by jsendak | Oct 9, 2025 | Computer Science
Expert Commentary
The study on large language models (LLMs) and their susceptibility to psychological context provides valuable insights into the potential biases and vulnerabilities of these autonomous agents. The findings that exposure to anxiety-inducing narratives led to a decrease in the nutritional quality of shopping baskets across all tested models and budget constraints highlight the impact of emotional states on decision-making processes, even in AI systems.
Understanding Human-Like Emotional Biases
Stress and anxiety are known to affect human decision-making, often leading to impulsive or suboptimal choices. The fact that LLM agents exhibited similar vulnerabilities underscores the need for further research and safeguards when deploying these models in real-world contexts.
Implications for Digital Health and Consumer Safety
These results have significant implications for digital health applications that rely on LLMs for generating recommendations or providing personalized advice. If these models are susceptible to emotional biases, there is a risk that they may inadvertently influence users’ behavior in ways that are not in their best interest.
Ethical Considerations in AI Deployment
The study also raises important ethical considerations regarding the deployment of LLMs in consumer-facing applications. As AI systems become more autonomous and integrated into everyday decision-making processes, ensuring that they are free from biases and vulnerabilities is crucial for maintaining trust and accountability.
Overall, the study sheds light on a new class of vulnerabilities in LLM agents and underscores the importance of improving our understanding of how these models operate in different psychological contexts. Addressing these vulnerabilities will be crucial for the responsible development and deployment of AI technologies in the future.
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by jsendak | Oct 8, 2025 | Computer Science
arXiv:2510.05839v1 Announce Type: new
Abstract: Multimodal fake news detection (MFND) has become an urgent task with the emergence of huge multimodal fake content on social media platforms. Previous studies mainly focus on complex feature extraction and fusion to learn discriminative information from multimodal content. However, in real-world applications, multimedia news may naturally lose some information during dissemination, resulting in modality incompleteness, which is detrimental to the generalization and robustness of existing models. To this end, we propose a novel generic and robust multimodal fusion strategy, termed Multi-expert Modality-incomplete Learning Network (MMLNet), which is simple yet effective. It consists of three key steps: (1) Multi-Expert Collaborative Reasoning to compensate for missing modalities by dynamically leveraging complementary information through multiple experts. (2) Incomplete Modality Adapters compensates for the missing information by leveraging the new feature distribution. (3) Modality Missing Learning leveraging an label-aware adaptive weighting strategy to learn a robust representation with contrastive learning. We evaluate MMLNet on three real-world benchmarks across two languages, demonstrating superior performance compared to state-of-the-art methods while maintaining relative simplicity. By ensuring the accuracy of fake news detection in incomplete modality scenarios caused by information propagation, MMLNet effectively curbs the spread of malicious misinformation. Code is publicly available at https://github.com/zhyhome/MMLNet.
Expert Commentary on Multimodal Fake News Detection
Fake news detection in the era of social media has become an increasingly important and challenging task. With the rise of multimodal content, traditional methods focusing solely on text analysis are no longer sufficient. This article highlights the significance of multimodal fake news detection (MFND) and addresses the issue of modality incompleteness that can affect the accuracy of existing models.
Multi-disciplinary Concepts
The concepts discussed in this article encompass various disciplines such as artificial intelligence, computer vision, natural language processing, and information theory. The fusion of different modalities (text, image, video) requires a multi-disciplinary approach that combines methods from different fields to effectively detect fake news.
Related Fields
Multi-expert Modality-incomplete Learning Network (MMLNet) incorporates concepts and techniques related to multimedia information systems, animations, artificial reality, augmented reality, and virtual realities. By leveraging multiple experts for collaborative reasoning and adapting to incomplete modalities, MMLNet showcases the integration of different fields to enhance fake news detection.
Future Directions
The proposed MMLNet provides a strong foundation for improving the robustness and generalization of fake news detection models in real-world applications. Future research in this area could explore the development of even more sophisticated fusion strategies and adaptive learning techniques to address evolving challenges in misinformation dissemination.
Overall, this article sheds light on the complexities of detecting fake news in a multimodal environment and underscores the importance of multi-disciplinary approaches for effective solutions.
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by jsendak | Oct 8, 2025 | Computer Science
Expert Commentary
Hashemi and Kapur’s algorithm for Groebner basis conversion, which involves truncating polynomials based on monomial order, was a significant development in the field. However, as with any algorithm, there are bound to be cases where it may not produce the desired results. In this case, the presentation of a counterexample is essential for highlighting the limitations of the algorithm and potentially inspiring further refinement.
