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Current Vision-Language Models (VLMs) are celebrated for their multilingual capabilities, yet they operate under a flawed assumption: that one language corresponds to a single writing system. This overlooks billions of users of multi-script languages like Punjabi, Serbian, Hindi-Urdu, Kurdish, among many others, for whom a model&#39;s capability may be fractured by orthographic bias. We introduce PuMVR (Punjabi Multimodal Visual Reasoning), the first benchmark designed to quantify script-dependent bias through 375 culturally grounded image-reasoning tasks across Punjabi&#39;s three active scripts (Gurmukhi, Shahmukhi, Roman). Evaluating 10 state-of-the-art VLMs, we expose a substantial Script Gap: models frequently solve visual puzzles in one script while failing identical tasks in another, with accuracy deltas reaching 16% and Script Consistency Rates (SCR) as low as 24.8%. Crucially, visual input boosts absolute performance but does not close this gap, the relative bias persists. Our analysis suggests reasoning patterns show limited cross-script transferability, and Chain-of-Thought pathways diverge based on script alone. We propose SCR as a core metric for script-agnostic evaluation, challenging current multilingual assessment paradigms and providing a framework for equitable AI.

ACL 2026

Beyond &#39;One Language, One Script&#39;: Quantifying Orthographic Bias in Multilingual VLMs with PuMVR

punjabi

script evaluation

orthographic bias

multimodal reasoning

vision-language models

multilingual nlp

benchmark

**Welcome to ACL-IJCNLP 2021!**

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ACL-IJCNLP 2021

The great event is jointly organized by the Association for Computational Linguistics (ACL) and Asian Federation of Natural Language Processing (AFNLP).

Current Vision-Language Models (VLMs) are celebrated for their multilingual capabilities, yet they operate under a flawed assumption: that one language corresponds to a single writing system. This overlooks billions of users of multi-script languages like Punjabi, Serbian, Hindi-Urdu, Kurdish, among many others, for whom a model's capability may be fractured by orthographic bias. We introduce PuMVR (Punjabi Multimodal Visual Reasoning), the first benchmark designed to quantify script-dependent bias through 375 culturally grounded image-reasoning tasks across Punjabi's three active scripts (Gurmukhi, Shahmukhi, Roman). Evaluating 10 state-of-the-art VLMs, we expose a substantial Script Gap: models frequently solve visual puzzles in one script while failing identical tasks in another, with accuracy deltas reaching 16% and Script Consistency Rates (SCR) as low as 24.8%. Crucially, visual input boosts absolute performance but does not close this gap, the relative bias persists. Our analysis suggests reasoning patterns show limited cross-script transferability, and Chain-of-Thought pathways diverge based on script alone. We propose SCR as a core metric for script-agnostic evaluation, challenging current multilingual assessment paradigms and providing a framework for equitable AI.

Beyond 'One Language, One Script': Quantifying Orthographic Bias in Multilingual VLMs with PuMVR

workshop paper

It is my great pleasure and honor to welcome you to the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026), held in San Diego, California, USA, July 2–7, 2026. ACL 2026 continues our fields 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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The rapid evolution of text-to-image generation has blurred the perceptual boundary between natural and synthetic imagery. However, it remains questionable whether the statistical structure of generated visual content mirrors the information density of the physical visual world. Drawing upon principles from statisti-cal linguistics, this study investigates the visual language of generative models through the lens of Zipfian dynamics. By analyzing a large-scale corpus of real and synthetic images, we uncover a fundamental divergence between visual syntax and semantics. We find that while generative models have successfully replicated the low-level physics of light, their high-level texture vocabulary exhibits distinct statistical signatures. Our analysis reveals a spectrum of entropy, identifying architectural fingerprints unique to each model. Furthermore, we investigate the relationship between generated images and prompt complexity, and find that increasing the semantic specificity of text prompts systematically degrades the statistical realism of the generated output.

A Zipfian Analysis of Visual Token Distributions for AI-Generated Images

Vision foundation models produce features that generalize across visual domains without fine-tuning, yet naively transferring labels through these feature spaces fails under large distribution shifts.
We propose SAOT Semantically Aware Optimal Transport), which learns a transport cost within a fused unbalanced optimal transport formulation for dense label transfer from frozen vision transformer features to new domains.
\method{} combines a learnable appearance metric with semantic class-prototype priors, unbalanced transport for partial matching under distribution shift, and a block-sparse solver for tractable inference.
We pair this with a two-stage decoder: an MLP trained on SAOT pseudo-labels, then refined via EMA-teacher self-training with class-balanced sampling.
On GTA5$\to$Cityscapes with frozen DINOv2 ViT-L/14 features, SAOT+Decoder reaches 25.7\% mIoU, a \textbf{3.8$\times$} improvement over nearest-neighbor transfer (6.7\%), without any backbone adaptation.
Per-class results show large gains on spatially coherent classes (road 90.3\%, car 76.2\%, building 71.5\%), demonstrating that learned semantic transport costs capture domain-invariant structure even under severe synthetic-to-real shifts. On VOC train$\to$val with frozen ViT-B/16 features, the full pipeline reaches 47.5\% mIoU, indicating that the approach extends beyond synthetic-to-real adaptation.

