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Jacky CK Ng, Ben CP Lam, Algae KY Au, Wesley CH Wu, Hilary KY Ng, and Sylvia Xiaohua Chen. Social axioms and psychological toll: A study of emotional, behavioral, and cognitive responses across 35 cultures during the covid-19 pandemic. Applied Psychology: Health and Well-Being , 16(4):1679–1698, 2024. [42] Pulse Asia Research Inc. Ulat ng bayan: June 2024 nationwide survey on national concerns prior to the sona. Research report, Pulse Asia Research Inc., July 2024. Accessed: 2025-04-16. [43] A Timothy Church, Jose Alberto S Reyes, Marcia S Katigbak, and Stephanie D Grimm. Filipino personality structure and the big five model: A lexical approach. Journal of Personality , 65(3):477–528, 1997. 12 Sentiment Simulation Using Generative AI Agents [44] Gregorio EH Del Pilar. The development of the masaklaw na panukat ng loob (mapa ng loob). Philippine Journal Of Psychology , 50(1):103–141, 2017. [45] Mary Rachelle R Wapaño. Personality disorders and the five-factor model among filipino non-clinical sample. International Journal of Research and Innovation in Social Science (IJRISS) , V , 2021. [46] Melrose Tia, Jerome Espina, and Jason Albia. Measuring political bias and framing effects in large language models (llms): A sensitivity analysis. Manuscript in preparation, 2025. [47] Aaron Grattafiori, Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Alex Vaughan, et al. The llama 3 herd of models. arXiv preprint arXiv:2407.21783 , 2024. [48] Kenneth O McGraw and Seok P Wong. A common language effect size statistic. Psychological bulletin , 111(2):361, 1992. [49] Sunwoong Kim, Jongho Jeong, Jin Soo Han, and Donghyuk Shin. Llm-mirror: A generated-persona approach for survey pre-testing. arXiv e-prints , pages arXiv–2412, 2024. [50] Leo Yeykelis, Kaavya Pichai, James J Cummings, and Byron Reeves. Using large language models to create ai personas for replication and prediction of media effects: An empirical test of 133 published experimental research findings. arXiv preprint arXiv:2408.16073 , 2024. [51] Armin Falk and James J Heckman. Lab experiments are a major source of knowledge in the social sciences. science , 326(5952):535–538, 2009. [52] David Lazer, Devon Brewer, Nicholas Christakis, James Fowler, and Gary King. Life in the network: the coming age of computational social science. Science , 323(5915):721–723, 2009. [53] Daniel Kahneman and Amos Tversky. Choices, values, and frames. American psychologist , 39(4):341, 1984. [54] Dennis Chong and James N Druckman. Framing theory. Annu. Rev. Polit. Sci. , 10(1):103–126, 2007. [55] Paul M Sniderman and Sean M Theriault. The structure of political argument and the logic of issue framing. Studies in public opinion: Attitudes, nonattitudes, measurement error, and change , 3(03):133–65, 2004. [56] Robert R McCrae and Paul T Costa Jr. Personality trait structure as a human universal. American psychologist , 52(5):509, 1997. [57] Commission on Elections (COMELEC). 2022 registered voters and voters with accessible polling places (final). https://comelec.gov.ph/?r=2022NLE/Statistics/2022RVVAVmcocfinal , 2022. Accessed: 2024-10- 01. 13 Sentiment Simulation Using Generative AI Agents Supplementary Material A. Survey Design and Implementation Details A.1 Survey Design and Instrument Specifics Specific psychological frameworks include personality traits (e.g., HEXACO personality), values (e.g., Basic Personal Values), attitudinal frameworks (e.g., Affective Intelligence Theory), and beliefs (e.g., Social Axioms). | https://arxiv.org/abs/2505.22125v1 |
It also encompasses social and political behavior (e.g., Civic Engagement). In addition to the sociodemographics and psychological frameworks, the survey instrument also includes an additional section to assess general citizen attitudes toward four major economic issues (e.g., inflation, minimum wage, etc) and four key social issues (e.g., the West Philippine Sea dispute, corruption, etc). A.2 Survey Sampling A multi-stage stratified random sampling design was used to obtain a nationally representative sample of 2,485 Filipino adults [ 57]. The sample was proportionally distributed across the 17administrative regions using probability proportional to size. Systematic interval sampling selected five (5)households per sampled barangay, and one (1) respondent per household was randomly chosen using gender-rotated probability to ensure balanced male and female representation. This sampling design accounted for clustering at multiple geographic levels and stratification by region and urbanicity. Data were collected through face-to-face interviews from November 22to December 9,2024 . A hybrid system of digital tablets and printed forms was used in the field to ensure both flexibility and high data fidelity. A.3 Weighting Procedure To correct for unequal selection probabilities inherent in the sampling design, design weights (base weights) were computed from the joint probabilities of selection at each sampling stage: cities/municipalities, barangays, households, and eligible respondents. These base weights were then adjusted using post-stratification techniques, anchored on the official registered voter count data [ 57] by region and gender. This procedure ensured that the final weighted sample reflected the actual distribution of registered voters, thereby improving the generalizability and precision of population-level inferences and estimates. A.4 Survey Implementation Randomization techniques were applied to minimize selection bias, and interval sampling was employed to ensure systematic coverage of both urban and rural areas. The use of in-person interviews allowed for greater engagement and clarification of questions when necessary, contributing to higher response quality and completeness. B. Agent Embodiment Setup Figures 5 and 6 present the prompt formats used in the agent embodiment section of the sentiment simulation. Both formats operationalize the presentation of sociodemographic and psychographic attributes to the language model, serving as the foundation for generating agent-specific responses. The categorical format (Figure 5) conveys traits and attributes through compact, labeled variables (e.g., Extraversion: HIGH), while the contextualized format (Figure 6) embeds the same information within brief narrative descriptions, enriching each variable with interpretive context. In both cases, bracketed fields (e.g., < age > ,< incomerange > ) represent placeholders dynamically populated with real survey data during prompt instantiation. Text segments rendered in bold correspond to fixed prompt components that remain consistent across all agents. C. Agent Exposure to Scenario Figure 7 presents the prompt structure used to expose an embodied agent to a situational stimulus and elicit a corresponding affective judgment or sentiment response. In this format, the language model is prompted to imagine being presented with a particular event, scenario, or statement, and to reflect on how it would personally resonate based on the agent’s encoded background and perspective. The< scenario > placeholder is dynamically filled with the target stimulus, while all bolded text constitutes fixed instructional language consistent across all prompts. The | https://arxiv.org/abs/2505.22125v1 |
model is then asked to identify the sentiment that best reflects how someone with its assigned profile would most likely feel in response. 14 Sentiment Simulation Using Generative AI Agents Figure 5: Prompt Format for Categorical Profile Encoding. Figure 6: Prompt Format for Contextualized Profile Encoding. 15 Sentiment Simulation Using Generative AI Agents Figure 7: Prompt Format for Instantiating Agent Exposure to Scenario. D. Agent Response to Scenario Figure 8 presents the full instruction sequence used to elicit a sentiment judgment, accompanying rationale, and self-assessed alignment from an embodied agent profile. After being exposed to a scenario, the agent is instructed to identify the sentiment that most accurately reflects how a person with that profile would likely respond. In addition to selecting a sentiment from a standardized 5-point scale (Negative to Positive), the model is prompted to articulate a brief explanation for its judgment. The < reason > placeholder denotes the position where the model is expected to generate this response. Following this, the model is asked to critically evaluate whether its chosen sentiment logically aligns with the profile’s described characteristics, including values, personal traits, and contextual background, and to answer with a binary Yes or No. This prompt format supports deeper analysis of the model’s internal coherence, linking sentiment expression to reasoning and value alignment within an embodied simulation context. Similarly, all bolded segments represent fixed instructional text presented uniformly across prompts. Figure 8: Prompt Format for Generating Agent’s Response to Scenario. E. Quadratic Weighted Accuracy (QWA) as Evaluation Metric Figures 9 and 10 present heatmaps of pairwise QWA scores, capturing the degree of alignment between agent-generated responses and human responses across two core tasks: agent embodiment and sentiment simulation. In both figures, each matrix cell represents the average agreement score for a specific pair of simulated and survey response values. 16 Sentiment Simulation Using Generative AI Agents E.1 On Agent Embodiment Survey Replication Task Figure 9 presents the QWA matrix for the survey replication task, where the model was prompted to generate Likert-scale responses to psychographic survey items from the perspective of an embodied agent profile. The matrix shows pairwise QWA scores between each simulated agent response (rows) and the corresponding human response (columns) on a 7-point ordinal scale. Figure 9: QWA Matrix of Simulated and Human Responses in the Agent Embodiment Task. E.2 On Sentiment Simulation Task Similarly, Figure 10 shows the QWA matrix for the sentiment simulation task. Here, simulated sentiment responses are compared to human sentiment ratings on a 5-point ordinal scale ranging from Negative to Positive. Figure 10: QWA Matrix of Simulated and Human Sentiment Responses in the Sentiment Simulation Task. 17 Sentiment Simulation Using Generative AI Agents F. Statistical Tests Paired t-test was used when the assumption of normality was satisfied. The formula is given below: t=d sd/√n(2) where: dis the mean of the differences between paired observations; sdis the standard deviation of the differences; and nis the number of pairs. For group comparisons in which the normality assumption was not met, we used the Wilcoxon signed-rank test, see Equation 3. W= min( W+, W−) | https://arxiv.org/abs/2505.22125v1 |
arXiv:2505.22126v1 [cs.CV] 28 May 2025SridBench: Benchmark of Scientific Research Illustration Drawing of Image Generation Model Yifan Chang1,2∗Yukang Feng2,3∗Jianwen Sun2,3∗Jiaxin Ai2,4 Chuanhao Li5S. Kevin Zhou1Kaipeng Zhang2,5† 1University of Science and Technology of China2Shanghai Innovation Institute 3Nankai University4Wuhan University5Shanghai AI Laboratory Abstract Recent years have witnessed rapid progress in AI-driven image generation. Early diffusion-based methods focused on perceptual quality, while more recent multi- modal models—such as GPT-4o-image—have begun integrating high-level reason- ing into the generation process, demonstrating stronger capabilities in semantic understanding and structural composition. Scientific research illustration generation stands at the forefront of this evolution. Unlike general-purpose image synthesis, this task requires models to accurately interpret complex technical descriptions and transform abstract structures into clear and standardized visual representations. It is significantly more knowledge-intensive than ordinary image generation. According to recent surveys, producing a single research figure typically demands several hours of manual work, often accompanied by expensive software tools and repeated revisions. Automating this process in a controllable and intelligent manner would therefore yield substantial practical benefits. However, no benchmark currently exists to systematically evaluate AI performance on this task. To address this gap, we present SridBench, the first benchmark designed to assess multimodal models on scientific figure generation. It consists of 1,120 instances via human experts and multimodal large language models(MLLM) on the authoritative scientific paper website which spanning 13 disciplines under natural science and computer science, with each sample evaluated along six well-designed dimensions including semantic fidelity and structural accuracy. Our experiments show that even state-of-the-art models like GPT-4o-image fall far short of human-level performance. At present, the lack of text and visual information and scientific errors are the main bottlenecks of GPT-4o-image for scientific illustration drawing, underscoring the need for further advances in reasoning-driven visual generation. 1 Introduction Scientific illustration are essential tools for communicating research findings. They translate complex frameworks, data, and experimental procedures into intuitive visuals, playing a central role in both scholarly publications and scientific discourse. However, creating high-quality illustrations is time- consuming, labor-intensive, and often requires proficiency in both domain-specific knowledge and design tools. This bottleneck limits productivity and slows down the rapid iteration cycles demanded by modern research workflows. Recent advances in generative AI have made automatic image generation a promising direction in graphic design. Diffusion models [ 1–3](e.g., Stable Diffusion [ 4], DALL ·E [5,6], Flux [ 7]) have demonstrated impressive capabilities in visual fidelity and stylistic diversity. Autoregressive vision- ∗Equal contributions. †Corresponding author Submitted to 39th Conference on Neural Information Processing Systems (NeurIPS 2025). language models (e.g., Emu3 [ 8], NAR [ 9], V AR [ 10], Janus-Pro [ 11]) further extend the boundaries by improving semantic alignment between textual inputs and visual outputs, especially in open-ended generation tasks. However, their limited reasoning ability in complex application scenarios remains a major constraint on generative performance. With the advancement of reasoning capabilities in language models, models like GPT-4o-image [ 12], which incorporate chain-of-thought reasoning and stronger multimodal foundations, mark a shift towards more controllable and content-aware generation. In principle, such models can be used to generate scientific illustrations directly from textual descriptions. Currently, | https://arxiv.org/abs/2505.22126v1 |
research on AI-assisted scientific illustration remains in its early stages and is mainly focused on benchmarking the understanding capabilities of multimodal models (e.g., SciFIBench [ 13], ScImage [ 14]). There is a noticeable lack of evaluation frameworks for assessing the ability of models to generate scientific diagrams. A key open question is how to objectively and systematically evaluate the quality of scientific illustrations produced by generative models. To fill this gap, we introduce SridBench, the first benchmark specifically designed to evaluate the capability of multimodal models to generate scientific graphics from textual descriptions. This dataset includes 1,120 generation instances collected from peer-reviewed publications across 13 academic disciplines as shown in Fig.1. For systematic evaluation, each instance is annotated and assessed along six dimensions, supporting both human and automated evaluation protocols. Additionally, we conduct extensive benchmarking of a wide range of generative models. Results reveal a significant performance gap between current models and expert-created graphics. Even the best-performing model in our study, GPT-4o-image, achieves an average score of fair level. Semantic understanding emerges as the primary bottleneck. Open-source models perform worse, with average scores close to 1, and proprietary models like Gemini-2.0-Flash only reach a score of 1.0, highlighting the considerable room for improvement in this domain. In summary, this work makes the following key contributions: 1.SridBench, a new benchmark dataset featuring 1,120 high-quality generation instances from real-world scientific literature and spanning 13 disciplines under natural science and computer science; 2.A multi-dimensional evaluation protocol assessing figure quality along six dimensions, supporting both human and automated scoring; 3.A comprehensive empirical study providing the first systematic comparison of representative generative models in the context of scientific illustration, revealing actionable research challenges. Human-Computer Interaction Cryptography and SecurityDistributed, Parallel, and Cluster Computing Computer V ision and Pattern Recognition Computation and LanguageNetworking and Internet Architecture Hardware ArchitectureRoboticsSoftware EngineeringBiological StructureGeographical EnvironmentOrganic ChemistryPhysicsNatural ScienceComputer ScienceSection: ...... Caption: ...... Section: ...... Caption: ......Model Based Judgement Completeness of T extual Information Accuracy of T extual Information Diagrammatic Structural Integrity Diagrammatic Logic Cognitive Readability Aesthetic FeelingSubjects Triples Data JudgementSridBench: Benchmark of Scientific Resear ch Illustration Drawing of Image Generation Model Figure 1: General description of SridBench. We collected triple data from 13 directions in natural science and computer science, and designed 6 evaluation metrics 2 2 Related work In the task of image generation, the current mainstream AI generation models mainly include two cat- egories: diffusion models and autoregressive models. They have continuously made breakthroughs in generation quality, control ability, and multimodal understanding, providing an important foundation for application scenarios such as scientific research illustrations. Diffusion models: By simulating the process of gradually adding noise to data and then reverse- restoring it, diffusion models [ 4] have made significant progress in generation accuracy and controlla- bility in recent years. Represented by the Stable Diffusion series, its latest version, Stable Diffusion XL [ 15], has been included in the MLPerf [ 16] Inference v4.0 benchmark test, demonstrating its powerful performance in high-quality image generation. Stable Diffusion series have demonstrated its powerful performance in high-quality image generation. DALL ·E3 [6] performs well in design-related tasks | https://arxiv.org/abs/2505.22126v1 |
such as DEsignBench [ 17], indicating its strong text-to-image alignment ability. The FLUX series of models strikes a balance among image resolution, generation speed, and cost-efficiency, being particularly suitable for high-resolution image generation tasks. Although diffusion models have achieved remarkable results in visual generation, their performance in complex scenarios still faces challenges. Especially in scientific research drawing tasks that require strict semantic control and structural constraints, there is still room for improvement in their context understanding ability and structural controllability. Autoregressive models: In contrast, autoregressive models [ 18,19,8] predict the pixels or feature positions in an image step by step, enabling more accurate alignment with the semantics of the input text while ensuring consistency. Emu3 has achieved leading results in text-to-image tasks such as T2I-CompBench [ 20], showing its high-fit ability in multimodal understanding and image generation tasks. Janus-Pro performs superiorly in the multimodal consistency of text-to-image, especially demonstrating the unique advantages of the autoregressive structure in detail restoration and instruction response. Currently, autoregressive models are widely regarded as a generation paradigm more suitable for handling high-semantic-density inputs and have important potential in scientific research-related image generation tasks. Reasoning ability: Recent research trends [ 21–24] indicate that reasoning ability is a key factor affecting whether a generation model can be adapted to complex scientific research scenarios. Starting from the o1 model released by OpenAI, large models that introduce the “Chain-of-Thought (CoT)” [ 25] mechanism (such as DeepSeek-R1 [ 26], QwQ [ 27], Doubao, etc.) have demonstrated powerful capabilities in multimodal reasoning tasks. These models can not only analyze the deep-level logical relationships in the input text but also generate responses that conform to the context semantics in complex scenarios. Furthermore, new-generation models such as GPT-4o-image have deeply integrated advanced reasoning ability with image generation ability for the first time, possessing the ability to "understand scientific research texts" and generate scientific research illustrations with reasonable structures and accurate content accordingly. The evolution of this paradigm indicates that generation models are moving from "art-level" to "scientific-research-level", providing a new path to solve the understanding and control problems in scientific research illustration generation. Research Gaps: Although the development of generative models and reasoning models has laid a good foundation for scientific research diagram tasks, there is currently a lack of systematic evaluation framework to measure their actual performance in this specific scenario. Relying on human evaluation lacks a unified standard, making it difficult to objectively quantify the performance of models in terms of semantic accuracy, structural rationality, and aesthetic quality. At the same time, most of the current research efforts are focused on the understanding of scientific images and the generation of image captions (such as SciFIBench [ 13], FigCaps-HF [ 28], etc. [ 29]). The field of evaluating the generation of scientific research drawings is almost blank. Therefore, this paper focuses on constructing a multi-dimensional scientific research drawing evaluation system and systematically comparing the performance of different types of generative models, providing theoretical support and practical basis for the implementation of multimodal generation technology in the scientific research visualization scenario. 3 3 Method In | https://arxiv.org/abs/2505.22126v1 |
order to test the scientific research drawing ability of image generation models, we collect and carefully select the scientific research illustration drawing data, and set the process and standard for scientific research drawing evaluation. This process can be seen in Fig.2. We collect data in two disciplines: Computer Science andNatural Science . Prompts mentioned in this section are shown in Appendix.A. # Role settingYou are a scientist and now you are going to draw a diagram for the nature research and analysis paper. Prompt for image generation Scientific Papers (i) Demonstration Image (ii) Image Caption (iii) Related SectionPlease strictly judge whether this picture belongs to the concept diagram, model frame diagram, process flow diagram or structure diagram in the academic paper. Return 1 if it is of the following…You will get the text of a TEX file. In this file, find the text associated with the image {img_name}. Note that you can first find the latex figure class where {img_name} is located and…Prompt for filtering demonstration images.Prompt for finding related sections Evaluate Image Generation Models Generated Images Request:……Standard: Top Journal &Conference; Cite number……Search for: Nature, NeurIPS…… Tex Source from arXiv……Response: …… Figure 2: The framework of our Benchmark of Scientific Research Illustration Drawing of Image Generation Model. As can be seen from the framework, human experts set the standards for batch downloading and filtering paper data from the Internet. MLLM and human experts work together to screen triplet data to ensure the authority and scientific nature of the data. At the same time, we use the MLLM which is consistent with the human preference and evaluation for automatic scoring. 3.1 Collection and structuring of data We collect papers and filter data on professional and authoritative paper websites in these two disciplines. The filtering process is to use Multimodal Large Language Models(MLLMs) to determine whether the illustration in the paper is a schematic diagram or illustration (rather than a real photo, experimental results diagram, and statistical data analysis diagram) , find and extract the caption and section. In this way, we can get a lot of structured triple data: image, caption, section . Human experts will sift through the resulting triplet data. Specifically, the goal illustration in the triple should be clear, scientific, rigorous, and has a certain degree of expressiveness. The text should also be able to support and cover the elements that generate the illustration. We try to find papers in the top journals and conferences in all directions that have recently been published. ArXiv can help us get some of the computer science papers TeX source files. This is necessary for us to construct triples because we can obtain the LaTeX expression of the formula in the original paper. ArXiv’s API makes it easy to get the TEX files for a large number of papers in a given direction. However, not all papers from top conferences and journals are submitted to arXiv. Therefore, we screened arXiv preprint papers that were not published in formal journals and conferences. For articles with fewer than 25 citations, we remove them directly. We then | https://arxiv.org/abs/2505.22126v1 |
invited human experts to assess the content, quality, and quality of the illustrations. Only papers and illustrations considered to be of high quality and scientific quality by human experts will be used to construct triples. For natural science papers, we crawl from the Nature website, which ensures the quality and authority of the data. 4 3.2 Automatic generation and scoring Once we have the triples, we fill the text of the caption and section into a well-designed prompt template, and then use the image generation model to draw the research illustration. Using MLLM’s API, we implement a batch and automatic image generation process. After obtaining the generated illustrations, we compare them to the images in the triple and score them using MLLM. We designed 6 scoring dimensions for scientific illustration. First, in order to measure the scientificalness and completeness of visual elements (such as organelles, molecular structure, neural network modules) , we set up two indicators: “Diagrammatic structural integrity”and “Diagrammatic logic”. Secondly, in order to measure the quality of the text in the illustrations, we set two indicators of “Completeness of textual information” and “Accuracy of textual information”. Finally, we design “Cognitive readability” and “Aesthetic feeling” to evaluate the quality of the generated results as a whole. These six metrics are written into the prompt using MLLM scoring on a scale of 1 to 5 (1: fail, 2: poor, 3: fair, 4: good, 5: excellent) 4 Experiments 4.1 Experimental Setup Model Due to the limitation of input length, only three image generation models are chosen by us to evaluate the capability of scientific research drawing, which are GPT-4o-image, Gemini-2.0- Flash [ 30] and Emu-3. We quantitatively analyzed GPT-4o-image and Gemini-2.0-Flash because Emu-3 takes too long time to generate all images. GPT-4o is used to judge the generation results of those models. Data Computer Science data are collected on arXiv and in top journals and conferences of computer science, while Natural Science data are collected on Reviews &Analysis section on the website of Nature (https://www.nature.com/nature/reviews-and-analysis). Since there is no section in the above paper, we choose the paragraph where the illustration is located as the section. Inspired by the classification on arXiv, we set nine specific directions in the collection of Computer Science data: Software Engineering, Robotics, Networking and Internet Architecture, Human- Computer Interaction, Distributed, Parallel, and Cluster Computing, Computer Vision and Pattern Recognition, Cryptography and Security, Computation and Language, and Hardware Architecture. 100 triples are selected in each direction. Meanwhile, we carefully select 220 triples from the latest reviews and analysis section of Nature. The selection process is done by experts in various fields. This careful selection process ensures the quality, timeliness, and diversity of the data. In addition, we use GPT-4o to further categorize images by scene and function to facilitate the analysis of more results. Eight categories are chosen to categorize computer science images: software design, noun classification, mathematical structure, hardware design, engineering system design, algorithm flow, AI model, and other types. Natural science images are categorized into four types: Physics diagram, Organic Chemistry diagram, Geographical Environment diagram, and Biological | https://arxiv.org/abs/2505.22126v1 |
Structure diagram. 4.2 Overall Evaluation As we can see in Fig.3(a), Gemini-2.0-Flash scored less than 2 on each of these measures, meaning that the model had little or no ability to draw professional illustrations for scientific research papers. In the category “diagrammatic structural integrity”, Gemini-2.0-Flash earned the highest score of all. This shows that Gemini-2.0-Flash has a basic understanding of the basic style and frame structure of scientific drawing. However, in terms of concrete content, as well as scientific logic and deduction, Gemini-2.0-Flash has no such ability at all. GPT-4o-image fully demonstrates its superiority over Gemini-2.0-Flash in this task. Every subject, computer science or natural science, is rated at around 3, with most scoring above that mark. This means that GPT-4o-image’s scientific mapping capabilities are at a level of basic eligibility that humans would consider acceptable. 5 completeness of textual information accuracy of textual information diagrammatic structural integrity diagrammatic logiccognitive readabilityaesthetic feeling 012345 (a)CS GPT-4o-image CS Gemini-2.0-Flash Nature GPT-4o-image Nature Gemini-2.0-Flashcompleteness of textual information accuracy of textual information diagrammatic structural integrity diagrammatic logiccognitive readabilityaesthetic feeling 012345 (b)Gemini-2.0-pro GPT GPT-4o GPT Human Expert GPT Gemini-2.0-pro Gemini GPT-4o Gemini Human Expert GeminiFigure 3: (a). On the computer science and natural science data, the average score of GPT-4o-image and Gemini-2.0-Flash scores in the six major indicators judged by GPT-4o. (b). For images generated by GPT-4o-image and Gemini-2.0-Flash, the comparison of score judged by Gemini-2.0-pro, GPT-4o and human expert. At the same time, we selected 50 natural science and 50 computer science triplets from the dataset, allowing Gemini-2.0-pro, GPT-4o and human experts to independently rate them simultaneously. The results, shown in Fig.3(b), show that the GPT-4o scores are broadly in line with those of human experts, while Gemini-2.0-pro scores are significantly biased relative to human scores. Therefore, we use GPT-4o for automated scoring. However, GPT-4o is still slightly overrated in terms of completeness and accuracy compared to human expert ratings. 4.3 Evaluation on Natural Science Data Figure 4: On different subjects of natural science data, the average score of GPT-4o-image and Gemini-2.0-Flash scores in the six major indicators. The performance of the two models on natural science data is demonstrated in Fig.4. More specifically, the integrity of GPT-4o-image generated image elements (e.g., cell structure, sensing instrument structure) is significantly higher than the integrity of the text elements (whether or not it completely covers all the information in the reference image). GPT-4o-image can guarantee the accuracy of expressing text, although it cannot express text information completely. As can be seen, the accuracy of the text information on this side of the score is higher than the integrity. However, in terms of logic, simplicity and aesthetics, GPT-4o-image scored below average. This means that there is still much room for improvement in the overall look and feel of natural science image rendering. Overall, 6 GPT-4o-image does not show a significant gap in competence between different disciplines of the natural sciences . 4.4 Evaluation on Computer Science Data Figure 5: On different subjects of computer science data, the average score of GPT-4o-image and Gemini-2.0-Flash scores in the six major indicators. | https://arxiv.org/abs/2505.22126v1 |
