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arXiv

Benchmarking Document Parsers on Mathematical Formula Extraction from PDFs

Pius Horn, Janis Keuper

Correctly parsing mathematical formulas from PDFs is critical for training large language models and building scientific knowledge bases from academic literature, yet existing benchmarks either exclude formulas entirely or lack semantically-aware evaluation metrics. We introduce a benchmarking framework centered on synthetically generated PDFs with precise LaTeX ground truth, enabling systematic control over layout, formulas, and content characteristics. For evaluation, we apply LLM-as-a-judge to assess semantic equivalence of parsed formulas, capturing mathematical meaning beyond surface-level notation differences. We validate this approach through a human study (250 formula pairs, 750 ratings from 30 evaluators), showing a Pearson correlation of r=0.78 with human judgment, compared to r=0.34 for character-level matching (CDM) and r~0 for text similarity. Our robust two-stage matching pipeline combining LLM-based extraction with fuzzy validation reliably aligns parsed formulas with ground truth despite format inconsistencies across parsers. Evaluating 20+ contemporary PDF parsers across 100 synthetic documents with 2,000+ formulas reveals significant performance disparities, providing actionable guidance for practitioners selecting parsers for downstream applications. Code and benchmark data: https://github.com/phorn1/pdf-parse-bench and https://github.com/phorn1/formula-metric-study

arXiv

Guardian: Detecting Robotic Planning and Execution Errors with Vision-Language Models

Paul Pacaud, Ricardo Garcia, Shizhe Chen, Cordelia Schmid

Robust robotic manipulation requires reliable failure detection and recovery. Although recent Vision-Language Models (VLMs) show promise in robot failure detection, their generalization is severely limited by the scarcity and narrow coverage of failure data. To address this bottleneck, we propose an automatic framework for generating diverse robotic planning and execution failures across both simulated and real-world environments. Our approach perturbs successful manipulation trajectories to synthesize failures that reflect realistic failure distributions, and leverages VLMs to produce structured step-by-step reasoning traces. This yields GuardianFail-36k, a large-scale failure reasoning dataset built upon the RLBench simulator and the BridgeDataV2 real-robot dataset. Using GuardianFail-36k, we train Guardian, a multi-view reasoning VLM for unified planning and execution verification. Guardian achieves state-of-the-art performance on three unseen real-world benchmarks: RoboFail, RoboVQA, and our newly introduced UR5-Fail. When integrated with a state-of-the-art LLM-based manipulation policy, it consistently boosts task success rates in both simulation and real-world deployment. These results demonstrate that scaling high-quality failure reasoning data is critical for improving generalization in robotic failure detection. Code, Data, and Models are available at https://www.di.ens.fr/willow/research/guardian/.

arXiv

Motion-Aware Vision-Reference Alignment for Referring Multi-Object Tracking

Weiyi Lv, Ning Zhang, Hanyang Sun, Haoran Jiang, Kai Zhao, Yixiao Gu, Jing Xiao, Dan Zeng

Referring Multi-Object Tracking (RMOT) extends conventional multi-object tracking (MOT) by introducing natural language references for multi-modal fusion tracking. RMOT benchmarks only describe the object's appearance, relative positions, and initial motion states. This so-called static regulation fails to capture dynamic changes of the object motion, including velocity changes and motion direction shifts. This limitation not only causes a temporal discrepancy between static references and dynamic vision modality but also constrains multi-modal tracking performance. To address this limitation, we propose a novel motion-aware vision-reference alignment framework, named VMRMOT. VMRMOT introduces a motion modality derived from object dynamics to facilitate the alignment between the vision modality and language references. Specifically, we introduce motion-aware descriptions derived from object dynamic behaviors and encode them into motion features as the motion modality through multi-modal large language models (MLLMs). We further design a Vision-Motion-Reference Alignment (VMRA) module to hierarchically align visual queries with motion and reference cues, enhancing their cross-modal consistency. In addition, a Motion-Guided Prediction Head (MGPH) is developed to explore motion modality to enhance the performance of the prediction head. To the best of our knowledge, VMRMOT is the first motion-aware vision-reference alignment framework for the RMOT task. Extensive experiments on multiple RMOT benchmarks demonstrate that VMRMOT outperforms existing state-of-the-art methods. The code is available at https://github.com/Kroery/VMRMOT.

