Latest AI Papers July 7 2025 Agent Technology Misinformation Detection And More
This article provides an in-depth overview of the latest research papers published on July 7, 2025, focusing on key areas within artificial intelligence. For an enhanced reading experience and access to more papers, please visit the Github page.
Agent Technology: Advancing Autonomous Systems
The agent technology field continues to rapidly evolve, with researchers exploring various facets of autonomous systems. This section highlights the latest advancements in agent-based research, covering topics from web search to business process management and hardware design. The development of robust and reliable agents is crucial for a wide range of applications, and these papers offer valuable insights into the current state of the art.
From Web Search towards Agentic Deep Research: Incentivizing Search with Reasoning Agents
This groundbreaking paper, titled "From Web Search towards Agentic Deep Research: Incentivizing Search with Reasoning Agents," delves into the integration of reasoning agents with web search functionalities. The core idea revolves around incentivizing search processes to achieve deeper research outcomes. By equipping agents with reasoning capabilities, the search becomes more targeted and efficient. This approach has the potential to revolutionize how information is gathered and synthesized from the web, making research processes more streamlined and effective. The authors propose innovative methods to motivate agents to explore and evaluate information, ultimately leading to more comprehensive and insightful results.
Agentic Business Process Management: Practitioner Perspectives on Agent Governance in Business Processes
"Agentic Business Process Management: Practitioner Perspectives on Agent Governance in Business Processes" offers a practical perspective on implementing agent technology within business environments. The paper, accepted for Responsible BPM 2025, focuses on the governance aspects of agent integration in business processes. It presents practitioner viewpoints, providing a real-world understanding of the challenges and opportunities in this domain. The research explores how agent governance can be effectively managed to ensure compliance, security, and ethical considerations are addressed. This is particularly relevant as businesses increasingly adopt AI-driven solutions, requiring a structured approach to govern these intelligent systems.
Establishing Best Practices for Building Rigorous Agentic Benchmarks
Creating robust benchmarks is essential for evaluating and comparing agentic systems. The paper "Establishing Best Practices for Building Rigorous Agentic Benchmarks" addresses this need by proposing guidelines for developing comprehensive benchmarks. Spanning 39 pages and featuring 15 tables and 6 figures, this work provides a detailed framework for assessing agent performance. Rigorous benchmarks ensure that agent capabilities are accurately measured, fostering fair comparisons and driving progress in the field. The authors emphasize the importance of considering various factors, such as task complexity, environmental dynamics, and agent autonomy, in the benchmark design process.
KERAP: A Knowledge-Enhanced Reasoning Approach for Accurate Zero-shot Diagnosis Prediction Using Multi-agent LLMs
"KERAP: A Knowledge-Enhanced Reasoning Approach for Accurate Zero-shot Diagnosis Prediction Using Multi-agent LLMs" introduces a novel approach to zero-shot diagnosis prediction by leveraging multi-agent Large Language Models (LLMs). This method, known as KERAP, enhances reasoning capabilities through knowledge integration, enabling more accurate diagnoses without prior training on specific datasets. Knowledge-enhanced reasoning is crucial in medical diagnosis, where vast amounts of information must be processed to arrive at informed decisions. The paper demonstrates the effectiveness of KERAP in improving the accuracy and reliability of diagnostic systems.
Multi-agent Auditory Scene Analysis
The paper "Multi-agent Auditory Scene Analysis", submitted to Applied Intelligence, explores the application of multi-agent systems in auditory scene analysis. This research focuses on how multiple agents can collaborate to interpret complex audio environments, a critical capability for applications like smart surveillance and assistive technologies. By distributing the task of auditory analysis across multiple agents, the system can handle noisy and dynamic environments more effectively. This multi-agent approach offers a robust solution for understanding and interpreting auditory information.
Mind2Web 2: Evaluating Agentic Search with Agent-as-a-Judge
"Mind2Web 2: Evaluating Agentic Search with Agent-as-a-Judge" introduces an innovative evaluation framework for agentic search. The paper proposes using an "Agent-as-a-Judge" approach, where agents evaluate the performance of other agents. This method offers a more objective and scalable way to assess the effectiveness of search agents. The project, with a dedicated homepage at https://osu-nlp-group.github.io/Mind2Web-2/, aims to advance the evaluation methodologies for agentic systems. By using agents to judge other agents, the framework provides a nuanced understanding of agent capabilities and limitations.
