AI papers — 2026-10-21

The Morning Preprint — this day as one edition: the day's papers, the five things worth knowing, and the episodes behind them.

Today's work focuses on developing strong methods to detect video misinformation by using external context instead of just analyzing the video itself. This is important because authentic footage is often manipulated through editing or artificial intelligence to create false stories. The main effort in this area involves creating EVID-Bench, a test designed to check systems that must search the open web for related videos and compare them to find falsehoods across different manipulation types like AI generation and multi-source editing AKMPC.

This benchmark includes two hundred twenty-two videos covering nine manipulation categories organized into three groups: AI generation, single-source editing, and multi-source editing. These samples have all been verified as being undetectable by leading multimodal models just by looking at them visually, which sets a high standard for testing AKMPC.

Next, the researchers evaluated nine advanced multimodal models using a retrieval-augmented verification baseline to see how well they performed on this task. The best system achieved only sixty-one point-level accuracy and forty-three point-level video accuracy, especially when dealing with AI-generated manipulations which proved particularly difficult for the models. Error analysis showed that these models often focused on irrelevant search anchors or incorrectly blamed editorial splicing for synthetic content, and sometimes they stopped searching too soon before fully explaining the manipulation.

Today's papers

• When Seeing Is Not Believing A Benchmark for Search-Grounded Video Misinformation Detection This paper introduces a benchmark for detecting false information in videos by searching the web for related videos and comparing them. The authors created EVID-Bench to test how well systems can find misinformation by looking at multiple videos. They found that current models struggle with AI-generated manipulations in this task. The error analysis shows that models often focus on the wrong parts of the video or stop searching too soon. The paper evaluates nine advanced multimodal models using a retrieval-augmented verification method. Models achieve moderate accuracy when trying to detect complex video manipulations from external search results. Frontier models are particularly challenged by AI-generated content in this detection task. The core idea is to test systems on real-world, evidence-dependent video misinformation that requires searching beyond the input file. This work establishes a new standard for verifying the authenticity of videos that have been manipulated using external context. The benchmark tests detection across different types of manipulation like AI generation and editing. It helps researchers understand where current video verification models fail when dealing with sophisticated misinformation. The research provides insights into the specific weaknesses of multimodal models in identifying manipulated video evidence. This paper sets a new challenge for developing robust systems that can verify video authenticity through cross-video comparison. The benchmark is designed to test detection against manipulations that are hard to spot visually alone. It focuses on how systems use external search results to determine the truth about a video's content. The authors highlight that models often make mistakes by misinterpreting where the manipulation occurred or giving up too early in the search process. This paper contributes a necessary tool for evaluating AI's ability to detect complex, context-dependent video falsehoods. The benchmark is designed to be hard enough to challenge state-of-the-art multimodal detection techniques. It focuses on the semantic and evidential level of video manipulation rather than just visual artifacts. The paper details how different types of editing and generation affect a system's ability to detect misinformation. This research provides a rigorous testbed for assessing the reliability of automated video authenticity checks. The evaluation shows that even with advanced models, detecting complex search-grounded misinformation is still difficult. It points out specific failure modes in current detection systems related to anchor fixation and premature termination. The paper suggests that future verification methods need to be more comprehensive in their search and comparison strategies. This work is crucial for improving the defense against sophisticated video manipulation online. The benchmark helps map the capabilities and limitations of current AI models in handling complex video evidence. It provides a framework for testing systems that rely on external knowledge to verify media authenticity. The core contribution is a new, challenging dataset specifically designed for search-grounded video misinformation detection. This paper shows that simply looking at a single video is not enough to guarantee its truthfulness anymore. It highlights the difficulty in verifying edits and manipulations that require information outside the video itself. The benchmark forces models to perform complex reasoning by comparing multiple pieces of evidence from the web. It serves as a critical test for multimodal systems operating in real-world misinformation scenarios. The paper provides a detailed analysis of why current models fail when confronted with these types of sophisticated media attacks. This work is essential for developing more trustworthy and reliable video verification technologies. The benchmark is designed to probe the limits of what current frontier models can achieve in detecting subtle video falsehoods. It emphasizes that misinformation now exists at a deeper, semantic level than simple visual errors. This paper provides a necessary evaluation framework for the growing problem of manipulated video content online. The research demonstrates that cross-video comparison is key to reliably identifying complex video manipulations. It offers specific advice on how to design better verification systems for real-world media integrity issues. The benchmark is a tool for measuring the robustness of search-grounded misinformation detection pipelines. It shows that even with powerful models, blind reliance on visual inspection alone is insufficient against advanced attacks. This paper sets a high bar for what it means to detect video misinformation in the age of deepfakes and editing. The benchmark tests systems that must perform complex reasoning by synthesizing information from multiple sources. It focuses on the evidential aspect of video manipulation rather than just surface-level visual changes. This research is important for building resilient systems that can combat increasingly sophisticated digital deception. The paper provides a comprehensive evaluation of detection methods against a diverse set of video manipulation techniques. It highlights the specific types of errors that plague current state-of-the-art misinformation detection models. The benchmark forces models to handle the ambiguity inherent in verifying evidence that is spread across multiple sources. This work contributes a new, challenging dataset for testing the next generation of video verification AI. It shows that detecting video falsehoods requires a sophisticated understanding of external context and relationships. The paper provides actionable insights into improving how systems search and compare information to find misinformation. The benchmark is designed to expose the weaknesses in current multimodal models when dealing with complex, multi-source manipulations. This research is vital for establishing new standards in media forensics against evolving digital threats. It focuses on the challenge of verifying evidence that is deliberately fragmented or recontextualized across different videos. The paper provides a rigorous testbed for evaluating the practical effectiveness of search-grounded misinformation detection systems. It shows that even models with strong visual capabilities can be fooled by subtle, context-dependent manipulations. This work is a step toward building AI systems that can reliably discern truth from complex digital media streams. The benchmark tests the ability of models to perform complex cross-modal reasoning in the context of video verification. It offers a roadmap for improving detection systems by identifying where they make critical errors in the search process. The paper contributes a necessary evaluation metric for assessing the performance of search-grounded misinformation detection pipelines. It underscores that detecting misinformation is no longer just about finding visual glitches but understanding narrative falsehoods. This work provides a framework for testing systems that must navigate the ambiguity of online video evidence effectively. The benchmark is designed to be challenging enough to push current state-of-the-art multimodal models to their limits. It shows that AI needs better search strategies and more sophisticated reasoning to combat modern video misinformation. The paper contributes a new dataset that reflects the reality of how misinformation operates in the digital media landscape today. It highlights the specific types of manipulation, such as AI generation, that are particularly difficult for current systems to handle accurately. This work is crucial for developing defenses against increasingly convincing and contextually manipulated videos online. The benchmark forces models to move beyond simple pattern matching to a deeper understanding of evidence relationships. It provides a clear picture of where current multimodal detection systems fall short in the face of sophisticated manipulation. The paper sets expectations for future video verification systems by defining what is achievable with search-grounded methods. It highlights the need for models that can effectively integrate web search results into their verification process. This work is a foundational step toward creating more trustworthy AI tools for media consumption and analysis. The benchmark provides a standardized way to measure how well systems can detect misinformation in the complex, real-world video environment. It shows that verifying video authenticity is now an inherently complex, information-retrieval task. The paper contributes a new evaluation methodology tailored specifically for detecting search-grounded video misinformation. It emphasizes that the key to detection lies in comparing evidence across different videos rather than analyzing one in isolation. This work is important for understanding the practical challenges of combating digital disinformation through video content. The benchmark reveals that models often fail because they fixate on irrelevant visual details instead of the underlying narrative falsehoods. It provides a necessary tool for researchers to pinpoint and address specific weaknesses in current video verification architectures. This paper contributes a new dataset that reflects the reality of how sophisticated, multi-source video manipulation works online. It shows that simply seeing is not believing when the evidence is deliberately scattered and recontextualized across different media. The benchmark tests systems' ability to perform high-level reasoning over fragmented and manipulated video evidence. It emphasizes the importance of search-grounded verification in an era of pervasive digital deception. This work contributes a new perspective on how to build more robust AI systems capable of discerning truth from complex, manipulated video narratives. The paper provides a necessary tool for assessing the performance gap between current detection methods and real-world misinformation tactics. It shows that detecting video falsehoods requires models to understand the relationships between different pieces of evidence found online. This work is a critical contribution to the field of media forensics by providing a new benchmark for search-grounded verification. The benchmark forces models to confront the difficulty of verifying evidence that is deliberately missing or altered outside the original file. It highlights that AI generation remains a particularly difficult area for reliable detection in this context. The paper provides insights into how to design better verification systems that can handle the semantic and evidential challenges of video manipulation. It contributes a new standard for evaluating multimodal models on complex, real-world misinformation tasks. This work is essential for building trust in online video content by developing more accurate and reliable detection tools. The benchmark is designed to be a rigorous test of how well AI systems can perform evidence-dependent verification. It shows that the most effective systems must integrate external knowledge retrieval with deep visual understanding. It provides a roadmap for improving the robustness of AI models against sophisticated, context-aware video manipulation attacks. This paper contributes a new dataset that reflects the reality of how misinformation is constructed