Semantically Aware Optimal Transport for Dense Label Transfer

Vision-language model (VLM)-powered agents are increasingly enabling new forms of automation across various human tasks. While prior work has primarily focused on well-defined problems with explicit goals, the capabilities of agents in creative graphic design, where goals are inherently open-ended and subjective, remain largely underexplored. To bridge this gap, we introduce GraphicWeaver, a planning benchmark for graphic design comprising 1,079 diverse user queries and associated images spanning four design categories. Comprehensive experiments with six models reveal that current VLM-based agents struggle to handle such complex planning tasks, which require taking into account both explicit design constraints specified in queries and implicit commonsense design principles. We attribute these failures to challenges in (1) retrieving appropriate parameters for tool usage, (2) understanding spatial relationships across design components, and (3) coordinating dependencies across agents. We envision GraphicWeaver as a challenging yet valuable testbed for advancing VLM agent planning in creative design contexts.

GraphicWeaver: Benchmarking Agentic Planning for Graphic Design Generation

Spatial embodied intelligence under partial observability requires agents to actively acquire missing information rather than passively consume complete observations. While multimodal foundation models excel at passive perception and reasoning, their ability to support self-directed exploration for building and maintaining coherent spatial beliefs remains understudied. We propose \tos, defined as an agent’s ability to construct, revise, and exploit a spatial belief through active exploration under partial observability. We implement \tos as a benchmark in textual and visual environments, where the goal is curiosity-driven exploration to build a complete and accurate spatial belief. A key innovation is spatial belief probing, which prompts agents to externalize their internal spatial belief as a cognitive map at each step, enabling direct measurement of belief quality. Evaluating state-of-the-art models on downstream tasks reveals three bottlenecks: (1) the \textbf{Active-Passive Gap}, where performance drops when agents must autonomously gather information (e.g., \textsc{GPT-5.2}: $57.1{\to}46.0$); (2) \textbf{Inefficiency}, with redundant and unsystematic exploration; and (3) unstable global beliefs, where spatial knowledge degrades over time. A false-belief paradigm further reveals \textbf{Belief Inertia}, especially severe in vision-based models

Theory of Space: Active Exploration and Spatial Belief Probing for Foundation Models

Vision--Language Models (VLMs) have shown strong performance on multimodal reasoning tasks, yet most evaluations focus on short videos and assume unconstrained computational resources. In industrial settings such as pharmaceutical content understanding, practitioners must process long-form videos under strict GPU, latency, and cost constraints, where many existing approaches fail to scale. In this work, we present an industrial GenAI framework that processes over 200{,}000 PDFs, 25{,}326 videos across eight formats (e.g., MP4, M4V, etc.), and 888 multilingual audio files in more than 20 languages. Our study makes three contributions: (i) an industrial large-scale architecture for multimodal reasoning in pharmaceutical domains; (ii) empirical analysis of over 40 VLMs on two leading benchmarks (Video-MME and MMBench) and proprietary dataset of 25{,}326 videos across 14 disease areas; and (iii) four findings relevant to long-form video reasoning: the role of multimodality, attention mechanism trade-offs, temporal reasoning limits, and challenges of video splitting under GPU constraints. Results show 3--8$\times$ efficiency gains with SDPA attention on commodity GPUs, multimodality improving up to 8/12 task domains (especially length-dependent tasks), and clear bottlenecks in temporal alignment and keyframe detection across open- and closed-source VLMs. Rather than proposing a new "A+B" model, this paper characterizes practical limits, trade-offs, and failure patterns of current VLMs under realistic deployment constraints, and provide actionable guidance for both researchers and practitioners designing scalable multimodal systems for long-form video understanding in industrial domains.

Scaling Vision–Language Models for Pharmaceutical Long-Form Video Reasoning on Industrial GenAI Platform

Advances in AI have introduced several strong models in computational pathology to usher it into the era of multi-modal diagnosis, analysis, and interpretation. However, the current pathology-specific visual language models still lack capacities in making diagnosis with rigorous reasoning paths as well as handling divergent tasks, and thus challenges of building AI Copilots for real scenarios still exist. Here we introduce TeamPath, an AI system powered by reinforcement learning and router-enhanced solutions based on large-scale histopathology multimodal datasets, to work as a virtual assistant for expert-level disease diagnosis, patch-level information summarization, and cross-modality generation to integrate transcriptomic information for the clinical usage. We also collaborate with pathologists from Yale School of Medicine to demonstrate that TeamPath can assist them in working more efficiently by identifying and correcting expert conclusions and reasoning paths. Overall, TeamPath can flexibly choose the best settings according to the needs, and serve as an innovative and reliable system for information communication across different modalities and experts.

