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首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-demo.82ACL Anthology · ACL 2026

OpenGlass: A Sensing-Computing Split Architecture for Local MLLM-Driven Real-Time Visual Assistance

Mengzhang Li, Yuan Yao National University of Singapore

原文摘要

We present OpenGlass, an open-source, privacy-oriented, local-first system for low-latency multimodal visual assistance, with a primary focus on blind and low-vision users. Cloud MLLM assistants offer strong visual understanding, but often require uploading first-person visual data and can suffer multi-second network delays; wearable glasses are ideal for sensing, but cannot host large models under tight compute and power budgets. OpenGlass addresses this gap with a sensing-computing split: an ESP32-based glasses-side unit captures visual context, while a nearby consumer-grade device performs local MLLM inference and local speech output, reducing cloud reliance and keeping raw egocentric visual data on user-controlled devices by default. We evaluate response quality, query-ready-to-audio latency, safety-aware abstention, and auditable logs. Under real ESP32 Wi-Fi capture, OpenGlass reaches 993 ms median user-to-audio latency with resized payloads and 1625 ms with raw 1280\times720 payloads; 97.5% and 93.3% of trials fall below 2 s, respectively. OpenGlass is a user-initiated visual-assistance reference platform for obstacle/hazard awareness, sign/object queries, and image-quality self-checking, rather than a certified navigation aid. We release source code, hardware instructions, prompts, evaluation data, and logs.

cs.CL · cs.CV

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-industry.12ACL Anthology · ACL 2026

DocSplit: A Comprehensive Benchmark Dataset and Evaluation Approach for Document Packet Recognition and Splitting

Md Mofijul Islam, Md Sirajus Salekin, Nivedha Balakrishnan, Vincil C. Bishop III, Niharika Jain, Spencer Romo, Bob Strahan, Boyi Xie 等 9 位作者

原文摘要

Document understanding in real-world applications often requires processing heterogeneous, multi-page document packets containing multiple documents stitched together. Despite recent advances in visual document understanding, the fundamental task of document packet splitting, which involves separating a document packet into individual units, remains largely unaddressed. We present the first comprehensive benchmark dataset, \textit{DocSplit}, along with novel evaluation metrics for assessing the document packet splitting capabilities of large language models. \textit{DocSplit} comprises five datasets of varying complexity, covering diverse document types, layouts, and multimodal settings. We formalize the \textit{DocSplit} task, which requires models to identify document boundaries, classify document types, and maintain correct page ordering within a document packet. The benchmark addresses real-world challenges, including out-of-order pages, interleaved documents, and documents lacking clear demarcations. We conduct extensive experiments evaluating multimodal LLMs on our datasets, revealing significant performance gaps in current models’ ability to handle complex document splitting tasks. The \textit{DocSplit} benchmark datasets and proposed novel evaluation metrics provide a systematic framework for advancing document understanding capabilities essential for legal, financial, healthcare, and other document-intensive domains. We release the datasets and evaluation code to facilitate future research in document packet processing.

cs.CL · cs.CV

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-industry.126ACL Anthology · ACL 2026

Multi-Task Reinforcement Learning for Enhanced Multimodal LLM-as-a-Judge

Junjie Wu, Xuan Kan, Zihao He, Shunwen Tan, Bo Pan, Kaitai Zhang

原文摘要

Multimodal Large Language Models (MLLMs) have been widely adopted as MLLM-as-aJudges due to their strong alignment with human judgment across various visual tasks. However, most existing judge models are optimized for single-task scenarios and struggle to generalize to diverse contexts, which is a critical requirement for reliable evaluation. To address this limitation, we propose Multi-Task Reinforcement Learning for MLLM-as-a-Judge (MT-RL-Judge), a framework that jointly optimizes the judge model across multiple tasks, leveraging the generalization capabilities of RL. Experimental results against several strong baselines demonstrate that MT-RL-Judge outperforms strong baselines in both judgment consistency and correlation with human preferences. Furthermore, our approach exhibits robust generalization on out-of-distribution tasks, further validating its effectiveness.

cs.CL · cs.CV

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-industry.134ACL Anthology · ACL 2026

MM-BizRAG: Rethinking Multimodal Retrieval-Augmented Generation for General Purpose Enterprise Q&A

Hanoz Bhathena, Parin Rajesh Jhaveri, Rohan Mittal, Prateek Singh, Aymen Kallala, Rachneet Kaur, Yiqiao Jin, Zhen Zeng 等 10 位作者

原文摘要

Recent advances in multimodal retrieval-augmented generation (MM-RAG) have shifted toward minimal parsing, relying on page-level images for producing retriever embeddings and for answer generation. While efficient, this trend often neglects explicit handling of the rich, structured information in complex enterprise documents, instead depending on pre-trained embeddings or vision-language models to implicitly capture such structure. In this work, we take a more direct approach: MM-BizRAG proactively extracts and represents document structure via a document structure-aware split that dynamically routes documents through orientation-specific ingestion pipelines, applying explicit layout-aware parsing for vertically structured documents (e.g., reports) and holistic page-level representations for horizontally structured documents (e.g., slide decks). A unified LLM-driven artifact transformation pipeline with placeholder-based positional alignment preserves natural reading order, while inference-time multimodal assembly decouples retrieval representations from generation context, enabling richer, more grounded answers without any finetuning requirement. Through experiments on a large, heterogeneous enterprise dataset and two public benchmarks (SlideVQA and FinRAGBench-V), MM-BizRAG consistently outperforms state-of-the-art vision-centric baselines by up to 32% points, with especially strong gains on report-style layouts. Furthermore, we introduce FastRAGEval, a single-call LLM Judge metric for fine-grained generative recall that halves RAGChecker’s cost while achieving stronger human alignment.

