Morocco

Graphical User Interface (GUI) grounding is critical for effective GUI agents. Despite recent progress, key challenges remain: 1) existing grounding models and benchmarks are skewed toward web and mobile environments, neglecting desktop interfaces (especially windows); and 2) grounding capability is assessed using accuracy on a single &quot;best&quot; instruction per UI element. However, users can refer to a UI element in diverse valid ways -- via visual attributes, spatial relations, etc, and a capable grounding model should produce consistent outputs across such variations. Focusing on desktop environments, we introduce GUI Grounding Sensitivity Benchmark, which investigates the model sensitivity to multiple descriptions of the same UI element. We design an automatic pipeline to generate multiple valid instructions per UI element, and develop nuanced data validation methods, as frontier models even hallucinate to produce a single instruction. Evaluation of 12 models reveals they are reasonably sensitive and their performance on existing benchmarks does not reflect their true ability. Building on the insight that a given grounding model struggles more with certain instructions or relations, we introduce the GUI Grounding Diagnosis Agent, which generates challenging instructions using model feedback and iterative refinement. Our agent reports high success rate (upto 84%) in generating instructions that fail the state-of-the-art GUI grounding models.

EACL 2026

Do GUI Grounders Truly Understand UI Elements?

gui grounders

gui agents

evaluation

### Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

Message from the General Chair: 
*It is my great pleasure and honor to welcome you to the 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025), held in beautiful Vienna, Austria, from July 27 to August 1, 2025. ACL2025continues our field’s tradition of excellence in scholarship, innovation, and inclusivity, and I am deeply grateful to the many volunteers who have worked tirelessly to bring this event to life.* 
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ACL 2025

Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

Graphical User Interface (GUI) grounding is critical for effective GUI agents. Despite recent progress, key challenges remain: 1) existing grounding models and benchmarks are skewed toward web and mobile environments, neglecting desktop interfaces (especially windows); and 2) grounding capability is assessed using accuracy on a single "best" instruction per UI element. However, users can refer to a UI element in diverse valid ways -- via visual attributes, spatial relations, etc, and a capable grounding model should produce consistent outputs across such variations. Focusing on desktop environments, we introduce GUI Grounding Sensitivity Benchmark, which investigates the model sensitivity to multiple descriptions of the same UI element. We design an automatic pipeline to generate multiple valid instructions per UI element, and develop nuanced data validation methods, as frontier models even hallucinate to produce a single instruction. Evaluation of 12 models reveals they are reasonably sensitive and their performance on existing benchmarks does not reflect their true ability. Building on the insight that a given grounding model struggles more with certain instructions or relations, we introduce the GUI Grounding Diagnosis Agent, which generates challenging instructions using model feedback and iterative refinement. Our agent reports high success rate (upto 84%) in generating instructions that fail the state-of-the-art GUI grounding models.

poster

#### *Message from the General Chair, Aline Villavicencio*
I’m delighted and honoured to welcome you to the 19th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2026), taking place in the beautiful city of Rabat, in Morocco, in March 24-29, 2026. EACL is the flagship European conference of the Association and EACL 2026 proudly continues our field’s tradition of excellence in scholarship, innovation, and inclusivity. I am deeply grateful to the many volunteers whose dedication, generosity, and tireless efforts have made this conference possible.
For the first time EACL is being hosted in the African continent. This is an important milestone for our community, and we are grateful to our Moroccan hosts for enabling this historic moment by bringing this edition of EACL to Rabat. We are also delighted that the Second Arabic NLP School is co-located with EACL. We hope attendees enjoy this wonderful opportunity to strengthen ties with the Computational Linguistics communities across the African continent. *[Read full message](https://drive.google.com/file/d/14NlmHvwM6fPJuMmOvVh7K0vtQbEyv3SZ/view?usp=sharing)* 

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Welcome to the 19th Conference of the European Chapter of the Association for Computational Linguistics (EACL). EACL 2026 will be held in Rabat, Morocco, from March 24–29, 2026. 