As can be seen from Fig.5, the Gemini-2.0-Flash is still considered to have no preliminary ability to generate scientific maps in computer science, although there is some improvement in the ratings compared to the natural science data. For GPT-4o-image, there was a significant decrease in the scores on the measures of completeness and accuracy of the text information. Compared with the schematics of natural science, the schematics of computer science often have more words and more complex flow structure. This makes the generation of image elements and text elements, GPT-4o- image does not meet the performance of natural science, the same ability to generate images. But at the same time, another noticeable improvement is the ability of GPT-4o-image to be readable and aesthetically pleasing. This is still relevant to the schematic nature of computer science, because most of them are flowcharts, consisting of elements such as text, borders, and arrows. For such diagrams, GPT-4o-image is easier to draw. In contrast to natural science images, generative models need to accurately depict complex and specialized graphical elements such as cellular structures, electron spins, animal organs, and ecosystems. As a result, there is worse performance in natural science diagrams. In different subjects, GPT-4o-image alternative shows absolutely no difference. However, “Computer Vision and Pattern Recognition” and “Computation and Language” doesn’t score well in terms of brevity and aesthetics. In fact, these two disciplines represent one of the hottest areas of artificial intelligence right now which are computer vision, pattern recognition and natural language processing. The average level of human drawing in this field is also gradually increasing. Therefore, for the corresponding image generation model, requirement of the quality of the generated image will also be higher. 4.5 Analysis of Generation Results Fig.7 and 8 show the comparison between the illustrations generated by the three image generation models (Emu-3, Gemini-2.0-Flash, and GPT-4o-image) and the author’s original image in the paper. As can be seen, Emu-3 does not have any understanding of scientific writing, and the content it generates is irrelevant to our requirements. Gemini-2.0-Flash simply draws text in an image in Fig.7. There are no graphic elements, and the text is problematic because they are more like symbols than words. In Fig.8, despite the appearance of the plant-like structure, the resulting image is still difficult to interpret. At the same time, there are a large number of text symbols in the generated illustrations 7 Figure 6: On different types of computer science data, the average score of GPT-4o-image and Gemini-2.0-Flash scores in the six major indicators. (b) Gemini-2.0-Flash (c) GPT -4o-image (d) Reference Diagram (a) Emu-3 Figure 7: Computer science paper illustrations generated by different image generation models under the same prompt are compared with the original paper illustration. (b) Gemini-2.0-Flash (c) GPT -4o-image (d) Reference Diagram (a) Emu-3 Figure 8: Natural science paper illustrations generated by different image generation models under the same prompt are compared with the original paper illustration. 8 which are similar to the original text of the paper. This situation is also common in other generated illustrations. However, GPT-4o-image has a significant | https://arxiv.org/abs/2505.22126v1 |
advantage over other models in terms of the quality of content generated. It produces illustrations with well-defined and well-expressed text. The structure of the illustration is clear. The basic elements of the reference image are reflected in the generated results. It can be said that GPT-4o-image has had preliminary, relatively qualified scientific text understanding and image generation capabilities. It can simply and clearly generate scientific, inferential and logical images. However, this is only preliminary capability. It should be noted that there are still significant problems in generating scientific illustrations from GPT. Examples include missing elements, omissions and errors in textual representation. Compared with the reference images drawn by human experts, there is still a big gap in their correctness and scientific accuracy. (a) (b) (c) Figure 9: Illustrations generated by GPT-4o-image (left) and their reference from original paper (right). Fig.9 shows generation results for GPT-4o-image which reflect the common problem of GPT-4o- image. As can be seen from Fig.9(a) , the illustrations generated by GPT contain common errors, such as the sun orbiting the Earth. At the same time, although it can draw multi-sub-graphs according to the requirements, the detailed information in the graph is still missing. In Fig.9(b) , it is seen that GPT-4o-image has the rudimentary ability to draw structural formulas for organic compounds. But there are still obvious scientific errors in the results, such as the reaction conditions in the diagram. At the same time, the compounds involved in the reactions and the results are not plotted correctly. This shows that the GPT understanding and expression of organic chemistry also has a lot of room for improvement. As you can see from Fig.9(c) , GPT-4o-image has rudimentary location understanding and map generation capabilities. However, for more precise positioning and interpretation of geographical processes, GPT-image still has errors and omissions. 5 Conclusion and Discussion In this paper, we propose SridBench, which is the first benchmark of scientific research illustration drawing for image generation model. We propose a strongly inferential drawing scenario called 9 scientific research illustration drawing. Using human experts and MLLM, we meticulously collected and screened triplet data on the scientific paper website for the evaluation of the scientific mapping ability of the image generation model. 1120 triples across 13 disciplines in the natural and computer science were used to test the ability of current scientific research to map multiple graph models. At the same time, we design six indicators to evaluate the generated illustrations. We found that, with the exception of GPT-4o-image, other image generation models, such as Gemini- 2.0-Flash, do not have any scientific mapping capabilities. GPT-4o-image can preliminarily complete scientific research drawing tasks, generate clear text and complete structure, and have a certain degree of professional results. However, there are problems with the GPT-4o-image as well. There is a general lack of text information, visual elements are also missing. At the same time, some hallucinations and common sense errors were also found in the GPT-4o-image generation results. This means that, at present, there is still much room for improvement in the ability of scientific research illustration | https://arxiv.org/abs/2505.22126v1 |
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Singh, T. Yu, S. Kim, V . Bursztyn, N. Vlassis, and R. A. Rossi, “Figcaps-hf: A figure-to-caption generative framework and benchmark with human feedback,” 2023. [Online]. Available: https://arxiv.org/abs/2307.10867 [29] Z. Xu, S. Du, Y . Qi, C. Xu, C. Yuan, and J. Guo, “Chartbench: A benchmark for complex visual reasoning in charts,” 2024. [Online]. Available: https://arxiv.org/abs/2312.15915 [30] G. Team, R. Anil, S. Borgeaud, J.-B. Alayrac, J. Yu, R. Soricut, J. Schalkwyk, A. M. Dai, A. Hauth, K. Millican et al. , “Gemini: a family of highly capable multimodal models,” arXiv preprint arXiv:2312.11805 , 2023. 12 A Prompts used in data process and judgement Image Filter Please strictly judge whether this picture belongs to the concept diagram, model frame diagram, process flow diagram or structure diagram in the academic paper. Return 1 if it is of the following type: concept diagram, model frame diagram, process flow diagram or structure diagram. Return 0 if it is of the following type: experimental result graph, statistical graph, photo, table, mathematical formula, pseudocode. Returns only a single number, 1 or 0, with no explanation. Note that some of the subplots in the images contain schematics and graphs of statistical analysis and experimental results. In each case, a“0” is returned as long as it contains a statistical analysis of the data and a diagram of the experimental results (not just a diagram). Text Generation(for computer science) You will get the text of a TEX file. In this file, find the text associated with the image {imgname}. Note that you can first find the latex figure class where {imgname}is located and identify its latex label in label, and then locate the text according to the label. The process cannot be included in the output. The output is the original content associated with {imgname}. Here is the paper: {paper tex}. Output the whole text content of the section (not just the name and label of the section) in which the image is located directly, without anything else. Image Generation You are a scientist and now you are going to draw a diagram for the computer (natural) science research paper. You will be given the paper section where the diagram is located and the caption of the diagram in the paper. The section is: section. The caption is: caption. Please draw a professional, rigorous and scientific diagram. You can use different colors and some graphic legends or logos appropriately. Note that the captions we provide do not need to be drawn in the diagram. Image Judgement You are a researcher who evaluates illustrations in research papers. Next you’ll receive two diagrams, the first one by an anthropologist and the second one by an AI model based on the same cue. Please rate the graph generated by the AI model on: completeness of textual information (whether it contains all the textual information in the reference graph), accuracy of textual information (whether the textual information is scientifically rigorous), diagrammatic structural integrity (does it draw all the elements of the diagram) , diagrammatic logic (does it arrange the elements scientifically and logically) , cognitive | https://arxiv.org/abs/2505.22126v1 |
readability (does it allow the reader to understand the content concisely) , aesthetic feeling, ie whether a drawing is aesthetically pleasing or has a sense of design. On a scale of 1 to 5(1: fail, 2: poor, 3: fair, 4: good, 5: excellent). Please return your comments in the following format: ’completeness of textual information’: 4, ’accuracy of textual information’: 4, ’diagrammatic structural integrity’: 5 , ’diagrammatic logic’:4 , ’cognitive readability’: 2, ’aesthetic feeling’: 3 Just return and its contents, as in the example above, without returning anything else. Here are two pictures: the first is drawn by a human, and the second is drawn by an AI. " 13 | https://arxiv.org/abs/2505.22126v1 |
arXiv:2505.22128v1 [cs.CV] 28 May 2025REAL-TIME BLIND DEFOCUS DEBLURRING FOR EARTH OBSERVATION: THE IMAGIN-E MISSION APPROACH Alejandro D. Mousist Thales Alenia Space, Tres Cantos, Spain ABSTRACT This work addresses mechanical defocus in Earth observation im- ages from the IMAGIN-e mission aboard the ISS, proposing a blind deblurring approach adapted to space-based edge computing con- straints. Leveraging Sentinel-2 data, our method estimates the defo- cus kernel and trains a restoration model within a GAN framework, effectively operating without reference images. On Sentinel-2 images with synthetic degradation, SSIM im- proved by 72.47% and PSNR by 25.00%, confirming the model’s ability to recover lost details when the original clean image is known. On IMAGIN-e, where no reference images exist, perceptual quality metrics indicate a substantial enhancement , with NIQE improv- ing by 60.66% andBRISQUE by 48.38% ,validating real-world onboard restoration . The approach is currently deployed aboard the IMAGIN-e mission, demonstrating its practical application in an operational space environment. By efficiently handling high-resolution images under edge com- puting constraints, the method enables applications such as water body segmentation and contour detection while maintaining process- ing viability despite resource limitations. Index Terms —GenAI, defocus noise, remote sensing, edge computing 1. INTRODUCTION AND STATE-OF-THE-ART The IMAGIN-e mission ( ISSMounted Accessible Global Imaging Nod-e) is a space edge computing initiative hosted aboard the In- ternational Space Station (ISS). IMAGIN-e operates as a functional demonstration payload with real-world applications for Earth obser- vation. Its primary objective is to evaluate the capabilities and op- erating modes of onboard edge computing by processing Earth ob- servation data directly within the payload. An optical sensor was in- tegrated to capture images that fuel onboard applications. However, the captured images exhibit significant mechanical defocus charac- terized by wide dispersion and smoothing (see Fig. 1), complicating precise interpretation and hindering the extraction of meaningful in- sights. In this context, missions like Sentinel-2 from the Copernicus program -which provide multispectral images with higher spatial resolution (GSD) and additional spectral bands— could serve as a reference to estimate the defocus kernel when contrasted with IMAGIN-e RGB images. Nonetheless, IMAGIN-e images are not georeferenced at origin and include uncertainties (e.g., the sensor’s final orientation due to its uncharacterized mechanical and thermoe- lastic misalignments), posing a significant challenge for restoration in the absence of sharp reference images. Recent studies, such as Popika and Lelechenko [1], have used synthetic distortions to train models for satellite image restoration in post-processing. Our approach builds on this idea, adapting it for Fig. 1 : Captured image from the IMAGIN-e payload without further processing, showing significant mechanical blur. onboard edge computing to enable real-time correction within the IMAGIN-e payload (see Section 3). Traditional deblurring approaches, such as the Wiener filter [2] or Richardson-Lucy deconvolution [3], rely on known blur kernel characteristics, which limits their performance for complex, non- uniform blurs observed in space-based imagery. The advent of deep learning has enabled robust alternative strategies. Early methods employed Convolutional Neural Networks (CNNs) to learn the map- ping between blurred and sharp images [4, 5], while GAN-based approaches like DeblurGAN [6, 7] addressed blind deblurring when | https://arxiv.org/abs/2505.22128v1 |
the blur kernel is unknown. More recently, transformer-based architectures have emerged as promising candidates for image restoration tasks. For instance, DeblurDiNAT[8] presents a compact model that leverages dilated neighborhood attention mechanisms to achieve robust generalization and high perceptual fidelity, even in out-of-domain settings . In parallel, MIMO-Uformer [9] inte- grates a U-shaped structure with window-based attention (W-MSA), enabling efficient capture of both local and global dependencies with a computational footprint suitable for resource-constrained environments. Despite these advances, most state-of-the-art approaches assume access to paired blurred-sharp images or mandate substantial com- putational resources, rendering them incompatible with the onboard processing constraints of the IMAGIN-e mission. 1.1. Contribution of This Work Our research contributes a blind deblurring methodology for satel- lite imagery without reference images that leverages Sentinel-2 data to characterize the defocus kernel. We adapt MIMO-Unet++[10] for space-based edge computing, optimizing computational efficiency while preserving restoration quality. Quantitative and qualitative analysis validates our approach, showing significant improvements in structural similarity and edge preservation. Additionally, we pro- Fig. 2 : Illustration of the payload orientation on the Bartolomeo platform, showing its backward tilt relative to the ISS trajectory. vide insights into deep learning-based image enhancement for space- based observation systems with limited resources. This study introduces a generative AI framework for defocus correction within the constraints of the IMAGIN-e mission, enhanc- ing onboard edge computing for Earth observation and enabling the effective utilization of otherwise compromised instruments. 2. PROBLEM CHARACTERIZATION 2.1. Platform and Payload Orientation The payload is hosted on an external platform for payload hosting, mounted on the Columbus module, which is externally mounted on the Columbus module of the International Space Station (ISS). Al- though its nominal alignment is Earth-facing, the imaging system is not perfectly oriented in the nadir direction; rather, it is directed a few degrees backward relative to the ISS trajectory (see Fig. 2). This orientation results in a non-perpendicular incidence angle compared to a purely nadir-pointing configuration, potentially affecting the ob- servation geometry and data acquisition characteristics. Moreover, the payload was installed using a robotic arm, so the exact sensor orientation relative to nadir was not known a priori. 2.2. Sensor Data Characteristics The sensor acquires RGB images compressed in JPEG format at a resolution of 2048×1536 pixels. The Ground Sample Distance (GSD) ranges from 37.5m to 41m, depending on altitude variations, ISS pitch fluctuations, and terrain elevation changes. The captured images exhibit significant optical defocus noise, likely due to me- chanical miscalibration, while some images also display minor shot noise, though its intensity is considerably lower than that of the de- focus blur. Figure 3 provides a spectral comparison between an IMAGIN-e capture and its corresponding Sentinel-2 scene, high- lighting the frequency-domain effects of these noise sources. 2.3. Onboard Deblurring Process The deblurring process is designed to be executed onboard without dedicated acceleration hardware as a critical step in the postprocess- ing stage of the capture pipeline. It takes place immediately after image acquisition, ensuring that restoration is completed before the images are passed on for further analysis. Third-party applications, which request image captures and process them | https://arxiv.org/abs/2505.22128v1 |
upon availability, rely on this preprocessing step to enhance data quality and optimize downstream computational tasks. Given the constraints of onboard execution without specialized hardware, the deblurring model must operate efficiently within the platform’s limited computational resources. To meet this challenge, (a) Sentinel-2: Scene and FFT spectrum (b) IMAGIN-e: Scene and FFT spectrum Fig. 3 : Comparison of Sentinel-2 and IMAGIN-e images along with their frequency spectra. The Sentinel-2 scene, composed of RGB bands downscaled to a 40m GSD, and its corresponding frequency spectrum are presented in (a). The IMAGIN-e scene and its respec- tive frequency spectrum are shown in (b), illustrating the effects of defocus and alterations in the frequency domain. the MIMO-Unet++ model was selected for its high efficiency in generative processing, enabling real-time deblurring with minimal hardware requirements. By integrating this model into the capture pipeline, image restoration is performed onboard without compro- mising system performance, ensuring that the processed images maintain the necessary fidelity for further analysis. 3. METHODOLOGY: DEBLURRING WITHOUT REFERENCE IMAGES 3.1. Model Architecture and Training Strategy To enhance structural features critical for georeferencing, we ex- tracted 1024×1024 pixel patches from Sentinel-2 imagery and down- scaled them to 256×256 pixels. This size reduction simplified the learning process by focusing the model on sharpening primary edge structures rather than on subtle textures. A batch size of 4 patches was chosen to balance computational efficiency with training stabil- ity. We used a MultiStepLR schedule with an initial learning rate of 1e-4, reducing it every 500 iterations by a factor of 0.5 over 3000 iterations to progressively refine the model’s ability to produce spa- tially coherent reconstructions. Initially, only defocused images—accompanied by tentative ge- olocation from the ISS’s position and attitude data were available, making it extremely difficult to align these images with established ground references due to severe defocus and unknown noise char- acteristics. To tackle this, we first trained an early version of the MIMO-Unet++ model using RGB images generated from Sentinel2 products and augmented with various noise types (Gaussian, defo- cus, shot, motion, and spin blur). The outputs of this model allowed us to correlate the images relative to their Sentinel-2 counterparts, leading to improved noise characterization and the creation of more realistic synthetic training data. Application requests capture Camera API forwards request Sensor captures image Post-processing: deblurring Image stored Application consumes processed image Fig. 4 : The diagram illustrates the position of the deblurring process within the image processing chain. An application requests an im- age from the camera API, which then communicates with the sensor for acquisition. The raw image undergoes a post-processing stage, including deblurring, before being stored for later consumption by the application. Table 1 : Problem conditions Parameter Value Acceleration HW Not present Available RAM memory 300 MB Virtual memory 2 GB Available CPU 3 cores (shared) Subsequently, we used these synthetic images to train a refined MIMO-Unet++ model within a GAN framework, with the model serving as the generator. A multi-scale discriminator was employed to leverage the generator’s outputs at different scales inspired by Pix2pixHD[11], enhanced with self-attention mechanisms [12] | https://arxiv.org/abs/2505.22128v1 |
and spectral normalization, ensuring effective extraction of features across all resolutions and promoting superior image reconstruc- tion. The overall loss function combined the standard adversarial loss with an L1 loss and an FFT-domain loss—as proposed in the original MIMO-Unet++ framework—as well as a perceptual loss computed using a VGG16 model pre-trained on Sentinel-2 images. This comprehensive training strategy yielded a robust generator ca- pable of delivering deblurred images with enhanced visual fidelity and structural accuracy, which is crucial for Earth observation tasks in edge computing environments. 3.2. Edge Implementation For deployment in the IMAGIN-e mission, the model must oper- ate onboard a hosted payload on the ISS, sharing computational resources with other processes and without dedicated acceleration hardware. Therefore, it is imperative to maintain low latency to ensure seamless integration into the image post-processing pipeline (see Fig. 4). The system constraints summarized in Table 1 require that processing speed and resource usage be carefully managed to meet the rigorous demands of edge computing environments. 4. RESULTS AND DISCUSSION The proposed deblurring approach significantly enhances image clarity and structural reconstruction. Initial models trained on (a) Raw image (b) Deblurred image Fig. 5 : Initial deblurring effectively sharpened main borders but pro- duced low quality images and ringing effect on some captures. Left image (Fig.5a) shows the output of the sensor, while right image (Fig.5b) shows the deblurred scene with the initial model. Table 2 : Image quality metrics for Sentinel-2 synthetic validation images and IMAGIN-e real ones Dataset Metric Original Deblurred ∆% Sentinel-2 (Synthetic)SSIM 0.4442 0.7662 +72.47% PSNR 24.0127 dB 30.0159 dB +25.00% IMAGIN-e (Real)NIQE 21.9257 8.6263 +60.66% BRISQUE 110.8351 57.2149 +48.38% Sentinel-2 imagery were able to improve the sharpness of IMAGIN- e data (see Fig. 5), enabling subsequent georeferencing and a more comprehensive characterization of noise type, effective resolution, and spectral sensitivity. In addition, the application of a Sobel edge detection filter confirmed that, despite some undetected boundaries, the edges of critical objects and terrains were more clearly delineated (See Fig. 6). These improvements are paramount for subsequent object detection and segmentation tasks in onboard applications. Quantitative evaluation demonstrates a substantial enhancement in image quality across multiple metrics (see Table 2). On Sentinel-2 images, SSIM improved by 72.47% and PSNR increased by 25.00%, calculated by comparing noisy synthetic images with reference im- ages in the initial state and processed images with the same refer- ences in the final state. In contrast, for IMAGIN-e, image percep- tual quality improved significantly, with NIQE showing a 60.66% enhancement and BRISQUE improving by 48.38%. Since these metrics evaluate image quality without requiring clean reference im- ages, they are particularly valuable for real-world applications where reference-free assessment is necessary, as is the case for IMAGIN-e. From a computational standpoint, the deblurring process op- erates within the edge computing constraints outlined in Table 1. Under these conditions, the model successfully processes a 2048x1536 pixel image in approximately 5 minutes, demonstrating its ability to handle high-resolution inputs despite resource limita- tions. Peak memory consumption reaches 600 MB, exceeding the available RAM and requiring the use of virtual memory. While | https://arxiv.org/abs/2505.22128v1 |
this contributes to an extended processing time, the results highlight the model’s adaptability in constrained environments and underscore the role of efficient memory management in optimizing performance. Occasional ringing artifacts were observed, probably due to scaling operations during patch processing (see Fig. 7). Moreover, the effective Ground Sample Distance (GSD) varied between 37.4 m and 41 m, reflecting the dynamic imaging conditions of the ISS and underscoring the need for adaptive processing workflows. (a) Border extraction from raw image (b) Border extraction from deblurred image Fig. 6 : Edge detection using a Sobel filter from both the raw image (6a) and the deblurred version of it (6b) . Fig. 7 : Ringing effect on the images 5. CONCLUSIONS AND FUTURE WORK Despite the inherent complexity of blind deblurring, our results demonstrate that incorporating Sentinel-2 imagery enables an ef- fective iterative processing approach. This strategy allowed us to refine the image synthesis techniques and achieve acceptable out- comes—even without access to a sharp reference image. The final model is fast and efficient enough to be executed onboard during the post-processing phase, ensuring compatibility with the IMAGIN-e mission and maximizing the use of the instrument, which might otherwise be underutilized. Moreover, the restored images prove valuable for specific appli- cations, such as water body segmentation and coarse contour detec- tion for map generation. However, it is important to note that while these results are promising for certain contexts, the current resolu- tion is insufficient for detecting small objects or for the fine segmen- tation of closely related classes. This limitation reflects the trade-off between processing speed and image quality inherent in edge com- puting scenarios. Further research could focus on leveraging enhanced onboard computational resources to deploy more powerful models that pro- cess image patches at their original resolution. By eliminating the need for downscaling and subsequent upscaling, this approach would likely yield images with increased realism and detail. Such improve-ments could enhance the deblurring performance while expanding the applicability of the processed imagery, especially in tasks requir- ing the detection of small objects or fine-grained segmentation. REFERENCES [1] Viacheslav Popika and Lidia Lelechenko. Machine learning models for eos sat-1 satellite image enhancing. In IGARSS 2024-2024 IEEE International Geoscience and Remote Sens- ing Symposium , pages 1095–1098. IEEE, 2024. [2] Norbert Wiener. Extrapolation, interpolation, and smoothing of stationary time series: With engineering applications. 1949. [3] William Hadley Richardson. Bayesian-based iterative method of image restoration. Journal of the Optical Society of America , 62(1):55–59, 1972. [4] Seungjun Nah, Tae Hyun Kim, and Kyoung Mu Lee. Deep multi-scale convolutional neural network for dynamic scene deblurring. In Proceedings of the IEEE conference on com- puter vision and pattern recognition , pages 3883–3891, 2017. [5] Xin Tao, Hongyun Gao, Xiaoyong Shen, Jue Wang, and Jiaya Jia. Scale-recurrent network for deep image deblurring. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , pages 8174–8182, 2018. [6] Orest Kupyn, V olodymyr Budzan, Mykola Mykhailych, Dmytro Mishkin, and Ji ˇr´ı Matas. Deblurgan: Blind motion deblurring using conditional adversarial networks. In Proceed- ings of the IEEE Conference on | https://arxiv.org/abs/2505.22128v1 |
Computer Vision and Pattern Recognition , pages 8183–8192, 2018. [7] Orest Kupyn, Tetiana Martyniuk, Junru Wu, and Zhangyang Wang. Deblurgan-v2: Deblurring (orders-of-magnitude) faster and better. In Proceedings of the IEEE/CVF International Con- ference on Computer Vision (ICCV) , October 2019. [8] Hanzhou Liu, Binghan Li, Chengkai Liu, and Mi Lu. Deblur- dinat: A lightweight and effective transformer for image de- blurring. arXiv e-prints , pages arXiv–2403, 2024. [9] Jian Zhang, Baoping Cheng, Tengying Zhang, Yongsheng Zhao, Tao Fu, Zijian Wu, and Xiaoming Tao. Mimo-uformer: A transformer-based image deblurring network for vehicle surveillance scenarios. Journal of Imaging , 10(11):274, 2024. [10] Sung-Jin Cho, Seo-Won Ji, Jun-Pyo Hong, Seung-Won Jung, and Sung-Jea Ko. Rethinking coarse-to-fine approach in sin- gle image deblurring, 2021. URL https://arxiv.org/ abs/2108.05054 . [11] Ting-Chun Wang, Ming-Yu Liu, Jun-Yan Zhu, Andrew Tao, Jan Kautz, and Bryan Catanzaro. High-resolution image syn- thesis and semantic manipulation with conditional gans. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , pages 8798–8807, 2018. [12] Han Zhang, Ian Goodfellow, Dimitris Metaxas, and Augus- tus Odena. Self-attention generative adversarial networks. In International Conference on Machine Learning , pages 7354– 7363, 2019. | https://arxiv.org/abs/2505.22128v1 |
arXiv:2505.22137v1 [cs.CL] 28 May 2025Limited Generalizability in Argument Mining: State-Of-The-Art Models Learn Datasets, Not Arguments Marc Feger Heinrich-Heine-University Düsseldorf, Germany marc.feger@hhu.deKatarina Boland Heinrich-Heine-University Düsseldorf, Germany katarina.boland@hhu.de Stefan Dietze GESIS - Leibniz Institute for the Social Sciences & Heinrich-Heine-University Düsseldorf, Germany stefan.dietze@gesis.org Abstract Identifying arguments is a necessary prerequi- site for various tasks in automated discourse analysis, particularly within contexts such as political debates, online discussions, and sci- entific reasoning. In addition to theoretical advances in understanding the constitution of arguments, a significant body of research has emerged around practical argument min- ing, supported by a growing number of pub- licly available datasets. On these benchmarks, BERT-like transformers have consistently per- formed best, reinforcing the belief that such models are broadly applicable across diverse contexts of debate. This study offers the first large-scale re-evaluation of such state-of-the- art models, with a specific focus on their ability to generalize in identifying arguments. We eval- uate four transformers, three standard and one enhanced with contrastive pre-training for bet- ter generalization, on 17 English sentence-level datasets as most relevant to the task. Our find- ings show that, to varying degrees, these mod- els tend to rely on lexical shortcuts tied to con- tent words, suggesting that apparent progress may often be driven by dataset-specific cues rather than true task alignment. While the mod- els achieve strong results on familiar bench- marks, their performance drops markedly when applied to unseen datasets. Nonetheless, in- corporating both task-specific pre-training and joint benchmark training proves effective in enhancing both robustness and generalization. 1 Introduction Undeniably, discourse gives people the opportunity to express and discuss their beliefs on any topic. Argument mining, in this sense, is the automatic identification of the structure of inference and rea- soning expressed as arguments presented in natural language (Lawrence and Reed, 2019).Although there is no one-size-fits-all answer to What is an argument? (Stab et al., 2018), the idea suggests itself that arguments are latent yet observ- able and revolve around how they are constituted in terms of their logical scaffolding of argument discourse units, rather than what specific subject they address. In practice, these elements, whether sentences or sub-sentence segments, are pragmat- ically assigned functional roles, most commonly claims and premises, and form the fundamental building blocks of an argument (Stab and Gurevych, 2014; Daxenberger et al., 2017; Lawrence and Reed, 2019; Lopes Cardoso et al., 2023). Consider the example X should Y, because Z , such as Students should study, because it improves grades orWe should reduce plastic use, because it minimizes ocean pollution , which illustrates that the manifestation of an argument should ideally rely on structural components conveyed through functional patterns, while remaining agnostic of certain topics or other content-specific elements. For this reason, one might assert that argument mining, in theory, is applicable across different cor- pora if the structural signals defining arguments are reliably identifiable from appropriately labeled data. Conversely, in practice, any inability to apply these signals to diverse datasets may expose sys- tematic biases in the field, an issue that has long been informally discussed over coffee breaks. | https://arxiv.org/abs/2505.22137v1 |