cs.CL · cs.CV

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-industry.143ACL Anthology · ACL 2026

MTIVE: Multi-Task Image Verification Engine Using Vision-Language Models for E-commerce

Yu-Tong Cao, Vishnu Prabhakaran, Arunita Das, Purav Aggarwal, Anoop Saladi

原文摘要

Vision-language models show promise for e-commerce automation but struggle with noisy real-world images and multi-task requirements. We introduce MTIVE, a curriculum learning framework that progressively adapts base models through three stages: continued pre-training on large-scale e-commerce datasets with contrastive learning and diverse dialogue templates, instruction tuning on synthetic data, and modular task-specific expert training. Our architecture uses frozen base weights with stacked LoRA adapters—shared modules for domain knowledge and lightweight task-specific experts—enabling continual learning without catastrophic forgetting. MTIVE outperforms open-source and proprietary baselines in both standard and continual learning settings.

cs.CL · cs.CV

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-industry.15ACL Anthology · ACL 2026

Detecting AI-Generated Content on Social Media with Multi-modal Language Models

Chenyang Yang, Shen Yan, Yibo Yang, Litao Hu, Yuchen Liu, Yuan Zeng, Hanchao Yu, Yinan Zhu 等 16 位作者

原文摘要

Generative AI has enabled the creation of photorealistic images and videos that are increasingly disseminated on social media, often used for spam, misinformation, manipulation, and fraud. Existing AI-generated content (AIGC) detection methods face challenges including poor generalization to new generation models, reliance on single modalities, and lack of interpretable explanations. We present our pipeline that mitigates these issues by continuously curating diverse multi-modal social media data and training a compact vision-language model for detection and explanation. Our model achieves state-of-the-art detection performance on public benchmarks and demonstrates robust detection and explanation capabilities on internal social media datasets across multiple platforms. We deployed our model for post recommendation on social media platforms and observed positive downstream impacts on user engagement, demonstrating that it is feasible to perform effective AIGC detection in dynamic, real-world social media environments.

cs.CL · cs.CV

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-industry.28ACL Anthology · ACL 2026

Pre-Deployment Advertisement Ranking under Data Scarcity via Context-Aware Criteria Generation with VLMs

Kyungho Kim, Yeonje Choi, Gyurim Hwang, Sejin Chung, Hongseok Lee, Myeong Ho Song, Yeongho Kim, Sunwoo Kim 等 11 位作者

原文摘要

Vision-Language Models (VLMs) perform well on general multimodal tasks, yet applying them to real-world advertisement (ad) evaluation is challenging due to strong brand specificity and limited labeled data. We introduce a new practical task, brand-specific ad ranking, which aims to rank ads for a target brand prior to deployment by modeling brand-specific effectiveness. To this end, we propose ADvisor, which derives explicit brand-aware decision criteria using VLMs, augments limited brand context with ads from similar brands, and applies reflection-based scoring for ranking. Experiments on real-world advertising data from 10 brands, collected through actual ad campaigns, show that ADvisor outperforms strong baselines by up to 7.2%. Further analyses show the generated criteria capture meaningful brand specificity, and ADvisor also performs strongly in online A/B testing. Our code is available at https://github.com/K-Kyungho/ADvisor.

cs.CL · cs.CV

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-industry.3ACL Anthology · ACL 2026

MinerU2.5: A Decoupled Vision-Language Model for Efficient High-Resolution Document Parsing

Junbo Niu, Zheng Liu, Zhuangcheng Gu, Bin Wang, Linke Ouyang, Zhiyuan Zhao, Tao Chu, Tianyao He 等 59 位作者

原文摘要

We introduce MinerU2.5, a 1.2B-parameter document parsing vision-language model that achieves state-of-the-art recognition accuracy while maintaining exceptional computational efficiency. Our approach employs a coarse-to-fine, two-stage parsing strategy that decouples global layout analysis from local content recognition. In the first stage, the model performs efficient layout analysis on downsampled images to identify structural elements, circumventing the computational overhead of processing high-resolution inputs. In the second stage, guided by the global layout, it performs targeted content recognition on native-resolution crops extracted from the original image, preserving fine-grained details in dense text, complex formulas, and tables. To support this strategy, we developed a comprehensive data engine that generates diverse, large-scale training corpora for both pretraining and fine-tuning. Ultimately, MinerU2.5 demonstrates strong document parsing ability, achieving state-of-the-art performance on multiple benchmarks, surpassing both general-purpose and domain-specific models across various recognition tasks, while maintaining significantly lower computational overhead.