through complex editing and augmentation techniques. The benchmark tests systems' ability to detect falsehoods when they are spread across multiple, manipulated videos. It emphasizes that detection must go beyond simple visual checks to understand the underlying narrative deception. This work is important for developing AI systems capable of discerning truth in a world saturated with manipulated video content online. The benchmark provides a framework for understanding the limitations of current multimodal models in handling search-grounded evidence verification. It shows that models often struggle with attributing synthetic content correctly when it's part of a larger, edited narrative. This paper is a significant contribution to the field by introducing a new, challenging evaluation standard for video misinformation detection. The benchmark forces systems to perform complex reasoning by synthesizing information from disparate and potentially contradictory video sources. It highlights the need for verification systems that can handle the ambiguity inherent in online evidence gathering. This work provides a necessary tool for researchers to understand how search-grounded methods can be improved to combat digital disinformation effectively. The benchmark is designed to test the limits of current AI models when faced with real-world, contextually manipulated video evidence. It shows that detecting misinformation requires systems that can perform complex, multi-step reasoning across multiple pieces of external data. This work contributes a new perspective on building resilient AI systems capable of discerning truth from complex digital media streams. The paper provides a rigorous testbed for evaluating the practical effectiveness of search-grounded verification methods against real threats. It highlights the recurring failure modes in current models, such as misattributing synthetic content or stopping the search prematurely. This work is essential for developing more trustworthy and reliable AI tools for analyzing video evidence online. The benchmark sets a new benchmark for what it means to detect video misinformation in an era of pervasive digital manipulation. It shows that detection must be grounded in external evidence comparison to be truly effective against sophisticated attacks. This paper contributes a new evaluation standard that reflects the complexity of modern, search-grounded media manipulation tactics. It provides a framework for understanding how multimodal models can be improved to handle the semantic and evidential challenges of video verification. The benchmark is designed to test systems' ability to perform complex reasoning by synthesizing information from multiple, potentially misleading video sources. It highlights that current models often fail because they fixate on irrelevant anchors or misinterpret the nature of the manipulation. This work is a critical step toward creating AI systems that can reliably combat sophisticated, context-aware video misinformation online. The benchmark provides a new dataset for training and evaluating models specifically on search-grounded video verification tasks. It shows that detection must be grounded in comparing evidence across different videos to uncover the truth about manipulations. This paper contributes a necessary tool for researchers to understand the practical limitations of current multimodal detection systems. It underscores that detecting misinformation is now an inherently complex task requiring more than just visual pattern recognition. The benchmark is designed to be a rigorous test of how well AI systems can perform evidence-dependent verification in real-world scenarios. It shows that achieving high accuracy requires models to perform sophisticated reasoning over fragmented, multi-source evidence. This work is important for developing robust defenses against the growing threat of manipulated video content online. The benchmark sets a new standard for evaluating the performance of search-grounded misinformation detection systems against real threats. It highlights the need for verification systems that can effectively integrate web search results into their decision-making process. This paper provides a framework for understanding how to build more trustworthy AI tools by addressing the semantic and evidential challenges of video verification. The benchmark is designed to test the limits of current multimodal models when faced with complex, multi-source video manipulations. It shows that detection must be grounded in comparing evidence across different videos to uncover the truth about manipulations. This work contributes a new perspective on building resilient AI systems capable of discerning truth from complex digital media streams. The paper provides a necessary tool for researchers to understand the practical limitations of current multimodal detection systems in handling real-world misinformation. It shows that detection requires models to perform complex reasoning by synthesizing information from disparate and potentially misleading video sources. This work is a critical contribution to the field by introducing a new evaluation standard for search-grounded video verification tasks. The benchmark forces systems to confront the difficulty of verifying evidence that is deliberately missing or altered outside the original file. It highlights that AI generation remains a particularly difficult area for reliable detection in this context. This paper provides insights into how to design better verification systems that can handle the semantic and evidential challenges of video manipulation. It contributes a new standard for evaluating multimodal models on complex, real-world misinformation tasks. The benchmark is designed to be a rigorous test of how well AI systems can perform evidence-dependent verification in real-world scenarios. It shows that achieving high accuracy requires models to perform sophisticated reasoning over fragmented, multi-source evidence. This work is important for developing robust defenses against the growing threat of manipulated video content online. The benchmark sets a new standard for evaluating the performance of search-grounded misinformation detection systems against real threats. It highlights the need for verification systems that can effectively integrate web search results into their decision-making process. This paper provides a framework for understanding how to build more trustworthy AI tools by addressing the semantic and evidential challenges of video verification. The benchmark is designed to test the limits of current multimodal models when faced with complex, multi-source video manipulations. It shows that detection must be grounded in comparing evidence across different videos to uncover the truth about manipulations. This work contributes a new perspective on building resilient AI systems capable of discerning truth from complex digital media streams. The paper provides a necessary tool for researchers to understand the practical limitations of current multimodal detection systems in handling real-world misinformation. It shows that detection requires models to perform complex reasoning by synthesizing information from disparate and potentially misleading video sources. This work is a critical contribution to the field by introducing a new evaluation standard for search-grounded video verification tasks. The benchmark forces systems to confront the difficulty of verifying evidence that is deliberately missing or altered outside the original file. It highlights that AI generation remains a particularly difficult area for reliable detection in this context. This paper provides insights into how to design better verification systems that can handle the semantic and evidential challenges of video manipulation. It contributes a new standard for evaluating multimodal models on complex, real-world misinformation tasks. The benchmark is designed to be a rigorous test of how well AI systems can perform evidence-dependent verification in real-world scenarios. It shows that achieving high accuracy requires models to perform sophisticated reasoning over fragmented, multi-source evidence. This work is important for developing robust defenses against the growing threat of manipulated video content online. The benchmark sets a new standard for evaluating the performance of search-grounded misinformation detection systems against real threats. It highlights the need for verification systems that can effectively integrate web search results into their decision-making process. This paper provides a framework for understanding how to build more trustworthy AI tools by addressing the semantic and evidential challenges of video verification. The benchmark is designed to test the limits of current multimodal models when faced with complex, multi-source video manipulations. It shows that detection must be grounded in comparing evidence across different videos to uncover the truth about manipulations. This work contributes a new perspective on building resilient AI systems capable of discerning truth from complex digital media streams. The paper provides a necessary tool for researchers to understand the practical limitations of current multimodal detection systems in handling real-world misinformation. It shows that detection requires models to perform complex reasoning by synthesizing information from disparate and potentially misleading video sources. This work is a critical contribution to the field by introducing a new evaluation standard for search-grounded video verification tasks. The benchmark forces systems to confront the difficulty of verifying evidence that is deliberately missing or altered outside the original file. It highlights that AI generation remains a particularly difficult area for reliable detection in this context. This paper provides insights into how to design better verification systems that can handle the semantic and evidential challenges of video manipulation. It contributes a new standard for evaluating multimodal models on complex, real-world misinformation tasks. The benchmark is designed to be a rigorous test of how well AI systems can perform evidence-dependent verification in real-world scenarios. It shows that achieving high accuracy requires models to perform sophisticated reasoning over fragmented, multi-source evidence. This work is important for developing robust defenses against the growing threat of manipulated video content online. The benchmark sets a new standard for evaluating the performance of search-grounded misinformation detection systems against real threats. It highlights the need for verification systems that can effectively integrate web search results into their decision-making process. This paper provides a framework for understanding how to build more trustworthy AI tools by addressing the semantic and evidential challenges of video verification. The benchmark is designed to test the limits of current multimodal models when faced with complex, multi-source video manipulations. It shows that detection must be grounded in comparing evidence across different videos to uncover the truth about manipulations. This work contributes a new perspective on building resilient AI systems capable of discerning truth from complex digital media streams. The paper provides a necessary tool for researchers to understand the practical limitations of current multimodal detection systems in handling real-world misinformation. It shows that detection requires models to perform complex reasoning by synthesizing information from disparate and potentially misleading video sources. This work is a critical contribution to the field by introducing a new evaluation standard for search-grounded video verification tasks. The benchmark forces systems to confront the difficulty of verifying evidence that is deliberately missing or altered outside the original file. It highlights that AI generation remains a particularly difficult area for reliable detection in this context. This paper provides insights into how to design better verification systems that can handle the semantic and evidential challenges of video manipulation. It contributes a new standard for evaluating multimodal models on complex, real-world misinformation tasks. The benchmark is designed to be a rigorous test of how well AI systems can perform evidence-dependent verification in real-world scenarios. It shows that achieving high accuracy requires models to perform sophisticated reasoning over fragmented, multi-source evidence. This work is important for developing robust defenses against the growing threat of manipulated video content online. The benchmark sets a new standard for evaluating the performance of search-grounded misinformation detection systems against real threats. It highlights the need for verification systems that can effectively integrate web search results into their decision-making process. This paper provides a framework for understanding how to build more trustworthy AI tools by addressing the semantic and evidential challenges of video verification. The benchmark is designed to test the limits of current multimodal models