arXiv

TCSA-UDA: Text-Driven Cross-Semantic Alignment for Unsupervised Domain Adaptation in Medical Image Segmentation

Lalit Maurya, Honghai LIU, Reyer Zwiggelaar

Unsupervised domain adaptation (UDA) for medical image segmentation remains challenging due to substantial domain shifts across imaging modalities, such as CT and MRI. Although recent vision-language representation learning methods have shown promise in medical image analysis, their role in cross-modality UDA segmentation remains underexplored. To address this problem, we propose TCSA-UDA, a Text-driven Cross-Semantic Alignment framework that uses modality-aware textual prompting to guide domain-invariant visual representation learning. Specifically, we introduce a vision-language covariance cosine loss (VLCoL) that aligns inter-class visual feature relationships with text-derived semantic relationships, encouraging the image encoder to learn semantically structured and modality-robust representations. In addition, we incorporate a prototype alignment module to reduce residual class-level discrepancies between source and target domains by aligning high-level class prototypes. Extensive experiments on cross-modality cardiac, abdominal, and brain tumor segmentation benchmarks demonstrate that TCSA-UDA consistently improves adaptation performance and outperforms state-of-the-art UDA methods. These results highlight the potential of language-driven semantic guidance for domain-adaptive medical image segmentation. The code is available at https://github.com/lalitmaurya47/TCSA_UDA

arXiv

VinciCoder: Unifying Multimodal Code Generation via Coarse-to-fine Visual Reinforcement Learning

Xuanle Zhao, Deyang Jiang, Zhixiong Zeng, Lei Chen, Haoyue Yang, Haibo Qiu, Jing Huang, Yufeng Zhong, Liming Zheng, Yilin Cao, Lin Ma

While recent specialized multimodal code generation models excel in tasks like chart-to-code generation, their reliance on single-task training limits generalization and hinders the development of \textbf{VI}sio\textbf{N} \textbf{C}ode \textbf{I}ntelligence. In this work, we introduce \textbf{VinciCoder}, a unified framework designed for generalized multimodal code generation. We first curate a large-scale SFT corpus comprising 1.3M direct generation pairs and 300k visual-based refinement tasks. This composition fosters self-refinement capabilities, enabling the model to directly rectify code to align with input images. Subsequently, we propose coarse-to-fine Visual Reinforcement Learning (ViRL) to overcome the brittleness of textual metrics in handling semantically equivalent but syntactically diverse code. By quantifying visual similarity across multi-scale patches, ViRL provides an implementation-agnostic reward mechanism that ensures high-fidelity alignment between rendered outputs and input visuals. Extensive experimental results across diverse benchmarks demonstrate that VinciCoder achieves superior performance, while comprehensive ablation studies validate the effectiveness of our proposed ViRL strategy. The data, code and model are available at https://github.com/DocTron-hub/VinciCoder.

arXiv

ExtrinSplat: Decoupling Geometry and Semantics for Open-Vocabulary Understanding in 3D Gaussian Splatting

Jiayu Ding, Xinpeng Liu, Zhiyi Pan, Shiqiang Long, Ge Li

Lifting 2D open-vocabulary understanding into 3D Gaussian Splatting (3DGS) scenes is a critical challenge. Mainstream methods, built on an embedding paradigm, suffer from three key flaws: (i) geometry-semantic inconsistency, where points, rather than objects, serve as the semantic basis, limiting semantic fidelity; (ii) semantic bloat from injecting gigabytes of feature data into the geometry; and (iii) semantic rigidity, as one feature per Gaussian struggles to capture rich polysemy. To overcome these limitations, we introduce ExtrinSplat, a framework built on the extrinsic paradigm that decouples geometry from semantics. Instead of embedding features, ExtrinSplat clusters Gaussians into multi-granularity, overlapping 3D object groups. A Vision-Language Model (VLM) then interprets these groups to generate lightweight textual hypotheses, creating an extrinsic index layer that natively supports complex polysemy. By replacing costly feature embedding with lightweight indices, ExtrinSplat reduces scene adaptation time from hours to minutes and lowers storage overhead by several orders of magnitude. On benchmark tasks for open-vocabulary 3D object selection and semantic segmentation, ExtrinSplat outperforms established embedding-based frameworks, validating the efficacy and efficiency of the proposed extrinsic paradigm.