Control at Stake: Evaluating the Security Landscape of LLM-Driven Email Agents
The security of LLM-driven email agents is a significant concern, and the paper "Control at Stake: Evaluating the Security Landscape of LLM-Driven Email Agents" addresses this critical issue. The research evaluates the security vulnerabilities and potential risks associated with using LLMs in email communication. This work highlights the need for robust security measures to protect sensitive information and prevent malicious attacks. The evaluation of the security landscape is crucial for the responsible deployment of LLM-based email agents.
Multi-Agent Reinforcement Learning for Dynamic Pricing in Supply Chains: Benchmarking Strategic Agent Behaviours under Realistically Simulated Market Conditions
"Multi-Agent Reinforcement Learning for Dynamic Pricing in Supply Chains: Benchmarking Strategic Agent Behaviours under Realistically Simulated Market Conditions" explores the use of multi-agent reinforcement learning in optimizing dynamic pricing strategies within supply chains. The research benchmarks strategic agent behaviors under realistically simulated market conditions, providing insights into effective pricing strategies. Dynamic pricing is a critical aspect of supply chain management, and this paper offers a data-driven approach to optimizing pricing decisions. The use of reinforcement learning allows agents to adapt to changing market dynamics, leading to improved profitability and efficiency.
Hey AI, Generate Me a Hardware Code! Agentic AI-based Hardware Design & Verification
This innovative paper, "Hey AI, Generate Me a Hardware Code! Agentic AI-based Hardware Design & Verification," investigates the application of agentic AI in hardware design and verification. Scheduled to appear at the 38th SBC/SBMicro/IEEE Symposium on Integrated Circuits and Systems Design (SBCCI), the research explores how AI agents can generate hardware code. This approach has the potential to transform hardware design, making it more efficient and accessible. The paper demonstrates the feasibility of using AI agents to automate the complex tasks involved in hardware development.
VRAgent-R1: Boosting Video Recommendation with MLLM-based Agents via Reinforcement Learning
"VRAgent-R1: Boosting Video Recommendation with MLLM-based Agents via Reinforcement Learning" introduces a video recommendation system powered by MLLM-based agents using reinforcement learning. This approach enhances the accuracy and personalization of video recommendations. Reinforcement learning allows the agents to learn user preferences and adapt recommendations accordingly. By leveraging MLLMs, the system can better understand video content and user interests, leading to improved recommendation performance.
DynamiCare: A Dynamic Multi-Agent Framework for Interactive and Open-Ended Medical Decision-Making
"DynamiCare: A Dynamic Multi-Agent Framework for Interactive and Open-Ended Medical Decision-Making" presents a multi-agent framework for medical decision-making. The 16-page paper details DynamiCare, a system designed to facilitate interactive and open-ended medical decision processes. This framework aims to support healthcare professionals in making informed decisions by providing a collaborative platform. The dynamic nature of DynamiCare allows it to adapt to changing medical scenarios, making it a valuable tool in complex medical environments.
MedAide: Information Fusion and Anatomy of Medical Intents via LLM-based Agent Collaboration
"MedAide: Information Fusion and Anatomy of Medical Intents via LLM-based Agent Collaboration" explores the use of LLM-based multi-agent collaboration in medical applications. The paper focuses on information fusion and the anatomy of medical intents, aiming to improve the accuracy and efficiency of medical decision support systems. MedAide leverages LLMs to process and interpret medical information, enabling agents to collaborate effectively. This approach enhances the ability to understand and respond to complex medical needs.
WebSailor: Navigating Super-human Reasoning for Web Agent
"WebSailor: Navigating Super-human Reasoning for Web Agent" introduces a web agent designed to navigate the web with super-human reasoning capabilities. This research aims to develop agents that can effectively process and utilize web-based information. WebSailor represents a significant step towards creating more intelligent and autonomous web agents. The ability to reason at a super-human level allows the agent to handle complex tasks and extract valuable insights from the vast amount of data available on the web.
AI Research Agents for Machine Learning: Search, Exploration, and Generalization in MLE-bench
This paper, "AI Research Agents for Machine Learning: Search, Exploration, and Generalization in MLE-bench," investigates the use of AI research agents in machine learning. The study focuses on search, exploration, and generalization within the MLE-bench environment. The code for this project is available at https://github.com/facebookresearch/aira-dojo. This research explores how AI agents can automate and enhance the machine learning research process, leading to more efficient and effective model development.