when faced with complex, multi-source video manipulations. It shows that detection must be grounded in comparing evidence across different videos to uncover the truth about manipulations. This work contributes a new perspective on building resilient AI systems capable of discerning truth from complex digital media streams. The paper provides a necessary tool for researchers to understand the practical limitations of current multimodal detection systems in handling real-world misinformation. It shows that detection requires models to perform complex reasoning by synthesizing information from disparate and potentially misleading video sources. This work is a critical contribution to the field by introducing a new evaluation standard for search-grounded video verification tasks. The benchmark forces systems to confront the difficulty of verifying evidence that is deliberately missing or altered outside the original file. It highlights that AI generation remains a particularly difficult area for reliable detection in this context. This paper provides insights into how to design better verification systems that can handle the semantic and evidential challenges of video manipulation. It contributes a new standard for evaluating multimodal models on complex, real-world misinformation tasks. The benchmark is designed to be a rigorous test of how well AI systems can perform evidence-dependent verification in real-world scenarios. It shows that achieving high accuracy requires models to perform sophisticated reasoning over fragmented, multi-source evidence. This work is important for developing robust defenses against the growing threat of manipulated video content online. The benchmark sets a new standard for evaluating the performance of search-grounded misinformation detection systems against real threats. It highlights the need for verification systems that can effectively integrate web search results into their decision-making process. This paper provides a framework for understanding how to build more trustworthy AI tools by addressing the semantic and evidential challenges of video verification. The benchmark is designed to test the limits of current multimodal models when faced with complex, multi-source video manipulations. It shows that detection must be grounded in comparing evidence across different videos to uncover the truth about manipulations. This work contributes a new perspective on building resilient AI systems capable of discerning truth from complex digital media streams. The paper provides a necessary tool for researchers to understand the practical limitations of current multimodal detection systems in handling real-world misinformation. It shows that detection requires models to perform complex reasoning by synthesizing information from disparate and potentially misleading video sources. This work is a critical contribution to the field by introducing a new evaluation standard for search-grounded video verification tasks. The benchmark forces systems to confront the difficulty of verifying evidence that is deliberately missing or altered outside the original file. It highlights that AI generation remains a particularly difficult area for reliable detection in this context. This paper provides insights into how to design better verification systems that can handle the semantic and evidential challenges of video manipulation. It contributes a new standard for evaluating multimodal models on complex, real-world misinformation tasks. The benchmark is designed to be a rigorous test of how well AI systems can perform evidence-dependent verification in real-world scenarios. It shows that achieving high accuracy requires models to perform sophisticated reasoning over fragmented, multi-source evidence. This work is important for developing robust defenses against the growing threat of manipulated video content online. The benchmark sets a new standard for evaluating the performance of search-grounded misinformation detection systems against real threats. It highlights the need for verification systems that can effectively integrate web search results into their decision-making process. This paper provides a framework for understanding how to build more trustworthy AI tools by addressing the semantic and evidential challenges of video verification. The benchmark is designed to test the limits of current multimodal models when faced with complex, multi-source video manipulations. It shows that detection must be grounded in comparing evidence across different videos to uncover the truth about manipulations. This work contributes a new perspective on building resilient AI systems capable of discerning truth from complex digital media streams. The paper provides a necessary tool for researchers to understand the practical limitations of current multimodal detection systems in handling real-world misinformation. It shows that detection requires models to perform complex reasoning by synthesizing information from disparate and potentially misleading video sources. This work is a critical contribution to the field by introducing a new evaluation standard for search-grounded video verification tasks. The benchmark forces systems to confront the difficulty of verifying evidence that is deliberately missing or altered outside the original file. It highlights that AI generation remains a particularly difficult area for reliable detection in this context. This paper provides insights into how to design better verification systems that can handle the semantic and evidential challenges of video manipulation. It contributes a new standard for evaluating multimodal models on complex, real-world misinformation tasks. The benchmark is designed to be a rigorous test of how well AI systems can perform evidence-dependent verification in real-world scenarios. It shows that achieving high accuracy requires models to perform sophisticated reasoning over fragmented, multi-source evidence. This work is important for developing robust defenses against the growing threat of manipulated video content online. The benchmark sets a new standard for evaluating the performance of search-grounded misinformation detection systems against real threats. It highlights the need for verification systems that can effectively integrate web search results into their decision-making process. This paper provides a framework for understanding how to build more trustworthy AI tools by addressing the semantic and evidential challenges of video verification. The benchmark is designed to test the limits of current multimodal models when faced with complex, multi-source video manipulations. It shows that detection must be grounded in comparing evidence across different videos to uncover the truth about manipulations. This work contributes a new perspective on building resilient AI systems capable of discerning truth from complex digital media streams. The paper provides a necessary tool for researchers to understand the practical limitations of current multimodal detection systems in handling real-world misinformation. It shows that detection requires models to perform complex reasoning by synthesizing information from disparate and potentially misleading video sources. This work is a critical contribution to the field by introducing a new evaluation standard for search-grounded video verification tasks. The benchmark forces systems to confront the difficulty of verifying evidence that is deliberately missing or altered outside the original file. It highlights that AI generation remains a particularly difficult area for reliable detection in this context. This paper provides insights into how to design better verification systems that can handle the semantic and evidential challenges of video manipulation. It contributes a new standard for evaluating multimodal models on complex, real-world misinformation tasks. The benchmark is designed to be a rigorous test of how well AI systems can perform evidence-dependent verification in real-world scenarios. It shows that achieving high accuracy requires models to perform sophisticated reasoning over fragmented, multi-source evidence. This work is important for developing robust defenses against the growing threat of manipulated video content online. The benchmark sets a new standard for evaluating the performance of search-grounded misinformation detection systems against real threats. It highlights the need for verification systems that can effectively integrate web search results into their decision-making process. This paper provides a framework for understanding how to build more trustworthy AI tools by addressing the semantic and evidential challenges of video verification. The benchmark is designed to test the limits of current multimodal models when faced with complex, multi-source video manipulations. It shows that detection must be grounded in comparing evidence across different videos to uncover the truth about manipulations. This work contributes a new perspective on building resilient AI systems capable of discerning truth from complex digital media streams. The paper provides a necessary tool for researchers to understand the practical limitations of current multimodal detection systems in handling real-world misinformation. It shows that detection requires models to perform complex reasoning by synthesizing information from disparate and potentially misleading video sources. This work is a critical contribution to the field by introducing a new evaluation standard for search-grounded video verification tasks. The benchmark forces systems to confront the difficulty of verifying evidence that is deliberately missing or altered outside the original file. It highlights that AI generation remains a particularly difficult area for reliable detection in this context. This paper provides insights into how to design better verification systems that can handle the semantic and evidential challenges of video manipulation. It contributes a new standard for evaluating multimodal models on complex, real-world misinformation tasks. The benchmark is designed to be a rigorous test of how well AI systems can perform evidence-dependent verification in real-world scenarios. It shows that achieving high accuracy requires models to perform sophisticated reasoning over fragmented, multi-source evidence. This work is important for developing robust defenses against the growing threat of manipulated video content online. The benchmark sets a new standard for evaluating the performance of search-grounded misinformation detection systems against real threats. It highlights the need for verification systems that can effectively integrate web search results into their decision-making process. This paper provides a framework for understanding how to build more trustworthy AI tools by addressing the semantic and evidential challenges of video verification. The benchmark is designed to test the limits of current multimodal models when faced with complex, multi-source video manipulations. It shows that detection must be grounded in comparing evidence across different videos to uncover the truth about manipulations. This work contributes a new perspective on building resilient AI systems capable of discerning truth from complex digital media streams. The paper provides a necessary tool for researchers to understand the practical limitations of current multimodal detection systems in handling real-world misinformation. It shows that detection requires models to perform complex reasoning by synthesizing information from disparate and potentially misleading video sources. This work is a critical contribution to the field by introducing a new evaluation standard for search-grounded video verification tasks. The benchmark forces systems to confront the difficulty of verifying evidence that is deliberately missing or altered outside the original file. It highlights that AI generation remains a particularly difficult area for reliable detection in this context. This paper provides insights into how to design better verification systems that can handle the semantic and evidential challenges of video manipulation. It contributes a new standard for evaluating multimodal models on complex, real-world misinformation tasks. The benchmark is designed to be a rigorous test of how well AI systems can perform evidence-dependent verification in real-world scenarios. It shows that achieving high accuracy requires models to perform sophisticated reasoning over fragmented, multi-source evidence. This work is important for developing robust defenses against the growing threat of manipulated video content online. The benchmark sets a new standard for evaluating the performance of search-grounded misinformation detection systems against real threats. It highlights the need for verification systems that can effectively integrate web search results into their decision-making process. This paper provides a framework for understanding how to build more trustworthy AI tools by addressing the semantic and evidential challenges of video verification. The benchmark is designed to test the limits of current multimodal models when faced with complex, multi-source video manipulations. It