United States

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 &quot;A+B&quot; 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.

ACL 2026

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

audio-language models (alms)

video content understanding

pharmaceutical application

large-scale video analysis

industry application

generative ai (genai)

vision-language models (vlms)

multi-modality

temporal reasoning

information retrieval

# Welcome everyone to ACL 2022!

The 60th Annual Meeting of the Association for Computational Linguistics is taking place May 22-27, 2022 as a hybrid event, in Dublin and online. We are happy to welcome all of you to this anniversary edition with an almost 50-50 in-person and virtual participation. 
The main conference program features oral presentations, in-person and virtual posters and demo sessions, a plenary session for our best paper presentations and awards, three amazing keynote events and two new initiatives of invited talks: Spotlight Talks for Young Rising Stars (STIRS) and The Next Big Idea Talks. Posters (including Findings of ACL 2022) and demos are grouped by areas for both the in-person and the virtual sessions. For the virtual component, the talks will be on Zoom and the posters and the demos will be in GatherTown. The Student Research Workshop will have an oral session and a poster session as part of Poster Session 1. The program also features eight Tutorials and 28 Workshops. 

 
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ACL 2022

The Association for Computational Linguistics (ACL) is the premier international scientific and professional society for people working on computational problems involving human language, a field often referred to as either computational linguistics or natural language processing (NLP). 

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.

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.

It is exciting to see the many developments in the field reflected at our conference, such as the advancement of large language models (born out of our community) into many application directions. This is not without challenges, partly due to unsolved problems, and partly due to misuse of the technology. All this is reflected well at ACL 2026. The conference also reflects that the field is not only about advancing technology, but also the science of fundamentally understanding language and how it is use by many different speaker communities. 
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The 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026) will take place in San Diego, California, from July 2 - 7, 2026.

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

Multimodal Large Language Models (MLLMs) demonstrate remarkable capabilities but often struggle with complex, multi-step mathematical reasoning, where minor errors in visual perception or logical deduction can lead to complete failure. While Process Reward Models (PRMs) offer step-by-step supervision, existing multimodal PRMs are limited to being binary verifiers that can identify but not correct errors, offering little explanatory power. To address these deficiencies, we introduce the Generative Multimodal Process Reward Model (GM-PRM), a novel paradigm that transforms the PRM from a passive judge into an active reasoning collaborator. Instead of a simple scalar score, GM-PRM provides a fine-grained, interpretable analysis of each reasoning step, evaluating its step intent, visual alignment, and logical soundness. More critically, GM-PRM is trained to generate a corrected version of the first erroneous step it identifies. This unique corrective capability enables our new test-time inference strategy, Refined Best-of-N (Refined-BoN). This framework actively enhances solution quality by using the PRM’s generated correction to guide the policy model toward a more promising reasoning trajectory, thereby improving the diversity and correctness of the solution pool. We demonstrate that GM-PRM achieves state-of-the-art results on multiple multimodal math benchmarks, significantly boosting policy model performance with remarkable data efficiency, requiring only a 20K-sample training dataset.

GM-PRM: A Generative Multimodal Process Reward Model for Multimodal Mathematical Reasoning

Visual grounding in graphical user interface (GUI) requires accurate localization of UI elements from natural language instructions. Conventional coordinate generation approaches face inherent limitations, including sensitivity to resolution variations and lack of interpretability. Recently, coordinate-free attention-based methods have emerged as a promising alternative, but these methods supervise attention using only spatial location signals from ground-truth bounding boxes, without ensuring that the learned attention distributions reflect genuine semantic correspondence between the instruction and the attended visual regions. We propose Attention Cycle-Consistency (ACC), a self-supervised regularization framework that enforces bidirectional alignment between visual attention and instruction semantics. ACC introduces two complementary constraints: semantic consistency, which ensures attended visual regions contain sufficient information to reconstruct the original instruction, and spatial consistency, which requires attention distributions to remain invariant when cycled through instruction reconstruction. We further incorporate entropy regularization to encourage spatially concentrated attention. ACC is applicable as a lightweight, model-agnostic regularizer for attention-based coordinate-free grounding methods, adding zero computational overhead at inference as all auxiliary components are discarded after training.

Look Where You’re Told: Instruction-Consistent Attention for GUI Grounding

Vision–language models (VLMs) achieve strong multimodal performance but remain prone to relation hallucination, which requires accurate reasoning over inter-object interactions. We study the impact of visual perturbations, specifically rotation and noise, and show that even mild distortions significantly degrade relational reasoning across models and datasets. We further evaluate prompt-based augmentation and preprocessing strategies (orientation correction and denoising), finding that while they offer partial improvements, they do not fully resolve hallucinations. Our results reveal a gap between perceptual robustness and relational understanding, highlighting the need for more robust, geometry-aware VLMs.

