com/czy1999/MultiTQ.