arXiv

Optimal Transport for Handwritten Text Recognition in a Low-Resource Regime

Petros Georgoulas Wraight, Giorgos Sfikas, Ioannis Kordonis, Petros Maragos, George Retsinas

Handwritten Text Recognition (HTR) is a task of central importance in the field of document image understanding. State-of-the-art methods for HTR require the use of extensive annotated sets for training, making them impractical for low-resource domains like historical archives or limited-size modern collections. This paper introduces a novel framework that, unlike the standard HTR model paradigm, can leverage mild prior knowledge of lexical characteristics; this is ideal for scenarios where labeled data are scarce. We propose an iterative bootstrapping approach that aligns visual features extracted from unlabeled images with semantic word representations using Optimal Transport (OT). Starting with a minimal set of labeled examples, the framework iteratively matches word images to text labels, generates pseudo-labels for high-confidence alignments, and retrains the recognizer on the growing dataset. Numerical experiments demonstrate that our iterative visual-semantic alignment scheme significantly improves recognition accuracy on low-resource HTR benchmarks.

arXiv

Using Wavelet Domain Fingerprints to Improve Source Camera Identification

Xinle Tian, Matthew Nunes, Emiko Dupont, Shaunagh Downing, Freddie Lichtenstein, Matt Burns

Camera fingerprint detection plays a crucial role in source identification and image forensics, with wavelet denoising approaches proving particularly effective for extracting sensor pattern noise (SPN). In this article, we introduce the concept of a wavelet domain (WD) fingerprint, redefining the representation of the extracted fingerprint from the conventional image domain to the native wavelet coefficient domain. Rather than reconstructing the fingerprint as a spatial domain image, fingerprint comparison is performed directly on the wavelet coefficients, eliminating the final inverse transform and subsequent image-domain post-processing. This reformulation streamlines the fingerprint extraction and comparison pipeline while preserving the information required for source camera identification. The proposed framework is applicable to existing wavelet-based SPN extraction methods and is demonstrated using two representative state-of-the-art pipelines. Experimental results on real-world datasets show that the proposed approach significantly reduces computational cost, making it well-suited for large-scale source camera identification applications.

arXiv

QIRL: Optimized Question-Image Relation Learning for Bias-Robust Visual Question Answering

Quanxing Xu, Ling Zhou, Xian Zhong, Feifei Zhang, Rubing Huang

Existing bias mitigation methods for Visual Question Answering (VQA), a typical Artificial intelligence application, endure two main limitations. First, they fail to capture the optimal relation between images and texts, as prevailing learning frameworks lack the capacity to extract deep correlations from highly contrasting samples. Second, they overlook assessing Question-Image (QI) relevance during inference, since prior work has not examined the degree of input relevance in debiasing studies. To address these issues, we propose a novel neural network framework termed Optimized Question-Image Relation Learning (QIRL), which provides a reliable implementation of artificial intelligence for VQA tasks and improves the robustness of conventional VQA models through a generation-driven self-supervised learning strategy. Specifically, two modules are introduced. The Negative Image Generation (NIG) module automatically produces highly irrelevant QI pairs during training to strengthen relational learning. In contrast, the Irrelevant Sample Identification (ISI) module enhances model robustness by detecting and filtering out irrelevant inputs, thereby reducing prediction errors. Moreover, to verify the effectiveness of filtering out unrelated QI pairs in mitigating output errors, we propose a specialized metric to evaluate the ISI module's performance. Notably, our approach is model-agnostic and can be seamlessly integrated with various VQA architectures. Extensive experiments on VQA-CPv2 and VQA-v2 datasets demonstrate the effectiveness and generalization ability of our method. Our approach achieves state-of-the-art performance.

arXiv

Reference-free Human-Object Interaction Editing

Jiun Tian Hoe, Weipeng Hu, Wei Zhou, Chao Xie, Ziwei Wang, Xudong Jiang, Yap-Peng Tan, Chee Seng Chan