Embodied AI Agents: Modeling the World
"Embodied AI Agents: Modeling the World" explores the critical aspect of world modeling for embodied AI agents. This research focuses on how agents can create and use models of their environment to interact more effectively with the world. Embodied AI agents need to understand their surroundings to perform tasks and achieve goals. This paper provides insights into the techniques and challenges involved in creating effective world models for AI agents.
Misinformation Detection: Combating False Information
Misinformation detection is a crucial area of research in today's digital age. The spread of false information can have significant societal impacts, making it essential to develop effective detection and mitigation strategies. This section reviews the latest papers focusing on various aspects of misinformation detection, from multimodal approaches to low-resource settings.
Towards Explainable Bilingual Multimodal Misinformation Detection and Localization
This paper, "Towards Explainable Bilingual Multimodal Misinformation Detection and Localization," addresses the critical need for explainable methods in misinformation detection. The research focuses on developing techniques that can detect and localize misinformation in bilingual content, providing insights into the reasoning behind the detection. Explainability is vital for building trust in misinformation detection systems, as it allows users to understand why certain information is flagged as false. This paper contributes to the development of transparent and reliable misinformation detection tools.
MisinfoTeleGraph: Network-driven Misinformation Detection for German Telegram Messages
"MisinfoTeleGraph: Network-driven Misinformation Detection for German Telegram Messages" presents a network-driven approach to detect misinformation on the German Telegram platform. The research leverages network analysis techniques to identify patterns and behaviors indicative of misinformation spread. This approach recognizes that misinformation often propagates through social networks, making network analysis a valuable tool for detection. The paper provides insights into the specific challenges and strategies for detecting misinformation in the context of German Telegram messages.
Multimodal Misinformation Detection Using Early Fusion of Linguistic, Visual, and Social Features
The paper "Multimodal Misinformation Detection Using Early Fusion of Linguistic, Visual, and Social Features" explores the use of multiple modalities to detect misinformation. By integrating linguistic, visual, and social features, the research aims to improve the accuracy and robustness of detection systems. Multimodal approaches are particularly effective as they can capture different aspects of misinformation, such as deceptive language, manipulated images, and suspicious social interactions. This paper demonstrates the benefits of combining these features for enhanced misinformation detection.
E-FreeM2: Efficient Training-Free Multi-Scale and Cross-Modal News Verification via MLLMs
"E-FreeM2: Efficient Training-Free Multi-Scale and Cross-Modal News Verification via MLLMs", accepted to AsiaCCS 2025 @ SCID, introduces an efficient, training-free method for news verification. The approach, named E-FreeM2, leverages Multi-Modal Large Language Models (MLLMs) to perform multi-scale and cross-modal verification without the need for extensive training data. This method offers a practical solution for quickly verifying news content by leveraging the pre-trained knowledge of MLLMs. The absence of a training requirement makes E-FreeM2 a valuable tool for rapid misinformation detection.
A Guide to Misinformation Detection Data and Evaluation
"A Guide to Misinformation Detection Data and Evaluation" provides a comprehensive overview of the data and evaluation methods used in misinformation detection research. This paper serves as a valuable resource for researchers, offering guidance on data selection, preprocessing, and evaluation metrics. Standardized data and evaluation procedures are crucial for ensuring the comparability and reproducibility of research findings. This guide helps to promote best practices in the field of misinformation detection.
GraphRAG-Causal: A novel graph-augmented framework for causal reasoning and annotation in news
"GraphRAG-Causal: A novel graph-augmented framework for causal reasoning and annotation in news" introduces a novel framework for causal reasoning and annotation in news content. The 18-page paper, featuring 8 figures, details how graph-based methods can enhance the understanding of causal relationships in news articles. Causal reasoning is essential for accurately assessing the credibility of news information. By using graph-augmented techniques, the framework can identify and analyze causal links, aiding in the detection of misinformation.
A Survey of Datasets for Information Diffusion Tasks
"A Survey of Datasets for Information Diffusion Tasks" provides a comprehensive overview of datasets used in information diffusion research. This survey is a valuable resource for researchers, offering insights into the characteristics and availability of various datasets. Information diffusion is closely related to the spread of misinformation, making these datasets relevant for detection research. The paper helps researchers identify appropriate datasets for their studies.