Analysis of the Counterexample
The counterexample provided serves as a crucial test case for evaluating the effectiveness and reliability of Hashemi and Kapur’s algorithm. By identifying scenarios where the algorithm fails to deliver correct results, researchers can gain valuable insights into its underlying mechanisms and shortcomings. This can lead to the development of more robust and efficient algorithms in the future.
It is essential to note that encountering a counterexample does not diminish the significance of the original algorithm. On the contrary, it is a natural part of the scientific process to test and validate new methods rigorously. The identification of weaknesses or edge cases can ultimately drive innovation and improvements in the field of Groebner basis conversion.
Future Directions
Moving forward, researchers could explore alternative approaches to Groebner basis conversion that address the limitations highlighted by the presented counterexample. This could involve modifying the existing algorithm, incorporating additional criteria for polynomial truncation, or exploring entirely new methodologies. By building upon existing research and learning from counterexamples, the field can continue to evolve and advance.
In conclusion, the presentation of a counterexample to Hashemi and Kapur’s algorithm for Groebner basis conversion underscores the importance of rigorous testing and validation in computational mathematics. While setbacks are inevitable, they provide valuable opportunities for learning and improvement. By addressing the challenges posed by counterexamples, researchers can push the boundaries of knowledge and contribute to the development of more robust algorithms in the future.
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by jsendak | Oct 7, 2025 | Computer Science
arXiv:2510.03965v1 Announce Type: new
Abstract: Predicting corporate earnings surprises is a profitable yet challenging task, as accurate forecasts can inform significant investment decisions. However, progress in this domain has been constrained by a reliance on expensive, proprietary, and text-only data, limiting the development of advanced models. To address this gap, we introduce textbf{FinCall-Surprise} (Financial Conference Call for Earning Surprise Prediction), the first large-scale, open-source, and multi-modal dataset for earnings surprise prediction. Comprising 2,688 unique corporate conference calls from 2019 to 2021, our dataset features word-to-word conference call textual transcripts, full audio recordings, and corresponding presentation slides. We establish a comprehensive benchmark by evaluating 26 state-of-the-art unimodal and multi-modal LLMs. Our findings reveal that (1) while many models achieve high accuracy, this performance is often an illusion caused by significant class imbalance in the real-world data. (2) Some specialized financial models demonstrate unexpected weaknesses in instruction-following and language generation. (3) Although incorporating audio and visual modalities provides some performance gains, current models still struggle to leverage these signals effectively. These results highlight critical limitations in the financial reasoning capabilities of existing LLMs and establish a challenging new baseline for future research.
Expert Commentary: Exploring the Multi-Disciplinary Nature of Financial Earnings Surprise Prediction
In the realm of corporate finance, predicting earnings surprises is a critical task that can have significant implications for investment decisions. The introduction of the FinCall-Surprise dataset represents a groundbreaking development in this field, as it combines text, audio, and visual data from corporate conference calls to create a multi-modal dataset for earnings surprise prediction.
This approach highlights the multi-disciplinary nature of the concepts involved in financial forecasting. By incorporating a variety of modalities, including textual transcripts, audio recordings, and presentation slides, researchers are able to capture a more comprehensive view of the data and potentially uncover hidden patterns and insights that may not be apparent from a single source of information. This multi-modal approach aligns with the broader field of multimedia information systems, which explores the integration of various types of media to enhance understanding and decision-making.
Furthermore, the evaluation of 26 state-of-the-art unimodal and multi-modal language models (LLMs) reveals interesting insights into the performance of these models in the context of financial earnings surprise prediction. The findings indicate that while many models achieve high accuracy, there are significant challenges posed by class imbalances in real-world data. Additionally, some specialized financial models exhibit unexpected weaknesses in instruction-following and language generation, underscoring the need for further refinement and improvement in this area.
From a broader perspective, the results of this study have implications for the fields of Artificial Reality, Augmented Reality, and Virtual Realities as well. The incorporation of audio and visual modalities in financial forecasting represents a step towards creating more immersive and interactive experiences for analysts and investors. However, the challenges in leveraging these signals effectively highlight the complexities involved in bridging the gap between traditional financial analysis and emerging technologies.
Overall, the FinCall-Surprise dataset and the insights gained from evaluating various LLMs shed light on the critical limitations of existing models in the context of financial reasoning and set a challenging new baseline for future research in this field.