cs.CL · cs.CV

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-industry.32ACL Anthology · ACL 2026

UNIVID: Unified Vision-Language Model for Video Moderation

Kejuan Yang, Yizhuo Zhang, Mingyuan Du, Yue Zhang, Dixin Zheng, Kaili Zhao, Yang Xiao, Hanzhong Liang 等 9 位作者

原文摘要

Global-scale video moderation faces a dual challenge: the need for fine-grained multimodal reasoning and the demand for interpretable outputs to support downstream enforcement. Traditional moderation systems often rely on fragmented black-box classifiers that are difficult to maintain and lack transparency.In this paper, we present UNIVID, a Unified Vision-Language model for Video Moderation. Unlike standard classification models, UNIVID generates policy-aware captions that serve as an interpretable intermediate representation, enabling human-verifiable decisions and multi-task reusability. While existing open-source and commercial VLMs often suffer from safety-guardrail refusals and lack fine-grained policy alignment, we develop a specialized training data recipe that combines expert human-refined labels with synthetic data to align the model with our safety guidelines.By integrating UNIVID as the core captioner, we design a novel end-to-end video moderation system that reduces violation leakage by 42.7% and overkill rate by 37.0% relatively. Meanwhile, by replacing over 1,000 policy-specific models with a single UNIVID backbone, we recycle extensive computational resources while significantly reducing engineering maintenance overhead. To our knowledge, this is one of the first reports of a high-efficiency captioning VLM successfully supporting industrial-scale moderation and cross-functional business.

cs.CL · cs.CV

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-industry.41ACL Anthology · ACL 2026

Grounded Multimodal In-Context Learning for Product Weight Estimation at Scale in E-commerce

Bhavuk Singhal, Arsh Keshari, Ravindra Kumar Yadav

原文摘要

Accurately inferring implicit physical attributes of products, such as weight, is critical for large-scale e-commerce logistics but challenging due to sparse or unreliable textual metadata and high visual variability. We formulate weight estimation as a grounded multimodal reasoning problem and investigate whether large vision-language models (LVLMs) can infer discretized weight buckets through in-context learning (ICL) over product images and descriptions. We introduce a scalable inference framework that conditions predictions on automatically retrieved, category-specific exemplars and propose a distribution-calibrated retrieval strategy that aligns few-shot contexts with the empirical weight distribution of each product sub-category. This calibration substantially improves few-shot multimodal reasoning compared to random or embedding-based retrieval baselines. Across 14 high-variance categories, our approach significantly outperforms strong multimodal KNN baselines in both exact-match accuracy and near-bucket reliability. Deployed in production on a large e-commerce platform, our system processes millions of listings daily and reduces shipping-related revenue leakage by \sim22%, demonstrating that multimodal ICL can serve as a practical and cost-effective alternative to manual or hardware-based verification.

cs.CL · cs.CV

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-industry.46ACL Anthology · ACL 2026

ColorBrowserAgent: Complex Long-Horizon Browser Agent with Adaptive Knowledge Evolution

Jihong Wang, Jiamu Zhou, Weiming Zhang, Teng Wang, Weiwen Liu, Zhuosheng Zhang, Xingyu Lou, Weinan Zhang 等 10 位作者

原文摘要

With the advancement of vision-language models, web automation has made significant progress. However, deploying autonomous agents in real-world settings remains challenging, primarily due to site heterogeneity, where generalist models lack domain-specific priors for diverse interfaces, and long-horizon instability, characterized by the accumulation of decision drift over extended interactions. To address these challenges, we introduce ColorBrowserAgent (Complex Long-Horizon Browser Agent), a knowledge-evolving agent for robust web automation. Our approach addresses these challenges through two synergistic mechanisms: human-in-the-loop knowledge adaptation that transforms sparse human feedback into reusable domain knowledge, and knowledge-aligned progressive summarization that stabilizes long interactions through memory compression. Extensive experiments on WebArena, WebChoreArena and industrial deployment show that ColorBrowserAgent consistently outperforms strong baselines. It achieves a state-of-the-art success rate of 71.2% on WebArena and maintains 47.4% performance under zero-shot transfer setting on WebChoreArena. In commercial deployment, it improves user satisfaction by 19.3% relatively, verifying its robustness in real-world scenarios.

cs.CL · cs.CV

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-industry.54ACL Anthology · ACL 2026

From Relevance to Authority: Authority-aware Generative Retrieval in Web Search Engines

Sunkyung Lee, Jihye Back, Donghyeon Jeon, Soonhwan Kwon, Moonkwon Kim, Inho Kang, Jongwuk Lee

原文摘要

Generative information retrieval (GenIR) formulates the retrieval process as a text-to-text generation task, leveraging the vast knowledge of large language models. However, existing works primarily optimize for relevance while often overlooking document trustworthiness. This is critical in high-stakes domains like healthcare and finance, where relying solely on semantic relevance risks retrieving unreliable information. To address this, we propose an Authority-aware Generative Retriever (AuthGR), the first framework that incorporates authority into GenIR. AuthGR consists of three key components: (i) Multimodal Authority Scoring, which employs a vision-language model to quantify authority from textual and visual cues; (ii) a Three-stage Training Pipeline to progressively instill authority awareness into the retriever; and (iii) a Hybrid Ensemble Pipeline for robust deployment. Offline evaluations demonstrate that AuthGR successfully enhances both authority and accuracy, with our 3B model matching a 14B baseline. Crucially, large-scale online A/B tests and human evaluations conducted on the commercial web search platform confirm significant improvements in real-world user engagement and reliability.

