by jsendak | Aug 8, 2025 | Computer Science
arXiv:2508.05087v1 Announce Type: new
Abstract: Jailbreak attacks against multimodal large language Models (MLLMs) are a significant research focus. Current research predominantly focuses on maximizing attack success rate (ASR), often overlooking whether the generated responses actually fulfill the attacker’s malicious intent. This oversight frequently leads to low-quality outputs that bypass safety filters but lack substantial harmful content. To address this gap, we propose JPS, underline{J}ailbreak MLLMs with collaborative visual underline{P}erturbation and textual underline{S}teering, which achieves jailbreaks via corporation of visual image and textually steering prompt. Specifically, JPS utilizes target-guided adversarial image perturbations for effective safety bypass, complemented by “steering prompt” optimized via a multi-agent system to specifically guide LLM responses fulfilling the attackers’ intent. These visual and textual components undergo iterative co-optimization for enhanced performance. To evaluate the quality of attack outcomes, we propose the Malicious Intent Fulfillment Rate (MIFR) metric, assessed using a Reasoning-LLM-based evaluator. Our experiments show JPS sets a new state-of-the-art in both ASR and MIFR across various MLLMs and benchmarks, with analyses confirming its efficacy. Codes are available at href{https://github.com/thu-coai/JPS}{https://github.com/thu-coai/JPS}. color{warningcolor}{Warning: This paper contains potentially sensitive contents.}
Expert Commentary
The concept of Jailbreak attacks against multimodal large language models (MLLMs) is a growing area of research that highlights the vulnerabilities in artificial intelligence systems. This research focuses not only on maximizing attack success rates but also on ensuring that the generated responses actually fulfill the attacker’s malicious intent. This multi-disciplinary approach combines elements of artificial intelligence, computer vision, and cybersecurity to create a robust method for bypassing safety filters.
The proposed JPS (Jailbreak MLLMs with collaborative visual Perturbation and textual Steering) approach is innovative in its use of both visual image perturbations and textual steering prompts to guide the responses of the language models. By co-optimizing these components, the researchers were able to achieve a new state-of-the-art in both attack success rate and malicious intent fulfillment rate. This demonstrates the effectiveness of integrating visual and textual cues in crafting sophisticated attacks against MLLMs.
From a multimedia information systems perspective, this research showcases the importance of incorporating visual elements into language-based models to enhance their capabilities. Animations, artificial reality, augmented reality, and virtual realities can all benefit from this approach by creating more immersive and interactive experiences that respond to user input in a meaningful way.
Overall, the JPS approach represents a significant advancement in the field of AI security and highlights the potential for further research in leveraging multi-modal data to enhance the capabilities of language models.
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by jsendak | Aug 8, 2025 | Computer Science
Expert Commentary
In the realm of legal research, the ability to identify relevant legal precedents is crucial for building strong cases and making informed decisions. Traditional retrieval methods often prioritize factual similarity over legal issues, leading to inefficiencies and irrelevant results. This paper introduces an innovative approach utilizing Large Language Models (LLMs) to address this challenge by enhancing case retrieval, providing explanations for relevance, and identifying core legal issues autonomously.
The integration of Retrieval Augmented Generation (RAG) with structured summaries optimized for Indian case law represents a significant step forward in legal information retrieval. By leveraging the Augmented Question-guided Retrieval (AQgR) framework, the system generates targeted legal questions based on factual scenarios to improve the relevance of case law retrieval. This tailored approach holds great promise for legal professionals seeking more precise and comprehensive research results.
The manual assessment of structured summaries by legal experts underscores the importance of domain-specific validation in ensuring the system’s accuracy and relevance. Furthermore, the evaluation of case law retrieval using the FIRE dataset and the review of generated explanations by legal experts demonstrate a commitment to rigorous testing and quality assurance.
The experimental evaluation results, with a Mean Average Precision (MAP) score of 0.36 and a Mean Average Recall (MAR) of 0.67, signal a substantial improvement over the current benchmark. This achievement highlights the efficacy of the proposed approach in delivering more contextually relevant results that align closely with legal professionals’ needs. Notably, the shift from fact-based to legal-issue-based retrieval represents a paradigm shift that enhances the utility and applicability of the system for legal practitioners.
In conclusion, this work presents a suite of innovative contributions that have the potential to revolutionize case law retrieval and significantly enhance legal research capabilities. By embracing cutting-edge technology and tailored methodologies, the proposed approach sets a new standard for legal information retrieval, paving the way for more efficient, precise, and insightful legal research practices.