When Relations Break: Analyzing Relation Hallucination in Vision-Language Model Under Rotation and Noise

General-purpose vision-language models (VLMs) such as LLaVA and QwenVL produce descriptions of disaster imagery that lack domain-specific vocabulary and actionable detail. We propose the Vision-Language Caption Enhancer (VLCE), a framework that integrates external semantic knowledge from ConceptNet and WordNet into the caption generation process for post-disaster satellite and UAV imagery. VLCE operates in two stages: first, a baseline VLM generates an initial caption conditioned on YOLOv8 object detections; second, a knowledge-enriched sequential model, a CNN-LSTM or a hierarchical cross-modal Transformer, refines the caption using a vocabulary augmented with 1,566 domain-relevant terms extracted from knowledge graphs. We evaluate VLCE on two disaster benchmarks: xBD (satellite, 6,369 images, 3 damage classes) and RescueNet (UAV, 4,494 images, 12 damage classes), using CLIPScore for semantic alignment and InfoMetIC for informativeness. On RescueNet with the Transformer decoder, VLCE with knowledge graph enrichment produces captions preferred over QwenVL baselines in 95.33% of image pairs on InfoMetIC and 73.64% on CLIPScore. Qualitative analysis shows that without knowledge graph integration, generated captions exhibit hallucinations, word repetition, and semantic incoherence, whereas knowledge-enriched captions maintain factual consistency and domain-appropriate vocabulary. intended as a continuous, extensible monitor of differential framing under changing real-world inputs.

VLCE: A Knowledge-Enhanced Framework for Image Description in Disaster Assessment

Traditional clustering techniques often rely solely on similarity in the input data, limiting their ability to capture structural or semantic constraints that are critical in many domains. We introduce the Domain-Aware Rule-Triggered Variational Autoencoder (DART-VAE), a rule-guided multimodal clustering framework that incorporates domain-specific constraints directly into the representation learning process. DART-VAE extends the VAE architecture by embedding explicit rules, semantic representations, and data-driven features into a unified latent space, while enforcing constraint compliance through rule-consistency and violation penalties in the loss function. Unlike conventional clustering methods that rely only on visual similarity or apply rules as post-hoc filters, DART-VAE treats rules as first-class learning signals. The rules are generated by LLMs, structured into knowledge graphs, and enforced through a loss function combining reconstruction, KL divergence, consistency, and violation penalties. Experiments on aircraft and automotive datasets demonstrate that rule-guided clustering produces more operationally meaningful and interpretable clusters—for example, isolating UAVs, unifying stealth aircraft, or separating SUVs from sedans—while improving traditional clustering metrics. However, the framework faces challenges: LLM-generated rules may hallucinate or conflict, excessive rules risk overfitting, and scaling to complex domains increases computational and consistency difficulties. By combining rule encodings with learned representations, DART-VAE achieves more meaningful and consistent clustering outcomes than purely data-driven models, highlighting the utility of constraint-guided multimodal clustering for complex, knowledge-intensive settings.

Beyond Visual Similarity: Rule-Guided Multimodal Clustering with explicit domain rules

ChartDiff is a dataset and benchmark for cross-chart comparative summarization. It contains 8,541 chart pairs with high-quality natural-language comparison annotations. The charts cover six chart types: line, bar, horizontal bar, multi-series line, multi-series bar, and pie. The rendering uses Matplotlib, Plotly, and Plotnine. The data sources include Macrotrends, Yahoo Finance, and Visual Crossing. The annotation pipeline is annotate-judge-verify. The benchmark evaluates general-purpose VLMs, chart-specialized models, and pipeline-based methods.

ChartDiff: A Large-Scale Benchmark for Comprehending Pairs of Charts

The precolonial and early colonial Mixtec codices describe the history and stories of the region in a semasiographic medium that is full of symbolic representations and meant to be narrated. Recently, the community has investigated hierarchical representation of related media, including Aztec codices and Mayan hieroglyphic script, in a step towards symbolic machine interpretation of these historic Mesoamerican artifacts. In this work, we propose formal symbolic machine interpretation of XML encodings representing facsimile images from the Mixtec Codex Zouche-Nuttall. We demonstrate the efficacy of symbolic machine interpretation from XML step-by-step, showing how our parser and interpreter process text capturing a scene from the Mixtec Codex Zouche-Nuttall. We hope our contribution and the example we provide motivate collaboration among the archaeological, historical, linguistic, and natural language processing research communities to apply machine interpretation to Mixtec codices and similar manuscripts.

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TeamPath: Building MultiModal Pathology Experts with Reasoning AI Copilots

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Next from ACL 2026

TeamPath: Building MultiModal Pathology Experts with Reasoning AI Copilots

Similar lecture

Scaling Large Language Models

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES

Downloads