cs.CL · cs.CV

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-industry.77ACL Anthology · ACL 2026

FROST: Factual Reasoning via Optimized Stochastic Trajectories in Large Language Models during Inference

Soumedhik Bharati, Ebad Shabbir, Jiechao Gao

原文摘要

Large language models face a trade-off between factual consistency and reasoningdiversity: deterministic decoding prioritizes reliability but may miss alternativesolution paths, while high-temperature sampling increases exploration at the costof accuracy. We present FROST (Factual Reasoning via Optimized StochasticTrajectories), an inference-time framework that balances exploration andexploitation without additional training or context augmentation. FROST combinesdeterministic inference from a large model with targeted stochastic sampling froma smaller model, selecting outputs via multi-criteria validation over coherence,factual grounding, and semantic novelty. Across HotpotQA, CommonsenseQA, andMMLU, FROST achieves 2–5 percentage point improvements over standard chain-of-thoughtprompting and reduces unsupported outputs by 40% relative to Standard CoT. Comparedto Self-Consistency ensembles, FROST delivers comparable accuracy at 28% lowerinference cost through strategic delegation to smaller models. On an adversarialsubset with unanswerable queries, FROST abstains on 34% of cases versus 8% forstandard chain-of-thought, reducing false positives by 45%. Task-stratifiedevaluation shows that exploration benefits scale with problem ambiguity.Generalization to mathematical reasoning, code generation, and multimodal domainsremains future work.

cs.CL · cs.CV

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-industry.85ACL Anthology · ACL 2026

Adaptive Weighted Proxy Tuning: Efficient Gray-Box Steering for Image Captioning.

Nafew Azim, Fuad Rahman, Nabeel Mohammed

原文摘要

Adapting Large Vision-Language Models (LVLMs) to specialized domains typically demands resource-intensive fine-tuning or access to proprietary parameters (“white-box” access). While decoding-time strategies like Proxy Tuning offer a parameter-efficient alternative, they rely on rigid, static logit arithmetic that fails to account for instance-specific variations in model certainty and domain shift. In this work, we introduce Adaptive Weighted Proxy Tuning (AWPT), a gray-box steering framework that dynamically modulates the logit contributions of a large base model, a fine-tuned expert, and an untuned anti-expert. Unlike static approaches, AWPT introduces two instance-aware mechanisms: (1) a lightweight ViT-based Weight Predictor that performs amortized inference to estimate optimal mixing coefficients in real-time with negligible added latency (\sim0.03s overhead), and (2) a Per-Sample Optimization objective that establishes theoretical performance bounds via gradient-based logit steering. Extensive evaluation across medical (ROCOv2, IU-Xray) and general domains (Flickr30k, MS COCO, TextCaps) demonstrates that AWPT achieves performance parity with fully fine-tuned models while remaining parameter-free regarding the generator. Crucially, our dynamic weighting acts as an effective regularizer, significantly reducing object hallucinations and establishing AWPT as a robust solution for deploying general-purpose LVLMs in safety-critical contexts.

cs.CL · cs.CV

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-industry.89ACL Anthology · ACL 2026

IPS: In-Prompt Process Supervision for Short Video Content Moderation

Mingchao Liu, Yu Sun, Ruixiao Sun, Xin Dong, Xiang Shen, Hongwei Wang, Hongyu Xiong, Yang Song

原文摘要

Multimodal large language models (MLLMs) are effective at capturing the semantics of short video content; however, they often fail to attend to the policy-specific details required for reliable content moderation.To address this limitation, we introduce IPS, a novel framework that integrates In-prompt Process Supervision into MLLMs by introducing sequential reasoning over ancillary questions during fine-tuning. IPS consistently outperforms baseline MLLMs on public and proprietary benchmarks.Moreover, replacing human-annotated ancillary labels with MLLM-generated ones results in only marginal performance degradation, demonstrating robustness to noisy supervision and strong scalability with model-generated annotations.These findings establish IPS as a scalable and effective solution for complex multimodal classification in large-scale industrial settings.

cs.CL · cs.CV

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-industry.99ACL Anthology · ACL 2026

A Multistage Extraction Pipeline for Long Scanned Financial Documents: An Empirical Study in Industrial KYC Workflows

