Recent advances in LLM agents have enabled complex cognitive capabilities, such as multi-step reasoning, planning, and tool use, that increasingly position these agents as collaborative partners in human workflows. Effective collaboration, however, requires collaborators to continuously maintain and align mental models of their own reasoning, partners’ intentions, and shared goals during the collaborative process. Today’s agents rarely develop such capabilities since they are primarily optimized for task completion rather than for maintaining the underlying mental models required for collaborative processes. Moreover, the community lacks authentic human collaboration data with action-level mental model annotations that could guide agents toward collaborative competence. To bridge this gap, we present ALMANAC, a dataset of Action-Level Mental model ANnotations for Agent Collaboration built from the Map Task, a classic dyadic routing task from social science. ALMANAC contains 2,987 collaboration actions, each paired with theory-informed mental model annotations that record the participants’ self-reasoning, perceived partner intent, and perceived team goal. We benchmark six LLMs on predicting humans’ next-turn behavior and mental models. Our results demonstrate ALMANAC’s utility in evaluating models’ ability to simulate human collaborative behaviors and infer their underlying mental models. The project page is available at https://human-agent-collab.hailab.io/almanac.

Recent advances in video-text retrieval (VTR) have largely relied on supervised learning and fine-tuning. In this paper, we introduce , a novel zero-shot VTR framework that leverages off-the-shelf video captioners, large language models (LLMs), and text retrieval methods—entirely without additional training or annotated data. Due to the limited power of captioning methods, the captions often miss important content in the video, resulting in unsatisfactory retrieval performance. To translate more information into video captions, we first generates initial captions for videos, then enhances them using a relevance-boosted captioning strategy powered by LLMs, enriching video descriptions with salient details. To further emphasize key content, we propose structural information extraction, organizing visual elements such as objects, events, and attributes into structured templates, further boosting the retrieval performance. Benefiting from the enriched captions and structuralized information, extensive experiments on several video-text retrieval benchmarks demonstrate the superiority of over existing fine-tuned and pretraining methods without any data. They also show that the enriched captions capture key details from the video with minimal noise. Code and data will be released to facilitate future research.

Generating cell-level descriptions for Jupyter Notebooks, which is a major resource consisting of codes, tables, and descriptions, has been attracting increasing research attention. However, existing methods for Jupyter Notebooks mostly focus on generating descriptions from code snippets or table outputs independently. On the other side, descriptions should be personalized as users have different purposes in different scenarios while previous work ignored this situation during description generation. In this work, we formulate a new task, personalized description generation with code, tables,and user-written guidelines in Jupyter Notebooks. To evaluate this new task, we collect and propose a benchmark, namely NBDESCRIB: , containing code, tables, and user-written guidelines as inputs and personalized descriptions as targets. Extensive experiments show that while existing models of text generation are able to generate fluent and readable descriptions, they still struggle to produce factually correct descriptions without user-written guidelines. CodeT5 achieved the highest scores in Orientation (1.27) and Correctness (-0.43) among foundation models in human evaluation, while the ground truth scored higher in Orientation (1.45) and Correctness (1.19). Common error patterns involve misalignment with guidelines, incorrect variable values, omission of im-031 portant code information, and reasoning errors.032 Moreover, ablation studies show that adding guidelines significantly enhances performance, both qualitatively and quantitatively.