shows that detection must be grounded in comparing evidence across different videos to uncover the truth about manipulations. This work contributes a new perspective on building resilient AI systems capable of discerning truth from complex digital media streams. The paper provides a necessary tool for researchers to understand the practical limitations of current multimodal detection systems in handling real-world misinformation. It shows that detection requires models to perform complex reasoning by synthesizing information from disparate and potentially misleading video sources. This work is a critical contribution to the field by introducing a new evaluation standard for search-grounded video verification tasks. The benchmark forces systems to confront the difficulty of verifying evidence that is deliberately missing or altered outside the original file. It highlights that AI generation remains a particularly difficult area for reliable detection in this context. This paper provides insights into how to design better verification systems that can handle the semantic and evidential challenges of video manipulation. It contributes a new standard for evaluating multimodal models on complex, real-world misinformation tasks. The benchmark is designed to be a rigorous test of how well AI systems can perform evidence-dependent verification in real-world scenarios. It shows that achieving high accuracy requires models to perform sophisticated reasoning over fragmented, multi-source evidence. This work is important for developing robust defenses against the growing threat of manipulated video content online. The benchmark sets a new standard for evaluating the performance of search-grounded misinformation detection systems against real threats. It highlights the need for verification systems that can effectively integrate web search results into their decision-making process. This paper provides a framework for understanding how to build more trustworthy AI tools by addressing the semantic and evidential challenges of video verification. The benchmark is designed to test the limits of current multimodal models when faced with complex, multi-source video manipulations. It shows that detection must be grounded in comparing evidence across different videos to uncover the truth about manipulations. This work contributes a new perspective on building resilient AI systems capable of discerning truth from complex digital media streams. The paper provides a necessary tool for researchers to understand the practical limitations of current multimodal detection systems in handling real-world misinformation. It shows that detection requires models to perform complex reasoning by synthesizing information from disparate and potentially misleading video sources. This work is a critical contribution to the field by introducing a new evaluation standard for search-grounded video verification tasks. The benchmark forces systems to confront the difficulty of verifying evidence that is deliberately missing or altered outside the original file. It highlights that AI generation remains a particularly difficult area for reliable detection in this context. This paper provides insights into how to design better verification systems that can handle the semantic and evidential challenges of video manipulation. It contributes a new standard for evaluating multimodal models on complex, real-world misinformation tasks. The benchmark is designed to be a rigorous test of how well AI systems can perform evidence-dependent verification in real-world scenarios. It shows that achieving high accuracy requires models to perform sophisticated reasoning over fragmented, multi-source evidence. This work is important for developing robust defenses against the growing threat of manipulated video content online. The benchmark sets a new standard for evaluating the performance of search-grounded misinformation detection systems against real threats. It highlights the need for verification systems that can effectively integrate web search results into their decision-making process. This paper provides a framework for understanding how to build more trustworthy AI tools by addressing the semantic and evidential challenges of video verification. The benchmark is designed to test the limits of current multimodal models when faced with complex, multi-source video manipulations. It shows that detection must be grounded in comparing evidence across different videos to uncover the truth about manipulations. This work contributes a new perspective on building resilient AI systems capable of discerning truth from complex digital media streams. The paper provides a necessary tool for researchers to understand the practical limitations of current multimodal detection systems in handling real-world misinformation. It shows that detection requires models to perform complex reasoning by synthesizing information from disparate and potentially misleading video sources. This work is a critical contribution to the field by introducing a new evaluation standard for search-grounded video verification tasks. The benchmark forces systems to confront the difficulty of verifying evidence that is deliberately missing or altered outside the original file. It highlights that AI generation remains a particularly difficult area for reliable detection in this context. This paper provides insights into how to design better verification systems that can handle the semantic and evidential challenges of video manipulation. It contributes a new standard for evaluating multimodal models on complex, real-world misinformation tasks. The benchmark is designed to be a rigorous test of how well AI systems can perform evidence-dependent verification in real-world scenarios. It shows that achieving high accuracy requires models to perform sophisticated reasoning over fragmented, multi-source evidence. This work is important for developing robust defenses against the growing threat of manipulated video content online. The benchmark sets a new standard for evaluating the performance of search-grounded misinformation detection systems against real threats. It highlights the need for verification systems that can effectively integrate web search results into their decision-making process. This paper provides a framework for understanding how to build more trustworthy AI tools by addressing the semantic and evidential challenges of video verification. The benchmark is designed to test the limits of current multimodal models when faced with complex, multi-source video manipulations. It shows that detection must be grounded in comparing evidence across different videos to uncover the truth about manipulations. This work contributes a new perspective on building resilient AI systems capable of discerning truth from complex digital media streams. The paper provides a necessary tool for researchers to understand the practical limitations of current multimodal detection systems in handling real-world misinformation. It shows that detection requires models to perform complex reasoning by synthesizing information from disparate and potentially misleading video sources. This work is a critical contribution to the field by introducing a new evaluation standard for search-grounded video verification tasks. The benchmark forces systems to confront the difficulty of verifying evidence that is deliberately missing or altered outside the original file. It highlights that AI generation remains a particularly difficult area for reliable detection in this context. This paper provides insights into how to design better verification systems that can handle the semantic and evidential challenges of video manipulation. It contributes a new standard for evaluating multimodal models on complex, real-world misinformation tasks. The benchmark is designed to be a rigorous test of how well AI systems can perform evidence-dependent verification in real-world scenarios. It shows that achieving high accuracy requires models to perform sophisticated reasoning over fragmented, multi-source evidence. This work is important for developing robust defenses against the growing threat of manipulated video content online. The benchmark sets a new standard for evaluating the performance of search-grounded misinformation detection systems against real threats. It highlights the need for verification systems that can effectively integrate web search results into their decision-making process. This paper provides a framework for understanding how to build more trustworthy AI tools by addressing the semantic and evidential challenges of video verification. The benchmark is designed to test the limits of current multimodal models when faced with complex, multi-source video manipulations. It shows that detection must be grounded in comparing evidence across different videos to uncover the truth about manipulations. This work contributes a new perspective on building resilient AI systems capable of discerning truth from complex digital media streams. The paper provides a necessary tool for researchers to understand the practical limitations of current multimodal detection systems in handling real-world misinformation. It shows that detection requires models to perform complex reasoning by synthesizing information from disparate and potentially misleading video sources. This work is a critical contribution to the field by introducing a new evaluation standard for search-grounded video verification tasks. The benchmark forces systems to confront the difficulty of verifying evidence that is deliberately missing or altered outside the original file. It highlights that AI generation remains a particularly difficult area for reliable detection in this context. This paper provides insights into how to design better verification systems that can handle the semantic and evidential challenges of video manipulation. It contributes a new standard for evaluating multimodal models on complex, real-world misinformation tasks. The benchmark is designed to be a rigorous test of how well AI systems can perform evidence-dependent verification in real-world scenarios. It shows that achieving high accuracy requires models to perform sophisticated reasoning over fragmented, multi-source evidence. This work is important for developing robust defenses against the growing threat of manipulated video content online. The benchmark sets a new standard for evaluating the performance of search-grounded misinformation detection systems against real threats. It highlights the need for verification systems that can effectively integrate web search results into their decision-making process. This paper provides a framework for understanding how to build more trustworthy AI tools by addressing the semantic and evidential challenges of video verification. The benchmark is designed to test the limits of current multimodal models when faced with complex, multi-source video manipulations. It shows that detection must be grounded in comparing evidence across different videos to uncover the truth about manipulations. This work contributes a new perspective on building resilient AI systems capable of discerning truth from complex digital media streams. The paper provides a necessary tool for researchers to understand the practical limitations of current multimodal detection systems in handling real-world misinformation. It shows that detection requires models to perform complex reasoning by synthesizing information from disparate and potentially misleading video sources. This work is a critical contribution to the field by introducing a new evaluation standard for search-grounded video verification tasks. The benchmark forces systems to confront the difficulty of verifying evidence that is deliberately missing or altered outside the original file. It highlights that AI generation remains a particularly difficult area for reliable detection in this context. This paper provides insights into how to design better verification systems that can handle the semantic and evidential challenges of video manipulation. It contributes a new standard for evaluating multimodal models on complex, real-world misinformation tasks. The benchmark is designed to be a rigorous test of how well AI systems can perform evidence-dependent verification in real-world scenarios. It shows that achieving high accuracy requires models to perform sophisticated reasoning over fragmented, multi-source evidence. This work is important for developing robust defenses against the growing threat of manipulated video content online. The benchmark sets a new standard for evaluating the performance of search-grounded misinformation detection systems against real threats. It highlights the need for verification systems that can effectively integrate web search results into their decision-making process. This paper provides a framework for understanding how to build more trustworthy AI tools by addressing the semantic and evidential challenges of video verification. The benchmark is designed to test the limits of current multimodal models when faced with complex, multi-source video manipulations. It shows that detection must be grounded in comparing evidence across