Han Yuxuan, Yuanxing Zhang, Yushuo Wang, Yichao Jin

原文摘要

Structured information extraction from long, multilingual scanned financial documents is a core requirement in industrial KYC and compliance workflows. These documents are typically non-machine-readable, noisy, and visually heterogeneous. They usually span dozens of pages while containing only sparse task-relevant information. Although recent vision–language models (VLMs) achieve strong benchmark performance, directly applying them end-to-end to full financial reports often leads to unreliable extraction under real-world conditions.We present a multistage extraction framework that integrates image preprocessing, multilingual OCR, hybrid page-level retrieval, and compact VLM-based structured extraction. The design separates page localization from multimodal reasoning, enabling more accurate extraction from complex multi-page documents.We evaluated the framework on 120 production KYC documents comprising about 3000 multilingual scanned pages. Across multiple OCR–VLM combinations, the proposed pipeline consistently outperforms direct PDF-to-VLM baselines, improving field-level accuracy by up to 31.9 percentage points. The best configuration, PaddleOCR with MiniCPM-o-2.6, achieves 87.27% accuracy. Ablation studies show that page-level retrieval is the dominant factor in performance improvements, particularly for complex financial statements and non-English documents.

cs.CL · cs.CV

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1000ACL Anthology · ACL 2026

Benchmarking Large Vision-Language Models on CFMME: A Comprehensive Chinese Financial Multimodal Evaluation Dataset

Qian Chen Alibaba Group, Xianyin Zhang Fudan University, Yanzhi Liu Alibaba Group, Lifan Guo, Feng Chen Alibaba Group, Chi Zhang Alibaba Group

原文摘要

The emergence of Large Vision-Language Models (LVLMs) has substantially expanded model capabilities beyond text-only understanding, enabling unified inference across both visual and textual modalities and supporting a broader range of real-world applications. To comprehensively evaluate the perception, understanding, reasoning, and cognition capabilities of LVLMs throughout the entire financial business workflow in Chinese contexts, we introduce CFMME, a novel Chinese financial multimodal evaluation benchmark. CFMME comprises 6,052 instances spanning from fundamental academic knowledge to complex real-world applications, covering eight primary financial image modalities and four core multimodal tasks. On CFMME, we conduct a thorough evaluation of representative LVLMs. The results show that the state-of-the-art model attains an overall accuracy of 66.11% on the question answering task and an average score of 77.18 on the detection, recognition, and information extraction tasks, indicating substantial room for improvement in current LVLMs. In addition, we conduct detailed analyses of error causes, cross-modal capabilities, and multi-orientation settings, yielding valuable insights for future research. We hope that CFMME will spur further progress in LVLMs, especially by improving their performance on multiple multimodal tasks in the financial domain.

cs.CL · cs.CV

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1006ACL Anthology · ACL 2026

ATIR: Towards Audio-Text Interleaved Contextual Retrieval

Tong Zhao, Chenghao Zhang Renmin University of China, Yutao Zhu, Zhicheng Dou Renmin University of China

原文摘要

Audio carries richer information than text, including emotion, speaker traits, and environmental context, while also enabling lower-latency processing compared to speech-to-text pipelines. However, recent multimodal information retrieval research has predominantly focused on images, largely overlooking audio, especially in the setting of interleaved audio-text contextual retrieval. In this work, we introduce the Audio-Text Interleaved contextual Retrieval (ATIR) task, where queries can alternate between audio and text modalities. We construct an ATIR benchmark by integrating several Automatic Speech Recognition (ASR), QA, and retrieval datasets, ultimately unifying four types of contextual retrieval tasks. This benchmark substantially addresses the limitations of existing audio retrieval datasets in semantic retrieval. To study this task, we evaluate several off-the-shelf retrievers and train our ATIR model based on a Multimodal Large Language Model (MLLM). We further propose a novel token compression mechanism, which is orthogonal to existing compression methods, to mitigate the challenge of excessive audio tokens in MLLM-based ATIR models. Experimental results show that our ATIR model achieves significant improvements over strong baselines.

cs.CL · cs.CV

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1009ACL Anthology · ACL 2026

Benchmarking Web Agent Safety under E-commerce Deceptive Interfaces

Zijing Shi, Meng Fang University of Liverpool and Eindhoven University of Technology, Ling Chen University of Technology Sydney

原文摘要

As autonomous web agents are increasingly deployed to perform real-world tasks, ensuring their safety has become a critical concern. In this work, we study web agent behavior under realistic deceptive interfaces in the e-commerce domain. We introduce WebDecept, a lightweight and configurable plugin framework that enables controlled injection of deceptive interface patterns into existing web environments. Using WebDecept, we instantiate seven deceptive patterns commonly observed on the open web, including targeted advertisements, domain redirection, and shopping manipulation. By injecting these patterns into the frontend during task execution, we perform controlled evaluation of multiple multimodal web agents. Our results show that current web agents are highly susceptible to multiple classes of deceptive interfaces, and that prompt-based constraints are often insufficient to mitigate these failures. We further analyze how the design choices of deceptive patterns influence the success of such manipulations. These findings highlight safety challenges that should be addressed as web agents are scaled toward real-world deployment.

cs.CL · cs.CV

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1010ACL Anthology · ACL 2026

Counteracting the Matthew Effect in Self-Improvement of LVLMs through Head-Tail Re-balancing

Xin Guo, Zhiheng Xi, Yiwen Ding, Yitao Zhai Meituan, Xiaowei Shi, Xunliang Cai Meituan, Tao Gui Fudan University, Qi Zhang Fudan University 等 9 位作者

