Modern science has moved decisively toward larger and more collaborative research teams. Yet an influential 2019 study reported that small teams are more disruptive than large teams, a conclusion difficult to reconcile with both the well-known trend toward increasing collaboration and the disproportionately large teams behind widely recognized disruptive research, such as work that has been awarded Nobel Prizes. Here we show that the reported disruptive advantage of small teams is largely an artifact of measurement rather than a fact about teams. Analyzing about 60 million scientific publications, we find that larger teams cite more references and that the standard disruption index declines mechanically as reference lists lengthen. Equalizing reference-count distributions across team sizes reverses the negative gradient within the index's own framework. Evaluating each paper against one reference at a time yields a reference-robust measure that distinguishes recognized breakthroughs from ordinary papers more sharply and more consistently than the standard disruption index across six independently curated benchmarks, including the Nobel Prize. Under this validated measurement, scientific disruption increases with team size across four scientific domains and in every decade from the 1970s to the 2010s. For scientific publications, we conclude that larger teams are more, not less, disruptive.
Zhongrui Gui, Junyu Xie, Tengda Han, Weidi Xie, Andrew Zisserman
Films introduce characters incrementally as they progress. We study the problem of identifying principal characters and assigning their names directly from the movie itself, without using external cast lists, actor photographs, or pre-built character banks. We introduce the character naming task: given a name mentioned in dialogue and the associated video context, the model must determine which visible character bears that name. This task is challenging because name resolution often depends on cinematic cues such as shot-reverse-shot structure, self-introduction, direct address, third-party reference, gaze, and temporal continuity. We provide a dataset and annotations for evaluating character naming and first character appearances, develop a video-and-dialogue model for the task, and compare it with proprietary multimodal large language models. We further introduce a progressive framework for building a character bank as the film unfolds. Together, these contributions support online, human-like movie understanding in which character identities are inferred from the film itself.
Fuqiang Niu, Zini Chen, Zhiyu Xie, Hu Huang University of Science and Technology of China, Qing Liao Harbin Institute of Technology and Pengcheng Laboratory, Qianlong Wang Shenzhen Technology University, Genan Dai Shenzhen Technology University, Bowen Zhang Shenzhen Technology University
Political user-level stance detection is vital for analyzing polarization, yet progress is hindered by the scarcity of high-quality benchmarks integrating linguistic and social signals. Existing datasets, largely relying on noisy heuristic or distant supervision, limit model robustness and generalizability. To address this, we introduce TwiUSD, a large-scale, expert-annotated benchmark for political user-level stance detection with explicit social network structure. TwiUSD comprises 16,211 users and 47,757 tweets, labeled by domain experts using a protocol that integrates both user content and followee signals, ensuring high-quality annotations (kappa > 0.9). Building upon TwiUSD, we propose MRFG, a Multi-scale Relevance Filtering and Graph-aware framework that leverages large language models to filter stance-relevant followee content and adaptively routes features based on structural informativeness. This design enables robust stance prediction by jointly modeling semantic and relational cues. Extensive experiments show that MRFG significantly outperforms strong baselines, highlighting the importance of relevance filtering and structure-aware modeling.
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cs.CL
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表
Yueqi Xie, Tao Qi Beijing University of Posts and Telecommunications, Jingwei Yi Beijing Academy of Artificial Intelligence, Xiyuan Yang, Ryan Whalen University of Hong Kong, Junming Huang Princeton University, Qian Ding Tsinghua University and Microsoft, Yu Xie 等 10 位作者
With the growing prevalence of generative AI, an increasing amount of content is no longer exclusively generated by humans but by generative AI models with human guidance. This shift presents notable challenges for the delineation of originality due to the varying degrees of human contribution in AI-assisted works. This study raises the research question of measuring human contribution in AI-assisted content generation and introduces a framework to address this question that is grounded in information theory. By calculating mutual information between human input and AI-assisted output relative to self-information of AI-assisted output, we quantify the proportional information contribution of humans in content generation. Our experimental results demonstrate that the proposed measure effectively discriminates between varying degrees of human contribution across multiple creative domains. To further enhance real-world applicability, we extend the framework to estimate the minimal necessary human contribution for any text without requiring human input and validate its effectiveness. We hope that this work lays a foundation for measuring human contributions in AI-assisted content generation in the era of generative AI.
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cs.CL
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表
Understanding and reconstructing the complex geometry and motion of dynamic scenes from video remains a formidable challenge in computer vision. This paper introduces D4RT, a simple yet powerful feedforward model designed to efficiently solve this task. D4RT utilizes a unified transformer architecture to jointly infer depth, spatio-temporal correspondence, and full camera parameters from a single video. Its core innovation is a novel querying mechanism that sidesteps the heavy computation of dense, per-frame decoding and the complexity of managing multiple, task-specific decoders. Our decoding interface allows the model to independently and flexibly probe the 3D position of any point in space and time. The result is a lightweight and highly scalable method that enables remarkably efficient training and inference. We demonstrate that our approach sets a new state of the art, outperforming previous methods across a wide spectrum of 4D reconstruction tasks. We refer to the project webpage for animated results: https://d4rt-paper.github.io/.
In this paper, we introduce MapBench-the first dataset specifically designed for human-readable, pixel-based map-based outdoor navigation, curated from complex path finding scenarios. MapBench comprises over 1600 pixel space map path finding problems from 100 diverse maps. In MapBench, LVLMs generate language-based navigation instructions given a map image and a query with beginning and end landmarks. For each map, MapBench provides Map Space Scene Graph (MSSG) as an indexing data structure to convert between natural language and evaluate LVLM-generated results. We demonstrate that MapBench significantly challenges state-of-the-art LVLMs both zero-shot prompting and a Chain-of-Thought (CoT) augmented reasoning framework that decomposes map navigation into sequential cognitive processes. Our evaluation of both open-source and closed-source LVLMs underscores the substantial difficulty posed by MapBench, revealing critical limitations in their spatial reasoning and structured decision-making capabilities. We release all the code and dataset in https://github.com/taco-group/MapBench.