This paper presents InteractEdit, a novel framework for reference-free Human-Object Interaction (HOI) editing that tackles the challenging task of transforming an existing interaction in an image into a new, desired interaction while preserving the identities of the subject and object. Unlike prior image editing tasks such as attribute manipulation, object replacement or style transfer, HOI editing involves complex spatial, contextual, and relational dependencies inherent in HOI. Existing methods often overfit to the source image structure, limiting adaptability to the substantial structural modifications demanded by the new interactions. To address this, InteractEdit disassembles each scene into subject, object, and background components to disentangle intricate HOI relationship, and introduces a selective inversion strategy combined with Selective-Rank Adaptation (SeRA) to leverage pretrained interaction priors while learning visual identity from the source image. This enables a balanced trade-off between interaction editing and identity preservation. We also introduce IEBench, a new benchmark for HOI editing, and a new metric that jointly evaluates the trade-off between successful interaction editing and identity preservation. Extensive experiments show that InteractEdit outperforms 23 existing methods, providing a strong baseline for future HOI editing research. Code and the dataset: https://jiuntian.github.io/InteractEdit/.

arXiv

QuantFormer: Learning to Quantize for Neural Activity Forecasting in Mouse Visual Cortex

Salvatore Calcagno, Isaak Kavasidis, Simone Palazzo, Marco Brondi, Luca Sità, Giacomo Turri, Daniela Giordano, Vladimir R Kostic, Tommaso Fellin, Massimiliano Pontil, Concetto Spampinato

Understanding complex animal behaviors hinges on deciphering the neural activity patterns within brain circuits, making the ability to forecast neural activity crucial for developing predictive models of brain dynamics. This capability holds immense value for neuroscience, particularly in applications such as real-time optogenetic interventions. While traditional encoding and decoding methods have been used to map external variables to neural activity and vice versa, they focus on interpreting past data. In contrast, neural forecasting aims to predict future neural activity, presenting a unique and challenging task due to the spatiotemporal sparsity and complex dependencies of neural signals. Existing transformer-based forecasting methods, while effective in many domains, struggle to capture the distinctiveness of neural signals characterized by spatiotemporal sparsity and intricate dependencies. To address this challenge, we here introduce QuantFormer, a transformer-based model specifically designed for forecasting neural activity from two-photon calcium imaging data. Unlike conventional regression-based approaches, QuantFormerreframes the forecasting task as a classification problem via dynamic signal quantization, enabling more effective learning of sparse neural activation patterns. Additionally, QuantFormer tackles the challenge of analyzing multivariate signals from an arbitrary number of neurons by incorporating neuron-specific tokens, allowing scalability across diverse neuronal populations. Trained with unsupervised quantization on the Allen dataset, QuantFormer sets a new benchmark in forecasting mouse visual cortex activity. It demonstrates robust performance and generalization across various stimuli and individuals, paving the way for a foundational model in neural signal prediction.

arXiv

When the Canonical Completion Is Wrong: Formalizing and Measuring the Jump in Large Language Models

Dai Shi, Xiaoyu Li, José Miguel Hernández-Lobato

Whether large language models (LLMs) can perform the abductive leap from evidence to a new system of axioms, commonly referred to as a jump, has recently attracted considerable debate. A prominent position holds that LLMs are structurally incapable of such jumps, while recent studies challenge both its mechanism and its evidence. However, the debate remains difficult to settle, since the field still lacks a formal definition of the jump and a measure to test either side. In this paper, we develop a formal account of the jump in four steps and measure the second. The steps ask what the default completion of partial data is, when abandoning it is forced, when the abandonment is correct, and how successive jumps compound. Specifically, we define a jump instance as a finite extension problem with a machine-checked certificate that a correct completion exists, is unique up to renaming, and differs from the canonical completion of the data. The canonical completion is given by the left and right Kan extensions and is also what models produce without constraints, so it serves as the default. We prove that jump instances are well-posed and establish a family theorem that certifies instances of unbounded difficulty without enumeration. We further formalize when a jump is correct and how successive jumps compound. Finally, we run the measurement on nine certified instances and four frontier models. The Kan-default rate is zero in all 248 constrained trials, so the models do jump at this step and abandon the excluded default every time. Failures at higher difficulty stem from exhausted reasoning budgets or constraint errors, never from reverting to the default. These results indicate that the second step is not the bottleneck. If the disputed incapacity is real, it lies in generating the constraints or inventing the framework. Code can be found at: https://github.com/EEthanShi/kan-jump-test.

arXiv

Investigating the Influence of Prompt and Response Languages on LLM Content Generation

Thi Thanh Nhan Nguyen, Mai Khoi Tieu, Michael A. Riegler, Pål Halvorsen, Thu Nguyen