Combating Misinformation in the Arab World: Challenges & Opportunities
"Combating Misinformation in the Arab World: Challenges & Opportunities" addresses the specific challenges and opportunities in combating misinformation in the Arab world. The paper focuses on disinformation, misinformation, factuality, harmfulness, and fake news in this context. Regional nuances are crucial in misinformation detection, as cultural and linguistic factors can influence the spread and impact of false information. This paper provides valuable insights into these factors, helping to tailor detection and mitigation strategies for the Arab world.
Fact-R1: Towards Explainable Video Misinformation Detection with Deep Reasoning
The paper "Fact-R1: Towards Explainable Video Misinformation Detection with Deep Reasoning" introduces a deep reasoning approach for explainable video misinformation detection. The 28-page paper, featuring 27 figures, details Fact-R1, a system designed to provide transparent reasoning behind its detection decisions. Explainable AI is particularly important in video misinformation detection, where complex visual and audio cues must be analyzed. Fact-R1 aims to make the detection process more understandable and trustworthy.
Truth over Tricks: Measuring and Mitigating Shortcut Learning in Misinformation Detection
"Truth over Tricks: Measuring and Mitigating Shortcut Learning in Misinformation Detection" addresses the issue of shortcut learning in misinformation detection models. The research focuses on measuring and mitigating this phenomenon, where models rely on superficial cues rather than genuine understanding. Shortcut learning can lead to poor generalization performance, making it crucial to develop strategies to address this issue. This paper offers insights into how to build more robust and reliable misinformation detection models.
Unified Large Language Models for Misinformation Detection in Low-Resource Linguistic Settings
"Unified Large Language Models for Misinformation Detection in Low-Resource Linguistic Settings" explores the use of Large Language Models (LLMs) for misinformation detection in low-resource linguistic settings. The research focuses on developing unified models that can effectively detect misinformation in languages with limited data. Low-resource settings pose a significant challenge for AI research, making this paper particularly relevant. The use of LLMs offers a promising approach to addressing this challenge.
RAEmoLLM: Retrieval Augmented LLMs for Cross-Domain Misinformation Detection Using In-Context Learning Based on Emotional Information
"RAEmoLLM: Retrieval Augmented LLMs for Cross-Domain Misinformation Detection Using In-Context Learning Based on Emotional Information", accepted by ACL 2025 (Main), introduces RAEmoLLM, a retrieval-augmented approach for cross-domain misinformation detection. The method leverages emotional information and in-context learning to enhance LLM performance. Emotional information can be a valuable cue in detecting misinformation, as emotionally charged content is often used to manipulate audiences. RAEmoLLM demonstrates the effectiveness of incorporating emotional context into the detection process.
CMIE: Combining MLLM Insights with External Evidence for Explainable Out-of-Context Misinformation Detection
"CMIE: Combining MLLM Insights with External Evidence for Explainable Out-of-Context Misinformation Detection" explores the combination of Multi-Modal Large Language Model (MLLM) insights with external evidence for explainable misinformation detection. The research focuses on detecting misinformation that is taken out of context, a common tactic used to spread false information. Contextual understanding is crucial in misinformation detection, and CMIE aims to provide a more comprehensive analysis by integrating MLLM insights with external evidence.
Debate-to-Detect: Reformulating Misinformation Detection as a Real-World Debate with Large Language Models
"Debate-to-Detect: Reformulating Misinformation Detection as a Real-World Debate with Large Language Models" proposes a novel approach to misinformation detection by reformulating it as a real-world debate. The research uses Large Language Models (LLMs) to simulate debates, aiming to identify misinformation through argumentation and counter-argumentation. This approach leverages the reasoning capabilities of LLMs to analyze claims and evidence, providing a dynamic and interactive method for misinformation detection.
Yesterday's News: Benchmarking Multi-Dimensional Out-of-Distribution Generalization of Misinformation Detection Models
"Yesterday's News: Benchmarking Multi-Dimensional Out-of-Distribution Generalization of Misinformation Detection Models" focuses on benchmarking the generalization performance of misinformation detection models. The research addresses the challenge of out-of-distribution generalization, where models must perform well on data that differs from their training set. Generalization ability is crucial for the practical application of misinformation detection systems. This paper provides insights into the factors that influence generalization performance and offers recommendations for building more robust models.
Large Language Models (LLMs): Advancing Natural Language Processing
Large Language Models (LLMs) continue to be a central focus in AI research, driving advancements in natural language processing and related fields. This section highlights the latest papers exploring various aspects of LLMs, from fine-tuning techniques to security concerns and applications in specific domains.