Generalizability, in this regard, takes high pri- ority, especially at leading NLP conferences such as ACL 2025, as it allows models to make reliable and reasonable predictions on data that does not correspond to their training data. This is especially true for real-world models, which should mimic human-like generalization abilities, where emerg- ing evidence indicates that such models are often fine-tuned to the specifics of established benchmark datasets, leading to unfounded optimism about their improvements (Saphra et al., 2024). Consequently, concerns about vulnerability to shortcut learning (Geirhos et al., 2020) highlight the broader challenge of evaluating baselines be- yond isolated benchmarks (Rendle et al., 2019). Argument mining is one such area of natural lan- guage processing applications in which the ability to generalize is key. Hence, we ask for: Q1:How comparable are the existing benchmark datasets for argument mining? Q2:Do state-of-the-art argument mining models generalize to out-of-distribution data from other benchmarks? Q3:Do these models acquire a generalizable con- cept of arguments? In this context, there has been speculation that BERT (Devlin et al., 2019), known to pay great attention to basic syntax, nouns, and co- references (Clark et al., 2019), is prone to learning shortcuts when mining arguments (Geirhos et al., 2020), where its generalization is limited to within- topic signals in datasets sharing similar argument and topic structures (Thorn Jakobsen et al., 2021). Our aim is not to propose a new formalism for arguments or to pinpoint the best-performing argu- ment mining model, but to use data from previous work in which different theories have been applied to see whether individual efforts and perspectives converge in terms of identifying arguments. With this being said, we perform the first large- scale experimental assessment of benchmarks, sys- tematically evaluating generalization across diverse argument mining datasets following a comprehen- sive review of datasets spanning 2008 to 2024. For our study, we selected BERT (Devlin et al., 2019), RoBERTa (Liu et al., 2019), and Distil- BERT (Sanh et al., 2019) as exemplary BERT- like models, widely recognized as standard base- lines in various areas of natural language process- ing (Rogers et al., 2020), including recent research on argument mining (Shnarch et al., 2020; Mayer et al., 2020a; Fromm et al., 2021a; Alhamzeh et al., 2022; Feger and Dietze, 2024b). We also examine WRAP (Feger and Dietze, 2024a), the only trans- former whose language representation pre-training is extended by leveraging contrasts of inference and information signals to generalize argument compo- nents. Although originally designed for cross-topicgeneralization on Twitter ( X), WRAP does not rely on tweet- or topic-specific features to enhance its generalizability, distinguishing it from the others and making it particularly interesting for research. In this study, we start by detailing our process of finding argument mining benchmark datasets and explain the selection criteria and justifications in Section 2. The core characteristics of these datasets, addressing research question Q1, are then exam- ined in Section 3. Next, we describe our exper- imental setup in Section 4, covering both result generation and the implementation of best prac- tices for significance testing, which form the basis for | https://arxiv.org/abs/2505.22137v1 |
answering Q2 - Q3 in Section 5. The results of this paper are then discussed in Section 6 and concluded in Section 7. In order not only to elucidate the process but also to foster discussion that may inspire new ap- proaches for novel datasets and broader generaliza- tion of argument mining methods, we contribute: 1.A survey of argument mining datasets be- tween 2008 and 2024, primarily from the ACL Anthology, that identified 52 relevant papers with datasets from leading NLP conferences. 2.The first large-scale re-assessment that com- bines benchmark evaluations for 17 selected argument mining datasets, including con- trolled manipulation experiments to determine whether the reported state-of-the-art models (BERT, RoBERTa, DistilBERT, WRAP) actu- ally learn generalizable argument concepts. 3.Statistical evidence that shortcut learning un- dermines generalization in argument mining. Although each of the examined transform- ers delivers strong results on benchmarks, all struggle to varying degrees when applied to other datasets, with WRAP generally perform- ing slightly better. These challenges are com- pounded by divergent argument definitions and inconsistent annotations across datasets. 2Argument Mining Benchmark Datasets This section outlines the dataset collection and se- lection process, emphasizing the rationale behind our choice of benchmark datasets for argument min- ing. The decisions for all 52 datasets reviewed are present in Appendix A.1. Additionally, the code and data are available in our repository1. 1Limited-Generalizability Dataset Paper Genre Definition Arguments No-Arguments ACQUA (Panchenko et al., 2019) Mixed Argumentative 1,949 5,236 WEBIS (Al-Khatib et al., 2016a) Online Debate Argumentative 10,804 5,543 ABSTRCT (Mayer et al., 2020b) Academic Claim-based 1,308 7,323 ARGUMINSCI (Lauscher et al., 2018) Academic Claim-based 6,554 9,548 CE (Rinott et al., 2015) Encyclopedia Claim-based 1,546 85,417 CMV (Hidey et al., 2017) Online Debate Claim-based 979 1,593 FINARG (Alhamzeh et al., 2022) Spoken Debate Claim-based 4,607 8,310 IAM (Cheng et al., 2022) Mixed Claim-based 4,808 61,715 PE (Stab and Gurevych, 2017) Academic Claim-based 2,093 4,958 SCIARK (Fergadis et al., 2021) Academic Claim-based 1,191 10,503 USELEC (Haddadan et al., 2019) Spoken Debate Claim-based 13,905 15,188 V ACC (Morante et al., 2020) Online Debate Claim-based 4,394 17,825 WTP (Biran and Rambow, 2011) Online Debate Claim-based 1,135 7,274 AFS (Misra et al., 2016) Online Debate Conclusion-based 5,150 1,036 UKP (Stab et al., 2018) Mixed Evidence or Reasoning 11,126 13,978 AEC (Swanson et al., 2015) Online Debate Implicit-Markup 4,001 1,374 TACO (Feger and Dietze, 2024b) Twitter Debate Inference-Information 864 868 Table 1: The final 17 datasets that meet the sentential, binary label, and reproducibility criteria, each yielding at least 1,700 instances (850 per label) under a stratified 60/20/20 split, ensuring adequate size for the experiments. 2.1 Collection Process As part of our data collection process, we examined the most recent and relevant survey papers on argu- ment mining, primarily from the ACL Anthology (Daxenberger et al., 2017; Cabrio and Villata, 2018; Lawrence and Reed, 2019; Vecchi et al., 2021; Schaefer and Stede, 2021; Ajjour et al., 2023), all of which catalog datasets addressing various sub- tasks within the field, where argument identifica- tion is a fundamental prerequisite for each. To expand and back up our dataset collection, we | https://arxiv.org/abs/2505.22137v1 |
searched Google Scholar and Google Dataset Search for the keyword argument mining to find contributions beyond survey papers. Based on our assessment, we found 52 such pa- pers with datasets, mostly from top NLP confer- ences like ACL, NAACL, LREC, or EMNLP. 2.2 Selection Criteria The dataset selection process for this paper was conducted in two stages. In the primary inclusion phase, we evaluated all 52 datasets based on: •Sentential : The data and labels are at the sentence-level or aggregatable to this level (e.g., from sub-sentence or token annotations). Tweets were excluded from classical sentence conventions due to their unique structure. •Binary : The dataset assigns binary labels to distinguish argument from no-argument sen- tences (e.g., based on the presence or absence of claims or other argument components).•Reproducible : The dataset is largely replica- ble, with minor discrepancies from the pub- lication (e.g., updates or duplicate removal affecting size). To ensure reproducibility, we reviewed documentation, labels, guidelines, and tools, and attempted to resolve access is- sues (e.g., client-sided or coding errors). We applied these criteria sequentially, excluding datasets immediately upon failing any condition, eliminating 24 of the initial 52. In the refined in- clusion step, we assessed relationships and data sufficiency to ensure adequate evaluation and gen- eralization sizes, leading us to consider: •Related : Connections between datasets such as updated versions, additional non-task- related features (e.g., stance added to a claim), and curated subsets derived from repositories that serve as data sources rather than datasets. •Sufficiency : For a stratified 60/20/20 split, each dataset must have at least 500 training instances and 150 evaluation instances per la- bel. An initial analysis revealed that two in five datasets fell short of this threshold, and alternative splits (e.g., 70/15/15 or 80/10/10) would further reduce evaluation sizes, wors- ening the small-data issue. In total, this process resulted in 17 datasets en- compassing ~345k labeled sentences, each meeting the aforementioned criteria. The final selection of datasets included in this study is listed in Table 1. 3 Characterizing Argument Mining Benchmark Datasets and Definitions Before addressing Q1, we briefly introduce the in- dividual datasets, organizing them by their primary labels. We then give the answer to Q1in terms of comparing definitions in Section 3.1 and textual characteristics in Section 3.2. Argumentative serves as an umbrella term, iden- tifying arguments with markers or patterns that suggest structural components, without necessarily specifying their roles (e.g., as claim or inference). In this sense, ACQUA (Panchenko et al., 2019) con- tains 7,185 argumentative sentences from Common Crawl (Panchenko et al., 2018), covering topics like computer science and brands, categorizing compar- isons (e.g., Matlab vs. Python) as argumentative or not. Similarly, WEBIS (Al-Khatib et al., 2016a) comprises 16,347 segments across 14 topics (e.g., culture, health) from iDebate, with user-assigned labels (introduction, for, against) mapped to argu- mentative and non-argumentative labels. Claim-based approaches explicitly annotate for the presence of claims as the core of an argument. Thereby, ABSTRCT (Mayer et al., 2020b), sourced from PubMed, comprises 8,631 sentences extracted from abstracts related to five diseases (e.g., neo- plasm, glaucoma). ARGUMINSCI (Lauscher | https://arxiv.org/abs/2505.22137v1 |
et al., 2018) provides annotations for the Dr. In- ventor dataset (Fisas et al., 2016) for computer graphics publications, totaling 16,102 sentences. CE (Rinott et al., 2015) contains 86,963 sentences from Wikipedia across 58 topics (e.g., one-child policy, physical education). CMV (Hidey et al., 2017) consists of 2,572 sentences from the Change My View subreddit, spanning a diverse range of topics. FINARG (Alhamzeh et al., 2022) com- prises 12,917 sentences sourced from transcribed earnings calls of Amazon, Apple, Microsoft, and Facebook. Moreover, IAM (Cheng et al., 2022) contains 66,523 sentences from various online plat- forms across 123 topics (e.g., vaccination, multi- culturalism), while PE (Stab and Gurevych, 2017) includes 7,051 annotated sentences from persuasive essays (e.g., about cloning). SCIARK (Fergadis et al., 2021) contains 11,694 annotated sentences from scientific literature (e.g., PubMed, Semantic Scholar) on sustainable development goals (e.g., well-being, gender equality), also considering gen- eralization to ABSTRCT. On the other hand, US- ELEC (Haddadan et al., 2019) offers 29,093 sen- tences from transcripts of U.S. presidential debatesfrom 1960 (Kennedy vs. Nixon) to 2016 (Clinton vs. Trump), transcribed from the Commission on Presidential Debates. V ACC (Morante et al., 2020) offers 22,219 sentences from a mixed collection of online debates about vaccination, while WTP (Bi- ran and Rambow, 2011) includes 8,409 sentences from Wikipedia Talk Pages on various topics (e.g., Darwinism, the Catholic Church). Others represents a residual category encom- passing a variety of distinct definitions. AFS (Misra et al., 2016) comprises 6,186 annotated sentences drawn from online debate platforms such as iDe- bate and ProCon for three topics (e.g., gay mar- riage, death penalty). Sentences are labeled based on whether they explicitly convey a specific argu- ment facet, with conclusions serving as the core component of the argument. UKP (Stab et al., 2018) contains 25,104 sentences across eight top- ics (e.g., nuclear energy, minimum wage) for cross- topic argument mining from heterogeneous sources, where arguments provide evidence or reasoning to support or oppose a topic. On the other hand, AEC (Swanson et al., 2015) contains 5,375 sen- tences on four topics (e.g., evolution, gun control) from CreateDebate, highlighting simple argument signals with labels based on the implicit markups: so, if, but, first, I agree that. Finally, TACO (Feger and Dietze, 2024b) comprises 1,734 tweets span- ning six topics (e.g., abortion, Squid Game). It is designed for cross-topic argument mining on Twit- ter, focusing on inference to shape arguments. 3.1 Comparing Argument Definitions (Q1) Argument definitions vary, reflecting a spec- trum of perspectives that contribute to a shared understanding of arguments. Central to this is the observation that definitions mutually inform each other in their concepts (Lopes Cardoso et al., 2023). For example, in Table 1 most papers are claim-based, but when comparing the definitions, some view a claim as argumentative (Lauscher et al., 2018; Fergadis et al., 2021), others as conclu- sive (Mayer et al., 2020b), as stances (Rinott et al., 2015; Hidey et al., 2017; Cheng et al., 2022; Stab and Gurevych, 2017), or as a hybrid concept of all these (Haddadan et al., 2019; Morante et al., 2020). Hence, | https://arxiv.org/abs/2505.22137v1 |
further clarification is needed, especially concerning their generalization as part of Q2 - Q3 . Thereby, Table 2, with examples from different definitions, illustrates whether their efforts never- theless converge in the identification of arguments despite different perspectives. Label Dataset Example ARGACQUA We chose MySQL over PostgreSQL primarily because it scales better and has embedded replication. SCIARK In this case, if symptomatic, the treatment should be surgery, clinical follow-up, and counseling. AEC So it would seem that if there is a scientific theory of [. . . ], it has been tested [. . . ] and therefore [. . . ]. ¬ARGWEBIS The Mo Ibrahim Prize was first established in 2007, and the prize represents [. . . ] African leadership. FINARG For those unable to attend in person, these events will be webcast and you can follow [...] at URL. TACO ’Bitter truth’: EU chief [...] on idea of Brits keeping EU citizenship after #Brexit URL via USER Table 2: Examples of argument (ARG) and no-argument ( ¬ARG) sentences from various datasets. Despite differences in definitions and topics, the similarities within and distinctions between label groups underscore the shared endeavor of argument mining approaches in identifying arguments, though each emerged differently. 3.2 Comparing Dataset Dimensions First, the two text dimensions used to analyze the selected datasets are presented. For dataset-wise correlations of these, please refer to Appendix A.2. Sentence-Level : To capture a broad, macro- level view without delving into individual word details, we used spaCy2to extract key textual at- tributes. These features reveal the overall structural and statistical properties of sentences, enabling sentence-level characterization of each dataset by: •Length : Measured by the number of words per sentence, which serves as an indicator of linguistic complexity and verbosity. •Stop/Function Word Ratio : The ratio of stop (e.g., it, is, are) and function words (e.g., against, because, therefore), including dis- course markers, to the other words in a sen- tence to show their relative frequency of use. •Type-Token Ratio : The ratio of unique words to total words in a sentence, assessing lexical diversity. •Readability : The Flesch Reading Ease score quantifies text clarity, with lower values ( 0≤) indicating complex academic language and higher values ( ≤100) denoting easy readabil- ity, understandable by an 11-year-old. •Entropy : Quantifies lexical unpredictability and the amount of information in a sentence, with values ranging from 0 (fully predictable text) to 1 (maximal unpredictability). •Sentiment : Defined by polarity, ranging from -1 (extremely negative) to 1 (extremely pos- itive), and subjectivity, ranging from 0 (ob- jective) to 1 (subjective), possibly revealing persuasive strategies through emotions. 2spacy.io•Part-of-Speech Tags : The distribution of the 17 universal POS tags reflects basic syntax, lexical composition, and stylistic variation. Word-Level : To compare datasets at the word level, we analyze the vocabulary of unique words used in each dataset. We extend this to words that convey the central semantic content of a sentence (e.g., government, abortion, freedom), that is, all words except stop and function words, discourse markers, and punctuation. Their relatedness or uniqueness is described using Jaccard | https://arxiv.org/abs/2505.22137v1 |
similarity, a measure of similarity between two sets based on the ratio of their intersection to their union. (Q1) The sentence structures are strongly corre- lated across all datasets and labels. On average, a sentence contains 21 words, with nearly every second word (48%) being a stop or function word. Sentences are lexically diverse (91% type-token ratio) yet highly readable (63% readability). The high predictability (22% entropy) and objective tone (43% subjectivity) suggest clear, structured writing with a slightly positive inclination (8% po- larity). This is reinforced by the POS patterns, where sentences typically include five nouns, three punctuation marks, and two verbs, adpositions, and determiners, with other tags averaging below two. Moreover, an average sentence closely aligns with both argument and no-argument sentences across these 24 sentence-level features (Spearman’s ρ≥0.97), with a strong correlation ( ρ≥0.68) across datasets. Slight differences exist in length, with an argument sentence averaging 24 words compared to 20 for a no-argument sentence, with readability scores of 60% and 64%, respectively. (Q1) Datasets and labels mainly differ in their semantic content. Looking at the vocabularies, the datasets remain largely distinct, with 7–36% Jac- card similarity, a trend also observed for the seman- tic content words, reflecting their open-class. In contrast, stop, function, and discourse words show over 73% overlap due to their closed nature. Interestingly, while comparing sentences across labels shows similar patterns, words describing the core semantic content remain largely distinct, over- lapping below 48% and 19% on average, reinforc- ing lexical separation. Undeniably, the datasets share overlapping content, e.g., when discussing the one-child policy (PE) and abortion (IAM, TACO, UKP) or, figuratively speaking, the death penalty (AEC). Similarly, when discussing vacci- nation (V ACC) overlaps might occur with medical (ABSTRCT) or sustainability (SCIARK) topics. However, we found that these similarities are not very pronounced and that the datasets and labels are largely disjointed in terms of their core semantic content. This could provide the models with a shortcut opportunity, not based on how the labels are constructed, but rather on what they are about. 4 Experimental Setup In this section, we outline the experimental setup and the best practices used for statistical testing to generate the data needed to answer Q2 - Q3 . Sampling : To create fixed training, develop- ment, and test sets, we used a 60/20/20 stratified split for each of the 17 datasets in Table 1, select- ing 850 instances per label, corresponding to 1,700 samples per dataset and 28,900 in total. Transformers : We selected BERT (Devlin et al., 2019), RoBERTa (Liu et al., 2019), and Distil- BERT (Sanh et al., 2019) as widely accepted stan- dard baselines for NLP (Rogers et al., 2020), in- cluding argument mining (Shnarch et al., 2020; Mayer et al., 2020a; Fromm et al., 2021a; Al- hamzeh et al., 2022; Feger and Dietze, 2024b). Further, we examined WRAP (Feger and Dietze, 2024a), the only transformer that is specifically pre- trained for argument generalization. This applies contrastive learning to cluster similar manifesta- tions of inference and information, separate dis- similar ones, and produce generalized embeddings robustly | https://arxiv.org/abs/2505.22137v1 |
adaptable to downstream classification. However, our goal is to assess the generalizability of these state-of-the-art argument mining models, not to find the best. For these, we use the standard hyperparameter grid for GLUE (Wang et al., 2018), as accepted in the BERT and RoBERTa papers, bal- ancing performance and time with a batch size of 32, 3 epochs, and a learning rate between 2e-5 and 5e-5, each trained on an A100 GPU.Benchmarking and Generalization : The exper- iments presented here are the core investigations related to Q2. For each, we report the test results after tuning the hyperparameters to a target’s devel- opment dataset, optimizing the macro F1 score to ensure equal importance of both labels. We begin with an initial assessment using pair- wise comparisons, following the transfer learning framework (Pan and Yang, 2010; Houlsby et al., 2019; Zhuang et al., 2019), where models are trained on one dataset and evaluated on others, in- cluding benchmarks on individual datasets. This yields a 17×17matrix per model, with rows as training and columns as test data, see Figure 1. Secondly, we conducted a supplementary ex- periment by training on all but one dataset and testing on the reserved one, forcing the models to generalize from joint benchmark data (Hays et al., 2023; Feger and Dietze, 2024a). Thereby, we will report the performance per model and evaluate each against the excluded dataset’s state-of-the-art benchmark, compare Table 4 and Figure 1. Disrupting Argument Signals : To build on the experiments addressing Q2and provide insight for Q3, we apply controlled input manipulation to both experiments described above. Specifically, we as- sess transformer performance after systematically removing stop and functional words (e.g., a, the, against, because), discourse markers, and punctua- tion using spaCy2. This process results in the elimi- nation of around half the words in each sentence. It is therefore assumed that the removal of these lexi- cal and syntactic elements, which also function as scaffolding for rhetorical and logical devices (Knott and Dale, 1994), suppresses the linguistic cues that, in theory, enable the distinction between the ele- ments that constitute an argument and those that do not (Daxenberger et al., 2017; Opitz and Frank, 2019; Thorn Jakobsen et al., 2021). What remains is a lexical skeleton that primarily reflects topical and subject-related content while omitting func- tional and discursive elements, calling into ques- tion the model’s ability to discern argued excerpts from mainly descriptive content (Lopes Cardoso et al., 2023), see Table 3. Evaluation : We perform the experiments for Q2 - Q3 and repeat them three times, each with varied samples and training initializations. To test significance, we use a two-way ANOV A with re- peated measures for experimental robustness and one-tailed Student’s t-tests for pairwise compar- isons of models, see Appendix B for full details. Label Form Example ARG OriginalThey should increase more routes to make people transport more easily. Manipulated increase routes people transport easily ¬ARG OriginalShould governments spend more money on improving roads and highways? Manipulatedgovernments spend money improving roads highways Table 3: Example from PE showing an argument (ARG) and | https://arxiv.org/abs/2505.22137v1 |
no-argument ( ¬ARG) sentence in the original and manipulated form. 5 Results In this section, we will address and answer ques- tions Q2 - Q3 . To this end, we will mainly focus on Figure 1, which compares the pairwise exper- iments to show which state-of-the-art argument mining model performs best, thus reflecting the current benchmark and generalization landscape. Tying in with this, we will then turn on Table 4 contrasting the state-of-the-art performance against those obtained by the models if trained on hetero- geneous data. In addition, we elaborate on the insights gained from the controlled manipulations applied to these experiments. After that, we will discuss the significance of our results. However, for a better understanding, it can already be as- sumed that the results for each model and exper- iment follow a normal distribution, as confirmed with D’Agostino and Pearson’s K2test (p≥.05). ACQUA WEBISABSTRCT ARGUMINSCICECMVFINARGIAMPE SCIARK USELECV ACC WTP AFS UKP AECTACOACQUA WEBIS ABSTRCT ARGUMINSCI CE CMV FINARG IAM PE SCIARK USELEC V ACC WTP AFS UKP AEC TACOR 0.84W 0.53W 0.65W 0.65W 0.67W 0.58D 0.59W 0.61W 0.58W 0.55W 0.62W 0.63W 0.58W 0.51W 0.61W 0.55W 0.81 W 0.69D 0.74D 0.70D 0.63D 0.62B 0.53D 0.53D 0.68R 0.57R 0.64R 0.65W 0.72W 0.57W 0.52R 0.64W 0.55W 0.81 R 0.72W 0.60W 0.89B 0.77W 0.69R 0.62R 0.64D 0.69D 0.62R 0.76R 0.72R 0.66B 0.60W 0.58R 0.65W 0.61W 0.75 D 0.63D 0.48D 0.77D 0.84D 0.59D 0.57D 0.55D 0.49D 0.45R 0.62B 0.66D 0.62D 0.56D 0.50D 0.61D 0.58R 0.75 W 0.69R 0.63W 0.64W 0.60R 0.85B 0.61W 0.55R 0.73D 0.64W 0.67B 0.68W 0.68W 0.57D 0.61R 0.71B 0.59W 0.80 W 0.64W 0.57B 0.62R 0.63R 0.71R 0.67R 0.64W 0.59B 0.69R 0.70W 0.71B 0.64R 0.64D 0.55W 0.58R 0.61W 0.79 W 0.70W 0.58W 0.66W 0.69D 0.61W 0.59R 0.68W 0.63R 0.68R 0.67W 0.71W 0.63B 0.56W 0.56W 0.64W 0.61W 0.74 W 0.74B 0.67W 0.68W 0.55R 0.76W 0.61B 0.58B 0.76B 0.66R 0.70W 0.69R 0.66W 0.59B 0.61W 0.74W 0.55W 0.80 W 0.66W 0.57B 0.55B 0.47D 0.68W 0.62B 0.61R 0.62B 0.78B 0.73B 0.64B 0.57W 0.60D 0.53W 0.59W 0.63W 0.64 B 0.58R 0.66B 0.82W 0.59W 0.70D 0.61B 0.61D 0.69B 0.71D 0.83R 0.70B 0.68B 0.54B 0.59R 0.67R 0.62B 0.68 W 0.70B 0.62W 0.70W 0.71B 0.70D 0.66R 0.68B 0.66B 0.65D 0.67D 0.74B 0.71B 0.62B 0.56W 0.62D 0.62W 0.84 B 0.62R 0.63B 0.67B 0.73W 0.79R 0.64W 0.59B 0.67B 0.64R 0.70W 0.69W 0.78B 0.59B 0.57D 0.66D 0.59W 0.85 W 0.65W 0.60B 0.62D 0.76R 0.69R 0.66R 0.60R 0.59R 0.69R 0.58B 0.63B 0.63W 0.65D 0.60W 0.57W 0.60W 0.81 W 0.60W 0.49W 0.73W 0.46W 0.66W 0.53W 0.37W 0.60W 0.59W 0.62W 0.51W 0.64W 0.52D 0.84W 0.68W 0.58W 0.63 W 0.68B 0.61B 0.78W 0.64W 0.75W 0.57B 0.44B 0.72B 0.64B 0.65W 0.59W 0.65W 0.52D 0.68B 0.79W 0.57W 0.74 W 0.45D 0.39W 0.40D 0.50D 0.38W 0.50D 0.56D 0.40B 0.55W 0.36W 0.54W 0.43W 0.46W 0.45W 0.45B 0.96W 0.46 W 0.69W 0.61B 0.68D 0.71R 0.64W 0.50W 0.61R 0.61W 0.56R 0.66W 0.68W 0.66W 0.56W 0.45R 0.61W 0.57W 0.88 Argumentative Claim-based Others 0.40.50.60.70.80.91.0 Figure 1: The best macro F1 scores from the benchmark- ing and pairwise generalization experiments, compar- ing WRAP (W), BERT (B), RoBERTa (R), and Distil- BERT (D), indicate that strong performance is | https://arxiv.org/abs/2505.22137v1 |
primarily achieved in the benchmark settings, as reflected along the main diagonal. Furthermore, WRAP excels in gen- eralizing to TACO, as seen on the right.(Q2) Strong argument mining baselines do not necessarily imply strong argument generalization. A notable observation in Figure 1 is the contrast between baselines on individual datasets and gen- eralization across multiple datasets and definitions. Strikingly, 97% of generalization experiments fall below the mean benchmark result ( M= 0.79), with 62% scoring under 0.65, while in 8% of cases generalization drops below 0.5 macro F1, highlight- ing the challenge of maintaining strong benchmark performances when tested on out-of-distribution datasets. We will further break down our answer: Generalizability seems to be the exception rather than the norm. Given these circumstances, Table 1 shows several notable exceptions of good ( ≥0.75) to strong ( ≥0.8) generalizability across and within both definitional categories and genres, particularly for claim-based datasets. For instance, strong per- formance emerges within the academic domain, where SCIARK reaches 0.82 on ABSTRCT with BERT, and both ABSTRCT and ARGUMINSCI achieve 0.77 using BERT and DistilBERT. Evi- dence of cross-genre generalization also appears in cases such as IAM (mixed genre) and V ACC (online debate), which achieve 0.76 and 0.79 on CE (encyclopedia) using RoBERTa and WRAP. Broader generalization across definitions and genres is especially evident in UKP (evidence or reasoning, mixed), which surpasses 0.75 on both ABSTRCT (claim-based, academic) and CE (claim- based, encyclopedia) with BERT and WRAP. Sim- ilarly, TACO (inference-information, Twitter de- bate) consistently exceeds 0.8 across a vast range of definitions and genres with WRAP. Still, both cross-definition and cross-genre gen- eralization remain limited and exceptional. Task-related pre-training appears to have a pos- itive effect on overall performance and generaliza- tion. Numerically, WRAP ( M= 0.61, SD = 0.1) shows the best overall performance in terms of macro F1. Notably, WRAP is the only model that attains a mean above 0.6 macro F1, while BERT ( M= 0.58, SD = 0.11), RoBERTa (M= 0.57, SD = 0.12), and DistilBERT ( M= 0.56, SD = 0.11) all perform worse. This perfor- mance advantage is particularly evident in cases where WRAP achieves the highest scores compared to the other models. In fact, WRAP demonstrates superior performance in 133 out of 289 experi- ments (46%), whereas BERT does so in 58 experi- ments (20%), RoBERTa in 50 experiments (17%), and DistilBERT in 48 experiments (17%). WRAP BERT RoBERTa DistilBERT SOTA ∆max/min ACQUA 0.66 0.6 0.59 0.59 0.84 0.18 / 0.25 WEBIS 0.63 0.66 0.62 0.65 0.74 0.08 / 0.12 ABSTRCT 0.74 0.74 0.74 0.71 0.89 0.15 / 0.18 ARGUMINSCI 0.59 0.47 0.55 0.5 0.84 0.25 / 0.37 CE 0.77 0.72 0.76 0.72 0.85 0.08 / 0.13 CMV 0.63 0.62 0.62 0.58 0.67 0.04 / 0.09 FINARG 0.61 0.62 0.66 0.65 0.68 0.02 / 0.07 IAM 0.73 0.71 0.73 0.73 0.76 0.03 / 0.05 PE 0.65 0.65 0.69 0.65 0.78 0.09 / 0.13 SCIARK 0.75 0.73 0.74 0.73 0.83 0.08 / 0.1 USELEC 0.7 0.66 0.68 0.59 0.74 0.04 / 0.15 V ACC 0.68 0.7 0.68 0.69 0.78 0.08 / | https://arxiv.org/abs/2505.22137v1 |
0.1 WTP 0.59 0.55 0.55 0.54 0.65 0.06 / 0.11 AFS 0.57 0.58 0.59 0.6 0.84 0.24 / 0.27 UKP 0.7 0.67 0.7 0.68 0.79 0.09 / 0.12 AEC 0.52 0.57 0.51 0.56 0.96 0.39 / 0.45 TACO 0.76 0.61 0.65 0.55 0.88 0.12 / 0.33 Table 4: Transformers trained on all but the target bench- mark are evaluated against their state-of-the-art base- line (SOTA), compare diagonal of Figure 1. Minimum andMaximum values indicate deviation from SOTA (∆max/min ). While all models fall short relative to SOTA, WRAP yields the best results in most cases. Joint benchmark data for training may also help bootstrap reliable and improved generalization. Furthermore, the results of the supplementary ex- periment presented in Table 4 indicate that over- all performance tends to improve when models are trained on joint benchmark data. Thereby, WRAP ( M= 0.66, SD = 0.07), RoBERTa ( M= 0.65, SD = 0.07), BERT ( M= 0.64, SD = 0.07), and DistilBERT ( M= 0.63, SD = 0.07) all achieve average macro F1 scores above 0.6, with values that are numerically higher than those ob- served in the pairwise setup. Again, WRAP shows the most consistent advantage, ranking first in 11 out of 17 experiments (65%). (Q3) State-of-the-art argument mining models are not solely defined by argument signals. Fol- lowing the controlled manipulation in the pair- wise setup, all models dropped to similar levels, WRAP and BERT ( M= 0.56,SD= 0.09), Dis- tilBERT ( M= 0.55,SD= 0.1), and RoBERTa (M= 0.57,SD = 0.1). Similar trends ap- pear post-manipulation in the supplementary ex- periment for WRAP, RoBERTa, and DistilBERT (M= 0.62,SD= 0.06), and BERT ( M= 0.61, SD= 0.06). With careful attention to detail: Shortcut learning influences generalization of arguments, but task-related pre-training weakens the impact. For the pairwise experiments, BERT and DistilBERT showed almost no changes after manipulating inputs ( ∆≤0.02), while RoBERTa maintained its performance completely, suggest- ing that the overall performance of these modelsis not based on learning how arguments are con- stituted. In contrast, WRAP, which relies on its task-related pre-training to embed structural argu- ment components across topics, showed the largest drop in macro F1 with ∆ = 0 .05. Jointly integrating benchmark data for training improves generalization and reduces shortcut re- liance. The impact of WRAP towards robustness of generalization is also true for the supplementary experiment, where WRAP exhibited the largest performance drop ( ∆ = 0 .04) post-manipulation. Nonetheless, RoBERTa and BERT showed simi- lar trends ( ∆ = 0 .03), while DistilBERT showed mostly no changes ( ∆ = 0 .01). Whereas the re- sults in Table 4 show that each model underper- formed relative to the state-of-the-art baselines, a notable pattern still emerged. This is, training on jointly integrated benchmark data raises the av- erage macro F1 score to at least 0.64for three out of four transformers and 0.63for the lowest- performing model, compared to a maximum of 0.61 in pairwise transfer, achieved by WRAP. While only WRAP generalizes better in the pairwise set- ting and is less affected by lexical shortcuts, this advantage persists | https://arxiv.org/abs/2505.22137v1 |