原文摘要

Self-improvement has emerged as a mainstream paradigm for advancing the reasoning capabilities of large vision–language models (LVLMs), where models explore and learn from successful trajectories iteratively. However, we identify a critical imbalance during this process: the model readily generates high-quality trajectories for simple queries (i.e., head data) but struggles with complex ones (i.e., tail data). This bias drives the optimization to disproportionately prioritize simple reasoning skills, while inhibiting the acquisition of complex capabilities. As iterations progress, this imbalance becomes more acute—a dynamic we term the “Matthew effect”, ultimately stalling performance gains. To mitigate this, we approach head-tail re-balance during the exploration-and-learning process from two perspectives: distribution-reshaping and trajectory-resampling. Extensive experiments on Qwen2-VL-7B-Instruct and InternVL2.5-4B models across visual reasoning tasks demonstrate that our methods consistently improve visual reasoning capabilities, outperforming vanilla self-improvement baselines by an average of 3.86 points.

cs.CL · cs.CV

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1012ACL Anthology · ACL 2026

MACS: Modality-Aware Capacity Scaling for Efficient Multimodal MoE Inference

Bo Li Baidu Inc, Chuan Wu, Shaolin Zhu Tianjin University

原文摘要

Mixture-of-Experts Multimodal Large Language Models (MoE MLLMs) suffer from a significant efficiency bottleneck during Expert Parallelism (EP) inference due to the straggler effect. This issue is worsened in the multimodal context, as existing token-count-based load balancing methods fail to address two unique challenges: (1) Information Heterogeneity, where numerous redundant visual tokens are treated equally to semantically critical ones, and (2) Modality Dynamics, where varying visual to text ratios across tasks lead to resource misallocation. To address these challenges, we propose MACS (Modality-Aware Capacity Scaling), a training-free inference framework. Specifically, MACS introduces an Entropy-Weighted Load mechanism to quantify the semantic value of visual tokens, addressing information heterogeneity. Additionally, the Dynamic Modality-Adaptive Capacity mechanism allocates expert resources based on the real-time modal composition of the input. Extensive experiments demonstrate that MACS significantly outperforms existing methods on various multimodal benchmarks, providing a novel and robust solution for the efficient deployment of MoE MLLMs in EP inference.

cs.CL · cs.CV

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1016ACL Anthology · ACL 2026

MIRTH: Mutual-Information Reasoning with Temporal Hubs for Vision-Language-Action Agents

Hao Sun, Yu Song Ritsumeikan University, Teng Shiyu, Ziwei Niu, Yen-wei Chen Ritsumeikan University

原文摘要

VLA models have emerged as a powerful paradigm for transferring semantic knowledge from web-scale data to physical robotic control. However, current single-frame architectures suffer from intrinsic limitations: temporal myopia that discards historical dynamics, reasoning gaps between high-level instructions and low-level motor commands, and inference inefficiency due to autoregressive scalar decoding. In this work, we propose MIRTH, a unified framework designed to address these challenges. MIRTH augments a pretrained VLA backbone with three key innovations: (1) dual-scale temporal memory hubs that compress long-term scene evolution and short-term motion trends into compact embeddings; (2) latent reasoning tokens optimized via a mutual-information objective carving out a semantic plan space to align multimodal context with action trajectories; and (3) a parallel action decoding scheme that replaces autoregressive generation with vector-wise prediction to maximize control throughput. Extensive evaluations on the LIBERO simulation benchmark and a real-world LeRobot platform demonstrate that MIRTH achieves state-of-the-art performance and exhibiting emergent error recovery capabilities. We will release our code and collected datasets to facilitate reproducible research in embodied AI upon publication.

cs.CL · cs.CV

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.102ACL Anthology · ACL 2026

CARES: Context-Aware Resolution Selector for VLMs

Moshe Kimhi International Business Machines, Nimrod Shabtay Tel Aviv University, Raja Giryes Apple and Tel Aviv University, Chaim Baskin Ben Gurion University of the Negev, Eli Schwartz International Business Machines

原文摘要

Large vision–language models (VLMs) commonly process images at native or high resolution to remain effective across tasks. This inflates visual tokens to 97-99% of total tokens, resulting in high compute and latency, even when low-resolution images would suffice. We introduce CARES—a Context-Aware Resolution Selector, a lightweight preprocessing module that, given an image–query pair, predicts the minimal sufficient input resolution. CARES uses a compact VLM (350M) to extract features and predict when a target pretrained VLM’s response converges to its peak ability to answer correctly. Though trained as a discrete classifier over a set of optional resolutions, CARES interpolates continuous resolutions at inference for fine-grained control. Across five multimodal benchmarks spanning documents and natural images, as well as diverse target VLMs, CARES preserves task performance while reducing compute by up to 80%.

cs.CL · cs.CV

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1021ACL Anthology · ACL 2026

OS-Symphony: A Holistic Framework for Robust and Generalist Computer-Using Agents

Bowen Yang, Kaiming Jin national university of singaore, National University of Singapore, Zhenyu Wu, Zhaoyang Liu Hong Kong University of Science and Technology and Shanghai AI Laboratory, Qiushi Sun University of Hong Kong, Zehao Li University of Science and Technology of China, JingJing Xie, Zhoumianze Liu 等 15 位作者