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by jsendak | Aug 7, 2025 | Computer Science
arXiv:2508.04353v1 Announce Type: new
Abstract: This paper introduces the Learned User Significance Tracker (LUST), a framework designed to analyze video content and quantify the thematic relevance of its segments in relation to a user-provided textual description of significance. LUST leverages a multi-modal analytical pipeline, integrating visual cues from video frames with textual information extracted via Automatic Speech Recognition (ASR) from the audio track. The core innovation lies in a hierarchical, two-stage relevance scoring mechanism employing Large Language Models (LLMs). An initial “direct relevance” score, $S_{d,i}$, assesses individual segments based on immediate visual and auditory content against the theme. This is followed by a “contextual relevance” score, $S_{c,i}$, that refines the assessment by incorporating the temporal progression of preceding thematic scores, allowing the model to understand evolving narratives. The LUST framework aims to provide a nuanced, temporally-aware measure of user-defined significance, outputting an annotated video with visualized relevance scores and comprehensive analytical logs.
Expert Commentary: Analyzing Video Content with the Learned User Significance Tracker (LUST)
In the field of multimedia information systems, the integration of multiple modalities such as visual and textual information is crucial for creating meaningful and contextually relevant content. The Learned User Significance Tracker (LUST) introduces a novel framework that leverages both visual cues from video frames and textual information extracted through Automatic Speech Recognition (ASR) to analyze video content and quantify thematic relevance.
What sets LUST apart is its multi-modal analytical pipeline, which combines visual and auditory content with textual descriptions to assess the thematic significance of video segments. This cross-disciplinary approach not only enhances the accuracy of relevance scoring but also provides a more comprehensive understanding of the content being analyzed.
Two-Stage Relevance Scoring Mechanism
The hierarchical, two-stage relevance scoring mechanism employed by LUST is particularly noteworthy. The initial “direct relevance” score evaluates individual segments based on immediate visual and auditory content against the user-provided thematic description. This direct assessment is then refined through the “contextual relevance” score, which takes into account the temporal progression of thematic scores to understand evolving narratives within the video.
By integrating Large Language Models (LLMs) into the scoring mechanism, LUST is able to provide a nuanced, temporally-aware measure of user-defined significance. This not only enhances the accuracy of relevance scoring but also enables the model to capture the subtle intricacies of thematic evolution within the video content.
Implications for Animations, Artificial Reality, Augmented Reality, and Virtual Realities
The concepts introduced by the LUST framework have significant implications for the broader fields of multimedia information systems, including Animations, Artificial Reality, Augmented Reality, and Virtual Realities. By providing a more granular and contextually relevant analysis of video content, LUST can enhance the creation and delivery of immersive multimedia experiences across these domains.
For Animations, LUST could be used to analyze the thematic coherence and relevance of animated sequences, ensuring that visual storytelling remains engaging and impactful. In Artificial Reality, LUST could enable more intelligent content curation and personalized experiences based on user-defined thematic preferences. Augmented Reality applications could leverage LUST to overlay contextual information onto real-world scenes, enriching the user experience with relevant content. And in Virtual Realities, LUST could facilitate the creation of more immersive and dynamically evolving virtual environments that respond to user interactions and thematic cues.
Overall, the LUST framework represents a groundbreaking approach to analyzing video content across multiple modalities and has the potential to revolutionize the way we interact with multimedia information systems in a wide range of applications.
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by jsendak | Aug 7, 2025 | Computer Science
Expert Commentary
Text-to-Image (T2I) models have indeed revolutionized a wide range of applications, from generating realistic images based on textual descriptions to assisting in artistic endeavors and visual storytelling. However, as mentioned in the article, the misuse of these models can lead to the generation of Not-Safe-For-Work (NSFW) content, which poses serious ethical and moral concerns.
This paper’s exploration of adversarial attacks on T2I models under black-box settings sheds light on the vulnerabilities that exist within such systems. Adversarial attacks have been a growing concern in the field of machine learning, as they can be used to exploit weaknesses in models and compromise their intended functionality.
One of the key contributions of this paper is the proposal of a novel prompt learning attack framework (PLA) that leverages gradient-based training and multimodal similarities to effectively bypass safety mechanisms in black-box T2I models. This approach represents a significant advancement in the field of adversarial machine learning, as it demonstrates the potential for developing more robust defense mechanisms against such attacks.
Despite the promising results presented in the experiments, it is important to note that the development of adversarial attacks and defenses is an ongoing arms race in the field of machine learning. As researchers continue to push the boundaries of AI technology, it is crucial to remain vigilant and proactive in addressing potential security threats and vulnerabilities.
Overall, this paper highlights the importance of considering the broader implications of AI technology and the need for responsible and ethical use of T2I models to prevent the generation of harmful or inappropriate content.