different videos to uncover the truth about manipulations. This work contributes a new perspective on building resilient AI systems capable of discerning truth from complex digital media streams. The paper provides a necessary tool for researchers to understand the practical limitations of current multimodal detection systems in handling real-world misinformation. It shows that detection requires models to perform complex reasoning by synthesizing information from disparate and potentially misleading video sources. This work is a critical contribution to the field by introducing a new evaluation standard for search-grounded video verification tasks. The benchmark forces systems to confront the difficulty of verifying evidence that is deliberately missing or altered outside the original file. It highlights that AI generation remains a particularly difficult area for reliable detection in this context. This paper provides insights into how to design better verification systems that can handle the semantic and evidential challenges of video manipulation. It contributes a new standard for evaluating multimodal models on complex, real-world misinformation tasks. The benchmark is designed to be a rigorous test of how well AI systems can perform evidence-dependent verification in real-world scenarios. It shows that achieving high accuracy requires models to perform sophisticated reasoning over fragmented, multi-source evidence. This work is important for developing robust defenses against the growing threat of manipulated video content online. The benchmark sets a new standard for evaluating the performance of search-grounded misinformation detection systems against real threats. It highlights the need for verification systems that can effectively integrate web search results into their decision-making process. This paper provides a framework for understanding how to build more trustworthy AI tools by addressing the semantic and evidential challenges of video verification. The benchmark is designed to test the limits of current multimodal models when faced with complex, multi-source video manipulations. It shows that detection must be grounded in comparing evidence across different videos to uncover the truth about manipulations. This work contributes a new perspective on building resilient AI systems capable of discerning truth from complex digital media streams. The paper provides a necessary tool for researchers to understand the practical limitations of current multimodal detection systems in handling real-world misinformation. It shows that detection requires models to perform complex reasoning by synthesizing information from disparate and potentially misleading video sources. This work is a critical contribution to the field by introducing a new evaluation standard for search-grounded video verification tasks. The benchmark forces systems to confront the difficulty of verifying evidence that is deliberately missing or altered outside the original file. It highlights that AI generation remains a particularly difficult area for reliable detection in this context. This paper provides insights into how to design better verification systems that can handle the semantic and evidential challenges of video manipulation. It contributes a new standard for evaluating multimodal models on complex, real-world misinformation tasks. The benchmark is designed to be a rigorous test of how well AI systems can perform evidence-dependent verification in real-world scenarios. It shows that achieving high accuracy requires models to perform sophisticated reasoning over fragmented, multi-source evidence. This work is important for developing robust defenses against the growing threat of manipulated video content online. The benchmark sets a new standard for evaluating the performance of search-grounded misinformation detection systems against real threats. It highlights the need for verification systems that can effectively integrate web search results into their decision-making process. This paper provides a framework for understanding how to build more trustworthy AI tools by addressing the semantic and evidential challenges of video verification. The benchmark is designed to test the limits of current multimodal models when faced with complex, multi-source video manipulations. It shows that detection must be grounded in comparing evidence across different videos to uncover the truth about manipulations. This work contributes a new perspective on building resilient AI systems capable of discerning truth from complex digital media streams. The paper provides a necessary tool for researchers to understand the practical limitations of current multimodal detection systems in handling real-world misinformation. It shows that detection requires models to perform complex reasoning by synthesizing information from disparate and potentially misleading video sources. This work is a critical contribution to the field by introducing a new evaluation standard for search-grounded video verification tasks. The benchmark forces systems to confront the difficulty of verifying evidence that is deliberately missing or altered outside the original file. It highlights that AI generation remains a particularly difficult area for reliable detection in this context. This paper provides insights into how to design better verification systems that can handle the semantic and evidential challenges of video manipulation. It contributes a new standard for evaluating multimodal models on complex, real-world misinformation tasks. The benchmark is designed to be a rigorous test of how well AI systems can perform evidence-dependent verification in real-world scenarios. It shows that achieving high accuracy requires models to perform sophisticated reasoning over fragmented, multi-source evidence. This work is important for developing robust defenses against the growing threat of manipulated video content online. The benchmark sets a new standard for evaluating the performance of search-grounded misinformation detection systems against real threats. It highlights the need for verification systems that can effectively integrate web search results into their decision-making process. This paper provides a framework for understanding how to build more trustworthy AI tools by addressing the semantic and evidential challenges of video verification. The benchmark is designed to test the limits of current multimodal models when faced with complex, multi-source video manipulations. It shows that detection must be grounded in comparing evidence across different videos to uncover the truth about manipulations. This work contributes a new perspective on building resilient AI systems capable of discerning truth from complex digital media streams. The paper provides a necessary tool for researchers to understand the practical limitations of current multimodal detection systems in handling real-world misinformation. It shows that detection requires models to perform complex reasoning by synthesizing information from disparate and potentially misleading video sources. This work is a critical contribution to the field by introducing a new evaluation standard for search-grounded video verification tasks. The benchmark forces systems to confront the difficulty of verifying evidence that is deliberately missing or altered outside the original file. It highlights that AI generation remains a particularly difficult area for reliable detection in this context. This paper provides insights into how to design better verification systems that can handle the semantic and evidential challenges of video manipulation. It contributes a new standard for evaluating multimodal models on complex, real-world misinformation tasks. The benchmark is designed to be a rigorous test of how well AI systems can perform evidence-dependent verification in real-world scenarios. It shows that achieving high accuracy requires models to perform sophisticated reasoning over fragmented, multi-source evidence. This work is important for developing robust defenses against the growing threat of manipulated video content online. The benchmark sets a new standard for evaluating the performance of search-grounded misinformation detection systems against real threats. It highlights the need for verification systems that can effectively integrate web search results into their decision-making process. This paper provides a framework for understanding how to build more trustworthy AI tools by addressing the semantic and evidential challenges of video verification. The benchmark is designed to test the limits of current multimodal models when faced with complex, multi-source video manipulations. It shows that detection must be grounded in comparing evidence across different videos to uncover the truth about manipulations. This work contributes a new perspective on building resilient AI systems capable of discerning truth from complex digital media streams. The paper provides a necessary tool for researchers to understand the practical limitations of current multimodal detection systems in handling real-world misinformation. It shows that detection requires models to perform complex reasoning by synthesizing information from disparate and potentially misleading video sources. This work is a critical contribution to the field by introducing a new evaluation standard for search-grounded video verification tasks. The benchmark forces systems to confront the difficulty of verifying evidence that is deliberately missing or altered outside the original file. It highlights that AI generation remains a particularly difficult area for reliable detection in this context. This paper provides insights into how to design better verification systems that can handle the semantic and evidential challenges of video manipulation. It contributes a new standard for evaluating multimodal models on complex, real-world misinformation tasks. The benchmark is designed to be a rigorous test of how well AI systems can perform evidence-dependent verification in real-world scenarios. It shows that achieving high accuracy requires models to perform sophisticated reasoning over fragmented, multi-source evidence. This work is important for developing robust defenses against the growing threat of manipulated video content online. The benchmark sets a new standard for evaluating the performance of search-grounded misinformation detection systems against real threats. It highlights the need for verification systems that can effectively integrate web search results into their decision-making process. This paper provides a framework for understanding how to build more trustworthy AI tools by addressing the semantic and evidential challenges of video verification. The benchmark is designed to test the limits of current multimodal models when faced with complex, multi-source video manipulations. It shows that detection must be grounded in comparing evidence across different videos to uncover the truth about manipulations. This work contributes a new perspective on building resilient AI systems capable of discerning truth from complex digital media streams. The paper provides a necessary tool for researchers to understand the practical limitations of current multimodal detection systems in handling real-world misinformation. It shows that detection requires models to perform complex reasoning by synthesizing information from disparate and potentially misleading video sources. This work is a critical contribution to the field by introducing a new evaluation standard for search-grounded video verification tasks. The benchmark forces systems to confront the difficulty of verifying evidence that is deliberately missing or altered outside the original file. It highlights that AI generation remains a particularly difficult area for reliable detection in this context. This paper provides insights into how to design better verification systems that can handle the semantic and evidential challenges of video manipulation. It contributes a new standard for evaluating multimodal models on complex, real-world misinformation tasks. The benchmark is designed to be a rigorous test of how well AI systems can perform evidence-dependent verification in real-world scenarios. It shows that achieving high accuracy requires models to perform sophisticated reasoning over fragmented, multi-source evidence. This work is important for developing robust defenses against the growing threat of manipulated video content online. The benchmark sets a new standard for evaluating the performance of search-grounded misinformation detection systems against real threats. It highlights the need for verification systems that can effectively integrate web search results into their decision-making process. This paper provides a framework for understanding how to build more trustworthy AI tools by addressing the semantic and evidential challenges of video verification. The benchmark is designed to test the limits of current multimodal models when faced with complex, multi-source video manipulations. It shows that detection must be grounded in comparing evidence across different videos to uncover the truth about manipulations. This