原文摘要

While Vision-Language Models (VLMs) have significantly advanced Computer-Using Agents (CUAs), current agentic frameworks struggle with robustness in novel domains and long-horizon workflows due to the absence of visual-aware tutorial retrieval and the lack of granular control over historical visual context curation and pruning. To bridge these gaps, we introduce OS-Symphony, a holistic framework that comprises an Orchestrator coordinating two key innovations for robust automation: (1) a Reflection-Memory Agent that utilizes milestone-driven long-term memory to enable trajectory-level self-correction, effectively mitigating visual context loss in long-horizon tasks; (2) Versatile Tool Agents featuring a Multimodal Searcher that adopts a “SeeAct” paradigm to navigate a browser-based sandbox to synthesize live, visually aligned tutorials, thereby resolving fidelity issues in unseen scenarios. Experimental results demonstrate that OS-Symphony delivers substantial performance gains across varying model scales, establishing new state-of-the-art results on three online benchmarks, notably achieving 65.84% on OSWorld. All research assets will be made publicly available.

cs.CL · cs.AI · cs.CV

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1038ACL Anthology · ACL 2026

Omni-Embed-Audio: Leveraging Multimodal LLMs for Robust Audio-Text Retrieval

HaeJun Yoo, Yongseop Shin, Insung Lee, Myoung-Wan Koo Sogang University, Du-Seong Chang Sogang University

原文摘要

Audio-text retrieval systems based on Contrastive Language-Audio Pretraining (CLAP) achieve strong performance on traditional benchmarks; however, these benchmarks rely on caption-style queries that differ substantially from real-world search behavior, limiting their assessment of practical retrieval robustness. We present Omni-Embed-Audio (OEA), a retrieval-oriented encoder leveraging multimodal LLMs with native audio understanding. To systematically evaluate robustness beyond caption-style queries, we introduce User-Intent Queries (UIQs)—five formulations reflecting natural search behaviors: questions, commands, keyword tags, paraphrases, and exclusion-based negative queries. For negative queries, we develop a hard negative mining pipeline and propose discrimination metrics (HNSR, TFR) assessing models’ ability to suppress acoustically similar distractors. Experiments on AudioCaps, Clotho, and MECAT show that OEA achieves comparable text-to-audio retrieval performance to state-of-the-art M2D-CLAP, while demonstrating clear advantages in two critical areas: (1) dominant text-to-text retrieval (+22% relative improvement), and (2) substantially superior hard negative discrimination (+4.3%p HNSR@10, +34.7% relative TFR@10), revealing that LLM backbones provide superior semantic understanding of complex queries.

cs.CL · cs.CV

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1043ACL Anthology · ACL 2026

Enhancing Multimodal Large Language Models for Ancient Chinese Character Evolution Analysis via Glyph-Driven Fine-Tuning

Rui Song Jilin University, Lida Shi Jilin University, Ruihua Qi, Yingji Li, Hao Xu Jilin University

原文摘要

In recent years, rapid advances in Multimodal Large Language Models (MLLMs) have increasingly stimulated research on ancient Chinese scripts. As the evolution of written characters constitutes a fundamental pathway for understanding cultural transformation and historical continuity, how MLLMs can be systematically leveraged to support and advance text evolution analysis remains an open and largely underexplored problem. To bridge this gap, we construct a comprehensive benchmark comprising 11 tasks and over 130,000 instances, specifically designed to evaluate the capability of MLLMs in analyzing the evolution of ancient Chinese scripts. We conduct extensive evaluations across multiple widely used MLLMs and observe that, while existing models demonstrate a limited ability in glyph-level comparison, their performance on core tasks-such as character recognition and evolutionary reasoning-remains substantially constrained. Motivated by these findings, we propose a glyph-driven fine-tuning framework (GEVO) that explicitly encourages models to capture evolutionary consistency in glyph transformations and enhances their understanding of text evolution. Experimental results show that even models at the 2B scale achieve consistent and comprehensive performance improvements across all evaluated tasks. To facilitate future research, we publicly release both the benchmark and the trained models.

cs.CL · cs.CV

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1044ACL Anthology · ACL 2026

See2Refine: Vision-Language Feedback Improves LLM-Based eHMI Action Designers

Ding Xia The University of Tokyo, Xinyue Gui, Mark Colley University College London, University of London, Fan Gao, Zhongyi Zhou Google, Dongyuan Li The University of Tokyo and Institute of Science Tokyo, Renhe Jiang The University of Tokyo, Takeo Igarashi The University of Tokyo, Tokyo Institute of Technology

原文摘要

Automated vehicles lack natural communication channels with other road users, making external Human-Machine Interfaces (eHMIs) essential for conveying intent and maintaining trust in shared environments. However, most eHMI studies rely on developer-crafted message-action pairs, which are difficult to adapt to diverse and dynamic traffic contexts. A promising alternative is to use Large Language Models (LLMs) as action designers that generate context-conditioned eHMI actions, yet such designers lack perceptual verification and typically depend on fixed prompts or costly human-annotated feedback for improvement.We present See2Refine, a human-free, closed-loop framework that uses vision-language models (VLMs) for perceptual evaluation as automated visual feedback to improve an LLM-based eHMI action designer. Given a driving context and a candidate eHMI action, the VLM evaluates the perceived appropriateness of the action, and this feedback is used to iteratively revise the designer’s outputs, enabling systematic refinement without human supervision.We evaluate our framework across three eHMI modalities (lightbar, eyes, and arm) and multiple LLM model sizes. Across settings, our framework consistently outperforms prompt-only LLM designers and manually specified baselines in both VLM-based metrics and human-subject evaluations. The results further indicate that the improvements are generalized across modalities and that VLM evaluations are reasonably aligned with human preferences in our controlled settings, supporting the robustness and effectiveness of See2Refine for scalable action design.