While chain-of-thought (CoT) prompting improves reasoning in large language models, its effectiveness in vision-language models (VLMs) remains limited due to over-reliance on textual cues and memorized knowledge. To investigate the visual reasoning capabilities of VLMs in complex real-world scenarios, we introduce DrivingVQA, a visual question answering dataset derived from driving theory exams, which contains 3,931 multiple-choice problems with expert-written explanations and grounded entities relevant to the reasoning process. Leveraging this dataset, we explore the benefits of incorporating entity-related information, such as entity names, spatial coordinates, and visual content, through supervised fine-tuning to enhance the modelХs reasoning abilities. Our experiments demonstrate that interleaving textual explanations with visual tokens extracted from entities relevant to the question improves answer accuracy by 3.1% and reasoning accuracy by 4.6% over vanilla CoT prompting. Furthermore, we demonstrate that this retrieval-based approach effectively scales to the larger A-OKVQA reasoning dataset by leveraging automatically generated pseudo-labels, outperforming CoT prompting.

DRIVINGVQA: A Dataset for Interleaved Visual Chain-of-Thought in Real-World Driving Scenarios

Dashboards are powerful visualization tools for data-driven decision-making, integrating multiple interactive views that allow users to explore, filter, and navigate data. Unlike static charts, dashboards support rich interactivity, which is essential for uncovering insights in real-world analytical workflows. However, existing question-answering benchmarks for data visualizations largely overlook this interactivity, focusing instead on static charts. This limitation severely constrains their ability to evaluate the capabilities of modern multimodal agents designed for GUI-based reasoning. To address this gap, we introduce DashboardQA, the first benchmark explicitly designed to assess how vision-language GUI agents comprehend and interact with real-world dashboards. The benchmark includes 292 tasks on 112 interactive dashboards, encompassing 405 question answer pairs overall. These questions span five categories: multiple-choice, factoid, hypothetical, multi-dashboard, and conversational. By assessing a variety of leading closed- and open-source GUI agents, our analysis reveals their key limitations, particularly in grounding dashboard elements, planning interaction trajectories, and performing reasoning. Our findings indicate that interactive dashboard reasoning is a challenging task overall for all the VLMs evaluated. Even the top-performing agents struggle; for instance, the best agent based on Gemini-Pro-2.5 achieves only 38.69% accuracy, while the OpenAI CUA agent reaches just 22.69%, demonstrating the benchmark's significant difficulty. We release DashboardQA at ..

DashboardQA: Benchmarking Multimodal Agents for Question Answering on Interactive Dashboards

We introduce VIGiA, a novel multimodal dialogue model designed to understand and reason over complex, multi-step instructional video action plans. Unlike prior work which focuses mainly on text-only guidance, or treats vision and language in isolation, VIGiA supports grounded, plan-aware dialogue that requires reasoning over visual inputs, instructional plans, and interleaved user interactions. To this end, VIGiA incorporates two key capabilities: (1) multimodal plan reasoning, enabling the model to align uni- and multimodal queries with the current task plan and respond accurately; and (2) plan-based retrieval, allowing it to retrieve relevant plan steps in either textual or visual representations. Experiments were done on a novel dataset with rich Instructional Video Dialogues aligned with Cooking and DIY plans. Our evaluation shows that VIGiA outperforms existing state-of-the-art models on all tasks in a conversational plan guidance setting, reaching over 90% accuracy on plan-aware VQA.