MOTIF: Modular Thinking via Reinforcement Fine-tuning in LLMs
The paper "MOTIF: Modular Thinking via Reinforcement Fine-tuning in LLMs" introduces a novel approach to enhance the thinking capabilities of LLMs. The method, known as MOTIF, uses reinforcement fine-tuning to promote modular thinking. Modular thinking involves breaking down complex problems into smaller, manageable parts, a critical skill for intelligent systems. This research demonstrates how reinforcement learning can be used to improve the reasoning and problem-solving abilities of LLMs.
Self-Correction Bench: Revealing and Addressing the Self-Correction Blind Spot in LLMs
"Self-Correction Bench: Revealing and Addressing the Self-Correction Blind Spot in LLMs" presents a comprehensive evaluation of the self-correction capabilities of LLMs. The 31-page paper, featuring 18 figures, reveals a self-correction blind spot in these models and proposes methods to address this issue. Self-correction is a crucial ability for AI systems, as it allows them to identify and rectify errors. This research provides valuable insights into the limitations of current LLMs and offers strategies for improvement.
KERAP: A Knowledge-Enhanced Reasoning Approach for Accurate Zero-shot Diagnosis Prediction Using Multi-agent LLMs
As mentioned earlier, "KERAP: A Knowledge-Enhanced Reasoning Approach for Accurate Zero-shot Diagnosis Prediction Using Multi-agent LLMs" introduces a method for zero-shot diagnosis prediction. It leverages multi-agent LLMs, enhancing reasoning capabilities through knowledge integration. This paper highlights the versatility of LLMs in medical diagnosis, showcasing their ability to improve accuracy and reliability.
Who's Sorry Now: User Preferences Among Rote, Empathic, and Explanatory Apologies from LLM Chatbots
"Who's Sorry Now: User Preferences Among Rote, Empathic, and Explanatory Apologies from LLM Chatbots" explores user preferences for different types of apologies from LLM chatbots. The research investigates rote, empathic, and explanatory apologies, aiming to understand which type is most effective in human-computer interactions. User experience is a critical factor in the adoption of chatbot technology. This paper provides insights into how to design chatbots that can effectively handle apologies and maintain positive user relationships.
Early Signs of Steganographic Capabilities in Frontier LLMs
"Early Signs of Steganographic Capabilities in Frontier LLMs" investigates the potential for steganography in frontier LLMs. Steganography is the practice of concealing messages within other messages or media. This research explores whether LLMs can be used to hide information within their output, raising significant security concerns. The paper highlights the need for vigilance and research into the potential misuse of LLMs for covert communication.
Meta SecAlign: A Secure Foundation LLM Against Prompt Injection Attacks
"Meta SecAlign: A Secure Foundation LLM Against Prompt Injection Attacks" introduces Meta SecAlign, a secure foundation LLM designed to protect against prompt injection attacks. Prompt injection is a type of attack where malicious input is used to manipulate the behavior of an LLM. This research demonstrates the importance of building security into the foundation of LLMs to prevent potential vulnerabilities.
Gradient-Based Model Fingerprinting for LLM Similarity Detection and Family Classification
"Gradient-Based Model Fingerprinting for LLM Similarity Detection and Family Classification" presents a method for fingerprinting LLMs based on their gradients. This technique can be used to detect similarity between models and classify them into families. Model fingerprinting is valuable for identifying copyright infringement, plagiarism, and other malicious activities. This research provides a tool for monitoring and managing the proliferation of LLMs.
Next-Token Prediction Task Assumes Optimal Data Ordering for LLM Training in Proof Generation
"Next-Token Prediction Task Assumes Optimal Data Ordering for LLM Training in Proof Generation" explores the impact of data ordering on LLM training for proof generation. The research demonstrates that the next-token prediction task assumes optimal data ordering, which may not always be the case in real-world scenarios. Data ordering can significantly affect the performance of LLMs. This paper provides insights into the importance of careful data preparation and ordering in training LLMs for specific tasks.
Control at Stake: Evaluating the Security Landscape of LLM-Driven Email Agents
As previously mentioned, "Control at Stake: Evaluating the Security Landscape of LLM-Driven Email Agents" addresses the security landscape of LLM-driven email agents. It highlights the vulnerabilities and risks associated with using LLMs in email communication, emphasizing the need for robust security measures.