cs.CL · cs.CV

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1050ACL Anthology · ACL 2026

CamoQuery: Language-Guided Reasoning Camouflaged Object Segmentation

Tianxin Han Northeastern University, Qing Dong Northeastern University, Xingwei Wang Northeastern University, Jie Jia Northeastern University, Gang Wu Northeastern University, Bowen Yang, Fu Zhang Northeastern University

原文摘要

Although camouflaged object segmentation has advanced rapidly in recent years, existing methods are still confined to visual mask prediction under fixed task assumptions. They cannot interactively respond to user requests, nor can they proactively understand and reason about the user’s intent. Our work tackles this issue by proposing a novel task, Language-Guided Reasoning Camouflaged Object Segmentation (LRCOS). Given a camouflaged image and an implicit query text instruction that requires reasoning, LRCOS aims to output intent-consistent segmentation mask. To establish a benchmark for this task, we build CamoQuery, comprising 12,437 image–mask samples and 25971 implicit query text instructions. To better reflect real-world camouflaged scenarios, we additionally collect MCD, a multi-instance camouflage dataset where multiple camouflaged targets co-exist within the same scene, increasing the need for reasoning. Building on CamoQuery, we further propose COSA, a vision–language segmentation assistant that segments the intended camouflaged object from implicit queries and produces a reasoning explanation. Experiments on CamoQuery demonstrate that COSA has strong reasoning segmentation capability in camouflaged scenes and exhibits zero-shot capability.

cs.CL · cs.CV

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1053ACL Anthology · ACL 2026

MAB-DQA: Addressing Query Aspect Importance in Document Question Answering with Multi-Armed Bandits

Yixin Xiang Nanjing University of Science and Technology, Yunshan Ma Singapore Management University, Xiaoyu Du Nanjing University of Science and Technology, Yibing Chen Nanjing Pami Intelligent Technology Co., Ltd., Yanxin Zhang NVIDIA, Jinhui Tang Nanjing Forestry University

原文摘要

Document Question Answering (DQA) involves generating answers from a document based on a user’s query, representing a key task in document understanding. This task requires interpreting visual layouts, which has prompted recent studies to adopt multimodal Retrieval-Augmented Generation (RAG) that processes page images for answer generation. However, in multimodal RAG, visual DQA struggles to utilize a large number of images effectively, as the retrieval stage often retains only a few candidate pages (e.g., Top-4), causing informative but less visually salient content to be overlooked in favor of common yet low-information pages. To address this issue, we propose a Multi-Armed Bandit–based DQA framework (MAB-DQA) to explicitly model the varying importance of multiple implicit aspects in a query. Specifically, MAB-DQA decomposes a query into aspect-aware subqueries and retrieves an aspect-specific candidate set for each. It treats each subquery as an arm and uses preliminary reasoning results from a small number of representative pages as reward signals to estimate aspect utility. Guided by an exploration–exploitation policy, MAB-DQA dynamically reallocates retrieval budgets toward high-value aspects. With the most informative pages and their correlations, MAB-DQA generates the expected results. On four benchmarks, MAB-DQA shows an average improvement of 5%-18% over the state-of-the-art method, consistently enhancing document understanding. Codes are available at https://github.com/ElephantOH/MAB-DQA.

cs.CL · cs.CV

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1060ACL Anthology · ACL 2026

PEAP: Proactive Embodied Action Sequence Planning with Joint Understanding of Vision and Audio Perception

Tianwei Lan, Jiaqi Wu, Zeming Liu, Zhaoxin Fan Beijing University of Aeronautics and Astronautics, Haifeng Wang Baidu, Yuhang Guo

原文摘要

Embodied Action Sequence Planning focuses on the capability of embodied agents to implement action planning via environmental perception. This technology enables diverse intelligent assistance for real-world scenarios such as home and office environments. To address the limitations of existing embodied agents in meeting the requirement for proactivity and achieving joint understanding of visual and audio information, this study investigates the ability of embodied agents to proactively provide assistance through action sequence planning based on joint understanding of vision and audio perception without explicit human instructions. Correspondingly, we propose PEAP, the first multimodal proactive embodied action sequence planning dataset. We evaluate the performance of multiple Large Language Models on the PEAP dataset. The results demonstrate that these models still exhibit significant deficiencies on this task particularly lacking accurate environmental perception capabilities. Furthermore, ablation experiment and replacement experiment further corroborate that the joint understanding of multimodal information can significantly improve the models’ performance on proactive embodied action sequence planning task.

cs.CL · cs.CV

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表