VIGiA: Instructional Video Guidance via Dialogue Reasoning and Retrieval

Large Language Models (LLMs) excel at mathematical reasoning in English, but their performance in low-resource languages remains underexplored. This gap is particularly critical for Indonesia, where equitable access to AI-powered education depends on robust multilingual reasoning across diverse local languages. We introduce MATH-IDN, a multilingual benchmark for mathematical problem solving in Indonesian, Javanese, Sundanese, and Buginese, with English as reference. Each subset contains expert-verified problems spanning seven subjects and five difficulty levels. We evaluate eight open-source LLM families, including math-specialized, Southeast-Asian-adapted, and general-purpose models, under a zero-shot chain-of-thought setting. Results show that MATH-IDN presents a challenging and discriminative benchmark, revealing substantial performance gaps in low-resource languagesвЂ”particularly for BugineseвЂ”and highlighting key limitations in current multilingual reasoning capabilities. Anonymous link to the data repository: https://anonymous.4open.science/r/MATH_IDN-EA85

MATH-IDN: A Multilingual Mathematical Problem Solving Dataset Featuring Local Languages in Indonesia

Mixture-of-Experts (MoE) benefits from a dynamic routing mechanism among their specialized experts, which existing Parameter- Efficient Fine-Tuning (PEFT) strategies often fail to leverage. This motivates us to investigate whether adaptation modules themselves should incorporate routing mechanisms to align with MoE's multi-expert architecture. We analyze dynamics of core components when applying PEFT to MoE language models, and examine how different routing strategies affect adaptation effectiveness. Extensive experiments adapting OLMoE-1B-7B and Mixtral-8Г—7B on various commonsense and math reasoning tasks validate the performance and efficiency of our routed approach. We identify optimal configurations for different scenarios and provide empirical analyses with practical insights to facilitate better PEFT and MoE applications.

Parameter-Efficient Routed Fine-Tuning: Mixture-of-Experts Demands Mixture of Adaptation Modules

Large language model context lengths have grown rapidly in recent years, from 512 tokens in GPT to 2M tokens in Gemini 1.5 Pro. Larger context windows enable models to condition on significantly more input tokens, leading to higher quality responses for some user prompts. However, longer contexts also pose challenges to system instruction adherence. In this work, we formalize verifiable instructions to evaluate model compliance based on clear, measurable criteria. From this criteria, we present **VerIFY**, a **Ver**ifiable **I**nstruction **F**ollowing **Y**ardstick dataset designed to benchmark the compliance and accuracy of LLMs in adhering to various types of instructions across multi-turn, long-context conversations. From experiments with open-source models, we reveal insights into instruction-following failures in long contexts, helping to improve the reliability, safety, and precision of these models. Furthermore, we implement and evaluate six mitigation strategies to enhance instruction compliance in extended contexts, achieving an improvement up to 79%. This is the first work to consider instruction following for multi-turn, long context conversations.

We Are What We Repeatedly Do: Improving Long Context Instruction Following

Benchmarks for language models have become essential tools for research. Yet, such benchmarks face a persistent contamination problem, with recent studies finding 25-50% of evaluation datasets appearing in training corpora. This is true even looking at the two-player zero-sum game setting, where most benchmarks are based on popular games, like chess, whose optimal strategies are all over the web. Such contamination hinders the possibility to differentiate memorization and reasoning skills. To rectify these problems, we introduce TCG-Bench, a benchmark based on a new two-player trading card game (TCG), similar in spirit to games like Magic: The Gathering. TCG-Bench offers three key innovations: (1) a contamination-resistant design by separating the publicly released game engine from hidden card implementations, (2) a continuous difficulty spectrum via Monte Carlo simulation that prevents benchmark saturation, and (3) a parallel implementation in English and Arabic, the first multilingual text-based game benchmark to do so. We also formalize a practical threat model and refresh protocol that preserves evaluation integrity even if specific cards leak. Our analysis across 17 models (50,000+ games) reveals that performance declines exponentially with difficulty, while model size correlates only weakly with strategic ability. We also observe cross-linguistic performance gaps between English and Arabic, with a gap of 47.4% at 32B, highlighting the need for multilingual game benchmarks that target reasoning capabilities in the target language. We host a leaderboard showcasing these results and welcome evaluation requests on our private cards.