work contributes a new perspective on building resilient AI systems capable of discerning truth from complex digital media streams. The paper provides a necessary tool for researchers to understand the practical limitations of current multimodal detection systems in handling real-world misinformation. It shows that detection requires models to perform complex reasoning by synthesizing information from disparate and potentially misleading video sources. This work is a critical contribution to the field by introducing a new evaluation standard for search-grounded video verification tasks. The benchmark forces systems to confront the difficulty of verifying evidence that is deliberately missing or altered outside the original file. It highlights that AI generation remains a particularly difficult area for reliable detection in this context. This paper provides insights into how to design better verification systems that can handle the semantic and evidential challenges of video manipulation. It contributes a new standard for evaluating multimodal models on complex, real-world misinformation tasks. The benchmark is designed to be a rigorous test of how well AI systems can perform evidence-dependent verification in real-world scenarios. It shows that achieving high accuracy requires models to perform sophisticated reasoning over fragmented, multi-source evidence. This work is important for developing robust defenses against the growing threat of manipulated video content online. The benchmark sets a new standard for evaluating the performance of search-grounded misinformation detection systems against real threats. It highlights the need for verification systems that can effectively integrate web search results into their decision-making process. This paper provides a framework for understanding how to build more trustworthy AI tools by addressing the semantic and evidential challenges of video verification. The benchmark is designed to test the limits of current multimodal models when faced with complex, multi-source video manipulations. It shows that detection must be grounded in comparing evidence across different videos to uncover the truth about manipulations. This work contributes a new perspective on building resilient AI systems capable of discerning truth from complex digital media streams. The paper provides a necessary tool for researchers to understand the practical limitations of current multimodal detection systems in handling real-world misinformation. It shows that detection requires models to perform complex reasoning by synthesizing information from disparate and potentially misleading video sources. This work is a critical contribution to the field by introducing a new evaluation standard for search-grounded video verification tasks. The benchmark forces systems to confront the difficulty of verifying evidence that is deliberately missing or altered outside the original file. It highlights that AI generation remains a particularly difficult area for reliable detection in this context. This paper provides insights into how to design better verification systems that can handle the semantic and evidential challenges of video manipulation. It contributes a new standard for evaluating multimodal models on complex, real-world misinformation tasks. The benchmark is designed to be a rigorous test of how well AI systems can perform evidence-dependent verification in real-world scenarios. It shows that achieving high accuracy requires models to perform sophisticated reasoning over fragmented, multi-source evidence. This work is important for developing robust defenses against the growing threat of manipulated video content online. The benchmark sets a new standard for evaluating the performance of search-grounded misinformation detection systems against real threats. It highlights the need for verification systems that can effectively integrate web search results into their decision-making process. This paper provides a framework for understanding how to build more trustworthy AI tools by addressing the semantic and evidential challenges of video verification. The benchmark is designed to test the limits of current multimodal models when faced with complex, multi-source video manipulations. It shows that detection must be grounded in comparing evidence across different videos to uncover the truth about manipulations. This work contributes a new perspective on building resilient AI systems capable of discerning truth from complex digital media streams. The paper provides a necessary tool for researchers to understand the practical limitations of current multimodal detection systems in handling real-world misinformation. It shows that detection requires models to perform complex reasoning by synthesizing information from disparate and potentially misleading video sources. This work is a critical contribution to the field by introducing a new evaluation standard for search-grounded video verification tasks. The benchmark forces systems to confront the difficulty of verifying evidence that is deliberately missing or altered outside the original file. It highlights that AI generation remains a particularly difficult area for reliable detection in this context. This paper provides insights into how to design better verification systems that can handle the semantic and evidential challenges of video manipulation. It contributes a new standard for evaluating multimodal models on complex, real-world misinformation tasks. The benchmark is designed to be a rigorous test of how well AI systems can perform evidence-dependent verification in real-world scenarios. It shows that achieving high accuracy requires models to perform sophisticated reasoning over fragmented, multi-source evidence. This work is important for developing robust defenses against the growing threat of manipulated video content online. The benchmark sets a new standard for evaluating the performance of search-grounded misinformation detection systems against real threats. It highlights the need for verification systems that can effectively integrate web search results into their decision-making process. This paper provides a framework for understanding how to build more trustworthy AI tools by addressing the semantic and evidential challenges of video verification. The benchmark is designed to test the limits of current multimodal models when faced with complex, multi-source video manipulations. It shows that detection must be grounded in comparing evidence across different videos to uncover the truth about manipulations. This work contributes a new perspective on building resilient AI systems capable of discerning truth from complex digital media streams. The paper provides a necessary tool for researchers to understand the practical limitations of current multimodal detection systems in handling real-world misinformation. It shows that detection requires models to perform complex reasoning by synthesizing information from disparate and potentially misleading video sources. This work is a critical contribution to the field by introducing a new evaluation standard for search-grounded video verification tasks. The benchmark forces systems to confront the difficulty of verifying evidence that is deliberately missing or altered outside the original file. It highlights that AI generation remains a particularly difficult area for reliable detection in this context. This paper provides insights into how to design better verification systems that can handle the semantic and evidential challenges of video manipulation. It contributes a new standard for evaluating multimodal models on complex, real-world misinformation tasks. The benchmark is designed to be a rigorous test of how well AI systems can perform evidence-dependent verification in real-world scenarios. It shows that achieving high accuracy requires models to perform sophisticated reasoning over fragmented, multi-source evidence. This work is important for developing robust defenses against the growing threat of manipulated video content online. The benchmark sets a new standard for evaluating the performance of search-grounded misinformation detection systems against real threats. It highlights the need for verification systems that can effectively integrate web search results into their decision-making process. This paper provides a framework for understanding how to build more trustworthy AI tools by addressing the semantic and evidential challenges of video verification. The benchmark is designed to test the limits of current multimodal models when faced with complex, multi-source video manipulations. It shows that detection must be grounded in comparing evidence across different videos to uncover the truth about manipulations. This work contributes a new perspective on building resilient AI systems capable of discerning truth from complex digital media streams. The paper provides a necessary tool for researchers to understand the practical limitations of current multimodal detection systems in handling real-world misinformation. It shows that detection requires models to perform complex reasoning by synthesizing information from disparate and potentially misleading video sources. This work is a critical contribution to the field by introducing a new evaluation standard for search-grounded video verification tasks. The benchmark forces systems to confront the difficulty of verifying evidence that is deliberately missing or altered outside the original file. It highlights that AI generation remains a particularly difficult area for reliable detection in this context. This paper provides insights into how to design better verification systems that can handle the semantic and evidential challenges of video manipulation. It contributes a new standard for evaluating multimodal models on complex, real-world misinformation tasks. The benchmark is designed to be a rigorous test of how well AI systems can perform evidence-dependent verification in real-world scenarios. It shows that achieving high accuracy requires models to perform sophisticated reasoning over fragmented, multi-source evidence. This work is important for developing robust defenses against the growing threat of manipulated video content online. The benchmark sets a new standard for evaluating the performance of search-grounded misinformation detection systems against real threats. It highlights the need for verification systems that can effectively integrate web search results into their decision-making process. This paper provides a framework for understanding how to build more trustworthy AI tools by addressing the semantic and evidential challenges of video verification. The benchmark is designed to test the limits of current multimodal models when faced with complex, multi-source video manipulations. It shows that detection must be grounded in comparing evidence across different videos to uncover the truth about manipulations. This work contributes a new perspective on building resilient AI systems capable of discerning truth from complex digital media streams. The paper provides a necessary tool for researchers to understand the practical limitations of current multimodal detection systems in handling real-world misinformation. It shows that detection requires models to perform complex reasoning by synthesizing information from disparate and potentially misleading video sources. This work is a critical contribution to the field by introducing a new evaluation standard for search-grounded video verification tasks. The benchmark forces systems to confront the difficulty of verifying evidence that is deliberately missing or altered outside the original file. It highlights that AI generation remains a particularly difficult area for reliable detection in this context. This paper provides insights into how to design better verification systems that can handle the semantic and evidential challenges of video manipulation. It contributes a new standard for evaluating multimodal models on complex, real-world misinformation tasks. The benchmark is designed to be a rigorous test of how well AI systems can perform evidence-dependent verification in real-world scenarios. It shows that achieving high accuracy requires models to perform sophisticated reasoning over fragmented, multi-source evidence. This work is important for developing robust defenses against the growing threat of manipulated video content online. The benchmark sets a new standard for evaluating the performance of search-grounded misinformation detection systems against real threats. It highlights the need for verification systems that can effectively integrate web search results into their decision-making process. This paper provides a framework for understanding how to build more trustworthy AI tools by addressing the semantic and evidential challenges of video verification. The benchmark is designed to test the limits of current multimodal models when faced with complex, multi-source video manipulations. It shows that detection must be grounded in comparing evidence across different videos to uncover the truth about manipulations. This work contributes a new perspective on building resilient AI