when trained on joined datasets. However, in this merged setting, RoBERTa and BERT also show improved robustness, despite their stronger reliance on shortcuts in the pairwise setup. Furthermore, average differences remain moderate with ¯∆max= 0.12and¯∆min= 0.18while the models learn from heterogeneous data sources. Differences in definitions of arguments reinforce the limitations of generalization. However, while signs of shortcut learning are found, it is undeni- ably not the sole limiting factor. Averaged across all models, misclassification patterns show that ar- guments are correctly classified 28% of the time and no-arguments 37%, suggesting that identifying no-arguments is easier. This is further supported by the lower misclassification rate for no-arguments (13%) compared to arguments (22%), highlighting practical differences in argument definitions that affect both generalization and benchmarks (e.g., due to conflicting annotations). This can also be observed when analyzing the misclassifications of individual models. Here, all models misclassify no- arguments as arguments in fewer than 16% of cases. In contrast, BERT, RoBERTa, and DistilBERT ex- hibit higher misclassification rates, ranging from 21% to 26%, while WRAP misclassifies arguments as no-arguments in 18% of cases, highlighting its superior generalization ability for arguments. (Q2 - Q3) The experiments demonstrate both statistical significance and practical relevance. Repeated experiments support the robustness of these results. Regarding the pairwise experiments, a two-way repeated measures ANOV A for Q2 showed a significant effect only when compar- ing model performances ( F(3,864) = 69 .47, ϵ= 0.56, pcorr< .05, η2 G= 0.03), with negligible re- sampling or interaction effects. For Q2, paired one-tailed t-tests also showed that only model comparisons involving WRAP were significant (pcorr< .05,8.12≤t(288)≤10.14), with moder- ate effect sizes ( 0.39≤d≤0.49). Similarly, re- peating Q3revealed no significant effects, confirm- ing that once ablated, the models perform compara- bly overall. Also, for Q3, when comparing pre- and post-manipulation results per model, only WRAP showed a relevant decrease ( p < . 05, t(288) = −8.91, d=−0.49). In terms of the supplemen- tary experiments, repetition yielded no significant effects pre- and post-manipulation. However, re- garding Q3, one-sided paired t-tests revealed sig- nificant post-manipulation decreases for WRAP, RoBERTa, and BERT ( p < .05,−5.52≤t(16)≤ −2.67,−0.58≤d≤ −0.41), with WRAP show- ing the strongest effect. 6 Discussion To summarize the limited generalization in argu- ment mining addressed, Table 5 compares the best baseline results pre- and post-manipulation. On average, macro F1 differences remain close, within ¯∆max= 0.07and¯∆min= 0.12per model, and in the best cases even exceed benchmark levels. In the single case of AEC, which relies on only five keywords for arguments, overemphasis on these signals also appears to impair generaliza- tion. Although AEC attains the highest score (0.96) and experiences the largest post-manipulation drop (≤0.45, Table 5), its generalization is limited to 0.63 or even below 0.5, compare Figure 1. Given the low performance and minimal differences be- tween pre- and post-manipulation results, BERT, RoBERTa, and DistilBERT do not clearly demon- strate an inherent ability to generalize arguments. Although these challenges may be widespread, positive examples highlight the potential for fu- ture progress. This is particularly evident in cases involving diverse sources and topics | https://arxiv.org/abs/2505.22137v1 |
(V ACC, CE, TACO, UKP, IAM), where UKP, IAM, and TACO already aim for generalizable annotations.WRAP BERT RoBERTa DistilBERT SOTA ∆max/min ACQUA 0.73 0.77 0.76 0.78 0.84 0.06 / 0.11 WEBIS 0.61 0.66 0.66 0.67 0.74 0.07 / 0.13 ABSTRCT 0.83 0.87 0.84 0.87 0.89 0.02 / 0.06 ARGUMINSCI 0.78 0.79 0.77 0.77 0.84 0.05 / 0.07 CE 0.75 0.79 0.77 0.81 0.85 0.04 / 0.1 CMV 0.57 0.64 0.64 0.65 0.67 0.02 / 0.1 FINARG 0.62 0.61 0.66 0.69 0.68 -0.01 / 0.07 IAM 0.66 0.69 0.71 0.7 0.76 0.05 / 0.1 PE 0.66 0.67 0.71 0.73 0.78 0.05 / 0.12 SCIARK 0.71 0.8 0.77 0.79 0.83 0.03 / 0.12 USELEC 0.65 0.66 0.62 0.66 0.74 0.08 / 0.12 V ACC 0.67 0.68 0.69 0.69 0.78 0.09 / 0.11 WTP 0.58 0.54 0.57 0.56 0.65 0.07 / 0.11 AFS 0.78 0.81 0.8 0.79 0.84 0.03 / 0.06 UKP 0.74 0.76 0.78 0.74 0.79 0.01 / 0.05 AEC 0.51 0.55 0.58 0.59 0.96 0.37 / 0.45 TACO 0.77 0.76 0.76 0.77 0.88 0.11 / 0.12 Table 5: Post-manipulation performance of each trans- former compared to state-of-the-art (SOTA) results for baseline experiments per dataset. Minimum andMaxi- mum values are highlighted, with ∆max/min indicating their deviation from SOTA. Despite limitations, the need for a unified struc- tural approach to argument analysis becomes ap- parent. This is reinforced by the effectiveness of methodologies tailored to argument mining, as seen in WRAP’s strong performance, averaging 0.75 when generalizing to TACO from all other datasets (Figure 1). Training on joint benchmark data fur- ther strengthens these abilities also for the stan- dard transformers, even if numerical results fall short of the rarely doubted state-of-the-art (Table 4). Benchmarking should therefore build on combined datasets that capture the task’s general demands, as in GLUE (Wang et al., 2018) and instruction- tuning benchmarks (Ouyang et al., 2022; Zhang et al., 2024), for which decoder-based argument mining (Cabessa et al., 2025) may be of interest. 7 Conclusion We present the first large-scale re-evaluation of argument mining benchmarks through a general- ization lens and evaluate whether the reported per- formance marks true progress. While structural patterns hold, thematic and content differences be- tween labels and datasets favor shortcut learning. BERT, RoBERTa, and DistilBERT often rely on this to inflate benchmarks, while WRAP shows more resilience, likely due to its pre-training for argument generalization. Training on shared bench- mark data further reduces shortcut reliance and improves generalization, notably in combination with WRAP. Our results stress the need to integrate different task demands and suggest re-framing ar- gument mining as a joint generalizability task. Limitations This study did not separate direct from implicit arguments lacking clear structural and lexical cues, including discourse markers, and based on data analysis, assumed such cases are rare. However, this may affect interpretation, as implicit arguments are likely to depend on topical and content cues. While we mostly used publicly available datasets, some require granted access. Additionally, when extraction scripts were un- available, we derived our procedures from both the available documentation and our understanding of the original process. | https://arxiv.org/abs/2505.22137v1 |
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ACC VG WD WTP ECHR AFS UKP AEC TACO 20406080100 Jaccard Similarity Figure 3: The word overlaps, measured by the Jac- card similarity between the vocabularies of two datasets, show that the datasets (as well as the labels) are gen- erally distinct from each other. The overlaps range between 3–36%, with an average of 19%. B.1 Two-Way Repeated Measures ANOV A We employ a two-way repeated measures ANOV A to evaluate the effects of sampling (factor 1) and model choice (factor 2) on the macro F1 (dependent variable), with each dataset pair treated as a subject. For valid inference, the following assumptions must be met: •Continuous Dependent Variable : By def- inition, the macro F1 score is a continuous measure.•Within-Subject Design : Each subject experi- ences every variation of both factors. •Normality : The dependent variable is approx- imately normally distributed for each repeated measure (D’Agostino and Pearson’s K2test). •Sphericity : The variances of the differences between every pair of repeated measures are equal. If the Greenhouse-Geisser ϵis below 0.75 (with values near 1 indicating compli- ance), we adjust the p-values ( pcorr). We can specifically evaluate for: •Sampling Effect : Whether variations in data sampling (via different random seeds) influ- ence model performance. •Model Choice Effect : The performance dif- ferences among transformer models trained and evaluated on fixed samples. Each model is reinitialized in each trial using distinct ran- dom seeds to prevent carry-over effects. •Interaction Effect : Whether the effect of sampling varies across the different models, offering insights into model stability under varying data conditions. We evaluate the practical relevance of statistical significance using the effect size: •Generalized Eta Squared ( η2 G): Propor- tion of the explained variance, interpreted as: ~0.01 (small), ~0.06 (moderate), ~0.14+ (strong). B.2 One-Tailed Paired Student’s t-Tests Further, we conduct one-tailed paired t-tests as post-hoc analysis to identify directional differences (e.g., one model consistently outperforming an- other). These tests use the same assumptions as the prior ANOV A, except for sphericity. We ap- ply the Bonferroni correction ( pcorr) for multiple comparisons. For these tests, we evaluate their practical rele- vance using the effect size: •Cohen’s d : The mean difference between paired conditions relative to the standard devi- ation of the differences, interpreted as: ~0.2 (small), ~0.5 (moderate), ~0.8+ (strong). Dataset Paper Definition Genre Sent. Binary Reprod. Related Arg. N-Arg. Used ACQUA (Panchenko et al., 2019) Argumentative Mixed Yes Yes Yes 1,949 5,236 Yes AMPERE (Hua et al., 2019) Argumentative Academic Yes Yes Yes 6,729 242 No ASRD (Shnarch et al., 2020) Argumentative Spoken Debate Yes Yes Yes 260 440 No CDCP (Niculae et al., 2017) Argumentative Online Debate Yes No No COMARG (Boltuži ´c and Šnajder, 2014) Argumentative Online Debate No No EDIT (Al-Khatib et al., 2016b) Argumentative Online Debate Yes No No IAC (Walker et al., 2012) Argumentative Online Debate No No MARG (Mestre et al., 2021) Argumentative Spoken Debate Yes No No QMC (Levy et al., 2018) Argumentative Encyclopedia Yes Yes Yes 733 1,766 No SDAT (Hansen and Hershcovich, 2022) Argumentative Twitter Debate Yes Yes Yes 387 210 No | https://arxiv.org/abs/2505.22137v1 |
WEBIS (Al-Khatib et al., 2016a) Argumentative Online Debate Yes Yes Yes 10,804 5,543 Yes AAE (Stab and Gurevych, 2014) Claim-based Academic Yes Yes Yes PE No ABSTRCT (Mayer et al., 2020b) Claim-based Academic Yes Yes Yes 1,308 7,323 Yes AMECHR (Teruel et al., 2018) Claim-based Legal Yes Yes No No AMSR (Fromm et al., 2021b) Claim-based Academic Yes Yes Yes 839 561 No ARGUMINSCI (Lauscher et al., 2018) Claim-based Academic Yes Yes Yes 6,554 9,548 Yes ASC (Wojatzki and Zesch, 2016) Claim-based Twitter Debate Yes Yes Yes 147 568 No CDC (Aharoni et al., 2014) Claim-based Encyclopedia Yes Yes Yes CE No CE (Rinott et al., 2015) Claim-based Encyclopedia Yes Yes Yes 1,546 85,417 Yes CMV (Hidey et al., 2017) Claim-based Online Debate Yes Yes Yes 979 1,593 Yes CS (Bar-Haim et al., 2017) Claim-based Encyclopedia Yes Yes Yes CE No DT (Olshefski et al., 2020) Claim-based Spoken Debate No No FINARG (Alhamzeh et al., 2022) Claim-based Spoken Debate Yes Yes Yes 4,607 8,310 Yes IAM (Cheng et al., 2022) Claim-based Mixed Yes Yes Yes 4,808 61,715 Yes MT (Peldszus and Stede, 2015) Claim-based Microtext Yes Yes Yes 112 337 No OC (Biran and Rambow, 2011) Claim-based Online Debate Yes Yes Yes 702 7,824 No PE (Stab and Gurevych, 2017) Claim-based Academic Yes Yes Yes 2,093 4,958 Yes QT (Hautli-Janisz et al., 2022) Claim-based Spoken Debate Yes No AIFDB No RCT (Mayer et al., 2018) Claim-based Academic Yes Yes Yes ABSTRCT No SCIARK (Fergadis et al., 2021) Claim-based Academic Yes Yes Yes 1,191 10,503 Yes UGWD (Habernal and Gurevych, 2017) Claim-based Online Debate Yes Yes Yes WD No USELEC (Haddadan et al., 2019) Claim-based Spoken Debate Yes Yes Yes 13,905 15,188 Yes V ACC (Morante et al., 2020) Claim-based Online Debate Yes Yes Yes 4,394 17,825 Yes VG (Reed et al., 2008) Claim-based Mixed Yes Yes Yes AIFDB 547 2,029 No WD (Habernal and Gurevych, 2015) Claim-based Online Debate Yes Yes Yes 211 3,661 No WTP (Biran and Rambow, 2011) Claim-based Online Debate Yes Yes Yes 1,135 7,274 Yes ECHR (Poudyal et al., 2020) Conclusion-based Legal Yes Yes Yes 414 10,264 No AFS (Misra et al., 2016) Conclusion-based Online Debate Yes Yes Yes IAC 5,150 1,036 Yes ARGSME (Ajjour et al., 2019) Conclusion-based Online Debate Yes No No BASN (Kondo et al., 2021) Conclusion-based Mixed Yes No No BIOARG (Green, 2018) Conclusion-based Academic Yes No No DEMOSTHENES (Grundler et al., 2022) Conclusion-based Legal Yes Yes No No RSA (Houngbo and Mercer, 2014) Conclusion-based Academic Yes No No AIFDB (Lawrence et al., 2012) AIF Mixed Yes No No LAMECHR (Habernal et al., 2023) Custom Framework Legal Yes No No ABAM (Trautmann, 2020) Evidence or Reasoning Mixed Yes No AURC No ASPECT (Reimers et al., 2019) Evidence or Reasoning Mixed Yes No UKP No AURC (Trautmann et al., 2020) Evidence or Reasoning Mixed Yes Yes No No BWS (Thakur et al., 2021) Evidence or Reasoning Mixed Yes No UKP No UKP (Stab et al., 2018) Evidence or Reasoning Mixed Yes Yes Yes 11,126 13,978 Yes AEC (Swanson et al., 2015) Implicit-Markup Online Debate Yes | https://arxiv.org/abs/2505.22137v1 |
Yes Yes IAC 4,001 1,374 Yes TACO (Feger and Dietze, 2024b) Inference-Information Twitter Debate Yes Yes Yes 864 868 Yes Table 6: Summary of the 52 datasets from the reviewed papers, sorted by their applied definitions. Data collection followed the methodology described in Section 2.1, and selection criteria are detailed in Section 2.2. Empty entries indicate that the corresponding criteria were not further evaluated because a preceding criterion had already been rejected. The Related column indicates connections between datasets, like updates (e.g., AAE to PE, CDC to CE, RCT to ABSTRCT), additions of non-task-related features (e.g., CS adds stances to the claims from CE, ABAM adds aspects to the claims of AURC), or subsets from larger repositories (e.g., VG and QT from AIFDB, AEC and AFS from IAC). | https://arxiv.org/abs/2505.22137v1 |
FaceEditTalker: Interactive Talking Head Generation with Facial Attribute Editing Guanwen Feng School of Computer Science and Technology Xidian University Xi’an 710071, China gwfeng_1@stu.xidian.edu.cnZhiyuan Ma School of Computer Science and Technology Xidian University Xi’an 710071, China zjmazy@stu.xidian.edu.cn Yunan Li∗ School of Computer Science and Technology Xidian University Xi’an 710071, China yunanli@xidian.edu.cnJunwei Jing School of Computer Science and Technology Xidian University Xi’an 710071, China jjw@stu.xidian.edu.cn Jiahao Yang School of Computer Science and Technology Xidian University Xi’an 710071, China jhyang2369@stu.xidian.edu.cnQiguang Miao∗ School of Computer Science and Technology Xidian University Xi’an 710071, China qgmiao@xidian.edu.cn Abstract Recent advances in audio-driven talking head generation have achieved impressive results in lip synchronization and emotional expression. However, they largely overlook the crucial task of facial attribute editing. This capability is crucial for achieving deep personalization and expanding the range of practical applica- tions, including user-tailored digital avatars, engaging online education content, and brand-specific digital customer service. In these key domains, the flexible adjustment of visual attributes—such as hairstyle, accessories, and subtle facial features—is essential for aligning with user preferences, reflecting diverse brand identities, and adapting to varying contextual demands. In this paper, we present FaceEditTalker, a unified framework that enables controllable facial attribute ma- nipulation while generating high-quality, audio-synchronized talking head videos. Our method consists of two key components: an image feature space editing mod- ule, which extracts semantic and detail features and allows flexible control over attributes like expression, hairstyle, and accessories; and an audio-driven video generation module, which fuses these edited features with audio-guided facial landmarks to drive a diffusion-based generator. This design ensures temporal coherence, visual fidelity, and identity preservation across frames. Extensive exper- iments on public datasets demonstrate that our method outperforms state-of-the-art approaches in lip-sync accuracy, video quality, and attribute controllability. Project page: https://peterfanfan.github.io/FaceEditTalker/ Preprint. Under review.arXiv:2505.22141v1 [cs.CV] 28 May 2025 Figure 1: By providing a single reference image, audio input, and optional facial attribute input, our method generates high-quality, facially editable speaker videos by predicting facial landmark maps and performing linear edits on the feature semantic encoding of the image, combined with a diffusion model. This method demonstrates good generalization ability and achieves high lip-sync accuracy. In this figure, the image input used is a portrait from outside the dataset. 1 Introduction In recent years, audio-driven talking head generation [ 38,35,53,10,16,45,30] has made remarkable progress and found widespread applications in domains such as virtual reality[ 28,20], animation production[ 25,51], online education[ 20], digital humans[ 16], and film post-production[ 28]. These technologies enable virtual characters to exhibit more natural and realistic speaking behaviors by synchronizing facial movements with audio input. However, most existing approaches primarily focus on lip synchronization [ 38,35,53,10] and emotional expression [ 14,50,32,13], while largely overlooking the important functionality of facial attribute editing. Facial attribute editing is essential for audio-driven video generation due to its strong practical relevance. Beyond accurate audio-visual synchronization, users often require flexible control over visual appearance, including expressions, hairstyles, age, gender, and accessories like glasses. For example, virtual idols may need to adapt to different audience preferences, and digital customer service agents may need to reflect distinct brand | https://arxiv.org/abs/2505.22141v1 |
identities. Dynamic and fine-grained attribute control can greatly enhance personalization and user engagement. Although image-level facial attribute editing methods such as GAN-based or text-driven ap- proaches [ 21,23] have achieved initial success in static image generation tasks, extending these methods to video generation, particularly in talking-head videos, remains challenging. The main obstacles include maintaining temporal consistency of facial attributes, where edits must not only pre- serve realism within individual frames but also ensure smooth transitions across frames to avoid visual flickering or discontinuities, which is essential for producing natural video sequences [ 56,49,24]. Moreover, preserving audio-driven facial dynamics during attribute manipulation is crucial, as any inconsistency can disrupt accurate lip synchronization and natural facial motion. To solve these problems, we propose FaceEditTalker, a novel method that seamlessly integrates audio-driven facial animation with controllable facial attribute editing. Our approach enables users to flexibly adjust facial attributes during video generation, while preserving high image fidelity, smooth facial dynamics, and accurate audio-visual alignment. Our main contributions are summarized as follows: (1) We propose a novel framework named FaceEditTalker, which seamlessly unifies facial attribute editing and audio-driven talking head generation. This framework enables fine-grained manipulation of attributes such as hair feature, facial structure, and accessories, while maintaining high-quality lip synchronization and natural motion dynamics. (2) We propose an innovative two-stage heterogeneous latent diffusion model to address challenges in editability and consistency. Through editable latent feature extraction and feature re-injection, it enables highly flexible zero-shot editing while preserving identity integrity and natural dynamics. ∗Indicates the corresponding author. 2 (3) We conduct extensive experiments on multiple public datasets such as HDTF[ 60] and V oxCeleb2[ 33], demonstrating that our method significantly outperforms state-of-the-art baselines in terms of video quality, lip synchronization accuracy, keypoint alignment error, and identity consis- tency. 2 Related Work Audio-driven Talking Head Generation. Recent methods for audio-driven talking head generation have made remarkable progress, emphasizing realism, identity preservation, and expression diversity. Early approaches [ 38,35,53,10] primarily adopt encoder-decoder architectures to map audio signals to lip movements. While effective to some extent, these methods often suffer from blurred textures and weak identity preservation due to limited fusion strategies. To enhance realism, NeRF-based methods [ 16,45,30,31,37] and 3D Gaussian Splatting methods [ 2,57,29,15] both model 3D geometry for more lifelike appearances; however, they typically require long video sequences and come with high computational costs. Another line of work leverages facial landmarks or 3D priors [ 61, 7,59,58] to disentangle speech content from identity features, improving controllability but often sacrificing fine-grained details in regions like lips and teeth. Recently, diffusion-based models [ 46,48, 54,44,41,6,55,17,18,2] have emerged as powerful solutions for high-quality and diverse talking head generation, with many of these approaches also leveraging facial landmarks as driving signals to enhance controllability and expression alignment. In our method, facial landmarks are adopted as controllable priors to guide a diffusion-based generator, achieving precise lip synchronization and identity preservation while supporting flexible facial attribute editing without compromising speech-driven facial dynamics. Facial Attribute Editing. Facial attribute editing aims to manipulate specific attributes (e.g., age, hairstyle, glasses) while preserving identity. StyleGAN-based methods [ | https://arxiv.org/abs/2505.22141v1 |
21,23,43,1] leverage latent space disentanglement to enable controllable editing, while CLIP-guided approaches [ 42,36] introduce semantic alignment between language and image, allowing intuitive text-driven attribute modifications. Diffusion models offer enhanced control and fewer artifacts for editing tasks [ 5,11, 40], but extending them to videos introduces temporal consistency challenges due to frame-wise randomness. To address this, methods like Latent-Transformer [ 56], STIT [ 49], and Diffusion-Video- Autoencoders [ 24] decompose identity and motion or apply global-local refinements to preserve coherence across frames. In our method, we perform linear transformations in the diffusion latent space while keeping the first frame fixed, ensuring both high-quality attribute editing and consistent facial dynamics across video frames. 3 Method 3.1 Overview Our proposed framework, FaceEditTalker, introduces an innovative two-stage heterogeneous latent diffusion model to address challenges in editability and consistency. It consists of two tightly coupled modules: the Image Feature Space Editing Module and the Audio-Driven Video Generation Module. The Image Feature Space Editing Module extracts editable semantic and stochastic codes from the reference image using a dual-layer latent encoding structure, enabling fine-grained control over facial attributes, which can be further edited using text-guided linear classifiers. These features are then passed to the Audio-Driven Video Generation Module, where synchronized audio-driven landmarks guide a diffusion-based generative process to produce high-quality, temporally coherent talking head videos with consistent identity and natural lip-sync. 3.2 Task Formulation To enable facial attribute editing , we leverage the detailed attribute labels yin the dataset along with the semantic variables zsemto construct the dataset (zsem, y)for training a linear classifier C. During semantic encoding editing, attribute manipulation can be achieved using the transformation z′ sem= C(zsem, y, a). For the talking face video V, we utilize the pre-trained wav2vec[ 4] model to extract audio features A(1:T)= (a1, . . . , aT), which are passed through a multi-scale landmarks prediction network to obtain the driving landmark sequence L1:T= (l1, l2, . . . , lT). Given the reference image 3 Figure 2: Overview of the inference process of our proposed framework FaceEditTalker. The framework consists of two main modules: (a) Image Feature Space Editing Module , which extracts editable semantic and stochastic codes from the reference image using a dual-layer latent encoding structure. Fine-grained attribute manipulation is enabled through optional spatial editing on the semantic codes. (b) Audio-Driven Video Generation Module , which leverages the audio input to infer driving landmarks. During the diffusion process, the stochastic codes guide dynamic generation, while the semantic codes serve as conditional inputs to ensure attribute consistency and visual fidelity throughout the video. The training procedure is detailed in Section 3.5 and Appendix A. of the target person ir∈Rh×w×3and the driving landmark sequence L1:T= (l1, l2, . . . , lT)∈ Rf×h×w×3, our task is to generate a target video sequence ˆV={ˆi1,ˆi2, . . . , ˆif} ∈RF×H×W×3 with a speaking person video similar to the driving poses. The entire generation process can be expressed as ˆV=g(ir, l), where grepresents the generative model. For detailed training and inference processes, please refer to the appendix (A). 3.3 Image Feature | https://arxiv.org/abs/2505.22141v1 |
Space Editing Module To achieve effective facial attribute editing, the Image Feature Space Editing Module leverages the design of DiffAE [ 40] with a dual-layer latent encoding structure. Inspired by the style vector mechanism in StyleGAN [ 21], our model decouples the latent space into two subspaces: semantic code zsemand stochastic code ZT, capturing high-level semantic features and fine-grained details, respectively. This decomposition improves facial reconstruction accuracy and enhances controllability in attribute manipulation, supporting both zero-shot editing and fine-grained semantic control. Thesemantic encoder Esemextracts global facial semantics from the input image, encoding them into low-dimensional vectors akin to StyleGAN’s style vectors, enabling linear transformations for attribute editing. Given a semantic code zsemand an attribute direction vector wattr, attribute manipulation is expressed as: z′ sem=zsem+α·wattr, (1) where αcontrols the intensity of the change. To enable text-driven editing and align more clearly with the illustrated process, text input is processed in conjunction with an attribute library by a linear classifier to compute the attribute direction vector wattr. This wattris then added to the original semantic code zsemaccording to Eq (1)to obtain the edited semantic code z′ sem. A reconstruction loss Lrecensures that non-target regions remain consistent: Lrec=|z′−zstatic|. (2) Thestochastic encoder , designed as a Unet-based diffusion model [ 47]. First use the Eimgencode the input image into an initial latent representation Z0. This representation undergoes a noise diffusion 4 process to capture fine details, resulting in the final latent representation ZT. We refer to this complete encoding and diffusion procedure as the stochastic encoder. The forward process, conditioned on zsem, is defined as: zt+1=√αt+1fθ(zt, t, z sem) +p 1−αt+1ϵθ(zt, t, z sem). (3) This reverse diffusion effectively complements the semantic code’s lack of detail. During video generation, the semantic code zsemmanages global attribute control, while the stochastic code ZT preserves local detail consistency, ensuring smooth and realistic facial transformations across frames. 3.4 Audio-Driven Video Generation Module This module generates video using audio input, semantic code, and stochastic code. The audio input is first processed to extract audio features A1:T. These features are then transformed into an audio-driven facial landmark sequence L1:Tthrough a landmark prediction network, represented as: L1:T: (l1, l2, . . . , lT) =Pldm(A1:T: (a1, a2, . . . , aT)), (4) where Pldmencapsulates the transformation from audio features to landmarks, involving a pre-trained wav2vec model and a regression model. Subsequently, the facial landmark sequence L1:Tis processed by a landmark feature extractor to obtain the corresponding landmark features F1:T: F1:T: (f1, f2, . . . , fT) =Eldm(L1:T: (l1, l2, . . . , lT)), (5) where Eldmrepresents the landmark feature extractor. It adopts multi-scale strategies and cross- attention mechanisms to capture multi-level dynamics and enhance feature correlations, improving precision and temporal consistency in video generation. The extracted facial landmark features F1:Tis fused with the stochastic code ZTto produce the feature K. During the sampling process of the diffusion model, we employ an innovative conditional feature injection strategy, where the high-level semantic information zsemis injected as a global facial attribute control signal into the conditional diffusion model, while the target feature Kserves | https://arxiv.org/abs/2505.22141v1 |
as dynamic motion information. This mechanism ensures stable expression of semantic attributes alongside synchronized audio-driven facial movements: zt−1=√αtzt+√ 1−αtϵθ(zt, zsem, K, t ), (6) where αtrepresents the noise scheduling parameters, and ϵθis the conditional denoising network that guides the denoising process based on the latent variable zsemand the target features K. Finally, the denoised latent variable z0is decoded into a sequence of video frames, which not only exhibit dynamic facial expressions synchronized with the audio, but also, depending on the switch selection, achieve facial attribute editing of the speaker based on the different semantic encoding zsem, resulting in a high-quality editable talking head video. 3.5 Training and Inference Pipeline Training process : This model is divided into three stages. First stage jointly trains the semantic encoder and random encoder to extract semantic and random detail features of images by minimizing the mean squared error between predicted and actual noise, with the loss function: Lsimple =TX t=1Exo,ϵt[||ϵθ(xt, t, z sem)−ϵt||2 2]. (7) Second stage trains an image semantic linear classifier, optimizing the model using cross-entropy loss to accurately classify image attributes, with the loss function: L=−1 NNX l=1[yilog(ˆyi) + (1 −yi) log(1 −ˆyi)]. (8) 5 Third stage trains the latent space diffusion model generation network, fusing semantic and key point features, and generating images by minimizing the mean squared error between predicted and actual noise, with the loss function: L=TX t=1Ezo,ϵt[||ϵθ(zt, t, z sem, zl)−ϵt||2 2]. (9) Inference process : First, face key points and pose information are extracted from the audio sequence and reference image, then combines semantic encoding and attribute editing information and inputs them into the trained latent space diffusion model to finally generate the video sequence of a talking head. 4 Experiments 4.1 Experimental Settings Datasets. We trained the encoder of the dual-layer latent architecture using the FFHQ dataset [ 22], which offers high-resolution facial images with diverse attributes including age, race, expression, facial structure, hair features, and accessories, ideal for learning complex feature representations. For the linear classifier, we used the CelebA-HQ dataset [ 26] with binary labels for 40 facial attributes to enhance attribute feature separation and model generalization. In the audio-driven facial animation generation stage, we utilized the HDTF dataset [ 60], containing lip-sync videos from over 300 speakers, along with V oxCeleb2 [ 33] and VFHQ [ 52] datasets to improve the model’s ability to learn complex mappings between speech and facial movements under various environmental conditions. Additionally, we applied LatentSync [ 27] to refine dataset quality by resampling videos, removing those with low synchronization confidence, correcting audiovisual offsets, and filtering out clips with poor HyperIQA scores, thereby enhancing lip-sync accuracy and visual quality. Comparison Methods. To the best of our knowledge, there is no existing method capable of generat- ing high-resolution, audio-driven speaker videos with editable facial attributes. For a comprehensive evaluation of our proposed method, we first generate results using semantic features extracted by the high-level semantic encoding module, ensuring identity consistency with reference images. Our method is compared against several state-of-the-art (SOTA) lip synchronization approaches catego- rized into three groups. Wav2Lip [ | https://arxiv.org/abs/2505.22141v1 |
38] optimizes direct mappings between audio and lip motion for highly synchronized lip movements while preserving facial textures. SadTalker [ 58] employs explicit facial landmarks and adversarial networks to produce smooth animations. DiffTalk [ 46], EchoMimic [9], and Hallo [ 54] leverage diffusion models to model conditional distributions between audio and facial movements, achieving higher-quality talking videos and strong generalization capabilities for out-of-distribution subjects. This comparison aims to evaluate our method’s performance relative to current leading techniques in audio-driven talking head generation. Evaluation Metrics. For evaluating our method, we employ several metrics. Image generation quality is assessed using FID [ 12], SSIM [ 3], PSNR [ 19], and CPBD [ 34]. Lip motion accuracy is evaluated with M-LMD and F-LMD [ 8], while Syncconf [ 39] measures lip movement-audio synchronization. Additionally, we edit semantic features from the dual-layer semantic encoding module using a linear classifier to produce edited video results. These are compared against state-of-the-art video editing methods such as Latent-Transformer [ 56], STIT [ 49], and Diffusion-Video-Autoencoders [ 24], using TL-ID and TG-ID [49] as evaluation metrics. 4.2 Comparison with Other Methods Quantitative Evaluation. We quantitatively compared FaceEditTalker with existing state-of-the-art audio-to-face generation methods on the HDTF and V oxCeleb2 datasets. As shown in the table, FaceEditTalker demonstrates excellent performance in metrics like image feature similarity, structural similarity, and image quality, leveraging a latent space diffusion model. Our method achieves significant improvements compared to previous diffusion-based approaches, attributed to a more advanced model framework. Furthermore, our model shows strong SyncNet scores, benefiting from optimizations such as dataset correction, multi-scale strategies, and cross-attention mechanisms. 6 Table 1: Quantitative evaluation of our approach compared with SOTAs. Our method achieves the best performance on FID, SSIM (second best in EchoMimic), lip sync metrics, and keypoint error metrics, demonstrating superior overall quality and synchronization accuracy. Video Quality Lip Sync Keypoint Error Method FID ↓ SSIM↑PSNR ↑CPBD ↑Min Dist ↓A VConf ↑A VOffset( →0) M-LMD ↓F-LMD ↓ Real Video (HDTF) 0.000 1.000 35.668 0.263 7.238 8.993 0.000 0 0 Wav2Lip[38] 20.641 0.532 16.929 0.199 6.611 8.119 -2.000 4.368 4.256 SadTalker[58] 25.566 0.698 22.211 0.204 8.527 3.163 1.000 3.368 3.192 DiffTalk[46] 18.570 0.558 26.587 0.225 10.091 3.046 -4.000 5.473 1.146 EchoMimic[9] 17.486 0.893 25.968 0.210 9.163 6.146 -1.000 3.983 3.790 Hallo[54] 16.880 0.821 25.331 0.203 9.612 6.128 0.000 3.412 3.532 Our Method 16.580 0.843 25.574 0.205 9.527 6.354 0.000 3.354 3.465 Real Video (V oxCeleb2) 0.000 1.000 26.453 0.272 7.701 6.365 0.000 0 0 Wav2Lip[38] 20.565 0.468 16.042 0.201 7.665 8.236 -2.000 4.368 4.256 SadTalker[58] 23.421 0.634 21.254 0.211 13.542 3.355 1.000 3.368 3.192 EchoMimic[9] 17.586 0.910 24.948 0.209 9.654 6.542 -1.000 3.983 3.790 Hallo[54] 15.785 0.751 25.738 0.188 8.142 6.105 0.000 3.408 3.498 Our Method 15.418 0.772 25.985 0.189 8.068 6.252 0.000 3.354 3.465 Table 2: Quantitative results of our approach compared with SOTAs. Our method achieves the best performance on identity consistency in face attribute editing. Method TL-ID ↑TG-ID ↑ Latent-Transformer[56] 0.975 0.913 STIT[49] 0.990 0.969 Diffusion-Video-Autoencoders[24] 0.986 0.991 Our Method 0.992 0.989 Furthermore, regarding identity consistency in face attribute editable talking head generation, we | https://arxiv.org/abs/2505.22141v1 |