This study examines how prompt and response language influence the behavior of large language models. Using five models, we evaluated answers to 68 non translation questions across four language conditions: English to English, English to Norwegian, Norwegian to Norwegian, and Norwegian to English. After removing refused items, the dataset contains 1348 responses. We measure length differences with Cohen d, semantic fidelity with LabSE cosine similarity, and cross lingual keyword overlap with both raw and soft Jaccard. Prompt language has a strong effect on response length. With English output, Norwegian prompts shorten responses by about thirty seven percent. With Norwegian output, English prompts shorten responses by about forty one percent. The largest cross lingual contrast shows a reduction in word count but a smaller reduction in tokens, reflecting tokenizer differences. Despite variation in length, semantic similarity remains high, and soft Jaccard reveals substantial conceptual overlap that raw Jaccard does not capture. Effect sizes vary across models, indicating heterogeneity. Prompt language is not neutral and systematically shapes output length and lexical realization, with implications for multilingual prompt design.

arXiv

LLM Agents Perform Controlled Experiments Using Simulation Models

Yuchen Xia, Michael Weyrich, Nasser Jazdi, Johannes Stümpfle, Johannes Sigel, Akshay Narla, Gavin K. Reynolds, Anna Jawor-Baczynska, Pol Llopart

Large language models (LLMs) have shown strong capabilities in reasoning, planning, and tool use, but many scientific and engineering tasks require more than plausible text and code generation. They require understanding how a system responds to intervention, which in practice depends on controlled experimentation. In this work, we propose a multi-agent framework that enables LLM agents to conduct controlled experiments with scientific simulation models for pharmaceutical process design. Given a user query and a baseline configuration, the system constructs a structured task representation, designs experiments, executes comparative simulation, interprets the resulting outcomes, and synthesizes evidence-based recommendations for process parameter optimization. By coupling language models with high-fidelity simulation models in an interactive agent framework, the proposed system supports reasoning through intervention, comparison, and observation. As a result, it produces more specific and actionable outputs than language-only reasoning. In an industrial application setting, this advantage is reflected in higher output specificity as well as improved user-rated correctness and helpfulness. Ablation studies and visualized case analyses further demonstrate the effectiveness and practical utility of simulation-integrated experimental reasoning.

arXiv

Lexical Perturbations Disrupt LLM Reasoning: An Empirical Study of Attention Diversion

Jiaqian Zhu, Yang Zhang, Junhua Ding, Xiaowei Yu

Large Language Models (LLMs) achieve strong reasoning performance, but their robustness to realistic lexical corruption remains poorly understood. We evaluate four open-weight instruction-tuned models and frontier models across four reasoning benchmarks under keyboard noise, character swaps, and filler insertion. Character-level perturbations substantially degrade accuracy, especially on multi-step reasoning tasks, while filler insertion has little effect. We trace this asymmetry to Attention Diversion: lexical corruption fragments subword tokenization, and the resulting fragments attract disproportionate attention mass, concentrated in middle and final transformer layers. Length-matched controls confirm that fragmentation, not prompt length, drives the loss. A factorial intervention then shows why the damage is hard to undo: fragmentation corrupts token content and attention allocation together, and the two are coupled. Restoring clean attention while the content remains corrupted is actively harmful, restoring content alone is insufficient, and only restoring both recovers a substantial share of the gap. This coupling explains why inference-time strategies, including chain-of-thought prompting, spell-checking, self-repair, and stronger repair models, fail to consistently recover performance: each addresses one channel at a time. Code and data are available at https://github.com/Jiaqian-Janelle/Attention-Diversion

arXiv

Measuring Stability and Failure Behavior in Language Models Under Structured Perturbations

Samira Golsefid

Language models are usually judged by a single accuracy score, which does not reveal how their performance degrades as inputs are perturbed. We present a graded, multi-family, failure-aware framework for stress-testing reasoning models. It perturbs each problem along a multi-level severity ladder across seven families: six that preserve the answer, paraphrase, input noise, formatting, irrelevant context, context load, and conflicting instructions, and a Knowledge Boundary family that removes answerability so that refusal becomes the correct response. Every test is validity-gated and labeled by its measured severity, and each model is summarized by per-level Accuracy, a magnitude-weighted Stability, and a per-family Collapse Point defined relative to the model's own baseline. Instantiated on the same 100 seed problems used by GSM-Symbolic, expanded into 4,473 gated tests and run on four models spanning capability tiers, the framework exposes structure that an aggregate score hides: the level at which a model fails is family-specific rather than global, and two stressors expose consistent weaknesses across all models: conflicting instructions and questions built on an impossible premise. Recognition of unanswerability is otherwise uneven, reliable on missing information and fabricated evidence but weak on impossible premises. These failure points are invisible to standard accuracy reporting.