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by jsendak | Oct 7, 2025 | Computer Science
Expert Commentary: The Evolution of Innovation Ecosystems
As the world continues to rapidly evolve, so too must our understanding of innovation ecosystems and the policies that govern them. The article’s findings highlight the shifting landscape of innovation, demonstrating how the traditional model of building upon foundational work within a single field is giving way to a more interconnected and collaborative approach.
Measuring the Influence of Innovations
The development of new measures to decompose the influence of innovations is a crucial step in understanding how innovation is progressing. By categorizing innovations as foundational, extensions, or generalizations, we can better grasp the impact of each type on the overall ecosystem. This allows policymakers and researchers to identify trends and make informed decisions about where to focus resources and support.
The Rise of Combinatorial Innovation
The study’s findings underscore the increasing importance of cross-disciplinary collaboration in driving innovation. As the world becomes more interconnected through the web, social media, and artificial intelligence, the ability to synthesize and modularize contributions from distant fields becomes paramount. This shift towards combinatorial innovation highlights the need for policies that promote collaboration and break down silos between disciplines.
Implications for Science Policy
With the locus of innovation moving from within fields to across the system as a whole, science policy must adapt to support this new paradigm. Policymakers should prioritize initiatives that foster interdisciplinary collaboration, incentivize knowledge sharing, and create opportunities for researchers to draw upon diverse expertise. By embracing these changes, we can ensure that innovation ecosystems continue to drive advancements in human health, welfare, security, and prosperity.
“Innovation ecosystems require careful policy stewardship to drive sustained advance in human health, welfare, security and prosperity.” – Expert Commentary
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by jsendak | Oct 6, 2025 | Computer Science
arXiv:2510.02746v1 Announce Type: new
Abstract: Music scores are used to precisely store music pieces for transmission and preservation. To represent and manipulate these complex objects, various formats have been tailored for different use cases. While music notation follows specific rules, digital formats usually enforce them leniently. Hence, digital music scores widely vary in quality, due to software and format specificity, conversion issues, and dubious user inputs. Problems range from minor engraving discrepancies to major notation mistakes. Yet, data quality is a major issue when dealing with musical information extraction and retrieval. We present an automated approach to detect notational errors, aiming at precisely localizing defects in scores. We identify two types of errors: i) rhythm/time inconsistencies in the encoding of individual musical elements, and ii) contextual errors, i.e. notation mistakes that break commonly accepted musical rules. We implement the latter using a modular state machine that can be easily extended to include rules representing the usual conventions from the common Western music notation. Finally, we apply this error-detection method to the piano score dataset ASAP. We highlight that around 40% of the scores contain at least one notational error, and manually fix multiple of them to enhance the dataset’s quality.
Expert Commentary: Music Score Quality Improvement Through Automated Error Detection
Music scores have long been used as a means to accurately store and preserve musical compositions. While traditional music notation follows strict rules, the advent of digital formats has introduced new challenges when it comes to ensuring the quality and accuracy of these scores. This article highlights the importance of data quality in the context of musical information extraction and retrieval, emphasizing the need for automated approaches to detect and correct notational errors.
The multidisciplinary nature of this work is evident in the intersection of music theory, computer science, and data analysis. By identifying rhythm/time inconsistencies and contextual errors in music scores, the researchers have developed a modular state machine that can effectively pinpoint deviations from commonly accepted musical conventions. This approach not only enhances the quality of the dataset but also showcases the potential for automated tools to improve the overall integrity of digital music scores.
From a broader perspective, this research contributes to the field of multimedia information systems by demonstrating the application of automated error detection in the realm of music scores. The concepts explored in this study have implications for other areas such as animations, artificial reality, augmented reality, and virtual realities, where precise representation and manipulation of complex objects are essential.
Future Implications and Directions
- Further refinement of automated error detection algorithms to address a wider range of notational errors.
- Exploration of how this approach can be applied to other types of musical scores beyond piano music.
- Integration of machine learning techniques to enhance the accuracy and efficiency of error detection processes.
- Collaboration with experts in music theory and information retrieval to validate the effectiveness of the proposed method.
In conclusion, this study represents a significant step towards improving the quality and reliability of digital music scores through automated error detection. By leveraging interdisciplinary expertise, researchers have demonstrated the potential for innovative solutions that bridge the gap between traditional music notation and modern digital formats.
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