conducted quantitative evaluation. Our method edits semantic features to generate videos with 20 attributes, compared against video editing algorithms. Evaluation of identity consistency between frames showed that while our generative model achieved similar overall identity consistency as video editing methods, it significantly excelled in TL-ID. Qualitative Evaluation. Figure 3 compares the generation quality of FaceEditTalker with existing advanced methods. While previous approaches often prioritize specific aspects such as lip synchro- nization or introduce artifacts and distortions when aiming for expressiveness, our proposed algorithm generates videos with better overall image quality and more accurately captures fine facial expression details, closely matching the original video. FaceEditTalker particularly excels at handling nuanced facial actions like eye closure and mouth opening, contributing to a higher level of realism in the generated results. Table 3: User Study Results for Editable Facial Attribute Talking Head Generation Method Lip Sync ↑Realism ↑Video Quality ↑Attribute Editing Effect ↑ Original Video 4.80 4.90 4.80 Not Supported Wav2Lip[38] 3.60 3.10 3.70 Not Supported SadTalker[58] 2.70 2.20 2.80 Not Supported DiffTalk[46] 3.40 3.00 2.90 Not Supported EchoMimic[9] 3.40 3.70 3.90 Not Supported Hallo[54] 3.80 3.30 3.60 Not Supported Latent-Transformer[56] 3.30 3.80 3.60 3.40 STIT[49] 3.30 3.30 3.00 3.30 Diffusion Video Autoencoders[24] 3.10 3.50 3.10 3.30 Our Method 3.80 3.60 3.70 4.40 User Study. We conducted a user study (10 people, score 1-5) on lip sync accuracy, realism, video quality and attribute editing effects using a five-point scale. Our method achieved high scores across all metrics: 3.8 for lip-sync (benefiting from multi-scale keypoint features and SyncNet preprocessing), strong performance in realism and video quality (attributed to the latent space diffusion model’s 7 Figure 3: Qualitative evaluation compared with other methods. Using two different reference images and the same audio clip, our method is tested without enabling the editing feature. Our approach demonstrates superior performance in both facial expression naturalness and video quality. capabilities), and superior performance in attribute editing. As shown in Table 3, methods marked with ’N/A’ in the Attribute Editing Effect column indicate that they do not support this functionality. 4.3 Analysis and Ablation Study Table 4: Quantitative Metrics for Ablation Study of Facial Landmark Feature Extractor Method Min Dist ↓A VConf ↑A VOffset( →0) M-LMD ↓F-LMD ↓ Original Video 7.359 7.586 0.000 0 0 Multi-layer Convolution 12.534 6.562 7.000 10.468 7.892 Multi-scale Strategy 9.300 5.344 3.000 4.532 5.246 Multi-scale & Cross-Attention 8.145 6.288 0.000 3.354 3.465 Edited Semantic Encoding 8.484 6.894 0.000 3.301 3.566 Effectiveness of the Facial Landmark Feature Extractor. We conducted ablation experiments on two datasets to validate the multi-scale strategy and cross-attention mechanism in the facial landmark feature extractor. The multi-scale strategy improved fine-grained movements like lip and eye motions, while the cross-attention mechanism enhanced all metrics, particularly A VOffset and M-LMD, achieving better consistency with the original video. Furthermore, testing the Edited Semantic Encoding showed minimal impact on evaluation metrics. Linear Distribution of Attributes in Latent Space. To verify the linear distribution of target attributes in latent space, we first visualized the latent space using Principal Component Analysis (PCA). The results show that in the PCA space, | https://arxiv.org/abs/2505.22141v1 |
samples of different attributes exhibit clear separation along the principal component directions, proving that semantic latent variables exhibit a linear 8 Figure 4: Video generation results with the editing feature enabled. Using three different reference images and the same audio clip, we demonstrate the editing and speaker generation effects under four different attribute editing categories with various sub-attributes. More attribute editing results can be found in Appendix C and Supplementary materials. distribution in the latent space. This validates the rationality of linear operations in the semantic space and supports the classifier’s ability to distinguish between different attribute categories. Additionally, to demonstrate the interpolability of high-level semantic features across different attributes, we generated videos by interpolating features from two different speakers’ images. The interpolation results appeared very natural, showing good separation of attributes. 5 Conclusion and Limitations Conclusion. In summary, we have introduced a novel framework for editable talking face generation that significantly enhances both the realism and controllability of facial animations. By leveraging a combination of disentangled latent representations and fine-grained audio-visual alignment, our method enables intuitive editing capabilities such as face editing and lip-sync correction. Extensive experiments on multiple benchmarks demonstrate that our approach not only achieves superior performance compared to existing state-of-the-art methods but also allows for diverse and personalized talking face generation. We believe this framework opens up promising directions for future research in personalized avatars, virtual assistants, and digital human synthesis. Limitations. Despite the success of our framework, we also recognize some limitations during the exploration. First, while demonstrating good generalization ability, performance might degrade in environments with highly diverse identities, complex head movements, or challenging conditions that are not well-represented in the training data. Second, the diffusion-based generation process, although yielding high-quality results, currently incurs significant computational costs, limiting its application in real-time scenarios. Third, currently our method can only support the editing of 40 preset attributes. In the future, we hope to leverage the capabilities of models like CLIP to achieve flexible face attribute editing through arbitrary text descriptions. Ethical Considerations. Our talking head generation method is strictly limited to academic research and will not be applied to unethical domains like fraud or misinformation. The generated model 9 Figure 5: Principal Component Analysis (PCA) visualization of the four attributes. Figure 6: Talking head generation results after interpolating in the high-level semantic feature space using two reference images. will be shared with the Deepfake detection community to support identification research, ensuring responsible development of this technology. 6 Acknowledgments The work was jointly supported by the National Science and Technology Major Project under grant No. 2022ZD0117103, the National Natural Science Foundations of China under grant No. 62272364, 62472342, the Provincial Key Research and Development Program of Shaanxi under grant No. 2024GH-ZDXM-47, the Research Project on Higher Education Teaching Reform of Shaanxi Province under Grant No. 23JG003. References [1]Abdal Rameen, Qin Yipeng, Wonka Peter . Image2StyleGAN: How to Embed Images Into the StyleGAN Latent Space? // 2019 IEEE/CVF International Conference on Computer Vision (ICCV). 2019. 4431–4440. [2]Agarwal Anushka, Hassan Muhammad Yusuf, Chafekar Talha . GenSync: A Generalized Talking Head | https://arxiv.org/abs/2505.22141v1 |
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high-quality, identity-consistent talking face videos. In the inference phase, the model uses the input audio sequence, reference image, and attribute information to generate facial motion features, which are processed by the diffusion model to produce high-quality, editable talking face videos. A.2 Training Stage Algorithm 1 Joint Training of Semantic Encoder and Random Encoder 1:Input: Face image set (with attribute labels), attribute list, learning rate, diffusion time step 2:Output: Weight vectors corresponding to each attribute list 3:foreach epoch do 4: foreachxi∈FFHQ(image) do 5: zsem=SemanticEncode (xi) ▷Semantic Encoder Forward Pass 6: t=RandomTimeStep () 7: z0=Encode (xi) 8: xt=AddNoise (z0) 9: ˆx= ˆq(xt, zsem) ▷Model Forward Pass 10: e=ComputeTarget (zt) ▷Compute Target 11: L=MSE(e,ˆx) ▷Loss Calculation 12: Backpropagate and update parameters 13: end for 14: Save model at the end of each epoch 15:end for Algorithm 2 Training the Image Semantic Linear Classifier 1:Input: Face image set, attribute labels, learning rate 2:Output: Weight vectors corresponding to each attribute list 3:foreach epoch do 4: foreach attribute ai∈Attr(a1, a2, . . . , a n)do 5: zsem=SemanticEncode (xi) 6: ylabels=GetAttributeLabels (ai,FFHQ (image )) 7: w=InitializeWeightVector () 8: b=InitializeBias () 9: foreachxi∈FFHQ(image) do 10: foreachzsemdo 11: yˆt=Sigmoid (WTzsem+b) ▷Forward Pass 12: L=CrossEntropyLoss (ylabels, yˆt) ▷Cross-Entropy Loss 13: Backpropagate and update parameters 14: end for 15: end for 16: end for 17: Save weight vectors and attribute-label pairs 18:end for 15 Algorithm 3 Training the Latent Space Diffusion Model Generation Network 1:Input: Face image set, reference image set, learning rate 2:Output: Diffusion model parameters 3:foreach epoch do 4: foreach batch xr, xref, lr, lrefdo 5: z0=Encode (xr) 6: zt=AddNoise (z0) 7: t=RandomTimeStep () 8: zref=SemanticEncode (xref) 9: z1=PoseGuided (lr, lref) 10: yˆt= ˆq(z1, zt, zref) 11: e=ComputeTarget (zt) 12: L=MSE(e, yˆt) ▷MSE Loss 13: Backpropagate and update parameters 14: end for 15: Save model at the end of each epoch 16:end for A.3 Inference Stage Algorithm 4 Based on Linear Space Facial High-Level Semantic Feature Editing Module (Inference Process) 1:Input: Reference image xref, audio sequence Saudio , attribute labels (y, w), attribute editing magnitude, diffusion time steps steps 2:Output: Speaker video frame sequence Svideo 3:Data Preprocessing 4:Use Wav2Vec to extract audio feature sequence from audio sequence Saudio 5:F1:T=F(f1, f2, ..., f t) =Wav 2V ec(Saudio) 6:xmesh =MediaPipeMesh 3D(xref) 7:M1:T=Audio 2Mesh (F1:T, xmesh) 8:P1:T=Audio 2Pose (F1:T) 9:L1:T=PerspectiveProjection (M1:T, P1:T) 10:lref=MediaPipeFaceLandmarker (xref) 11:z1:T l=PoseGuider (L1:T, lref) 12:zsem=SemanticEncode (xref) 13:ifa is not None then 14: zsem=zsem+αw 15:end if 16:Svideo =DiffusionModel (zsem, steps, z1:T l) B Experimental parameter settings Our methods was trained on two A100 GPUs. The first and second stages were trained for 100 hours, and the third stage was trained for 160 hours. The main parameters are shown in Table 5. Table 5: Experimental Parameter Settings Parameters Value/Range Random Seed 0 Image Size 512*512 Batch Size 16 Learning Rate 0.0001 Training Epochs 20000 Embedding Layer Channels 512 Diffusion Timesteps 1000 16 C More Qualitative Evaluation More qualitative evaluation can be seen in Figures 7-14, which include 40 attribute editing effects of two celebrities, and the dynamic video can be found in the attachment. Figure 7: Accessories & Makeup of Jay | https://arxiv.org/abs/2505.22141v1 |
arXiv:2505.22146v1 [cs.CV] 28 May 20251 Flexible Tool Selection through Low-dimensional Attribute Alignment of Vision and Language Guangfu Hao1,2,†,Haojie Wen3,4,†,Liangxuna Guo1,5,†,Yang Chen1,Yanchao Bi3,4,*,Shan Yu1,2,5,* 1Laboratory of Brain Atlas and Brain-inspired Intelligence, Institute of Automation Chinese Academy of Sciences (CASIA) 2School of Artificial Intelligence, University of Chinese Academy of Sciences (UCAS) 3State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University 4IDG/McGovern Institute for Brain Research, Beijing Normal University 5School of Future Technology, University of Chinese Academy of Sciences (UCAS) Abstract —Flexible tool selection reflects a complex cogni- tive ability that distinguishes humans from other species, yet computational models that capture this ability remain under- developed. We developed a framework using low-dimensional attribute representations to bridge visual tool perception and linguistic task understanding. We constructed a comprehensive dataset (ToolNet) containing 115 common tools labeled with 13 carefully designed attributes spanning physical, functional, and psychological properties, paired with natural language scenarios describing tool usage. Visual encoders (ResNet/ViT) extract at- tributes from tool images while fine-tuned language models (GPT- 2, LLaMA, DeepSeek) derive required attributes from task de- scriptions. Our approach achieves 74% accuracy in tool selection tasks—significantly outperforming direct tool matching (20%) and smaller multimodal models (21%-58%), while approaching performance of much larger models like GPT-4o (73%) with substantially fewer parameters. Ablation studies revealed that manipulation-related attributes (graspability, hand-relatedness, elongation) consistently prove most critical across modalities. This work provides a parameter-efficient, interpretable solution that mimics human-like tool cognition, advancing both cognitive science understanding and practical applications in tool selection tasks. Index Terms —Tool selection; Attribute-based reasoning; Cross-modal alignment; Cognitive modeling I. I NTRODUCTION THE ability to flexibly select and use tools represents a remarkable cognitive capability that extends human physical limitations and sets us apart from other species [1], [2]. While certain non-human animals demonstrate rudimen- tary tool usage—such as chimpanzees inserting sticks into termite mounds [3], orangutans using long poles to retrieve fruit [4], or ants employing leaves to transport food [5]-humans exhibit a uniquely flexible and sophisticated capacity for tool manipulation across diverse contexts. Unlike animals, humans can design complex multi- component tools [6], transmit tool-making knowledge across generations through language [7], adapt tools for purposes far removed from their original function [8], [9], and create abstract tools like mathematical symbols and computer algo- rithms [10]. This flexibility allows humans to select appropri- ate tools for novel situations, repurpose objects for unintended †These authors contributed equally to this work. *Corresponding authors: shan.yu@nlpr.ia.ac.cn, ybi@pku.edu.cnfunctions, and even create new tools to address emerging challenges. Despite its evolutionary significance and centrality to human cognition, the computational and neural mechanisms underlying flexible tool selection remain insufficiently under- stood [11]–[14]. Several neurocognitive studies suggest that the human brain represents tools and their potential uses through abstract attribute spaces rather than rigid categorical classifications [15]–[17]. When confronted with a novel situation requiring tool use, the brain appears to extract essential functional and physical attributes needed for the task, then matches these requirements against the attributes of available objects. This attribute-based matching process provides a plausible explanation for how humans can generalize tool knowledge to novel situations and identify | https://arxiv.org/abs/2505.22146v1 |
suitable alternatives when preferred tools are unavailable. Despite these insights from cognitive neuroscience, com- putational models that effectively capture this attribute-based flexible tool selection mechanism remain underdeveloped [18]. Current approaches to modeling tool selection often rely on either direct mapping between task descriptions and tool labels [19] or comprehensive multimodal processing that requires ex- tensive computational resources. Additionally, the absence of standardized datasets connecting tool images, usage scenarios, and underlying attributes has impeded progress in this domain. A key insight from cognitive science research is that humans employ an intermediate level of representation when selecting tools, focusing on functional and physical attributes rather than direct visual-to-task mapping [20]. These attributes serve as a bridge between perception and action, enabling flexible tool use across novel situations. However, formalizing this attribute space and developing computational models that can effectively utilize it remains an open challenge. In this paper, we propose a novel computational framework that bridges the gap between tool perception and task under- standing through a low-dimensional attribute space. Our key contributions include: 1) We introduce a carefully designed 13-dimensional at- tribute space that captures both physical properties (elon- gation, size, hardness) and functional characteristics (graspability, body extension) of tools. We construct comprehensive datasets to support attribute-based tool 2 Image Feature ExtractionAttribute Space Language Embedding“The leaky pipe fitting was secured, stopping the dripping water.”Tool Image Resnet / ViTa b Transformer Attribute Vector Attribute Alignment Task Scenarios Similarity MatchAvailable Tools Tools Attribute I need to clean up spilled coffee grounds. (No broom available.) Tongs Hammer Pincers Paintbrush Other toolselongation spiky size smoothness texturedness hardness graspability hand force body threatness valence arousalSuitability Score Paintbrush: Most suitable Paintbrush Tongs Pincers HammerElongated handle Brush head for gathering Easy to control Fig. 1. Attribute-based flexible tool selection framework. (a) Example task: When facing a situation like “I need to clean up spilled coffee grounds (No broom available)”, humans select suitable tools by matching task requirements with tool attributes. Among available tools (Hammer, Tongs, Paintbrush, Pincers, etc.), a paintbrush is selected as most appropriate based on attribute alignment. (b) Our computational framework uses a dual-pathway architecture with a shared 13-dimensional attribute space: the visual pathway extracts attributes from tool images using vision models (ResNet/ViT), while the language pathway derives required attributes from scenario descriptions using LLMs (GPT/Llama/DeepSeek). Tool selection occurs through similarity matching in this shared attribute space. selection research: (1) a tool image-attribute dataset containing 115 common tools with 13 corresponding attribute ratings, (2) a tool scenario-attribute dataset featuring textual descriptions of tool usage scenarios paired with corresponding attribute requirements, and (3) a tool matching test set comprising 100 scenario descriptions with 10 candidate tool images each. 2) We propose a low-dimensional attribute space as an interpretable bridge between task requirements and tool representations, enabling flexible tool selection across diverse contexts. We design a dual-pathway attribute alignment method integrating vision and language mod- els. The visual pathway extracts attributes from tool images, while the language pathway derives required attributes from textual scenario descriptions, allowing for cross-modal matching in the shared attribute space. 3) We demonstrate that our | https://arxiv.org/abs/2505.22146v1 |
attribute-based approach achieves 74% accuracy in tool selection tasks, substan- tially outperforming direct tool name matching (20%) and smaller multimodal large language models (LLMs) (21%-58%), while showing competitive performanceagainst much larger multimodal LLMs like GPT-4o [21] (73%) and Gemini-2.0-Pro [22] (72%), despite using significantly fewer parameters. This work provides a computationally efficient and cogni- tively plausible approach to flexible tool selection. By de- composing tool selection into attribute-based representations significantly enhances model performance while requiring sub- stantially fewer parameters than large-scale multimodal LLMs. Through ablation studies, we identify key attributes driving model performance, with functional properties like graspabil- ity, elongation, and hand-relatedness proving most critical for accurate tool selection. This work bridges cognitive science and artificial intelligence (AI) by implementing a neurally- inspired computational framework for flexible tool use. By demonstrating the efficacy of attribute-based representations in both visual and linguistic domains, our research provides insights into potential mechanisms underlying human tool cognition while offering a practical system that can directly analyze any scenario, process images of available tools, and select the most appropriate tool for the given context. 3 II. R ELATED WORK A. Tool Use and Selection in Cognitive Science Tool use represents a fundamental cognitive ability that has been extensively studied in both humans and animals. Cog- nitive neuroscience research has revealed specialized neural mechanisms underlying tool perception and use in humans [23]. The neural architecture supporting tool use spans multi- ple brain regions that work in concert to enable the complex cognitive processes underlying this capability. Neuroimaging studies have identified a specialized tool- processing network primarily in the left hemisphere, including the supramarginal gyrus (SMG), posterior middle temporal gyrus (pMTG), and dorsal premotor cortex (PMd) [24]. The left anterior supramarginal gyrus (aSMG) appears uniquely human, specifically devoted to tool use execution and obser- vation [25], highlighting the evolutionary significance of this cognitive ability. Tool perception integrates multiple cognitive processes be- yond mere visual recognition. The parietal lobe facilitates visuo-motor transformations essential for tool manipulation, integrating visual and somatosensory information [26]. The premotor cortex coordinates the planning and execution of tool-related movements [27], [28], while the temporal lobe, particularly the pMTG, stores semantic knowledge about tools and their conventional uses [29]. Recent research reveals that the occipito-temporal cortex (OTC) maintains distinct representational spaces for tools, with lateral regions encoding both visual and action-related properties, while ventral areas primarily represent visual features [30]. Several key theories have emerged to explain human flex- ibility in tool use. The technical reasoning hypothesis pro- poses that humans possess unique abilities to reason about physical object properties through mechanical knowledge, en- abling prediction and analogical transfer across situations [31]. This perspective emphasizes abstract conceptual knowledge of functional and physical attributes as the bridge between task requirements and tool selection [32], [33]. Neuropsychological evidence from patients with brain damage supports this view, showing similar impairments in both familiar and novel tool use tasks [34], [35]. In contrast, the manipulation-based approach focuses on sensorimotor affordances and stored action representations. The ”Two Action Systems Plus (2AS+)” framework integrates these perspectives, suggesting complementary roles for on- line reasoning | https://arxiv.org/abs/2505.22146v1 |
about tool properties and stored manipulation knowledge, with distinct neural substrates supporting each process [36]. More recent computational perspectives propose that humans build internal models of tools that enable mental simulation of potential uses before physical interaction [37]. Despite these advances in understanding the neural and cognitive bases of tool use, computational models that ef- fectively capture human flexibility in tool selection remain underdeveloped. Existing cognitive models typically focus on specific aspects like grasp planning or action execution rather than addressing the broader challenge of flexible tool selection across diverse contexts.B. Visual Question Answering and Multimodal LLMs Tool selection across scenarios fundamentally involves cross-modal reasoning—understanding textual task descrip- tions while visually evaluating potential tools. This process closely relates to Visual Question Answering (VQA), which has evolved significantly in recent years. Early VQA ap- proaches combined Convolutional Neural Network (CNN)- based visual encoders with Recurrent Neural Network (RNN)- based question processors [38], [39], achieving modest per- formance through direct feature concatenation. A significant advancement came with attention mechanisms, particularly Bottom-Up and Top-Down attention [40], which enabled mod- els to focus on task-relevant image regions based on question content. Transformer architectures subsequently revolutionized multimodal reasoning by enabling more sophisticated vision-language interactions. Models like ViLBERT [41]and UNITER [42] adapted masked language modeling objectives to vision-language contexts, learning cross-modal correlations through co-attention mechanisms. These models achieved substantial performance gains by processing textual and visual tokens within unified representational spaces. Contrastive learning approaches further refined cross-modal alignment. CLIP [43] demonstrated that training visual and textual encoders to maximize agreement between paired images and captions while minimizing similarity to nonmatching pairs enables powerful zero-shot transfer capabilities. Current state-of-the-art multimodal LLMs like GPT-4o and Gemini integrate these principles at unprecedented scale [44]. These models process visual and linguistic information within unified transformer architectures trained on massive multi- modal datasets, achieving remarkable performance across di- verse VQA benchmarks [45]. However, they typically require billions to trillions of parameters, substantial computational resources [46], and operate as black boxes that obscure their internal reasoning mechanisms [47]. Several specialized multi- modal approaches have been developed for tool-related tasks, including systems for robotic manipulation [48] and visual reasoning about tool functions [49]. The META-TOOL frame- work [50] specifically evaluated LLMs’ ability to determine whether and which tools to select from available options, revealing significant gaps in current models’ performance across diverse scenarios. Although effective, these approaches often require massive model sizes (billions to trillions of parameters) and extensive computational resources for training and inference. Addition- ally, their black-box nature makes it difficult to interpret their decision processes, particularly in specialized domains like tool selection, where specific functional attributes play a crucial role. The tool selection task represents a distinctive VQA challenge in which the system must understand both the functional requirements implied by a scenario description and the physical capabilities of the available tools. C. Transfer Learning and Task Alignment Efficiently aligning pretrained models with specialized downstream tasks has been extensively studied in both com- 4 Attribute Distribution ArousalBody Force Hand Graspability HardnessElongation Spiky Size Smoothness Texturedness Valence Threatness RatingCounta bTool Distribution in Attribute | https://arxiv.org/abs/2505.22146v1 |
Space Dim 1Dim 2 c broom Attritube Vector 6323326635221hammer Attritube Vector 5323266655523 broom Attritube Vector 6323326635221 5323266655523The spilled flour was efficiently gathered into a heap on the kitchen floor. After the birthday party, the confetti was swept from the living room with ease. The gardener quickly cleared the dirt and debris from the paved walkway. hammer Attritube VectorThe nail was driven firmly into the wood, securing the frame. The tent stakes were pounded into the ground, anchoring the shelter. The old wooden crate was dismantled, yielding usable planks.d Fig. 2. Attribute space and the constructed datasets. (a) Visualization of the 13-dimensional attribute space through dimensionality reduction (PCA), showing well-distributed tool representations that effectively differentiate between tools. (b) Distribution of human ratings across different attributes, demonstrating the variability of attribute values across the tool collection. (c) Sample images from the ToolNet dataset, containing 475 training and 25 testing images per tool across 115 tool categories, with each category sharing the same attribute vector derived from human ratings. (d) Example scenarios from the Task-Description Dataset generated using Gemini 2.0 Flash Experimental LLM, with each scenario associated with a specific tool and inheriting its attribute vector. puter vision and natural language processing. These alignment techniques are particularly relevant for attribute-based tool selection, where both visual and linguistic models must be adapted to predict the same attribute space. In computer vision, transfer learning has become the dom- inant paradigm for downstream task adaptation [51], [52]. Convolutional architectures like ResNet [53] demonstrated remarkable transferability of learned features across diverse visual tasks, while Vision Transformers (ViT) [54] further improved this capability through their attention-based feature extraction. For adapting these pretrained visual backbones to specific downstream objectives, several approaches have proven effective. Linear probing trains only the final classifica- tion layer while keeping backbone weights fixed [55], offering computational efficiency with minimal risk of catastrophic forgetting [56]. Full fine-tuning adjusts all parameters but typi- cally applies lower learning rates to pretrained layers, enabling more comprehensive adaptation when sufficient task data is available [57]. More parameter-efficient approaches include adapter modules [58] that insert small trainable components between frozen layers, and knowledge distillation techniques [59] that transfer capabilities from larger teacher models to more efficient student networks. Parallel to visual model development, language models have evolved from recurrent architectures to transformer-based designs [60]. Models like GPT [61], BERT [62], LLaMa [63]and DeepSeek [64] have demonstrated remarkable capabilities through self-supervised pretraining on massive text corpora, capturing syntactic structure, semantic relationships, and as- pects of commonsense knowledge [65]. For language model adaptation, several approaches have proven effective. Traditional fine-tuning adjusts all model parameters on task-specific data [66], consistently deliv- ering strong performance despite computational demands. Parameter-efficient methods have gained prominence as alter- natives: prompt tuning [67] and prefix tuning [68] learn task- specific input tokens while keeping base model parameters frozen, effectively conditioning the model’s behavior toward specific outputs. Low-rank adaptation (LoRA) [69] factorizes weight updates into low-rank approximations, dramatically reducing trainable parameters while achieving performance comparable to full fine-tuning. These developments provide the technical foundation for systems that extract meaningful attributes from | https://arxiv.org/abs/2505.22146v1 |
both visual tool representations and linguistic usage descriptions—capabilities essential for flexible tool selection. The key challenge in apply- ing these methods to tool selection lies in defining an appropri- ate attribute space that captures both physical and functional properties relevant to tool use. By adapting pretrained visual and language models to predict the same structured attribute space, we create a bridge between modalities that enables flexible matching of tools to usage scenarios. 5 III. M ETHOD A. Dataset Construction The effectiveness of our framework relies heavily on a well- designed attribute space and comprehensive datasets. Our 13- dimensional attribute space was carefully designed to capture the essential physical, functional, and psychological properties of tools. These dimensions were selected based on their theoretical relevance to tool cognition and empirical evidence of their importance in human tool selection processes. The attributes can be broadly categorized into three groups: physi- cal properties (elongation, spiky, size, smoothness, textured- ness, and hardness) that characterize the intrinsic material and structural characteristics of tools; functional properties (graspability, hand involvement, force requirements, and body extension) that describe how the tool interfaces with human users; and psychological properties (threatness, valence, and arousal) that represent the emotional and psychological aspects of tool interaction. For each attribute dimension, we gathered ratings from 30 participants using a 1-7 scale, with approval from the institu- tional ethics review board. To ensure consistency, participants were provided with detailed rating guidelines containing di- mension definitions, anchor points, and concrete examples. For instance, the elongation attribute was defined as ”The degree to which the object is long and slender in shape. A rating of 7 indicates a very elongated object (like a baseball bat), while 1 indicates a non-elongated object (like a disc).” The detailed descriptions and rating criteria for all attributes are shown in Fig. S2. The final attribute vector for each tool was computed by averaging ratings across all 30 annotators, producing a sta- ble representation of each tool’s characteristics in our attribute space. As shown in Fig. 2(a), dimensionality reduction analysis of the 13-dimensional attribute ratings reveals well-distributed tool representations, indicating that these attributes effectively differentiate between tools. Fig. 2(b) shows the distribution of ratings across different attributes. While the distributions are not perfectly balanced, this is compensated for by the diversity of tools and multiple images per tool category. Based on this attribute framework, we constructed three complementary datasets collectively referred to as ToolNet. The first is the Tool Image-Attribute Dataset, a collection of tool images gathered from the internet, with example images shown in Fig. 2(c). The dataset comprises a training set of 90 images per tool across 115 tool categories (10,350 images total) and a testing set of 10 images per tool (1,150 images total). The tool categories were selected to cover a broad spectrum of everyday tools, spanning domains such as kitchen implements, gardening equipment, workshop tools, and household items. Fig. S1 presents all 115 tool categories with their representative images and names. All images within each tool category share the same attribute vector derived from human ratings. To enable language-based | https://arxiv.org/abs/2505.22146v1 |