arXiv

SSE-Bio: A Structured Self-Evolving Agent with Agentic Retrieval Policy for Multi-Hop Biomedical Reasoning

Zhaohan Meng, Zaiqiao Meng, Siwei Liu, Hao Xu, Ke Yuan, Iadh Ounis

Biomedical multi-hop question answering (QA) requires models to connect evidence across intermediate entities such as diseases, drugs, proteins, and phenotypes. Existing agents typically rely on static retrieval workflows or coarse-grained prompt rewriting, which can lead to instruction drift when reasoning procedures need to be updated. We propose SSE-Bio, a structured self-evolving agent with an agentic retrieval policy for multi-hop biomedical reasoning. Instead of globally rewriting agent instructions, SSE-Bio maintains a structured state, selectively retrieves knowledge triplets and prior templates through a trainable proxy policy, and improves its reasoning memory through fine-grained template editing. To optimise retrieval decisions, we introduce a proxy-training strategy based on group relative policy optimization, where the proxy is improved through decision-contrastive groups over alternative retrieval choices. Experiments on three biomedical multi-hop QA benchmarks show that SSE-Bio consistently outperforms existing baselines, achieving an improvement of 6.56 absolute points over the strongest self-evolving baseline on BioHopR.

arXiv

PropUQ-MAS: Propagation-Aware Uncertainty Quantification for LLM Multi-Agent Systems

Yaokun Liu, Yifan Liu, Daniel Yue Zhang, Ruichen Yao, Zelin Li, Dong Wang

LLM-based multi-agent systems (MAS) solve complex tasks through communication among role-specialized agents. However, inter-agent dependencies introduce reliability risks beyond isolated agent failures. For instance, errors in intermediate messages could be inherited and amplified by downstream agents. Existing uncertainty quantification (UQ) methods mainly target isolated responses or single-agent reasoning, and therefore fail to capture uncertainty propagation in MAS. To this end, we propose PropUQ-MAS, an error propagation-aware UQ framework that represents MAS execution as a communication-structured graph and estimates each step's reliability by combining local uncertainty with uncertainty inherited from upstream messages. Extensive experiments demonstrate that PropUQ-MAS consistently improves UQ in MAS, with average relative gains of +6.10% in AUROC and +47.58% in PRR.

arXiv

Decoupled Physical Modeling and Execution for Physics Reasoning

Ye Zhang, Xuehang Guo, Rui Pan, Pengfei Yu, Denghui Zhang, Manling Li, Qingyun Wang

Physics reasoning requires constructing a consistent model of the underlying physical system rather than relying solely on symbolic or formula-based manipulation. Although large language models have shown strong ability in solving math and coding problems, they still struggle with physics problems, as these problems entangle the physical modeling process with mathematical calculations. Humans approach physics by first building a representation of the system before performing calculations. Inspired by this, we introduce a unified framework that distills intermediate representations that explicitly encode the physical modeling process and adopt a two-stage post-training strategy, where supervised fine-tuning establishes structured modeling, and reinforcement learning with rubric-based feedback improves the quality of the modeling process. Experiments on multiple multimodal physics benchmarks show that our approach generally improves physical reasoning performance across different models and datasets. Across PhysReason, PhyX, and SeePhys, physical modeling outperforms GRPO by ~3% on average. showing that explicit physical modeling is an effective strategy for improving physics reasoning in small VLMs.

arXiv

LLM assisted writing deserves empirical evaluation

Xuan Zhong Feng, Yi Lin, Yiye Zhang, Chunhua Weng, Yifan Peng

LLM-assisted writing is often treated as a detection problem, as it raises questions about clarity, integrity, equity, and evaluation. An analysis of 69,209 Health Informatics papers links it to more focused presentation, broader citation practices, and more globally distributed authorship. These patterns do not prove better science, but they support evaluating manuscripts by scholarly quality and accountability rather than by tool use.

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