TeamPath: Building MultiModal Pathology Experts with Reasoning AI Copilots

Current GUI agents struggle with multi-step digital device support. We investigate whether this failure is partly caused by a procedural knowledge deficit: agents often rely on zero-shot visual exploration instead of executing verified instructions. To address this, we introduce the Plan-Grounded GUI Agent (PGGA), framing interface navigation as a knowledge-execution problem by conditioning low-level actions on step-by-step text plans. Evaluated on our focused Device-Support Interaction Benchmark (DSIB), results reveal a sharp gap between knowing which operation to perform and grounding that operation on the screen: GTA1-7B reaches 99.59% Operation Accuracy with expert plans, but only 82.99% Element Accuracy and 45.61% Task Success Rate; without plans, its Task Success Rate is 0.00%. Our fine-tuned 2B-parameter PGGA achieves 54.39% Task Success Rate and 91.28% Element Accuracy when guided by expert plans, suggesting that explicit procedural grounding can substantially improve GUI execution when high-quality plans are available. Project Page: https://hsiung.cc/PGGA/

PGGA: A Plan-Grounded GUI Agent for Automated Device Support

Automated Essay Scoring (AES) is crucial for modern education, particularly with the increasing prevalence of multimodal assessments. However, traditional AES methods struggle with evaluation generalizability and multimodal perception, while even recent Multimodal Large Language Model (MLLM)-based approaches can produce hallucinated justifications and scores misaligned with human judgment. To address the limitations, we introduce CAFES, the first collaborative multi-agent framework specifically designed for AES. It orchestrates three specialized agents: an Initial Scorer for rapid, trait-specific evaluations; a Feedback Pool Manager to aggregate detailed and evidence-grounded feedback; and a Reflective Scorer that iteratively refines scores based on this feedback to enhance human alignment. Extensive experiments, using widely adopted MLLMs, achieve an average relative improvement of 21% in Quadratic Weighted Kappa (QWK) against ground truth, with particularly strong gains in grammatical and lexical diversity. Our proposed CAFES paves the way for an intelligent multimodal AES system.

CAFES: A Collaborative Multi-Agent Framework for Multi-Granular Multimodal Essay Scoring

Theory of Mind (ToM) - the ability to attribute beliefs and intents to others - is fundamental for social intelligence, yet Vision-Language Model (VLM) evaluations remain largely Western-centric. In this work, we introduce CulturalToM-VQA, a benchmark of 5,095 visually situated ToM probes across diverse cultural contexts, rituals, and social norms. Constructed through a frontier proprietary MLLM, human-verified pipeline, the dataset spans a taxonomy of six ToM tasks and four complexity levels. We benchmark 10 VLMs (2023-2025) and observe a significant performance leap: while earlier models struggle, frontier models achieve high accuracy (>93%). However, significant limitations persist: models struggle with false belief reasoning (19-83% accuracy) and show high regional variance (20-30% gaps). Crucially, we find that SOTA models exhibit social desirability bias - systematically favoring semantically positive answer choices over negative ones. Ablation experiments reveal that some frontier models rely heavily on parametric social priors, frequently defaulting to safety-aligned predictions. Furthermore, while Chain-of-Thought prompting aids older models, it yields minimal gains for newer ones. Overall, our work provides a testbed for cross-cultural social reasoning, underscoring that despite architectural gains, achieving robust, visually grounded understanding remains an open challenge.

Are Vision Language Models Cross-Cultural Theory of Mind Reasoners?

Stereotype repositories are critical to assess generative AI model safety, but currently lack adequate global coverage. It is imperative to prioritize targeted expansion, strategically addressing existing deficits, over merely increasing data volume. This work introduces a multilingual stereotype resource covering four sub-Saharan African countries that are severely underrepresented in NLP resources: Ghana, Kenya, Nigeria, and South Africa. By utilizing socioculturally-situated, community-engaged methods, including telephonic surveys moderated in native languages, we establish a reproducible methodology that is sensitive to the region's complex linguistic diversity and traditional orality. By deliberately balancing the sample across diverse ethnic and demographic backgrounds, we ensure broad coverage, resulting in a dataset of 3,534 stereotypes in English and 3,206 stereotypes across 15 native languages.

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