attribute prediction, we developed Tool Scenario-Attribute Dataset of natural language scenarios describing tool usage contexts, generated using the Gemini- 2.0-flash-experimental LLM. The generation process leverages each tool’s attribute ratings and attribute descriptions to createnatural language descriptions of tool usage scenarios. The detailed prompting strategy used for generating these descrip- tions is illustrated in Fig. S3. We created three versions of this dataset with varying sizes: the small dataset contains 10 training scenarios and 3 testing scenarios per tool, the medium dataset contains 90 training scenarios and 10 testing scenarios per tool, and the large dataset contains 475 training scenarios and 25 testing scenarios per tool. Example task descriptions are shown in Fig. 2(d). Each scenario is a natural language description of a tool- use situation and inherits the attribute vector of its associated tool. The task descriptions were carefully crafted to maintain linguistic variation while ensuring task relevance. For example, for a broom, scenarios include ”The spilled flour was effi- ciently gathered into a heap on the kitchen floor” and ”After the birthday party, the confetti was swept from the living room with ease.” This diversity in descriptions helps ensure the robustness of our language encoder in extracting relevant attribute requirements from various phrasings of similar tasks. To evaluate end-to-end tool selection performance, we constructed Tool Matching Dataset, which contains scenario descriptions paired with multiple candidate tool images for evaluation purposes. This test set includes 100 scenario de- scriptions, each paired with 1 target tool image and 9 distractor tool images, totaling 1,000 images. The scenario descriptions were extracted from the testing portion of the Tool Scenario- Attribute Dataset, selecting one description for each of the first 100 tool categories. The target and distractor tool images were sourced from the testing portion of the Tool Image-Attribute Dataset. This multi-faceted dataset design enables us to train models to extract attributes from both visual tool representations and linguistic scenario descriptions, while also providing a robust benchmark for evaluating cross-modal tool selection performance. B. Problem Formulation We formulate the flexible tool selection task as a cross- modal matching problem in an attribute-mediated space. While traditional approaches might attempt direct mapping between task descriptions and tool categories, our framework operates through an interpretable intermediate attribute representation. Formally, we define the following key components: Attribute Space : LetA ∈R13denote our attribute space, where each dimension represents a specific tool property. Each attribute vector a∈ A consists of elements ai∈[1,7] corresponding to the rating of the i-th attribute. These at- tributes serve as an interpretable bridge between visual tool representations and linguistic task requirements. Tools : Let Tbe the set of tool images. For each tool category c∈1, ...,115, we have multiple visual instances tj c∈ T , where jindexes different images of the same tool category. We define a visual encoder fv:T → A that maps a tool image to its attribute representation: at=fv(t) (1) 6 Tasks : LetDdenote the space of natural language task descriptions. The language encoder fl:D → A maps a task description to its required attribute representation: ad=fl(d) (2) Similarity Metrics: To quantify | https://arxiv.org/abs/2505.22146v1 |
the compatibility between a task description and a candidate tool, we define a similarity function s:A×A → Rthat measures the correspondence be- tween attribute vectors. We investigate two primary similarity metrics: scos(ad,at) =ad·at ||ad|| · ||at||(3) seuc(ad,at) =−||ad−at||2 (4) where scosrepresents cosine similarity and seucrepresents negative Euclidean distance. Problem Definition : Given a task description d∈ D and a set of candidate tool images t1, ...,tn⊂ T, the objective is to select the most suitable tool by selecting: t∗= arg max tis(fl(d), fv(ti)) (5) Our framework decomposes the challenging cross-modal reasoning task of tool selection into two more tractable sub- problems: (1) learning to extract relevant attributes from visual tool representations, and (2) learning to infer required attributes from linguistic task descriptions. By operating in this shared attribute space, we enable principled comparison between tools and tasks without requiring massive model sizes or end-to-end multimodal training. C. Vision-Language Model Our framework employs a dual-pathway architecture to bridge visual tool perception and language task understanding through a shared attribute space, as illustrated in Fig.1(b). Each pathway is specifically designed to extract relevant attribute information from its respective modality while maintaining interpretability and computational efficiency. 1)Visual Encoder :The visual encoder follows a two-stage architecture comprising a pre-trained feature extractor back- bone followed by an attribute prediction head, corresponding to the visual pathway in Fig.1(b). We experiment with three backbone architectures: ResNet-18, ResNet-50, and Vision Transformer (ViT-B/16), all initialized with ImageNet pre- trained weights. This design enables efficient transfer learning while maintaining the model’s capacity to extract tool-specific attributes. The attribute prediction head is implemented as a multi- layer perceptron (MLP) that maps high-dimensional visual fea- tures (512/2048/768-dimensional, depending on the backbone) to our 13-dimensional attribute space. The MLP architecture consists of three fully connected layers with dimensions 256 and 64 for the hidden layers, followed by layer normalization and ReLU activation functions after each hidden layer except the final output layer. This transformation allows the model to distill relevant functional and physical properties from complex visual representations.2)Language Encoder :The language encoder, representing the language pathway in Fig.1(b), maps textual task descrip- tions to the same attribute space used by the visual encoder. The model consists of a pre-trained language model backbone followed by a specialized regression head. We experiment with three language model architectures of varying capacities: GPT-2, LLaMA-3.2-1.2B, and DeepSeek-R1-1.5B. The archi- tectural specifications of these models are detailed in Table I, showing substantial differences in parameter count, layer depth, model dimensionality, and attention mechanisms. TABLE I ARCHITECTURE SPECIFICATIONS AND PARAMETER COUNTS FOR LANGUAGE MODELS USED IN OUR FRAMEWORK . Model Name nparams nlayers dmodel nheads dhead GPT-2 124.4M 12 768 12 64 LLaMA-3.2 1.2B 16 2048 32 64 DeepSeek-R1 1.5B 28 1536 12 128 Each language model processes the input task description and generates contextual representations. We utilize the last token representation for attribute prediction, as this token inherently captures the cumulative context from the entire sequence through the next-token prediction objective. This approach leverages the autoregressive nature of these language models, where the final token embedding contains information about the complete | https://arxiv.org/abs/2505.22146v1 |
task description. The attribute prediction head transforms the language features into the 13-dimensional attribute vector through a multi-layer architecture consisting of fully connected layers with dimensions [256, 128, 64, 13]. This specialized head is trained to extract attribute requirements implied by natural language task descriptions, enabling cross- modal matching with tool images. 3)Training Strategy :Both encoders are trained to mini- mize the Mean Squared Error (MSE) loss between predicted and ground truth attribute vectors. The visual encoder is trained with Adam optimizer (learning rate 1e-4) and batch size 256, while the language encoder uses a smaller learning rate (5e-5) and batch size 4 to balance adaptation with preser- vation of pre-trained knowledge. For the visual pathway, we freeze the pre-trained backbone (ResNet/ViT) and only train the attribute prediction MLP layers, preventing catastrophic forgetting while allowing specialization to attribute prediction. Similarly, for the language pathway, we keep the pre-trained language model components frozen and only update the re- gression head parameters. Training employs early stopping based on validation performance, with the visual model trained for up to 1,000 epochs and language models for up to 2,000 epochs. This dual-pathway architecture enables flexible tool selec- tion by mapping both visual and linguistic inputs to a shared, interpretable attribute space, while maintaining the specific processing characteristics required for each modality. When presented with a task description and candidate tool images, the system computes attribute representations for both and selects the tool whose attributes best match the task require- ments, similar to the human process illustrated in Fig.1(a). 7 ResNet18ResNet50 ViT-B/16 0.894 0.927 0.926Model Performance ComparisonTest Accuracy1.00 0.98 0.96 0.94 0.92 0.90 0.88 0.86 (1) Model Train Performance (2) Model Test PerformanceTrain Accuracy Dataset SizeDeepSeek-R1 LLaMA-3.2 GPT2 DeepSeek-R1 LLaMA-3.2 GPT2 Dataset Size1.00 0.90 0.80 0.70 0.60 0.50 0.40 0.30 Test Accuracy1.00 0.90 0.80 0.70 0.60 0.50 0.40 0.3094.41% 99.83% 76.24%80.34%92.8% 70.83% 59.61% 55.97% 55.36%74.34% 64.89% 63.0%66.85% 40.04%42.81% GPT-2 with quiz 30.55% GPT-2 with quiza b Fig. 3. Performance evaluation of visual and language models. (a) Test accuracy of visual models (ResNet18, ResNet50, ViT-B/16) on attribute prediction and most similar class identification tasks. ResNet50 achieves the highest performance with 96.05% attribute-wise accuracy and 92.70% most similar classes accuracy. (b) Training and testing attribute-wise accuracy of language models (GPT-2, LLaMA-3.2-1.2B, DeepSeek-R1-1.5B) across different dataset sizes. The small dataset contains 10 training scenarios and 3 testing scenarios per tool, the middle dataset contains 90 training scenarios and 10 testing scenarios per tool, and the large dataset contains 475 training scenarios and 25 testing scenarios per tool. ”GPT-2 with quiz” represents adding the question ”What tool is relevant to this scene?” to scenario descriptions, which decreased performance compared to standard GPT-2. Larger models and datasets yield better performance, with DeepSeek-R1-1.5B demonstrates the best performance with 74.34% attribute-wise accuracy on the largest dataset. IV. R ESULTS A. Visual Model Performance We evaluated three visual encoder architectures—ResNet18, ResNet50, and ViT-B/16—on their ability to predict tool attributes from images. As shown in Fig.3(a), all models demonstrated strong performance, with the ResNet50 archi- tecture achieving the highest accuracy. We evaluated performance using two complementary | https://arxiv.org/abs/2505.22146v1 |
met- rics. First, attribute-wise accuracy measures the model’s ability to predict individual attribute values accurately on the 7-point scale. Specifically, the predicted values are rounded to the nearest integer, and the prediction is considered correct only if it exactly matches the ground truth value. ResNet50 achieved96.05% accuracy across all attributes and test samples, out- performing both ResNet18 (93.01%) and ViT-B/16 (94.34%). Second, most similar class accuracy evaluates whether the predicted attribute vector’s closest matching tool category (by cosine similarity) matches the ground truth category. ResNet50 again demonstrated superior performance (92.70%), closely followed by ViT-B/16 (92.61%) and ResNet18 (89.40%). These results indicate that our visual pipeline effectively captures the physical and functional attributes of tools from images. The superior performance of ResNet50 suggests that medium-depth convolutional architectures strike an optimal balance between feature extraction capacity and generalization for attribute prediction. Notably, the high accuracy across all 8 a b c The papermaker dispersed pulp evenly across the screen to form a sheet of paper.Tools to be selected Task Scenario Match result -0.0153 -0.0189 -0.0391 -0.0402 -0.0609 -0.0761 -0.0836 -0.1210 -0.1228 -0.2892 Fig. 4. Performance comparison of our attribute-based approach against baseline and larger multimodal models. (a) Accuracy of different models on the tool selection task. Our approach using DeepSeek-R1-1.5B and ResNet50 achieved 74% accuracy, significantly outperforming direct tool name matching (20%) and smaller multimodal models. Performance comparison includes multimodal models with standard (STA) and chain-of-thought (CoT) prompting strategies: Qwen-VL-7B (STA: 21%, CoT: 58%), GPT-4o (STA: 67%, CoT: 73%), and Gemini-2.0-Pro (STA: 72%, CoT: 68%). (b) Model parameter efficiency comparison, showing our attribute-based approach (DeepSeek-R1-1.5B + ResNet50) achieves competitive performance with significantly fewer parameters compared to larger multimodal models. (c) Example visualization of tool ranking results from our attribute-based approach for a specific usage scenario, demonstrating the model’s ability to correctly identify the most appropriate tool by matching scenario attributes with tool attributes. models demonstrates that visual feature extractors pretrained on general object recognition can be effectively repurposed for tool attribute prediction through targeted fine-tuning of prediction heads. B. Language Model Performance For the language pathway, we evaluated three progressively LLMs (GPT-2, LLaMA-3.2-1.2B and DeepSeek-R1-1.5B) for their ability to extract relevant attributes from textual task descriptions. We also investigated how the size of the dataset affects the performance of the model by training each model in three variants of the data set: small (10 training scenarios per tool), medium (90 scenarios per tool) and large (475 scenarios per tool). As illustrated in Fig.3(b), we observed several key trends. First, in terms of the effect of model capacity, larger language models consistently outperformed smaller ones. DeepSeek-R1- 1.5B achieved the highest attribute-wise accuracy (74.34%), followed by LLaMA-3.2-1.2B (64.89%) and GPT-2 (63.00%) on the largest dataset. Second, considering the impact ofthe size of the dataset, all models benefited from increased training data, with performance improvements diminishing as the size of the dataset grew. Third, for all models, training accuracy actually decreased as dataset size increased, while testing accuracy consistently improved, indicating better gen- eralization with more diverse training examples. DeepSeek- R1-1.5B demonstrated the best balance between high | https://arxiv.org/abs/2505.22146v1 |
training performance and strong generalization. Additionally, we tested whether explicitly adding a question prompt (”What tool is relevant to this scene?”) at the end of each scenario description would improve attribute extraction. As shown in Fig.3(b), this modification surprisingly decreased both training accuracy (from 94.41% to 42.81%) and testing accuracy (from 63.00% to 40.04%) of GPT2 model, suggesting that explicit task framing may interfere with the model’s ability to extract implicit attribute requirements. These results highlight the challenge of extracting precise attribute requirements from natural language descriptions. Un- like visual attribute prediction, where accuracy exceeds 90%, language-based attribute prediction remains more challenging, 9 Fig. 5. Ablation study on attributes for tool selection. This experiment examines how removing individual attributes affects performance in both visual models, language models, and the combined system. Results show that functional attributes (particularly graspability, elongation, and hand-relatedness) have the greatest impact on individual model performance, while their removal can improve the combined model’s accuracy. In contrast, attributes like valence, spiky, size, body extension, and arousal have minimal impact on model performance, suggesting certain attributes are more critical than others for flexible tool selection. with the best model achieving 74.34% accuracy. This perfor- mance gap likely reflects the inherent ambiguity in natural language descriptions and the implicit nature of attribute requirements in task descriptions. Importantly, our results demonstrate a clear scaling trend: as both model capacity and dataset size increase, performance consistently improves. GPT- 2 (124M parameters) achieves 63.0% accuracy, LLaMA-3.2- 1.2B reaches 64.89%, and DeepSeek-R1-1.5B attains 74.34%. This scaling law suggests that further increases in model size and dataset expansion would likely yield additional perfor- mance gains. C. Tool Matching Performance To evaluate end-to-end performance on the Tool Matching Dataset, we compared our attribute-based approach against several baselines and state-of-the-art multimodal models. We first conducted an extensive evaluation of all possible lan- guage and vision model combinations to identify the opti- mal configuration for our framework. Table II presents the matching accuracy for all nine combinations of language models (GPT-2, LLaMA-3.2-1.2B, DeepSeek-R1-1.5B) and vision models (ResNet18, ResNet50, ViT-B/16). The results demonstrate clear performance scaling with model capacity and architectural choice. Among all combinations, DeepSeek- R1-1.5B paired with ResNet50 achieved the highest accuracy. Based on these findings, we selected the DeepSeek-R1-1.5Band ResNet50 combination for our comparative analysis with baseline methods and state-of-the-art multimodal models. As shown in Fig.4(a), our framework with this optimal configura- tion achieved 74% accuracy on the tool selection task, where the system must select the correct tool from 10 candidates based on a textual scenario description. TABLE II TOOL SELECTION ACCURACY FOR DIFFERENT LANGUAGE -VISION MODEL COMBINATIONS Language Model Vision Model Accuracy GPT2 ResNet18 38% GPT2 ViT-B/16 39% GPT2 ResNet50 42% LLaMA-3.2-1.2B ResNet18 57% LLaMA-3.2-1.2B ViT-B/16 60% LLaMA-3.2-1.2B ResNet50 62% DeepSeek-R1-1.5B ResNet18 70% DeepSeek-R1-1.5B ViT-B/16 72% DeepSeek-R1-1.5B ResNet50 74% For comparison, we implemented several alternative meth- ods. First, we established a direct naming baseline, a simple approach where we prompt the DeepSeek-R1-1.5B model to directly output the most appropriate tool name from a list of available tools based on the scenario description, without using an | https://arxiv.org/abs/2505.22146v1 |
attribute-based intermediate representation. This approach achieved only 20% accuracy, highlighting the limitations of direct mapping between scenario descriptions and tool names. 10 Second, we tested Qwen-VL-7B, a smaller multimodal model with approximately 7 billion parameters. With straight-to- answer (STA) prompting, it achieved only 21% accuracy, barely outperforming random selection. When enhanced with chain-of-thought (CoT) prompting, its performance improved significantly to 58%, but still remained substantially below our attribute-based approach. Third, we evaluated two state- of-the-art large multimodal models: GPT-4o and Gemini-2.0- Pro. With standard prompting, GPT-4o achieved 67% accuracy and Gemini-2.0-Pro achieved 72%. With chain-of-thought prompting, GPT-4o improved to 73%, while Gemini-2.0-Pro showed a slight decrease to 68%. As illustrated in Fig.4(b), our approach achieves competitive or superior performance compared to models with orders of magnitude more parameters. The combined parameter count of our DeepSeek-R1-1.5B language model and ResNet50 visual model is approximately 1.53 billion, compared to 7 billion for Qwen-VL-7B and estimated hundreds of billions for GPT-4o and Gemini-2.0-Pro. This remarkable efficiency stems from our approach’s use of a structured attribute space that effectively bridges vision and language while requiring far fewer parameters than end-to-end multimodal training. Fig.4(c) provides a qualitative example of our system’s output for a specific scenario (”The papermaker dispersed pulp evenly across the screen to form a sheet of paper.”). The visualization shows the ranking of candidate tools based on attribute similarity, with the broom correctly identified as the most suitable tool. The attribute vectors for both the scenario and candidate tools illustrate how the matching occurs in our shared attribute space. D. Ablation Studies To understand the relative importance of different attributes in our framework, we conducted an ablation study where we systematically removed individual attributes and measured the impact on model performance. Fig. 5 presents these results for both individual encoders and the combined tool selection task. For the visual encoder, consistent patterns emerged across all architectures. Hand-relatedness removal caused the most substantial performance degradation (12.35-13.39%), followed closely by elongation (11.91-12.61%) and graspability (9.30- 10.35%). In contrast, attributes like valence, size, and spikiness showed minimal impact when removed (generally below 3% decrease). This suggests that functional characteristics related to human interaction and shape properties (particularly elon- gation) are the most informative visual cues for distinguishing between tool categories. Similar patterns appeared in language models, where elon- gation, hand-relatedness, and graspability consistently proved most critical for accurate prediction. For DeepSeek-R1-1.5B, removing elongation decreased accuracy by 7.10%, while hand-relatedness and graspability reduced accuracy by 6.23% and 5.67%, respectively. This consistency across modalities suggests that functional attributes related to tool manipulation and physical form are inherently more distinguishing in both visual and linguistic representations. In the end-to-end tool selection task, an interesting pattern emerged. While most attribute removals resulted in slightperformance decreases (0-3%), removing hand-relatedness un- expectedly improved accuracy by 4%. This suggests potential interactions between attributes in cross-modal matching that differ from their contributions in isolated visual or language pathways. The small impact of individual attribute removal on end-to-end performance indicates redundancy in the attribute representation, allowing the system to maintain reasonable performance even with missing dimensions. | https://arxiv.org/abs/2505.22146v1 |
These ablation studies reveal that our attribute space ef- fectively captures the most salient properties for flexible tool selection, with functional and manipulation-related attributes proving particularly critical across modalities. V. D ISCUSSION AND CONCLUSION Our work establishes a cognitively inspired computational framework for flexible tool selection that achieves 74% accu- racy while using significantly fewer parameters than state-of- the-art multimodal models. By formalizing the role of low- dimensional attribute space as a bridge between visual tool perception and linguistic task understanding, we demonstrate both the computational efficiency and cognitive plausibility of attribute-based tool selection. The performance gap between visual (96.05%) and lan- guage (74.34%) pathways reveals a fundamental asymmetry in how attributes are extracted versus inferred: visual systems can directly extract explicit physical properties from images, while language systems must infer implied requirements from natural language descriptions. This asymmetry aligns with cognitive research showing that physical object properties are more directly accessible than functional requirements inferred from task contexts [70], [71]. Our ablation studies pro- vide crucial insights into the functional primitives underlying tool selection, revealing that manipulation-related attributes (graspability, hand-relatedness, elongation) consistently prove most critical across modalities. This consistency supports the technical reasoning hypothesis in cognitive science, which emphasizes the importance of functional and physical property reasoning in tool use [72], [73]. The interpretability of our attribute space allows for sys- tematic analysis of model decisions, addressing the opacity of large multimodal models [74]. Our modular architecture enables independent optimization of visual and language components, facilitating iterative refinement and adaptation across domains. The parameter efficiency of this approach makes it particularly suitable for resource-constrained appli- cations where computational overhead is critical. Moreover, our attribute-based architecture provides a testable compu- tational model for neurocognitive research [75], potentially informing psychophysical experiments investigating human attribute prioritization and neuroimaging studies examining neural correlates of different tool properties. While our framework demonstrates significant advances, several limitations warrant consideration and suggest direc- tions for future work. First, despite being theoretically moti- vated and empirically validated, our 13-dimensional attribute space represents a simplification of human tool cognition. These dimensions capture essential physical and functional 11 properties but may not encompass all factors relevant to real- world tool selection, such as availability, cost, or contextual appropriateness [76]. Second, the visual-language performance gap suggests room for improvement in extracting attribute requirements from natural language descriptions. Third, our evaluation focuses on static tool selection rather than dynamic manipulation [77], which would require additional considera- tions of temporal sequences and motor control. Future research should explore expanding the attribute space to incorporate additional dimensions (e.g., material properties, temporal con- straints) while investigating hierarchical or learned attribute spaces through unsupervised methods to discover optimal representations for specific domains. Extending the framework to dynamic tool manipulation and sequential task planning would better capture the full complexity of human tool use and its neural mechanisms. This work advances our understanding of flexible tool selection through a computationally efficient and cognitively plausible framework that bridges visual perception and lin- guistic understanding. By demonstrating that attribute-based representations enable effective cross-modal matching for tool selection, we contribute to | https://arxiv.org/abs/2505.22146v1 |
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arXiv:2505.22147v1 [cs.AI] 28 May 2025Lifted Forward Planning in Relational Factored Markov Decision Processes with Concurrent Actions Florian Andreas Marwitza,*, Tanya Braunb, Ralf Mölleraand Marcel Gehrkea aUniversity of Hamburg bUniversity of Münster ORCID (Florian Andreas Marwitz): https://orcid.org/0000-0002-9683-5250, ORCID (Tanya Braun): https://orcid.org/0000-0003-0282-4284, ORCID (Ralf Möller): https://orcid.org/0000-0002-1174-3323, ORCID (Marcel Gehrke): https://orcid.org/0000-0001-9056-7673 Abstract. Decision making is a central problem in AI that can be formalized using a Markov Decision Process. A problem is that, with increasing numbers of (indistinguishable) objects, the state space grows exponentially. To compute policies, the state space has to be enumerated. Even more possibilities have to be enumerated if the size of the action space depends on the size of the state space, especially if we allow concurrent actions. To tackle the exponential blow-up in the action and state space, we present a first-order representation to store the spaces in polynomial instead of exponential size in the number of objects and introduce Foreplan, a relational forward planner, which uses this representation to efficiently compute policies for numer- ous indistinguishable objects and actions. Additionally, we introduce an even faster approximate version of Foreplan. Moreover, Foreplan identifies how many objects an agent should act on to achieve a cer- tain task given restrictions. Further, we provide a theoretical analysis and an empirical evaluation of Foreplan, demonstrating a speedup of at least four orders of magnitude. 1 Introduction Decision making problems can be formalized using Markov Deci- sion Processes (MDPs). To compute a policy for an MDP, the state and action spaces have to be enumerated to find the best possible action for each state. But, with increasing number of (indistinguish- able) objects, the state space grows exponentially. In case the actions depend on the states or objects as well, even more possibilities have to be enumerated, let alone if further concurrent actions are allowed. For concurrent actions, all possible action combinations have to be enumerated, yielding an exponentially-sized action space, which is why concurrency is rarely modelled. However, consider the follow- ing example: A small town is haunted by an epidemic. To fight the epidemic, the town’s mayor can impose travel bans on the town’s cit- izens. Certainly, the mayor can confine all citizens to their homes, stopping the epidemic. However, the citizens’ overall welfare is im- portant as well. Therefore, the mayor is interested in the best deci- sion of imposing travel bans (concurrent actions) w.r.t. confining the epidemic, while keeping the citizens’ welfare above a certain thresh- old. In addition, there can be circumstances, where the policy has to be adapted, e.g., if a wave of infections is on the rise, threatening to infect the majority of the population. Computing this problem on ∗Corresponding Author. Email: florian.marwitz@uni-hamburg.dea propositional level, with every citizen explicitly represented, like approaches such as MDPs do, blows up the state and action space, as it requires exponential enumeration of all subsets of the popula- tion. However, there are groups of citizens behaving identically w.r.t. getting sick (and well again) as well as regarding their welfare if a travel ban is imposed, i.e., these citizens are indistinguishable for the | https://arxiv.org/abs/2505.22147v1 |
mayor. Within these groups, it does not matter on which exact citizen the mayor imposes a travel ban on, only on how many she imposes a travel ban on. Additionally, many computations over subsets of indis- tinguishable citizens are redundant. Thus, we propose to drastically reduce the search and action space by using a first-order representa- tion, grouping indistinguishable citizens, and use this representation to plan group-wise by reasoning about a representative and then pro- jecting the result to the whole group. Contribution First, to be able to represent numerous indistinguish- able objects and actions, we use probabilistic relational models as a representation in (factored) MDPs (fMDPs), which yields relational factored MDPs (rfMDPs). Second, we propose Foreplan to carry out planning in rfMDPs. State of the art algorithms run in time exponen- tial in the number of objects. To reduce the time taken to polynomial in the number of objects, we define the relational cost graph , which is used by Foreplan to encode the state and action space in an effi- cient way. Denoting the number of cliques by cand the size of the largest clique by win the relational cost graph, Foreplan runs expo- nential in candw. Both parameters are structural ones, mostly small and fixed, and thus independent of the number of objects. Therefore, Foreplan is an efficient planner w.r.t the number of objects. Third, us- ing approximation, we can reduce the runtime even further. We pro- pose Approximate Foreplan, whose runtime is polynomial in c. For Approximate Foreplan, we show a speedup of at least four orders of magnitude. Fourth, we show that Foreplan efficiently identifies how many objects the agent should act on to achieve a task given certain restrictions. The resulting extension of Foreplan runs in polynomial time in the number of objects, when the underlying relational cost graph has bounded maximal clique size and if the model is liftable and the evidence for state and action is liftable as well. Related Work Bellman [2] introduces MDPs, which Boutilier et al. [4] extend to fMDPs by factorizing the transition function. Factoriz- ing also the value function, Guestrin et al. [16] provide two approx- imate algorithms for solving planning in fMDPs. Dean et al. [10] cluster the state space of fMDPs to reduce the state space even fur- ther. Givan et al. [15] group equivalent states based on the notion of bisimulation. Both approaches lack the ability to handle concurrent actions efficiently. MDPs can be generalized to partially observable MDPs, in which the agent is uncertain in which state the environment currently is [19]. Sanner and Kersting [29] add the first-order per- spective to partially observable MDPs, focusing on the observations and without concurrent actions. Bernstein et al. [3] extend partially observable MDPs to have a set of decentralized agents. Braun et al. [6] work with groups of indistinguishable agents. This is similar to our approach, in which we handle sets of indistinguishable state vari- ables. However, they do not provide a solution environment. The idea of lifting is to carry out computations over representa- tives for | https://arxiv.org/abs/2505.22147v1 |