Can LLMs Identify Critical Limitations within Scientific Research? A Systematic Evaluation on AI Research Papers
"Can LLMs Identify Critical Limitations within Scientific Research? A Systematic Evaluation on AI Research Papers" investigates the ability of LLMs to identify critical limitations within scientific research. The research evaluates the performance of LLMs on AI research papers, assessing their capacity to analyze and critique scientific work. This paper explores the potential of LLMs to support scientific peer review and advance research quality.
FlowSpec: Continuous Pipelined Speculative Decoding for Efficient Distributed LLM Inference
"FlowSpec: Continuous Pipelined Speculative Decoding for Efficient Distributed LLM Inference" introduces FlowSpec, a technique for efficient distributed LLM inference. The 16-page paper, with the last 3 pages as an appendix, details a continuous pipelined speculative decoding approach. Efficient inference is crucial for deploying LLMs in real-world applications. FlowSpec offers a method to accelerate inference by using speculative decoding in a distributed environment.
Symbolic or Numerical? Understanding Physics Problem Solving in Reasoning LLMs
"Symbolic or Numerical? Understanding Physics Problem Solving in Reasoning LLMs" explores how LLMs solve physics problems, focusing on the distinction between symbolic and numerical reasoning. The research aims to understand the underlying mechanisms of LLM reasoning in scientific contexts. Physics problem-solving requires a combination of symbolic and numerical skills. This paper provides insights into how LLMs approach these problems, contributing to a better understanding of their reasoning abilities.
MedAide: Information Fusion and Anatomy of Medical Intents via LLM-based Agent Collaboration
As previously mentioned, "MedAide: Information Fusion and Anatomy of Medical Intents via LLM-based Agent Collaboration" explores the use of LLM-based multi-agent collaboration in medical applications. This paper highlights the potential of LLMs in improving medical decision support systems.
Aerial Vision-and-Language Navigation via Semantic-Topo-Metric Representation Guided LLM Reasoning
"Aerial Vision-and-Language Navigation via Semantic-Topo-Metric Representation Guided LLM Reasoning" explores the use of LLMs in aerial vision-and-language navigation. The research focuses on guiding LLM reasoning using semantic-topo-metric representations. This approach aims to improve the navigation capabilities of aerial vehicles by combining vision and language processing with LLM reasoning.
Continual Gradient Low-Rank Projection Fine-Tuning for LLMs
"Continual Gradient Low-Rank Projection Fine-Tuning for LLMs", accepted by ACL 2025 main, introduces a fine-tuning technique for LLMs. The 15-page paper, featuring 6 figures, details Continual Gradient Low-Rank Projection Fine-Tuning, a method designed to improve the efficiency and effectiveness of fine-tuning. Fine-tuning is a crucial step in adapting LLMs to specific tasks. This research offers a technique to enhance the fine-tuning process, leading to improved performance.
Representation Learning: Enhancing Data Understanding
Representation learning is a cornerstone of modern AI, focusing on how to effectively represent data for machine learning models. This section reviews recent papers exploring various aspects of representation learning, from urban region pre-training to causal representation learning and self-supervised methods.
Urban Region Pre-training and Prompting: A Graph-based Approach
"Urban Region Pre-training and Prompting: A Graph-based Approach", accepted at KDD 2025, explores a graph-based approach to urban region pre-training and prompting. This research focuses on how to effectively represent urban data for machine learning tasks. Urban data analysis is crucial for city planning, resource management, and other applications. This paper offers a novel method for pre-training models on urban data, leading to improved performance in downstream tasks.
MvHo-IB: Multi-View Higher-Order Information Bottleneck for Brain Disorder Diagnosis
"MvHo-IB: Multi-View Higher-Order Information Bottleneck for Brain Disorder Diagnosis", accepted by MICCAI-25, introduces a multi-view higher-order information bottleneck method for brain disorder diagnosis. The code is available at https://github.com/zky04/MvHo-IB. This research focuses on improving the accuracy of brain disorder diagnosis by leveraging multi-view data and information bottleneck techniques. The paper demonstrates the effectiveness of this approach in medical applications.
ASDA: Audio Spectrogram Differential Attention Mechanism for Self-Supervised Representation Learning
"ASDA: Audio Spectrogram Differential Attention Mechanism for Self-Supervised Representation Learning", accepted at Interspeech2025, presents an audio spectrogram differential attention mechanism for self-supervised representation learning. This research focuses on improving the representation of audio data through self-supervision. The ASDA method enhances the model's ability to capture relevant features from audio spectrograms, leading to improved performance in audio-related tasks.
Down with the Hierarchy: The 'H' in HNSW Stands for "Hubs"
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