Cards Against Contamination: TCG-Bench for Difficulty-Scalable Multilingual LLM Reasoning

Do large language models (LLMs) serve as proxies for human linguistic variation? We investigate this question through Hindi-English verb code-mixing, where speakers can use either a Hindi verb or an English verb with the light verb \textit{karna} (`do'). Both forms are grammatical, but speakers show gradient preferences without clear theoretical explanationвЂ”an ideal test of whether LLMs capture genuine sociolinguistic variation beyond formal rules. We collect preference ratings from 14 native speakers across 30 verb pairs in minimal pair contexts (moderate agreement, Krippendorff's О± = 0.365) and compare these to perplexity ratios from 7 base LLMs spanning families, sizes, and training compositions. Three experiments reveal systematic failure: unsupervised prediction performs at chance (F1 в‰¤ 0.33), supervised 4-way classification barely improves (F1 = 0.43), and binary classification achieves adequate performance (F1 = 0.81) for only one model after exhaustive feature searchвЂ”requiring the very human labels LLMs were meant to replace while discarding the gradient information linguists need. Models show no scaling effects and exhibit arbitrary systematic biases inconsistent with training data or human judgments. We conclude that current LLMs learn surface correlations without internalizing underlying sociolinguistic processes, and do not reliably model gradient linguistic variation. We release human ratings, perplexity ratios for 4,279 verb pairs across 7 models, and experimental materials.

Do LLMs model human linguistic variation? A case study in Hindi-English do-verb code-mixing

Transformers have become the standard in Natural Language Processing (NLP) and Computer Vision (CV) due to their strong performance, yet they remain highly sensitive to small input changes, often referred to as adversarial attacks, such as synonym swaps in text or pixel-level perturbations in images. These adversarial attacks can mislead predictions, while existing defenses are often domain-specific or lack formal robustness guarantees. We propose the Attention-Regularized Transformer (ART), a framework that enhances robustness across modalities. ART builds on the Attention Sensitivity Tensor (AST), which quantifies the effect of input perturbations on attention outputs. By incorporating an AST-based regularizer into training, ART encourages stable attention maps under adversarial perturbations in both text and image tasks. We evaluate ART on IMDB, QNLI, CIFAR-10, CIFAR-100, and Imagenette. Results show consistent robustness gains over strong baselines such as FreeLB and DSRM: up to +36.9% robust accuracy on IMDB and QNLI, and +5 – 25% on image benchmarks across multiple Vision Transformer (ViT) architectures, while maintaining or improving clean accuracy. ART is also highly efficient, training over 10x faster than adversarial methods on text and requiring only 1.25x the cost of standard training on images, compared to 1.5 – 5.5x for recent robust ViTs. Codes are available at [https://github.com/cliclab-um6p/ART](https://github.com/cliclab-um6p/ART)

ART: Attention-Regularized Transformers for Multi-Modal Robustness

The intersection of AI and legal systems presents a growing need for tools that support legal education, particularly in under-resourced languages such as Romanian. In this work, we aim to evaluate the capabilities of Large Language Models (LLMs) and Vision-Language Models (VLMs) in understanding and reasoning about Romanian driving law through textual and visual question-answering tasks. To facilitate this, we introduce RoD-TAL, a novel multimodal dataset comprising Romanian driving test questions, text-based and image-based, alongside annotated legal references and explanations written by human experts. We implement and assess retrieval-augmented generation (RAG) pipelines, dense retrievers, and reasoning-optimized models across tasks, including Information Retrieval (IR), Question Answering (QA), Visual IR, and Visual QA. Our experiments demonstrate that domain-specific fine-tuning significantly enhances retrieval performance. At the same time, chain-of-thought prompting and specialized reasoning models improve QA accuracy, surpassing the minimum grades required to pass driving exams. We highlight the potential and the limitations of applying LLMs and VLMs to legal education.

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DRIVINGVQA: A Dataset for Interleaved Visual Chain-of-Thought in Real-World Driving Scenarios

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