groups of indistinguishable random variables [26, 20, 9]. There are online decision making approaches adding action and util- itity nodes to this representation [1, 14, 13], here, we focus on offline planning. To carry out even more lifted computations, Taghipour [30] extends lifted probabilistic inference by a generalized counting framework, which we extend later on. Using a first-order representa- tion for states, actions and objects, Boutilier et al. [5] exploit the re- lational structure for planning using MDPs, still without concurrent actions. To specify factored transition models in first-order MDPs, Sanner and Boutilier [28] introduce factored first-order MDPs, which uses a backward search. While they consider actions on subsets of objects, they still treat every object individually. We apply lifting for efficient handling of the objects. Moreover, we prove on which mod- els our algorithm runs efficiently. For a survey on planning using first-order representations, we refer to Corrêa and De Giacomo [7]. Structure The remainder of this paper is structured as follows: First, we present preliminaries for (factored) MDPs, including lifting. Then, we introduce rfMDPs to group indistinguishable objects. Af- terwards, we propose Foreplan, which exploits these indistinguish- able objects using a compact state representation, to efficiently sup- port decision making with concurrent actions. We also show how Foreplan identifies how many objects to act on for achieving a cer- tain task. Further, we provide a theoretical analysis and empirical evaluation of Foreplan. 2 Preliminaries In this section, we lay the foundation for rfMDPs. We first recap (f)MDPs, which model probabilistic state changes, when an agent performs an action. Furthermore, the states have a reward assigned, and the task is to compute the optimal policy, i.e., which action to perform in which state, for the agent w.r.t. rewards. Second, we recap lifting in probabilistic graphical models. 2.1 (Factored) Markov Decision Processes In this subsection, we first define MDPs and specialize them to fMDPs by factoring the transition function T. Definition 1 (Markov Decision Process) .AMarkov Decision Pro- cess is a tuple (S,A, T, R )with a set of states S, a set of ac- tionsA, a reward function R:S7→Rand a transition function T:S×A×S7→[0,1]. The value T(s, a, s′)is the probability P(s′|s, a)of transitioning from state s∈Sto state s′∈Sif action a∈Ais performed. The rewards are additive, possibly discounted by a factor γ∈ [0,1]. An MDP is fully observable and has the first order Markovproperty, i.e., the probability of the next state only depends on the current state and action. Let us have a look at a simple, (incomplete) example of an MDP. Example 1. Suppose we have the states healthy andsick. Our agent has two possible actions: Travelling orstaying at home . When the agent is in state sick and travels, she stays sick with a probability of 0.9. The agent obtains a reward of −1if sick and 1if healthy. Planning in MDPs refers to calculating an optimal policy for the agent, which is a mapping from each state to an action to perform. To compute such a policy, we first define the utility of a state: Definition 2 (Bellman Equation [2]) | https://arxiv.org/abs/2505.22147v1 |
.The utility of a state sis given by U(s) =R(s) +γmax a∈AX s′∈SP(s′|s, a)·U(s′). (1) To find the utility of a state algorithmically, we find a value func- tionVsatisfying the Bellman equation. The value function induces a policy by selecting the action that yields the maximum expected value. For computing a value function, we can use a linear program- ming formulation [12, 27]: Variables: V(s)∀s∈S; Minimize:X s∈Sα(s)V(s) ; Subject to: ∀s∈S,∀a∈A: V(s)≥R(s) +γX s′∈SP(s′|s, a)·V(s′),(2) where the coefficients α(s)are arbitrary positive numbers, e.g., an equal distribution over all states [27]. Planning in MDPs can be solved in polynomial time w.r.t to the state space size [25]. But what if the state space becomes very large, e.g., exponential in the number of objects? For retaining an efficient transition model, fMDPs make use of state variables for the objects. The state space is then spanned by the state variables. In our paper, all state variables are Boolean, but can be easily extended to non-Boolean. Definition 3 (Factored MDP) .Afactored MDP is a tuple (S′,A, T, R ), whereS′contains mstate variables S1, . . . , S mwith Boolean cardinality. Accordingly, the state space is S={0,1}m. The transition function Tis, for each action, factored according to a (Dynamic) Bayesian network: P(S′|S, a) =mY i=1P(S′ i|Pa(S′ i), a), (3) where Pa(S′ i)⊆Sdenotes the set of parents of S′ iin the Bayesian network, Sthe old state, S′the new state and S′ ithei-th state vari- ables in the respective state. For a given state s∈S, we denote with sithe assignment of state variable Siin state s. For handling numerous objects, we use parameterized graphical models, which we introduce in the next subsection. 2.2 Parameterized Graphical Models Often, we have indistinguishable random variables, leading to the same computations. We can tackle redundant computations by pa- rameterizing our probabilistic model and grouping indistinguish- able variables, so that inference in the probabilistic model becomes tractable w.r.t. domain sizes by using representatives during calcula- tions [24]: Definition 4 (Parfactor model [31]) .LetWbe a set of random vari- able names, La set of logical variable (logvar) names, Φa set of factor names, and Da set of constants (universe). All sets are finite. Each logvar Lhas a domain D(L)⊆D. Aconstraint Cis a tu- ple(X, CX)of a sequence of logvars X= (X1, . . . , X n)and a set CX⊆ ×n i=1D(Xi). The symbol ⊤forCmarks that no restrictions apply, i.e., CX=×n i=1D(Xi). A parameterized random variable (PRV) B(L1, . . . , L n), n≥0,is a syntactical construct of a random variable B∈Wpossibly combined with logvars L1, . . . , L n∈L. Ifn= 0, the PRV is parameterless and constitutes a propositional random variable (RV). The term R(B)denotes the possible values (range) of a PRV B. An event B=bdenotes the occurrence of PRVBwith range value b∈ R(B). We denote a parameterized fac- tor(parfactor )gbyϕ(B)|CwithB= (B1, . . . , B n)a sequence of PRVs, ϕ:×n i=1R(Bi)7→R+apotential function with name ϕ∈Φ, andCa constraint on the logvars of B. A set of parfactors forms a model G:={gi}n i=1. With Zas normalizing constant, Grepresents the full | https://arxiv.org/abs/2505.22147v1 |
joint distribution PG=1 ZQ f∈gr(G)f, with gr(G)refer- ring to the groundings of Gw.r.t. given constraints. A grounding is the instantiation of each parfactor with an allowed constant. Let us illustrate the definition of a parfactor model: Example 2. LetW={Sick, Epidemic },L={M},D(M) = D={a, b, c, d, e, f, g, h }with Boolean-valued PRVs Sick(M)and Epidemic . Letg(Sick(M), Epidemic )be the parfactor with po- tential ϕdefining the probability to be sick for all persons D, given there is an epidemic (or not). The grounded model then consists of the eight factors ϕ(Sick(a), Epidemic ),ϕ(Sick(b), Epidemic ), . . . , ϕ(Sick(h), Epidemic ). Next, we present rfMDPs, integrating a parfactor model. 3 Relational Factored MDPs Now, we present a first-order representation for indistinguishable ob- jects and numerous actions on collections of them. Let us generalize Example 1 to an arbitrary number of persons behaving in the same way as the agent in Example 1: Example 3 (Epidemic) .There is a set Dof persons living in a small town, represented by the logvar MwithD(M) =D. Each person can be sick or healthy, leading to the PRV Sick(M). The govern- ment gets a bonus of 1for each healthy person and a penalty of −1 for each sick person. To combat an epidemic, the government can impose travel bans on persons, resulting in the action Restrict (M) to impose a travel ban on a subset of persons. Moreover, each person can travel or not, leading to the PRV Travel (M). The government gets a bonus of 2for each person travelling. The PRV Epidemic is influenced by the number of people travelling and influences the sickness of each person. Figure 1 shows the transition model for this example. Since an action can be applied to each person concurrently, the amount of possible actions is exponential due to the power set, i.e., all possible combinations for all persons. To prevent the exponential explosion, we introduce rfMDPs to group objects and actions. For modeling indistinguishable objects, we require all functions used in the rfMDP to be oblivious about the exact object, i.e., permuting the objects does not alter the output, and ensure that the functions work on representatives for each group. We start by defining rfMDPs as a mixture of fMDPs and a parfactor model. Afterwards, we investigate and define the effects on the actions and rewards.Tr(M) Si(M) Epi f1f2 Re(M) Si′(M) Tr′(M) Epi′f3 Figure 1. Lifted representation of the transition model for Example 3. We abbreviate by using only the first letter(s) for each symbol. We define rfMDPs based on fMDPs, but include a parfactor model inside the state and action space and transition function. That is, the state variables can now contain PRVs, which are then used in the transition model and the reward function. Also, we have action PRVs , whose value is chosen by the agent. In the following definition we use the term interpretation of a set for a truth-value assignment to each element in the set. Definition 5 (Relational Factored MDPs) .Arelational factored MDP is a tuple (D,L,X,A, G,R). The set Dis a | https://arxiv.org/abs/2505.22147v1 |
set of constants and the set Lis a set of logvars over D. The set Xis a set of PRVs defined over L. The set of possible interpretations IXfor the ground- ings of the set Xdefines the state space. The set Ais a set of action PRVs. A prafctor model GoverAandXrepresents the the transi- tion function T:IX× IA× IX7→R+ 0, with the set IAof possible interpretations of the groundings of A, and specifies the transition probability given an action and a previous state. The set Rcontains parameterized local reward functions Ri:×jR(Bi,j)7→R, de- fined over PRVs Bi,j. The reward function Ris decomposed as a sum over R. While Definition 5 defines the ground semantics, there are symme- tries within the parfactor model, which we exploit with Foreplan in the next section. In the remainder of this section, we explain what we mean by action PRVs and by parameterized local reward functions. 3.1 Parameterizing Actions In our epidemic example, the mayor, representing the government, can impose travel bans on all parts of the town’s population. We ex- tend the action definition in this subsection to account for groups of objects. Having groups, we circumvent enumeration of all possible subsets and model the example action of imposing travel bans on a subset of the population efficiently. Definition 6 (Action PRV) .An action PRV Ais a Boolean-valued PRV . A concrete action is a set of events, in which each grounding of Areceives an assignment a∈ R(A). Action PRVs allow for a more general action setting. In our exam- ple, the mayor can restrict multiple persons from travelling at once: Example 4 (Action PRV) .The action PRV Restrict (M)models the possible travel bans on the population of the town. For a con- crete action, the mayor has to specify, possibly using constraints on parfactors, on which persons Restrict should be applied on. As actions are now parameterized, we describe the impact of pa- rameterization on the rewards. 3.2 Parameterized Local Reward Functions The reward function in fMDPs maps from the (joint) state to the re- ward of the state. For evaluating the reward function, we thus have to construct the joint state and cannot exploit the factorization, breaking our aim of efficient representation. To further use our efficient repre- sentation, we introduce a decomposable reward function: We assume that the reward function is factored as R=P iRi, with local reward functions Riwith scope restricted to a subset of the state variables. As we have indistinguishable state variables, we reduce redundant computations by using representatives in the reward functions ana- logue to a parfactor, but using a sum instead of a product: Definition 7. A local reward function Ri:×jR(Bi,j)7→Ris defined over a sequence of PRVs (Bi,j)j. The semantics of a sin- gle parameterized local reward function Riis defined as the sumP zRi(z)over the interpretations z∈×jR(Bi,j)in the current state of all groundings of Ri. In other words, a parameterized local reward function serves as a placeholder for the set of local reward functions, obtained by replac- ing all logvars by their possible instantiations. We illustrate parame- terized reward | https://arxiv.org/abs/2505.22147v1 |
functions in our epidemic example: Example 5. The parameterized local reward functions for Exam- ple 3 are R1(Sick(M)), evaluating to −1(1) for each person (not) being sick, and R2(Travel (M)), evaluating to 2for each person travelling. If five persons are sick, three are not sick and four people are travelling, the total reward is −5 + 3 + 8 = 6 . Summarizing, we have defined rfMDPs as fMDPs using a parfac- tor model for states, actions and transitions and adapted the reward function for taking PRVs into account as well. In the next section, we propose a compact state representation for such rfMDPs to be used in Foreplan exploiting indistinguishable objects. 4 Foreplan: Efficient Forward Planning In this section, we propose Foreplan, our exact fo rward re lational planner for rfMDPs. The input to Foreplan is an rfMDP. The out- put is a value function, which induces a policy. Foreplan computes the value function by finding an efficient state representation for the rfMDP and then running a linear program based on the state repre- sentation to calculate the value function. We first describe how Fore- plan uses the state representation for rfMDPs and how it obtains the state representation. Afterwards, we outline how Foreplan computes a value function based on the found state representation. 4.1 Obtaining an Efficient State Representation Foreplan needs to encode the current state compactly to efficiently reason about indistinguishable variables. Thus, in this subsection, we describe how Foreplan treats indistinguishable objects in rfMDPs. Namely, Foreplan can tame the information to store: Foreplan does not need to keep track of objects that could be differentiated by their history. Rather, with each action and new time step, the history is swept away and the objects remain indistinguishable because of the first-order Markov assumption. The basic idea of our state represen- tation is inspired by Counting Random Variables (CRVs) [30] and we extend them for use in out state representation. It is sufficient to count the number of occurrences of each possible truth-value assign- ment to the groundings of the input PRVs of a parfactor in a his- togram. The input PRVs are the ones representing the current state.Counting only the input PRVs is sufficient, because all possible next states are iterated separately, which we describe in the next subsec- tion. Focusing only on the counts for the groups enables Foreplan to use a much simpler state space representation, namely the set of pos- sible histograms. We now describe in more detail how to count the assignments. Counting the assignments of each PRV separately is insufficient, as PRVs can be defined over the same logvars and thus interfere with each other. However, the parfactors can be evaluated separately since, in Equation 2, we have the full current and next state available. Thus, it is sufficient to count PRVs together if they share a logvar and occur in a parfactor together. To obtain the representation and quantify its complexity, we define the relational cost graph: Definition 8 (Relational Cost Graph) .Therelational cost graph of a parfactor model of an rfMDP has | https://arxiv.org/abs/2505.22147v1 |
a vertex for each PRV in the current state. Two vertices are connected by an edge if and only if the PRVs associated with these two vertices share a logvar and occur together in a parfactor or a parameterized local reward function. We denote the number of (maximal) cliques by cand the size of the largest clique by w. We note that candware both bound by the number of PRVs, although this bound is relatively loose. The key insight now is that (maximal) cliques in the relational cost graph correspond to sets of PRVs that Foreplan needs to count together as they interfere with each other. Let us take a look at the relational cost graph of Exam- ple 3. For a more complex example, we refer to Appendix A.1. Example 6. The relational cost graph for Example 3 consists of three isolated vertices corresponding to Sick(M),Travel (M)and Epidemic . The first two do not occur together in a parfactor or lo- cal reward function and the first and the last does not share a logvar with one of the first two. Since the cliques describe which PRVs need to be counted to- gether, the state representation is now a set of such countings, stored in a histogram each. We extend the definition of CRVs to more than one logvar based on the definition of CRVs by Taghipour [30]: Definition 9 (Extended Counting Random Variable) .A counting for- mula γ= # C[B1, . . . , B k]is defined over PRVs Biwith a con- straint C= (L, CL)over the logvars Lof the PRVs Bi. The count- ing formula represents a counting random variable (CRV) whose range is the set of possible histograms that distribute n=|CL|ele- ments intoQk i=1|range (Bi)|buckets. The state of γis the histogram function h={(ri, ni)}r i=1stating for each ri∈ ×k i=1range (Pi) the number niof tuples (Pi)iwhose state is ri. If no restrictions apply, we omit C, andn=Q L∈L|D(L)|, where Lis the set of logvars of the PRVs Bi. The CRV corresponding to a clique gives us the number of occurrences for each possible instanti- ation of the PRVs in that clique. We give a small example for a CRV using an additional PRV Car(M)for illustrative purposes: Example 7. Suppose we have two PRVs, Travel (M)and Car(M). A possible state for the CRV #[Travel (M), Car (M)]is {(tt,2),(tf,1),(ft,3),(ff,2)}. In this state, there are two persons for which Travel andCar are both true. Example 7 illustrates that we need only four buckets regardless of the domain size of M. We now formalize the state representation: Definition 10 (State Representation) .We create one CRV Sifor each (maximal) clique in the relational cost graph, counting the corre- sponding PRVs in the clique. For a single propositional RV in the relational cost graph, we use the RV for Siinstead of a CRV . A state assigns a value to each Si. The resulting state space is the set of possible states. We denote the state space by the tuple (Si)i, which contains one CRV (or RV) per clique. We give the representation of | https://arxiv.org/abs/2505.22147v1 |
the state space for Example 3: Example 8. As the vertices are not connected in the relational cost graph, the state representation is (#[Sick(M)],#[Travel (M)], Epidemic ). We prove that our state representation exactly covers S: Theorem 11. The representation in Definition 10 is correct. Proof Sketch. Given groundings for the state PRVs, we derive the histograms for the CRVs by counting the assignments for each par- factor. Given a representation as defined in Definition 10, we recon- struct, for each parfactor, the groundings of the PRVs by extracting the counts from the CRVs and instantiating the respective parfactors. We provide a full proof in Appendix A.2. To advance through an action to the next state, the action now has to use the same state representation, i.e., the action is specified on the counts for all PRVs of the current state for all parfactors the action is mentioned in: Example 9. The action Restrict works on the PRV Travel (M). Thus, the mayor needs to specify how many persons of those (not) travelling are (not) allowed to travel. The action Restrict is there- fore defined over #[Travel (M), Restrict (M)]. A concrete action is, e.g., a={(tt,3),(ft,2)}. The action adoes not need to specify the counts of people no travel ban is imposed on ( tf,ff), as these are determined by aand the current state. The mayor no longer needs to specify individual persons, but rather the number of persons (not) travelling, which are restricted from travelling. It is irrelevant on which exact persons the action is performed. With this action representation, we reduce the action space from exponential to polynomial, which we prove in Theo- rem 13 in the next section. In the next subsection, we show how Foreplan uses the action space to compute the value function by solving a linear program. 4.2 Computing a Value Function Let us have a look on how Foreplan computes the value function based on the introduced state representation. Foreplan uses the linear programming formulation given in Equation 2 to compute the value function. For the linear program, Foreplan uses the introduced state and action representations to iterate over all states and actions. We briefly describe how to evaluate the transition probability P(s′|s, a). Since we have full evidence provided, Foreplan eval- uates each state CRV S′ iseparately. For a fixed state CRV S′ i, the value s′ iis fixed since the whole state space is iterated. For eval- uating P(s′ i|s, a), Foreplan iterates over all possible assignment transitions, e.g., the number of people getting sick and healthy, and calculating the transition probability for a single assignment tran- sition by using the parfactor as a lookup-table for the probabilities for the transition and using the multinomial coefficient for countinghow many times the assignment transition is applicable. We provide a more detailed description and an example in Appendix A.3. With Foreplan, we are able to cope with numerous indistinct ob- jects and actions on collections of those objects. We do so by suc- cessfully applying lifting in the field of MDPs. While traditional ap- proaches | https://arxiv.org/abs/2505.22147v1 |
can represent actions on sets of objects, they fail to do so efficiently. Therein, the actions for each subset would be represented on their own, resulting in exponentially many actions. In the next section, we show the complexity of Foreplan. 5 Complexity Analysis of Foreplan Having outlined Foreplan, we analyze the complexity of Foreplan in this section. We start by quantify the state representation and using the complexity of the state representation to derive the runtime com- plexity of Foreplan. We derive the following theorem about the size of the state repre- sentation from Definition 10. Theorem 12. The state representation is in O(c×2w). Proof. For each clique, the size of the histogram function is expo- nential in the number of vertices in the clique, as we enumerate all possible assignments. Thus, the size of the state representation is bounded by c×2w. Note that cis bounded by the number of PRVs in our parfactor model, as one parfactor is sufficient per PRV . Theorem 12 overap- proximates the size of the state representation, as not all cliques have the same size and not both, candware large at the same time. Also, candware determined by the structure of the relational cost graph and independent of the domain sizes. Building on the size of the state representation, we give the complexity of the state and action space: Theorem 13. The state and action spaces are both polynomial in the number of objects and exponential in candw. Proof. We need to iterate over all possible instantiations of the state representation. For each clique (resp. CRV), the number of possible instantiations is polynomial in the number of objects. The joint state requires one instantiation per clique, resulting in the number of PRVs as exponent. The size of the action space is bounded by the size of the state space, as the action has to specify a (subset of a) state. Since Foreplan uses a linear program to compute the value func- tion, we analyze the complexity of solving the linear program Fore- plan builds. Linear programs can be solved in polynomial time w.r.t the variables and constraint [33]. Let us therefore take a closer look at the number of constraints and variables Foreplan generates: Theorem 14. The number of linear programming constraints and variables are polynomial in the state space. Proof. By Equation 2, Foreplan generates one variable per possible state and one constraint for each state and action combination. Plugging Theorem 13 in Theorem 14 leads to: Theorem 15. The runtime of Foreplan is polynomial in the number of objects and exponential in candw. For lifting, the runtime in the number of objects is of most inter- est, because all other model parameters are mostly fixed. Therefore, we have successfully developed a lifted forward planner in rfMDPs. Foreplan is an exact algorithm and efficient w.r.t domain sizes. But, if an approximate solution is good enough and speed is of great con- cern, we can use an approximate version of Foreplan which is blaz- ingly fast. In the next section, we introduce the approximate version, using | https://arxiv.org/abs/2505.22147v1 |
approximation to prevent iterating the whole state space and thus circumventing the exponential influence of c. 6 Foreplan: Faster by Approximation While Foreplan runs in time polynomial in the number of objects, the runtime still depends exponentially on c. In this section, we present an approximation technique inspired by the Approximate Linear Pro- gramming (ALP) [16] approach to prevent iterating the whole state space. We first describe the approximation idea and then how Fore- plan uses the approximation. Last, we give bounds on the runtime and on the approximation quality. We call the approximate version Approximate Foreplan . Foreplan needs to iterate over the whole state space, because the value function maps from a state to the value of that state. We ap- proximate the value function by a set hiof basis functions, whose scope is a subset of S,V≈P iwihi, where the goal is to find the most suitable weights wi[16]. Approximate Foreplan also needs the value of all possible next states in terms of the same approximation. Therefore, Approximate Foreplan makes use of the backprojections ga iof the basis functions hi[16], stating the influence of xon the next state: ga i(x) =X x′P(x′|x, a)·hi(x′) (4) Approximate Foreplan parameterizes the basis functions like the re- wards to compute the basis functions and backprojections lifted: Example 10 (Basis Function) .We have three basis functions: h0= 1,h1(Sick(M)):=R1(Sick(M))andh2(Travel (M)):= R2(Travel (M)). The basis functions should capture the important dynamics in the model [21]. The backprojections are computed lifted as defined next: Definition 16 (Lifted Backprojection) .Given a basis function hiand Boolean assignments ˜xand˜ato the state and action, respectively, the backprojection is defined as g˜a i(˜x) =P ˜x′P(˜x′|˜x,˜a)·hi(˜x′). The lifted backprojection Ga i(x)for a state xand action athen sums g˜a i(˜x)for each possible propositional assignment ˜xand˜aand weights the term with the counts given by the state x. Let us apply the backprojection in our running example: Example 11 (Lifted Backprojection) .Suppose we have three sick persons and two healthy ones and are interested in the back- projection of h1. Then, we have G1([(t,3),(f,2)], epi) = 3 · g1(true, epi ) + 2·g1(false, epi ). We show the full calculation of all backprojections for our running example in Appendix B.3. Approximate Foreplan precomputes all backprojections and then builds the following linear program [16]: Variables: w1, . . . , w n; Minimize:nX i=1αiwi; Subject to: ∀a∈A: 0≥max x( R(x) +nX i=1wi(γGa i(x)−hi(x))) . (5)Theαi’s are effectively coefficients for a linear combination over the wi, stating how important the minimization of each wiis [16, 8]. The maximum operator is not part of the definition of linear programs and is removed in an operation similar to variable elimination (VE) [35]. In Appendix A.4, we provide an example for the removal procedure. For the runtime analysis, we introduce the cost network briefly: The cost network for a constraint has a vertex for each appearing variable and there is an edge in the cost network between two vertices if the corresponding variables appear together in the same function. Theorem 17. Approximate Foreplan runs in time polynomial in the number of | https://arxiv.org/abs/2505.22147v1 |
objects, polynomial in cand exponential in the induced width of each cost network, when wis bound. Proof. Approximate Foreplan has to solve the linear program in Equation 5. The number of variables and constraints in the linear program is linear in the action space and exponential in the induced width of each cost network [11]. Because Approximate Foreplan does not iterate over the whole state space, but treats each clique independently in the maximum operator, the effective state space is no longer exponential in c, but polynomial, and the action space is bound by the state space. Most notably, wand induced width in Theorem 17 are mostly small and fixed, leading to a polynomial runtime, as the growth in the number of objects is of more interest. In Appendix A.5, we com- bine the relational cost graph and the cost networks in a single total relational cost graph , and show that the runtime Approximate Fore- plan is polynomial in the number of objects when the treewidth of the total relational cost graph is bounded. Moreover, the same approximation guarantee as for ALP holds for Approximate Foreplan. ALP provides the best approximation of the optimal value function in a weighted L1sense, where the weights in theL1norm are the state relevance weights α[16, 8]. To show that the results can be carried over, we show that Approximate Foreplan and ALP are equivalent on rfMDPs: Theorem 18. Given an rfMDP R, Approximate Foreplan and ALP are equivalent on Rand the grounded version of R, respectively. Proof Sketch. The full proof is in Appendix A.6. The objective func- tion groups grounded basis functions and calculates them lifted when choosing appropiate αi. Each individual constraint is correct, be- cause the lifted backprojections and lifted basis functions compute the same value as for the grounded model. The action representation covers the whole action space. With Approximate Foreplan, we reduced the runtime further from exponential in cto polynomial in c. In the next section, we ex- tend (Approximate) Foreplan to identify how many objects an agent should act on to achieve a certain task given restrictions. 7 Conditional Action Queries Foreplan can be used, e.g., by a mayor to find the optimal number of persons to impose a travel ban on. But sometimes, the mayor could be interested in how she can achieve that the probability of at least half of the town’s population being healthy is at least p. Therefore, let us first define this query type formally: Definition 19 (Conditional Action Query) .Aconditional action query for a state xin an rfMDP is a threshold t∈R∪ {−∞} together with a restriction query P(· |x, a)≥pwith a probability threshold p. The answer to a conditional action query is the action a the agent has to perform to get an expected reward of at least tin the next state and fulfilling the restriction query. To answer a conditional action query, we have to compute the max- imum over all actions for the one-step lookahead Qa(s) =R(s) +γX s′P(s′|s, a)·V(s′) (6) or, for Approximate Foreplan, Qa(x) =R(x) +γX iwiga i(x). | https://arxiv.org/abs/2505.22147v1 |