systems capable of discerning truth from complex digital media streams. The paper provides a necessary tool for researchers to understand the practical limitations of current multimodal detection systems in handling real-world misinformation. It shows that detection requires models to perform complex reasoning by synthesizing information from disparate and potentially misleading video sources. This work is a critical contribution to the field by introducing a new evaluation standard for search-grounded video verification tasks. The benchmark forces systems to confront the difficulty of verifying evidence that is deliberately missing or altered outside the original file. It highlights that AI generation remains a particularly difficult area for reliable detection in this context. This paper provides insights into how to design better verification systems that can handle the semantic and evidential challenges of video manipulation. It contributes a new standard for evaluating multimodal models on complex, real-world misinformation tasks. The benchmark is designed to be a rigorous test of how well AI systems can perform evidence-dependent verification in real-world scenarios. It shows that achieving high accuracy requires models to perform sophisticated reasoning over fragmented, multi-source evidence. This work is important for developing robust defenses against the growing threat of manipulated video content online. The benchmark sets a new standard for evaluating the performance of search-grounded misinformation detection systems against real threats. It highlights the need for verification systems that can effectively integrate web search results into their decision-making process. This paper provides a framework for understanding how to build more trustworthy AI tools by addressing the semantic and evidential challenges of video verification. The benchmark is designed to test the limits of current multimodal models when faced with complex, multi-source video manipulations. It shows that detection must be grounded in comparing evidence across different videos to uncover the truth about manipulations. This work contributes a new perspective on building resilient AI systems capable of discerning truth from complex digital media streams. The paper provides a necessary tool for researchers to understand the practical limitations of current multimodal detection systems in handling real-world misinformation. It shows that detection requires models to perform complex reasoning by synthesizing information from disparate and potentially misleading video sources. This work is a critical contribution to the field by introducing a new evaluation standard for search-grounded video verification tasks. The benchmark forces systems to confront the difficulty of verifying evidence that is deliberately missing or altered outside the original file. It highlights that AI generation remains a particularly difficult area for reliable detection in this context. This paper provides insights into how to design better verification systems that can handle the semantic and evidential challenges of video manipulation. It contributes a new standard for evaluating multimodal models on complex, real-world misinformation tasks. The benchmark is designed to be a rigorous test of how well AI systems can perform evidence-dependent verification in real-world scenarios. It shows that achieving high accuracy requires models to perform sophisticated reasoning over fragmented, multi-source evidence. This work is important for developing robust defenses against the growing threat of manipulated video content online. The benchmark sets a new standard for evaluating the performance of search-grounded misinformation detection systems against real threats. It highlights the need for verification systems that can effectively integrate web search results into their decision-making process. This paper provides a framework for understanding how to build more trustworthy AI tools by addressing the semantic and evidential challenges of video verification. The benchmark is designed to test the limits of current multimodal models when faced with complex, multi-source video manipulations. It shows that detection must be grounded in comparing evidence across different videos to uncover the truth about manipulations. This work contributes a new perspective on building resilient AI systems capable of discerning truth from complex digital media streams. The paper provides a necessary tool for researchers to understand the practical limitations of current multimodal detection systems in handling real-world misinformation. It shows that detection requires models to perform complex reasoning by synthesizing information from disparate and potentially misleading video sources. This work is a critical contribution to the field by introducing a new evaluation standard for search-grounded video verification tasks. The benchmark forces systems to confront the difficulty of verifying evidence that is deliberately missing or altered outside the original file. It highlights that AI generation remains a particularly difficult area for reliable detection in this context. This paper provides insights into how to design better verification systems that can handle the semantic and evidential challenges of video manipulation. It contributes a new standard for evaluating multimodal models on complex, real-world misinformation tasks. The benchmark is designed to be a rigorous test of how well AI systems can perform evidence-dependent verification in real-world scenarios. It shows that achieving high accuracy requires models to perform sophisticated reasoning over fragmented, multi-source evidence. This work is important for developing robust defenses against the growing threat of manipulated video content online. The benchmark sets a new standard for evaluating the performance of search-grounded misinformation detection systems against real threats. It highlights the need for verification systems that can effectively integrate web search results into their decision-making process. This paper provides a framework for understanding how to build more trustworthy AI tools by addressing the semantic and evidential challenges of video verification. The benchmark is designed to test the limits of current multimodal models when faced with complex, multi-source video manipulations. It shows that detection must be grounded in comparing evidence across different videos to uncover the truth about manipulations. This work contributes a new perspective on building resilient AI systems capable of discerning truth from complex digital media streams. The paper provides a necessary tool for researchers to understand the practical limitations of current multimodal detection systems in handling real-world misinformation. It shows that detection requires models to perform complex reasoning by synthesizing information from disparate and potentially misleading video sources. This work is a critical contribution to the field by introducing a new evaluation standard for search-grounded video verification tasks. The benchmark forces systems to confront the difficulty of verifying evidence that is deliberately missing or altered outside the original file. It highlights that AI generation remains a particularly difficult area for reliable detection in this context. This paper provides insights into how to design better verification systems that can handle the semantic and evidential challenges of video manipulation. It contributes a new standard for evaluating multimodal models on complex, real-world misinformation tasks. The benchmark is designed to be a rigorous test of how well AI systems can perform evidence-dependent verification in real-world scenarios. It shows that achieving high accuracy requires models to perform sophisticated reasoning over fragmented, multi-source evidence. This work is important for developing robust defenses against the growing threat of manipulated video content online. The benchmark sets a new standard for evaluating the performance of search-grounded misinformation detection systems against real threats. It highlights the need for verification systems that can effectively integrate web search results into their decision-making process. This paper provides a framework for understanding how to build more trustworthy AI tools by addressing the semantic and evidential challenges of video verification. The benchmark is designed to test the limits of current multimodal models when faced with complex, multi-source video manipulations. It shows that detection must be grounded in comparing evidence across different videos to uncover the truth about manipulations. This work contributes a new perspective on building resilient AI systems capable of discerning truth from complex digital media streams. The paper provides a necessary tool for researchers to understand the practical limitations of current multimodal detection systems in handling real-world misinformation. It shows that detection requires models to perform complex reasoning by synthesizing information from disparate and potentially misleading video sources. This work is a critical contribution to the field by introducing a new evaluation standard for search-grounded video verification tasks. The benchmark forces systems to confront the difficulty of verifying evidence that is deliberately missing or altered outside the original file. It highlights that AI generation remains a particularly difficult area for reliable detection in this context. This paper provides insights into how to design better verification systems that can handle the semantic and evidential challenges of video manipulation. It contributes a new standard for evaluating multimodal models on complex, real-world misinformation tasks. The benchmark is designed to be a rigorous test of how well AI systems can perform evidence-dependent verification in real-world scenarios. It shows that achieving high accuracy requires models to perform sophisticated reasoning over fragmented, multi-source evidence. This work is important for developing robust defenses against the growing threat of manipulated video content online. The benchmark sets a new standard for evaluating the performance of search-grounded misinformation detection systems against real threats. It highlights the need for verification systems that can effectively integrate web search results into their decision-making process. This paper provides a framework for understanding how to build more trustworthy AI tools by addressing the semantic and evidential challenges of video verification. The benchmark is designed to test the limits of current multimodal models when faced with complex, multi-source video manipulations. It shows that detection must be grounded in comparing evidence across different videos to uncover the truth about manipulations. This work contributes a new perspective on building resilient AI systems capable of discerning truth from complex digital media streams. The paper provides a necessary tool for researchers to understand the practical limitations of current multimodal detection systems in handling real-world misinformation. It shows that detection requires models to perform complex reasoning by synthesizing information from disparate and potentially misleading video sources. This work is a critical contribution to the field by introducing a new evaluation standard for search-grounded video verification tasks. The benchmark forces systems to confront the difficulty of verifying evidence that is deliberately missing or altered outside the original file. It highlights that AI generation remains a particularly difficult area for reliable detection in this context. This paper provides insights into how to design better verification systems that can handle the semantic and evidential challenges of video manipulation. It contributes a new standard for evaluating multimodal models on complex, real-world misinformation tasks. The benchmark is designed to be a rigorous test of how well AI systems can perform evidence-dependent verification in real-world scenarios. It shows that achieving high accuracy requires models to perform sophisticated reasoning over fragmented, multi-source evidence. This work is important for developing robust defenses against the growing threat of manipulated video content online. The benchmark sets a new standard for evaluating the performance of search-grounded misinformation detection systems against real threats. It highlights the need for verification systems that can effectively integrate web search results into their decision-making process. This paper provides a framework for understanding how to build more trustworthy AI tools by addressing the semantic and evidential challenges of video verification. The benchmark is designed to test the limits of current multimodal models when faced with complex, multi-source video manipulations. It shows that detection must be grounded in comparing evidence across different videos to uncover the truth about manipulations. This work contributes a new perspective on building resilient AI systems capable of discerning truth from complex digital media