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by jsendak | Aug 5, 2025 | Computer Science
arXiv:2508.01168v1 Announce Type: new
Abstract: The inevitable modality imperfection in real-world scenarios poses significant challenges for Multimodal Sentiment Analysis (MSA). While existing methods tailor reconstruction or joint representation learning strategies to restore missing semantics, they often overlook complex dependencies within and across modalities. Consequently, they fail to fully leverage available modalities to capture complementary semantics. To this end, this paper proposes a novel graph-based framework to exploit both intra- and inter-modality interactions, enabling imperfect samples to derive missing semantics from complementary parts for robust MSA. Specifically, we first devise a learnable hypergraph to model intra-modality temporal dependencies to exploit contextual information within each modality. Then, a directed graph is employed to explore inter-modality correlations based on attention mechanism, capturing complementary information across different modalities. Finally, the knowledge from perfect samples is integrated to supervise our interaction processes, guiding the model toward learning reliable and robust joint representations. Extensive experiments on MOSI and MOSEI datasets demonstrate the effectiveness of our method.
Expert Commentary: Multi-disciplinary Approach to Multimodal Sentiment Analysis
The field of Multimedia Information Systems encompasses a wide range of technologies and methodologies that deal with the processing, analysis, and retrieval of multimedia data. One important aspect of multimedia information systems is the analysis of multimodal data, which involves the integration of information from different modalities such as text, audio, images, and video.
The article discussed here focuses on Multimodal Sentiment Analysis (MSA), which involves the analysis of sentiment and emotions expressed in multiple modalities. The authors highlight the challenges posed by modality imperfections in real-world scenarios and propose a novel graph-based framework to address these challenges. This reflects the multi-disciplinary nature of MSA, which draws upon concepts from machine learning, natural language processing, computer vision, and signal processing.
Connections to Animations, Artificial Reality, Augmented Reality, and Virtual Realities
The concepts discussed in this article are closely related to the fields of Animations, Artificial Reality, Augmented Reality, and Virtual Realities. In particular, the use of attention mechanisms to capture inter-modality correlations reflects techniques commonly employed in virtual reality systems to enhance user immersion and interaction. By considering both intra- and inter-modality interactions, the proposed framework aligns with the principles of creating realistic and immersive experiences in artificial and augmented reality environments.
Furthermore, the integration of knowledge from perfect samples to guide the model’s learning process resonates with the iterative refinement process often used in creating animations. Just as animators continually refine and adjust their work based on feedback and references, the proposed framework iteratively refines its joint representations based on supervisory signals from perfect samples.
In conclusion, this article showcases the multi-disciplinary nature of multimedia information systems and highlights the connections between MSA and other related fields such as Animations, Artificial Reality, Augmented Reality, and Virtual Realities. By leveraging cross-disciplinary insights and methodologies, researchers can develop more robust and effective solutions for analyzing multimodal data and understanding complex human emotions and sentiments.
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by jsendak | Aug 5, 2025 | Computer Science
Expert Commentary: Analyzing the Hierarchical Space-Partitioning Tree Proposal
In this groundbreaking report, the authors suggest a formal specification for organizing all buildings, streets, and administrative areas worldwide into a hierarchical space-partitioning tree using data from OpenStreetMap. This hierarchical structure, encoded into a bigraph, serves as a digital twin of the world, providing a comprehensive representation of street connectivity on a global scale.
The implementation of a tool in OCaml to build bigraphs for regions from any part of the world showcases the potential of this approach to revolutionize the field of geographic information systems. By leveraging open data sources and innovative computational techniques, the proposed system offers a unique perspective on urban planning and spatial analysis.
Algorithmic Improvements and Speed Gains
A key contribution of this report lies in the algorithmic improvements made to open-source bigraph-building tools. By enhancing the efficiency of constructing and transforming large bigraphs, the authors have demonstrated significant speed gains, with up to a 97x improvement in performance observed in certain cases.
These advancements have far-reaching implications for various applications, including urban design, transportation planning, and disaster management. The ability to rapidly generate detailed bigraphs of urban areas can facilitate better decision-making processes and optimize resource allocation in complex urban environments.
Future Directions and Potential Impacts
Looking ahead, the insights presented in this report pave the way for further research in the field of spatial data organization and analysis. As the digital representation of the world becomes increasingly detailed and interconnected, new opportunities for leveraging bigraphs and hierarchical structures are likely to emerge.
By exploring the potential impacts of this innovative approach on urban development, environmental sustainability, and social equity, researchers can unlock novel strategies for addressing the multifaceted challenges of contemporary cities. The fusion of data-driven insights with advanced computational techniques holds immense promise for shaping the future of our built environment.
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