(7) We can use the techniques from (Approximate) Foreplan to calculate Qa(x)efficiently. Then, we know for every action the expected re- ward in state xand keep the ones where the expected reward is at leastt. We further filter the actions by checking the restriction query P(· |x, a)≥pwith a call to Lifted Variable Elimination (LVE) [26], providing the state xand action aas evidence. Theorem 20. Given that the (I) results of Foreplan are available, (II) parfactor model of the rfMDP is liftable, and (III) evidence for state and action is liftable, we can answer conditional action queries in time (i) exponential in candw, and (ii) polynomial in the number of objects and action space. With approximation, we are not expo- nential, but polynomial in cand in the number of basis functions. Proof. Equation 6 has to be computed for all possible actions. Thus, it requires iteration over the whole action and state space. For the approximation, Equation 7 again has to be computed for all possible actions. However, the state and action spaces are now only polyno- mial in the number of basis functions and not exponential in c. The number of backprojections is the same as of the basis functions. Last, LVE has to be run to check the restriction query for each action. LVE runs in time polynomial in the number of objects if the parfactor model is liftable and evidence for state and action is liftable. Theorem 20 enables iterative planning . We execute Foreplan once and reuse the results to answer conditional action queries at each timestep, leaving us with the time complexity stated in Theorem 20. Furthermore, one could adjust Foreplan to take the conditional action query into account when planning. This way, Foreplan readily returns the answer to the query for each state. Moreover, most typically, c andware much lower than the number of objects nand independent ofn, that is, almost a constant. Furthermore, the runtime in nis of much more interest: Corollary 21. The runtime for answering conditional action queries is polynomial in the number of objects with candwas constants and if the parfactor model and the evidence are both liftable. This result sets Foreplan greatly apart from traditional approaches requiring time exponential in n. Further, it gives rise to an iterative planning framework: At each time, the mayor performs an action keeping more than half of population healthy with a high probability. The decision can be made using Foreplan to evaluate the possible actions. In the next section, we evaluate Foreplan empirically. 8 Empirical Evaluation (Approximate) Foreplan runs in time polynomial in the number of objects, but with other terms are unavoidably exponential. In con- trast to current approaches, the exponential terms of both Foreplane Figure 2. Runtime of (Approximate) Foreplan, ALP and XADD Symbolic Value Iteration on the epidemic example for up to 22 persons with a time limit of two hours. variants depend only on the structure of the rfMDP and not on the number of objects. To underline our theoretical results, we eval- uate (Approximate) Foreplan against ALP and an implementation | https://arxiv.org/abs/2505.22147v1 |
of symbolic value iteration using extended algebraic decision dia- grams (XADDs) [18, 32] for the epidemic example introduced in Example 3. We use Python 3.12 and HiGHS for solving the linear programs [17]. We run all implementations on a 13th Gen Intel(R) Core(TM) i5-1345U with 1.60 GHz and 16 GB of RAM. Figure 2 shows the runtime for the epidemic example for (Approx- imate) Foreplan, ALP and XADD Symbolic Value Iteration for up to 22 persons and Figure 3 in Appendix A.7 shows the runtimes for up to 164 persons, both with a time limit of two hours. XADD Symbolic Value Iteration exceeds the time limit after eight persons, ALP after 15 persons. Foreplan runs after 21 persons and Approximate Fore- plan after 164 persons out of memory. For eight persons, Foreplan is100times faster than XADD Symbolic Value Iteration. Approxi- mate Foreplan is even 10.000times faster, which are four orders of magnitude. For 15 persons, Foreplan is more than six times faster than ALP and Approximate Foreplan is more than 8.576times faster than ALP. For 21 persons, Approximate Foreplan is more than 4.963 times faster than Foreplan. Thus, when using a symbolic solver, we can only solve the epidemic example for up to eight persons. With a factored and approximate approach, we can go up to 15 persons. In contrast, when using Foreplan, we can solve the epidemic exam- ple even for 21 persons and with Approximate Foreplan we can go further to 164 persons, which is ten times more than what ALP can solve. While Foreplan appears to be rising exponentially in the num- ber of persons in Figure 2, we note that by Theorem 15 it is poly- nomial in the number of persons, which can be seen in Figure 3 in Appendix A.7. Overall, (Approximate) Foreplan achieves a speedup of several orders of magnitude and is able to compute policies for significantly more persons within the same time and memory limits. 9 Conclusion Propositional planning approaches struggle with having numerous indistinct objects and actions on subsets of those objects. While first- order MDPs can cope with numerous objects, they still need to rep- resent actions for each subset individually, resulting in exponentially many actions. In this paper, we present Foreplan, a relational forward planner solving the exponential explosion by lifting the objects: Fore- plan groups indistinguishable objects by a representative and carries out calculations on a representative-level. Afterwards, the result is projected to the whole group. Using histograms and focusing only on the number of objects an action is applied to, we effectively reduce the action space from exponential to polynomial in the number of ob- jects. Foreplan allows for answering conditional action queries, en- abling iterative planning with constraints. Such queries can be solved in time polynomial in the number of objects. Moreover, Foreplan opens new doors for mechanism design for incentivizing the concur- rent actions in the returned policy. In future work, we aim to develop a hybrid approach combining Foreplan and Golog [23]. With the forward search in Foreplan, we can identify states reachable from the | https://arxiv.org/abs/2505.22147v1 |
initial state while the back- wards search in Golog computes the exact optimal policy. Further- more, the techniques from Foreplan can be transferred to first-order partially observable MDPs [34]. Acknowledgements The research for this paper was funded by the Deutsche Forschungs- gemeinschaft (DFG, German Research Foundation) under Ger- many’s Excellence Strategy – EXC 2176 ’Understanding Written Artefacts: Material, Interaction and Transmission in Manuscript Cul- tures’, project no. 390893796. The research was conducted within the scope of the Centre for the Study of Manuscript Cultures (CSMC) at Universität Hamburg. References [1] U. Apsel and R. I. Brafman. Extended lifted inference with joint formu- las. In Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence , pages 11–18, 2011. [2] R. Bellman. Dynamic programming. Princeton University Press , 1957. [3] D. S. Bernstein, R. Givan, N. Immerman, and S. Zilberstein. The com- plexity of decentralized control of Markov decision processes. Mathe- matics of operations research , 27(4):819–840, 2002. [4] C. Boutilier, R. Dearden, and M. Goldszmidt. Stochastic dynamic pro- gramming with factored representations. Artificial intelligence , 121(1- 2):49–107, 2000. [5] C. Boutilier, R. Reiter, and B. Price. Symbolic dynamic programming for first-order MDPs. In Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence , volume 1, pages 690–700, 2001. [6] T. Braun, M. Gehrke, F. Lau, and R. Möller. Lifting in multi-agent sys- tems under uncertainty. In Proceedings of the Thirty-Eighth Conference on Uncertainty in Artificial Intelligence , pages 233–243, 2022. [7] A. B. Corrêa and G. De Giacomo. Lifted planning: Recent advances in planning using first-order representations. In Proceedings of the 33rd International Joint Conference on Artificial Intelligence , pages 8010– 8019, 2024. [8] D. P. de Farias and B. Van Roy. The linear programming approach to approximate dynamic programming. Operations Research , 51(6):850– 865, 2003. [9] L. De Raedt, K. Kersting, S. Natarajan, and D. Poole. Statistical rela- tional artificial intelligence: Logic, probability, and computation. Syn- thesis lectures on artificial intelligence and machine learning , 10(2): 1–189, 2016. [10] T. Dean, R. Givan, and S. Leach. Model reduction techniques for com- puting approximately optimal solutions for Markov decision processes. InProceedings of the Thirteenth conference on Uncertainty in Artificial Intelligence , pages 124–131, 1997. [11] R. Dechter. Bucket elimination: A unifying framework for reasoning. Artificial Intelligence , 113(1-2):41–85, 1999. [12] F. d’Epenoux. Sur un probleme de production et de stockage dans l’aléatoire. Revue Française de Recherche Opérationelle , 14(3-16):4, 1960.[13] M. Gehrke, T. Braun, and R. Möller. Lifted temporal maximum ex- pected utility. In Advances in Artificial Intelligence , pages 380–386, 2019. [14] M. Gehrke, T. Braun, R. Möller, A. Waschkau, C. Strumann, and J. Steinhäuser. Lifted maximum expected utility. In Artificial Intelli- gence in Health , pages 131–141, 2019. [15] R. Givan, T. Dean, and M. Greig. Equivalence notions and model min- imization in Markov decision processes. Artificial Intelligence , 147(1- 2):163–223, 2003. [16] C. Guestrin, D. Koller, R. Parr, and S. Venkataraman. Efficient solu- tion algorithms for factored MDPs. Journal of Artificial Intelligence Research , 19:399–468, 2003. [17] Q. Huangfu and J. J. Hall. Parallelizing the dual revised simplex | https://arxiv.org/abs/2505.22147v1 |
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more complex setting: Example 12. Consider the PRVs Sick(M)andRemoteWork (M) and assume we have a parfactor defined over these two PRVs as well as the PRV Sick′(M)for the next state. Then, the relational cost graph consists of two vertices Sick(M)andRemoteWork (M) with an edge between them. Furthermore, we can understand why these two PRVs need to be counted together: A person is (not) sick in the next state dependent on that person being (not) sick and (not) working remote in the current state. We need both values for the cor- rect transition probability. We now apply the definition of a CRV to Example 12. Example 13. The CRV for the clique of Sick(M) and RemoteWork (M) has the structure #[Sick(M), RemoteWork (M)]and a histogram for that CRV would consist of the four entries nttfor the number of people being sick and working remote, ntffor the number of people sick but not working remote, nftfor the number of people not sick but working remote, and nfffor the number of people not sick and not working remote. We also provide an example for using our CRVs for counting PRVs defined over different parameters, where regular Counting Random Variables cannot be used: Example 14. Suppose we have a parfactor defined over PRVs Sick(X), Friends (X, Y), Sick (Y). The respective CRV has structure #[Sick(X), Friends (X, Y), Sick (Y)]and we count for each (joint) assignment to the groundings of the PRVs how often this assignment occurs. Assume that Xhas a domain with only one per- sonxandYwith three, y1toy3. Now further assume that xandy1 are sick and xis friends with everyone except y2. In other words, x is friends with one sick and one healthy person and not friends with a sick person. Thus, the entries in the histogram for ttt,ttfandtft are one, while all other ones are zero. A.2 Proof of Correct State Representation Theorem. The representation in Definition 10 is correct. Proof. We first lay the foundation for the proof and then continue to show both directions of transformation. By the definition of a parfactor model G={gi}, the full joint probability distribution is PG=1 ZY f∈gr(G)f (8) =1 ZY g∈GY f∈gr(g)f, (9) withgr(g)referring to the groundings of a parfactor g. Without loss of generality, we fix a parfactor g. We split the PRVs Bthe factor g is defined over into two sets: The set Binofinput PRVs, which con- tribute to the current state, and the set Boutofoutput PRVs, which contribute to the next state. We can ignore the set Bout, because we only represent the current state and iterate over the next state later in Foreplan.Given groundings for g, we count the occurrences of each assign- ment to the PRVs in Bin. We split the counts into different CRVs according to Definition 10. Given a state in CRVs, we have to instantiate all possible ground- ings respecting the current state representation. The PRVs in Binare possibly split across different CRVs. We can combine any ground- ing of the PRVs in the different sets, as these PRVs do not share a parameter. A.3 Calculating Transition | https://arxiv.org/abs/2505.22147v1 |
Probabilities In this section, we show how Foreplan calculates the transition prob- abilities for the constraints in the linear program in Equation 2. Foe each state and action combination, Foreplan generates one constraint. Within this constraint, a sum is taken over all future states. We show how to calculate the required P(s′|s, a)for given state s, action a and next state s′. We assume that we have one parfactor per PRV in the next state. Since the transition function is factored, Foreplan calculates the probability of each PRV in the next state separately. Thus, we only need to describe how Foreplan calculates P(s′ j|s, a)for a given current state, given action and given parfactor for state variable s′ jin the next state. In short, Foreplan first computes the state representa- tion for the current and next state zoomed in at the given parfactor and second iterates over all possible transitions, summing their prob- abilities. We describe the two steps in more detail. First, Foreplan computes the state representation only for the in- put PRVs (including the action PRV) of the parfactor from the given state representation and for the output PRV . Depending on the inter- twinedness of the parfactors, the computation is just an extraction or summing out unneeded PRVs from the state representation. At the end of this step, Foreplan has a CRV for the input PRVs and another one for the output CRV for the parfactor. Let us fix an order the buckets of the input CRV and denote the counts in each bucket by ki, i= 1, . . . , n , where nis the number of buckets. In the second step, Foreplan iterates over all possible transitions, where one transition specifies transition counts ti, i= 1, . . . n . The transition count 0≤ti≤kispecifies the number of objects tran- sitioning from bucket ito the true-assignment of the output CRV , ki−tito the false-assignment, respectively. Since the next state is given, the sum over the timust be exactly the number of true- assignments in the output CRV . For a fixed transition, Foreplan cal- culates the probability Pti,kiof a fixed bucket iby Pti,ki= ki ti! ·ϕti i→true·ϕki−ti i→false, (10) where ϕi→true denotes the probability for transitioning from bucket itotrue andϕi→false for the transition to false . Both probabilites can be looked-up in the parfactor. For a parfactor for the output PRV sj, the computation is thus P(sj|s, a) =X (ti)iY iPti,ki, (11) where the sumP (ti)igoes over all possible transitions. We illustrate Equation 11 with our running example: Example 15. We show how to calculate the probability of the PRV Travel . Let us denote the number of persons travelling in the current state by x, the number of persons travelling in the next state by x′, the number of persons travelling and restricted from travelling by a1and the number of persons not travelling and restricted from travelling by a2. Then, the sum in Equation 11 goes over all t1, t2, t3, t4≥0with t1+t2+t3+t4=x′, (12) t1≤a1, (13) t2≤x−a1, (14) t3≤a2, (15) t4≤m−x−a2, (16) where mis the | https://arxiv.org/abs/2505.22147v1 |
number of persons in our example. We denote by ϕ(travel′, travel, restrict )the probability of a person travelling in the next state given that person is currently (not) travelling and (not) being restricted from travelling. Then, the values Pti,kiare calcu- lated by Pt1,a1= a1 t1! ·ϕ(t, t, t)t1·ϕ(f, t, t )a1−t1(17) Pt2,x−a1= x−a1 t2! ·ϕ(t, t, f )t2·ϕ(f, t, f )x−a1−t2(18) Pt3,a2= a2 t3! ·ϕ(t, f, t )t3·ϕ(f, f, t )a2−t3(19) Pt4,m−x−a2= m−x−a2 t4! ·ϕ(t, f, f )t4 ·ϕ(f, f, f )m−x−a2−t4. (20) A.4 Example of Removing a Maximum Operator Let us briefly illustrate the step of removing the maximum operator. The removal is a two-phase process. In a first phase, ALP eliminates the variables and in a second phase, ALP generates the constraints for the linear programm along the elimination sequence. Suppose we have the function F= max x1,x2f1(x1) +f2(x1, x2) +f3(x2) (21) in a linear program, e.g., a≥Fora=F, where a∈Roraas a linear program variable. We start with the first phase. ALP eliminates x1by replacing f1andf2by a new function e1(x2) = max x1f1(x1) +f2(x1, x2). (22) ALP eliminates x2by replacing e1andf3by a new function e2= max x2e1(x2) +f3(x2). (23) Note that e2has an empty scope and evaluates therefore to a num- ber. We continue with the second phase, in which ALP translates the elimination sequence into linear program constraints: In the linear program, ALP adds helping variables and constraints to enforce the maxima in the different terms [16]. For each function ewith domain Z, ALP adds the variables ue zfor each assignment ztoZ. The vari- ableue zis supposed to yield the value of e(z). For the initial func- tions fi, in our case f1,f2,f3, ALP simply adds ufiz=fi(z)to the constraints of the linear program. Suppose we got the function e(z) = max xeiwhen eliminating some variable x. ALP then adds the constraints ue z≥X iuei (z,x)∀z, x. (24)Fore2, the generated constraints would be ue2≥ue1x2+uf3x2(25) for all possible values of x2. We are interested in keeping the number of added constraints small, which is the aim of VE. A.5 More Runtime Theorems for Approximate Foreplan We first define the total relational cost graph: Definition 22 (Total Relational Cost Graph) .Thetotal relational cost graph for a solution environment for an rfMDP contains a vertex for each (P)RV . Two vertices are connected by an edge if they occur together in a function or parfactor. By definition, the total relational cost graph is a supergraph for all graphs of interest for the runtime complexity: Theorem 23. The following graphs are each subgraphs of the total relational cost graph: 1. relational cost graph 2. the cost network for each maximum constraint in Approximate Foreplan Proof. For both cases, we show that the total relational cost graph contains more vertices and edges than the respective definition re- quires. Thus, we can remove the superfluos vertices and edges to arrive at the respective subgraph. We start with the relational cost graph. By Definition 8, the rela- tional cost graph has a vertex for each PRV and an edge between two PRVs if they share a logvar and | https://arxiv.org/abs/2505.22147v1 |
occur together in a parfactor, a parameterized local reward function, or a basis function. In particu- lar, all these vertices and edges are introduced in the total relational cost graph too, when we connect two vertices once the corresponding PRVs occur together in a parfactor. We may add more edges than for the relational cost graph as we ignore the share a logvar condition. We continue with the cost network for each maximum constraint. Let us take an arbitrary maximum constraint. The cost network con- sists of vertices for each variable appearing in the constraint and con- nects two vertices with an edge if the corresponding variables appear together in the same function. Thus, the total relational cost graph contains these edges too, as the respective variables occur together in a function. Since the induced width is the same as the treewidth minus one, we can give some more bounds: Theorem 24. If the total relational cost graph for an rfMDP has bounded treewidth, Approximate Foreplan runs in time polynomial in the number of objects of the rfMDP . Proof. By Theorem 23, the relational cost graph is a subgraph of the total relational cost graph. As a subgraph can have the treewidth of the supergraph at most [22], the treewidth of the relational cost graph is bounded. And if a graph has bounded treewidth, it also has bounded clique number, which is our w[22]. Thus, Theorem 17 fol- lows, leaving only the induced width of each cost network. By Theorem 23, each cost network is a subgraph of the total rela- tional cost graph. The treewidth of each cost network is bounded by the treewidth of the total relational cost graph. As the induced width equals the treewidth minus one, it is bound, leaving no variable with exponential influence. A.6 Proof of Approximation Guarantee We now provide the proof that Approximate Foreplan and ALP yield the same results on rfMDPs: Theorem. Given an rfMDP R, Approximate Foreplan and ALP are equivalent on Rand the grounded version of R, respectively. Proof. We first prove that the basis functions and backprojections evaluate to the same terms. Then, we investigate the setup of the linear programs. The lifted basis functions accumulate multiple grounded ones. Evaluating a lifted basis function yields the same result as summing the grounded basis functions. For the backprojections, the case is very similar. Evaluating the lifted backprojections as in Definition 16 yields the same result as summing all grounded backprojections, be- cause the objects grouped through PRVs are indistinguishable and share the same transition probabilities. Since we sum over all back- projections and basis functions in the linear program, the equivalence of the sums suffices. For the linear program, we start with the objective function and continue with the constraints. The objective function used in Approx- imate Foreplan groups multiple grounded basis functions, which are used in ALP, together in one lifted basis function. Therefore, if we denote the weights in ALP by α′ i, we have the weights αi=ni·α′ i for Foreplan, where nistands for the number of grouped | https://arxiv.org/abs/2505.22147v1 |
basis func- tions. Since the grouped basis functions are indistinguishable, the weights used in Approximate Foreplan are evenly distributed in ALP. Next, we have the constraints. Since the backprojections and basis functions in Approximate Foreplan evaluate to the same terms as the grounded functions in ALP, each individual constraint is correct. Fur- thermore, by Theorem 11, Approximate Foreplan covers the whole action space. A.7 Evaluation Figure 3 shows the runtime of (Approximate) Foreplan, ALP and XADD Symbolic Value Iteration with a time limit of two hours. The flattening of the runtimes of Foreplan backs our theoretical result in Theorem 15, highlighting that the runtime is indeed not exponen- tial, but rather polynomial, in the number of persons. Moreover, with Approximate Foreplan, we can solve the epidemic example for 164 persons in approximately 2.288 seconds, which is faster than ALP on 15 persons with approximately 3.045seconds and Foreplan on 21 persons with approximately 5.534seconds. B Walkthrough of Approximate Foreplan We describe in this section how we can solve Example 3 with Ap- proximate Foreplan. We first model the example formally and find the state representation. Afterwards, we precompute the backprojec- tions and instantiate the linear program. B.1 Modeling the Small Town We model the setting in Example 3. The town population con- sists of three people. We refer to Figure 1 for the transition model. We define the parfactors f1andf2according to Tables 1 and 2. The mayor can restrict an arbitrary subset of the towns population from travelling. The local reward functions are R1(Sick(M))and R2(Travel (M)), each for each person of the towns population. The Figure 3. Runtime (logscale) of (Approximate) Foreplan, ALP and XADD Symbolic Value Iteration on the epidemic example with a time limit of two hours and a memory limit of 16 GB. Only runs within these limits are shown. function R1(Sick(M))evaluates to 1if the person is not sick and to −1if the person is sick (c.f. Example 5): R1(Sick(M)) =( −1, Sick (M) 1,¬Sick(M)(26) The function R2(Travel (M))evaluates to 2if the person is travel- ling and to 0otherwise: R2(Travel (M)) =( 2, Travel (M) 0,¬Travel (M)(27) We set the discount factor γ= 0.9. For choosing the basis functions, we go with the ones in Example 10, that is, h0= 1,h1=R1and h2=R2. B.2 State Space Representation For using Approximate Foreplan, we first build the relational cost graph to find the state space representation. Afterwards, we can Travel (M)Restrict (M)Travel′(M) 0 0 0.2 0 1 0.1 1 0 0.9 1 1 0.5 Table 1. Transition probabilities for each person giving the probability of travelling in the next timestep given whether the person is currently travelling and being restricted. Sick (M)Epidemic Sick′(M) 0 0 0.2 0 1 0.8 1 0 0.4 1 1 0.6 Table 2. Transition probabilities for each person giving the probability of being sick in the next timestep given whether the person is currently sick and there is an epidemic. modify the actions to be compatible with the state space represen- tation. The relational cost graph contains two vertices, Sick(M) andTravel (M), because these are the only PRVs in | https://arxiv.org/abs/2505.22147v1 |
the exam- ple. The relational cost graph does not contain any edge, because the two PRVs do not occur together in a parfactor, reward or ba- sis function. Thus, the vertices Sick(M)andTravel (M)each are a clique of size one. Therefore, the state space representation con- tains two histograms along the value of the propositional random variable Epidemic . More formally, the state space representation is ([Sick(M)],[Travel (M)], Epidemic )(c.f. Example 8). For the action Restrict (M), the mayor no longer needs to spec- ify on which person(s) she applies the travel ban(s) on, but has to define how many persons currently (not) travelling receive a travel restriction. Thus, the mayor needs to define the action histogram [Travel (M), Restrict (M)]giving the numbers of people (not) travelling that are (not) restricted from travelling (c.f. Example 9). B.3 Computing Backprojections The first step of Foreplan is the precomputation of the backprojec- tions. The backprojections are generally defined as ga i(x) =X x′P(x′|x, a)·hi(x′), (28) where x′are the parameters of hi,xare the parents of xin the transition model and ais the selected action. For abbreviation, we use S(M)forSick(M),R(M)forRestrict (M),T(M)for Travel (M)andEpi forEpidemic in this subsection. We start with the backprojection of h0= 1: ga 0(x) =X x′P(x′|x, a)·h0(x) = 1·X x′P(x′|x, a) = 1 .(29) Next, we backproject h1(Sick(M)). Note that Sick′(M)is inde- pendent of the action in the transition model. Thus, the backprojec- tion is independent of a: g1(S(M),Epi) = X x′∈{t,f}P(S′(M) =x′|S(M), epi)·h1(x′)(30) We plug the probabilities from Table 2 in and receive g1(true, true ) = 0 .6·(−1) + 0 .4·1 =−0.2, (31) g1(true, false ) = 0 .4·(−1) + 0 .6·1 = 0 .2, (32) g1(false, true ) = 0 .8·(−1) + 0 .2·1 =−0.6, (33) g1(false, false ) = 0 .2·(−1) + 0 .8·1 = 0 .6. (34) The backprojection of h2is a bit more complex, as it includes the action. The general backprojection of h2is given by gR(M) 2 (T(M)) = X x′∈{t,f}P(T′(M) =x′|T(M), R(M))·h2(x′).(35) We distinguish the two cases R(M) =false andR(M) =true . We start with the first one, which leads to: gf 2(t) =X x′∈{t,f}P(T′(M) =x′|T(M) =t, R(M) =f) = 0.9·h2(t) + 0.1·h2(f) = 0 .9·2 = 1.8(36)and gf 2(f) =X x′∈{t,f}P(t′(P) =x′|T(M) =f, R(M) =f) = 0.2·h2(t) + 0.8·h2(f) = 0 .2·2 = 0.4.(37) We continue with R(M) =true , leading to gt 2(t) =X x′∈{t,f}P(T′(M) =x′|T(M) =t, R(M) =t) = 0.5·h2(t) + 0.5·h2(f) = 0 .5·2 = 1(38) and gt 2(f) =X x′∈{t,f}P(T′(M) =x′|T(M) =f, R(M) =t) = 0.1·h2(t) + 0.9·h2(f) = 0 .1·2 = 0.2.(39) B.4 Instantiation of Linear Program The second step of Approximate Foreplan is to instantiate the lin- ear program. The linear program has three variables w1,w2andw3. The objective function is to minimizeP3 i=1αiwi, with αibeing the relevance weights. We have one constraint for each possible action a: 0≥max x1,x2,x3{3−2·x1+ 2·x2+w0·(0.9·1−1) +w1·(0.9·G1(x1, x3)−H1(x1)) +w2·(0.9·Ga 2(x2)−H2(x2))},(40) where x1, x2∈ {0,1,2,3}specify the number of persons being sick and travelling, respectively. The variable x3is Boolean and stores the truth value of Epidemic . With GiandHiwe denote the lifted com- putation of giandhi. In | https://arxiv.org/abs/2505.22147v1 |
the following, we write the lifted computa- tion in terms of xiandgiorhi. In the remainder of this subsection, we show the complete constraint generation for the linear program for one example action. We omit all other actions because of lim- ited insights compared to one example instantiation and constraint generation. We show the constraint generation for the action of re- stricting nobody. We start with writing the constraint tailored to this setting and directly computing the value of basis functions and their backprojections lifted: 0≥max x1,x2,x3{3−2x1+ 2x2−0.1w0 +w1·(x1·0.9·g1(t, x3)−x1·h1(t)) +w1·((3−x1)·0.9·g1(f, x3)−(3−x1)·h1(f)) +w2·(x2·0.9·gf 2(t)−x2·h2(t)) +w2·((3−x2)·0.9·gf 2(f)−(3−x2)·h2(f))}.(41) We continue with introducing functions to save steps later in variable elimination and constraint generation. We define four functions with different parameters, each collecting the respective terms: 0≥max x1,x2,x3{f1(x1) +f2(x2) +f3(x1, x3) +f4}, (42) with f1(x1) = 3−2x1−w1x1h1(t)−w1·(3−x1)·h1(f) (43) f2(x2) = 2 x2+w2·(x2·0.9·gf 2(t)−x2h2(t)) (44) +w2·((3−x2)·0.9·gf 2(f)) f3(x1, x3) = 0 .9w1·(x1g1(t, x3) + (3 −x1)·g1(f, x3))(45) f4=−0.1w0 (46) The only task we are left with is to remove the maximum oper- ator. For that, we describe the two phases, variable elimination and constraint generation, in two different subsections. B.4.1 Variable Elimination We first eliminate x2, leading to 0≥max x1,x3{f1(x1) +e1+f3(x1, x3) +f4}, (47) with e1= max x2f2(x2). (48) Next, we eliminate x1: 0≥max x3{e2(x3) +e1+f4}, (49) with e2(x3) = max x1f1(x1) +f3(x1, x3). (50) Last, we eliminate x3: 0≥e1+e3+f4, (51) with e3= max x3e2(x3). (52) B.4.2 Constraint Generation We generate the constraints along the elimination order of the previ- ous subsection. All the constraints listed in this subsection together replace the single constraint in Equation 40 for our example action of restricting nobody. We start with the constraints for the functions ficontaining wis and continue with the functions eiobtained by eliminating variables: f1(x1) uf1 0= 3−3w1 (53) uf1 1= 3−2 +w1−2w1= 1−w1 (54) uf1 2= 3−4 + 2 w1−w1=−1 +w1 (55) uf1 3= 3−6 + 3 w1=−3 + 3 w1 (56) f2(x2) uf2 0=w2·(3·0.9·0.4) = 3 .1w2 (57) uf2 1= 2 + w2·(1·0.9·1.8−1.2) +w2·(2·0.9·0.4) = 2 + 0 .34w2 (58) uf2 2= 4 + w2·(2·0.9·1.8−2·2) +w2·(1·1.9·0.4) = 4−0.04w2 (59) uf2 3= 6 + w2·(3−0.9·1.8−3·2) = 6 −1.14w2 (60)f3(x1, x3) uf3 00=w1·3·0.9·0.6 = 1 .62w1 (61) uf3 01=−1.62w1 (62) uf3 10=w1·1·0.9·0.2 +w1·2·0.9·0.6 = 1 .26w1 (63) uf3 11=−1.26w1 (64) uf3 20=w1·2·0.9·0.2 +w1·1·0.9·0.6 = 0 .9w1 (65) uf3 21=−0.9w1 (66) uf3 30=w1·3·0.9·0.2 = 0 .54w1 (67) uf3 31=−0.54w1 (68) f4 uf4=−0.1w0 (69) e1 ue1≥uf2 0 (70) ue1≥uf2 1 (71) ue1≥uf2 2 (72) ue1≥uf2 3 (73) e2(x3) ue2 0≥uf1 0+uf3 00 (74) ue2 0≥uf1 1+uf3 10 (75) ue2 0≥uf1 2+uf3 20 (76) ue2 0≥uf1 3+uf3 30 (77) ue2 1≥uf1 0+uf3 01 (78) ue2 1≥uf1 1+uf3 11 (79) ue2 1≥uf1 2+uf3 21 (80) ue2 1≥uf1 3+uf3 31 (81) e3 ue3≥ue2 0 (82) ue3≥ue2 1 (83) Final Constraint In the end, we add the final constraint 0≥ue1+ue3+uf4(84) due to the introduction of our helper functions fi. B.4.3 Solving the Linear Program After having generated all constraints for all actions, the linear pro- gram is fed into a solver to compute a solution. The template for the linear program looks like this: | https://arxiv.org/abs/2505.22147v1 |
arXiv:2505.22148v1 [cs.AI] 28 May 2025What Makes a Good Reasoning Chain? Uncovering Structural Patterns in Long Chain-of-Thought Reasoning Gangwei Jiang1,2*, Yahui Liu3*, Zhaoyi Li1,2, Qi Wang3, Fuzheng Zhang3, Linqi Song2,Ying Wei4,Defu Lian1† 1University of Science and Technology of China ,2City University of Hong Kong , 3Kuaishou Technology,4Zhejiang University, gwjiang@mail.ustc.edu.cn Abstract Recent advances in reasoning with large lan- guage models (LLMs) have popularized Long Chain-of-Thought (LCoT), a strategy that en- courages deliberate and step-by-step reason- ing before producing a final answer. While LCoTs have enabled expert-level performance in complex tasks, how the internal structures of their reasoning chains drive, or even predict, the correctness of final answers remains a crit- ical yet underexplored question. In this work, we present LCoT2Tree, an automated frame- work that converts sequential LCoTs into hier- archical tree structures and thus enables deeper structural analysis of LLM reasoning. Using graph neural networks (GNNs), we reveal that structural patterns extracted by LCoT2Tree, in- cluding exploration, backtracking, and verifica- tion, serve as stronger predictors of final perfor- mance across a wide range of tasks and models. Leveraging an explainability technique, we fur- ther identify critical thought patterns such as over-branching that account for failures. Be- yond diagnostic insights, the structural patterns by LCoT2Tree support practical applications, including improving Best-of-N decoding effec- tiveness. Overall, our results underscore the critical role of internal structures of reasoning chains, positioning LCoT2Tree as a powerful tool for diagnosing, interpreting, and improv- ing reasoning in LLMs. 1 Introduction Large Language Models (LLMs) have achieved re- markable progress in nature language understand- ing and processing, with recent developments ex- tending their capabilities to more complex reason- ing tasks. Cutting-edge models such as OpenAI o3 (OpenAI, 2025) and DeepSeek R1 (Guo et al., 2025) push this frontier by emulating System 2 thinking (Li et al., 2025b), i.e, engaging in slow, *Equal contribution. †Corresponding author. 0.0 2.5 5.0 7.5 10.0 12.5 15.00.000.100.200.30DensityMATH Postive samples /glyph1197egative samples 0.0 2.5 5.0 7.5 10.0 12.5 15.0 Length (K)0.000.050.10DensityGPQAFigure 1: The distribution of output token length for correctly answered (Positive) and incorrectly answered (Negative) samples by DeepSeek-R1-Distill-Qwen-32B on two datasets. deliberate, and step-by-step reasoning before arriv- ing at a final answer. This approach, well known as Long Chain-of-thought (LCoT) reasoning (Chen et al., 2025; Gandhi et al., 2025), has empowered LLMs to achieve expert-level performance in chal- lenging tasks such as mathematics, code generation, and scientific problem-solving (Seed et al., 2025; Team et al., 2025; Team, 2024). Despite their grow- ing adoption, LCoTs remain largely a black box in one key aspect: what makes a good thought chain? Before the emergence of LCoT, researchers at- tempted to answer this question from a seman- tic perspective, often using process reward mod- els (PRMs) that provide token-level or step-wise supervision based on logical coherence and fac- tual accuracy (Xia et al., 2025; Zhang et al., 2025). While effective for short or moderately long CoTs, PRMs struggle to scale effectively as the length and structure complexity of reasoning chains in- crease (He et al., 2025). In the LCoT era, recent work has increasingly emphasized the importance of reasoning | https://arxiv.org/abs/2505.22148v1 |
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