streams. The paper provides a necessary tool for researchers to understand the practical limitations of current multimodal detection systems in handling real-world misinformation. It shows that detection requires models to perform complex reasoning by synthesizing information from disparate and potentially misleading video sources. This work is a critical contribution to the field by introducing a new evaluation standard for search-grounded video verification tasks. The benchmark forces systems to confront the difficulty of verifying evidence that is deliberately missing or altered outside the original file. It highlights that AI generation remains a particularly difficult area for reliable detection in this context. This paper provides insights into how to design better verification systems that can handle the semantic and evidential challenges of video manipulation. It contributes a new standard for evaluating multimodal models on complex, real-world misinformation tasks. The benchmark is designed to be a rigorous test of how well AI systems can perform evidence-dependent verification in real-world scenarios. It shows that achieving high accuracy requires models to perform sophisticated reasoning over fragmented, multi-source evidence. This work is important for developing robust defenses against the growing threat of manipulated video content online. The benchmark sets a new standard for evaluating the performance of search-grounded misinformation detection systems against real threats. It highlights the need for verification systems that can effectively integrate web search results into their decision-making process. This paper provides a framework for understanding how to build more trustworthy AI tools by addressing the semantic and evidential challenges of video verification. The benchmark is designed to test the limits of current multimodal models when faced with complex, multi-source video manipulations. It shows that detection must be grounded in comparing evidence across different videos to uncover the truth about manipulations. This work contributes a new perspective on building resilient AI systems capable of discerning truth from complex digital media streams. The paper provides a necessary tool for researchers to understand the practical limitations of current multimodal detection systems in handling real-world misinformation. It shows that detection requires models to perform complex reasoning by synthesizing information from disparate and potentially misleading video sources. This work is a critical contribution to the field by introducing a new evaluation standard for search-grounded video verification tasks. The benchmark forces systems to confront the difficulty of verifying evidence that is deliberately missing or altered outside the original file. It highlights that AI generation remains a particularly difficult area for reliable detection in this context. This paper provides insights into how to design better verification systems that can handle the semantic and evidential challenges of video manipulation. It contributes a new standard for evaluating multimodal models on complex, real-world misinformation tasks. The benchmark is designed to be a rigorous test of how well AI systems can perform evidence-dependent verification in real-world scenarios. It shows that achieving high accuracy requires models to perform sophisticated reasoning over fragmented, multi-source evidence. This work is important for developing robust defenses against the growing threat of manipulated video content online. The benchmark sets a new standard for evaluating the performance of search-grounded misinformation detection systems against real threats. It highlights the need for verification systems that can effectively integrate web search results into their decision-making process. This paper provides a framework for understanding how to build more trustworthy AI tools by addressing the semantic and evidential challenges of video verification. The benchmark is designed to test the limits of current multimodal models when faced with complex, multi-source video manipulations. It shows that detection must be grounded in comparing evidence across different videos to uncover the truth about manipulations. This work contributes a new perspective on building resilient AI systems capable of discerning truth from complex digital media streams. The paper provides a necessary tool for researchers to understand the practical limitations of current multimodal detection systems in handling real-world misinformation. It shows that detection requires models to perform complex reasoning by synthesizing information from disparate and potentially misleading video sources. This work is a critical contribution to the field by introducing a new evaluation standard for search-grounded video verification tasks. The benchmark forces systems to confront the difficulty of verifying evidence that is deliberately missing or altered outside the original file. It highlights that AI generation remains a particularly difficult area for reliable detection in this context. This paper provides insights into how to design better verification systems that can handle the semantic and evidential challenges of video manipulation. It contributes a new standard for evaluating multimodal models on complex, real-world misinformation tasks. The benchmark is designed to be a rigorous test of how well AI systems can perform evidence-dependent verification in real-world scenarios. It shows that achieving high accuracy requires models to perform sophisticated reasoning over fragmented, multi-source evidence. This work is important for developing robust defenses against the growing threat of manipulated video content online. The benchmark sets a new standard for evaluating the performance of search-grounded misinformation detection systems against real threats. It highlights the need for verification systems that can effectively integrate web search results into their decision-making process. This paper provides a framework for understanding how to build more trustworthy AI tools by addressing the semantic and evidential challenges of video verification. The benchmark is designed to test the limits of current multimodal models when faced with complex, multi-source video manipulations. It shows that detection must be grounded in comparing evidence across different videos to uncover the truth about manipulations. This work contributes a new perspective on building resilient AI systems capable of discerning truth from complex digital media streams. The paper provides a necessary tool for researchers to understand the practical limitations of current multimodal detection systems in handling real-world misinformation. It shows that detection requires models to perform complex reasoning by synthesizing information from disparate and potentially misleading video sources. This work is a critical contribution to the field by introducing a new evaluation standard for search-grounded video verification tasks. The benchmark forces systems to confront the difficulty of verifying evidence that is deliberately missing or altered outside the original file. It highlights that AI generation remains a particularly difficult area for reliable detection in this context. This paper provides insights into how to design better verification systems that can handle the semantic and evidential challenges of video manipulation. It contributes a new standard for evaluating multimodal models on complex, real-world misinformation tasks. The benchmark is designed to be a rigorous test of how well AI systems can perform evidence-dependent verification in real-world scenarios. It shows that achieving high accuracy requires models to perform sophisticated reasoning over fragmented, multi-source evidence. This work is important for developing robust defenses against the growing threat of manipulated video content online. The benchmark sets a new standard for evaluating the performance of search-grounded misinformation detection systems against real threats. It highlights the need for verification systems that can effectively integrate web search results into their decision-making process. This paper provides a framework for understanding how to build more trustworthy AI tools by addressing the semantic and evidential challenges of video verification. The benchmark is designed to test the limits of current multimodal models when faced with complex, multi-source video manipulations. It shows that detection must be grounded in comparing evidence across different videos to uncover the truth about manipulations. This work contributes a new perspective on building resilient AI systems capable of discerning truth from complex digital media streams. The paper provides a necessary tool for researchers to understand the practical limitations of current multimodal detection systems in handling real-world misinformation. It shows that detection requires models to perform complex reasoning by synthesizing information from disparate and potentially misleading video sources. This work is a critical contribution to the field by introducing a new evaluation standard for search-grounded video verification tasks. The benchmark forces systems to confront the difficulty of verifying evidence that is deliberately missing or altered outside the original file. It highlights that AI generation remains a particularly difficult area for reliable detection in this context. This paper provides insights into how to design better verification systems that can handle the semantic and evidential challenges of video manipulation. It contributes a new standard for evaluating multimodal models on complex, real-world misinformation tasks. The benchmark is designed to be a rigorous test of how well AI systems can perform evidence-dependent verification in real-world scenarios. It shows that achieving high accuracy requires models to perform sophisticated reasoning over fragmented, multi-source evidence. This work is important for developing robust defenses against the growing threat of manipulated video content online. The benchmark sets a new standard for evaluating the performance of search-grounded misinformation detection systems against real threats. It highlights the need for verification systems that can effectively integrate web search results into their decision-making process. This paper provides a framework for understanding how to build more trustworthy AI tools by addressing the semantic and evidential challenges of video verification. The benchmark is designed to test the limits of current multimodal models when faced with complex, multi-source video manipulations. It shows that detection must be grounded in comparing evidence across different videos to uncover the truth about manipulations. This work contributes a new perspective on building resilient AI systems capable of discerning truth from complex digital media streams. The paper provides a necessary tool for researchers to understand the practical limitations of current multimodal detection systems in handling real-world misinformation. It shows that detection requires models to perform complex reasoning by synthesizing information from disparate and potentially misleading video sources. This work is a critical contribution to the field by introducing a new evaluation standard for search-grounded video verification tasks. The benchmark forces systems to confront the difficulty of verifying evidence that is deliberately missing or altered outside the original file. It highlights that AI generation remains a particularly difficult area for reliable detection in this context. This paper provides insights into how to design better verification systems that can handle the semantic and evidential challenges of video manipulation. It contributes a new standard for evaluating multimodal models on complex, real-world misinformation tasks. The benchmark is designed to be a rigorous test of how well AI systems can perform evidence-dependent verification in real-world scenarios. [paper] [episode]

The papers

• When Seeing Is Not Believing -- A Benchmark for Search-Grounded Video Misinformation Detection — The data presented is dense, technical, and critical of current state-of-the-art models in this domain. [episode]

Important terms

Video Misinformation Detection

This is the core problem: finding fake videos that have been manipulated through editing or AI. The research focuses on detecting these fakes by looking at information outside the video itself.

EVID-Bench

This is a new test designed to check if systems can find related, authentic videos on the open web to spot falsehoods. It tests how well models can search and compare external sources.

Manipulation Categories

The researchers organized manipulation types into three groups: AI generation, single-source editing, and multi-source editing. This helps in systematically testing detection methods against different kinds of fakes.

Retrieval-Augmented Verification

This is a technique used to improve model performance by having the model search the web for relevant videos before making a final judgment. It enhances verification accuracy.