Top AI Research: Video & Multimodal Retrieval (Nov 2025)

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Top AI Research: Video & Multimodal Retrieval (Nov 2025)Hey everyone! Welcome to *DailyArXiv's* latest dive into the cutting-edge world of AI research. We're super excited to bring you the *hottest papers* that just dropped, specifically focusing on **Video Retrieval** and **Multimodal Retrieval**. These fields are absolutely exploding, pushing the boundaries of how machines understand and interact with visual and textual information. If you're into making sense of vast amounts of data, building smarter search engines, or just fascinated by the future of AI, then you're in the right place, guys! We've meticulously picked the most intriguing papers from November 2025, offering you a peek into what brilliant minds are cooking up. Get ready to explore some truly *groundbreaking ideas* that are setting the stage for the next generation of intelligent systems. Let's dig in and see what's new!## Dive into Video Retrieval InnovationsOkay, folks, let's kick things off with **Video Retrieval**. This area is *super crucial* because, let's be real, we're drowning in video content. From YouTube to TikTok, surveillance footage to medical imaging, the sheer volume is astronomical. The challenge? How do we find that *exact moment* or that *specific event* within hours of video data without watching every single second? That's where **video retrieval** steps in, aiming to develop sophisticated algorithms and models that can understand, index, and retrieve relevant video segments based on text queries, other videos, or even abstract concepts. Recent advancements, as highlighted in these papers, are pushing towards more *efficient*, *accurate*, and *context-aware* retrieval systems. We're talking about models that can grasp *long-term temporal dependencies*, understand *complex actions*, and even perform *reasoning* across video frames and associated text. The goal is to move beyond simple keyword matching to a deeper, semantic understanding of video content. Imagine asking an AI, "Show me all videos where someone is explaining how to fix a leaky faucet," and it not only finds the right videos but even pinpoints the exact timestamps of the repair steps. This isn't just about entertainment; it has massive implications for security, education, content moderation, and even autonomous systems that need to understand their surroundings from camera feeds. We're seeing a trend towards integrating large language models (LLMs) with visual data, creating powerful *vision-language models* (VLMs) that can interpret nuanced queries and context. Furthermore, the focus is shifting towards handling *long videos* more effectively, breaking them down into manageable segments and understanding their overall narrative. *Efficiency* is also a huge keyword here, as processing vast video datasets requires optimized retrieval systems. These papers represent the bleeding edge of this exciting domain, showcasing techniques from generative retrieval to adaptive pivot visual information extraction. So, buckle up, because the way we interact with video is about to get a whole lot smarter!### DualGR: Generative Retrieval with Long and Short-Term Interests ModelingThis paper, *DualGR*, introduces a fascinating approach to **generative retrieval** for videos. It focuses on how users' *long-term and short-term interests* can be modeled to provide more relevant results. Imagine a system that remembers your general preferences but also adapts to what you've just searched for – pretty neat, right?### VLA-R: Vision-Language Action Retrieval toward Open-World End-to-End Autonomous Driving*VLA-R* dives into an *open-world end-to-end autonomous driving* scenario. This is crucial for self-driving cars, enabling them to retrieve relevant actions based on visual cues and language commands. It's all about making sure autonomous vehicles can understand and react intelligently in complex, real-world situations.### Reasoning Text-to-Video Retrieval via Digital Twin Video Representations and Large Language ModelsHere's where **Large Language Models (LLMs)** meet video! This paper explores *reasoning text-to-video retrieval* by creating "digital twin" video representations. It allows LLMs to understand video content on a deeper, more conceptual level, translating complex textual queries into precise video segments.### Fusionista2.0: Efficiency Retrieval System for Large-Scale Datasets*Fusionista2.0* is all about *efficiency* – a critical factor when dealing with massive video datasets. This system focuses on making retrieval fast and scalable, ensuring that even with vast amounts of data, you can still get your results quickly and accurately. *Speed and scale*, guys, are key!### APVR: Hour-Level Long Video Understanding with Adaptive Pivot Visual Information RetrievalAccepted by AAAI 2026, *APVR* tackles the tough challenge of *hour-level long video understanding*. It uses an *adaptive pivot visual information retrieval* technique to make sense of extremely long videos, pinpointing key moments without getting lost in the details.### GCAgent: Long-Video Understanding via Schematic and Narrative Episodic MemoryAnother brilliant paper addressing *long-video understanding*, *GCAgent* introduces *schematic and narrative episodic memory*. This helps the AI grasp the overall story and structure of extended video content, making its comprehension more human-like.### Enhanced Partially Relevant Video Retrieval through Inter- and Intra-Sample Analysis with Coherence PredictionThis research focuses on *partially relevant video retrieval*, which is super important because sometimes you don't need the whole video, just a specific part. By using *inter- and intra-sample analysis* with *coherence prediction*, it enhances the ability to find those precise, relevant snippets.### DreamRunner: Fine-Grained Compositional Story-to-Video Generation with Retrieval-Augmented Motion Adaptation*DreamRunner*, accepted by AAAI 2026, takes us into the realm of *story-to-video generation*. It's about generating videos from narratives, and what's cool is it uses *retrieval-augmented motion adaptation* for fine-grained control, making the generated videos incredibly realistic and detailed.### Q2E: Query-to-Event Decomposition for Zero-Shot Multilingual Text-to-Video Retrieval*Q2E* (accepted in IJCNLP-AACL 2025) is a game-changer for *zero-shot multilingual text-to-video retrieval*. It decomposes queries into events, allowing the system to understand and retrieve videos even in languages it hasn't been explicitly trained on. Talk about versatility!### LoVR: A Benchmark for Long Video Retrieval in Multimodal ContextsBenchmarks are *essential* for progress, and *LoVR* provides just that: a *benchmark for long video retrieval in multimodal contexts*. This will help researchers properly evaluate and compare new models, pushing the entire field forward.### Explicit Temporal-Semantic Modeling for Dense Video Captioning via Context-Aware Cross-Modal InteractionAccepted to AAAI 2026, this paper tackles *dense video captioning*. It focuses on *explicit temporal-semantic modeling* to generate highly detailed and accurate captions for video events, understanding both *when* things happen and *what* they mean.### Learning a Thousand Tasks in a DayPublished in Science Robotics, this incredible work showcases how systems can *learn a thousand tasks in a day*. While broad, its implications for *robotics and action retrieval* are immense, as robots need to quickly adapt and understand diverse instructions from video demonstrations.### Surgical Agent Orchestration Platform for Voice-directed Patient Data InteractionThis paper introduces a *surgical agent orchestration platform* that allows for *voice-directed patient data interaction*. It's a fantastic example of how retrieval systems can be integrated into critical applications like healthcare, making patient data retrieval more intuitive and efficient.### Quality Over Quantity? LLM-Based Curation for a Data-Efficient Audio-Video Foundation ModelAccepted at EUSIPCO 2025, this research asks a crucial question: *Quality Over Quantity* in data? It proposes *LLM-based curation* to build *data-efficient audio-video foundation models*, showing that smart data selection can be more effective than simply hoarding more data.### StreamKV: Streaming Video Question-Answering with Segment-based KV Cache Retrieval and Compression*StreamKV* offers a smart solution for *streaming video question-answering*. It uses *segment-based KV cache retrieval and compression*, which means it can efficiently answer questions about live or continuously streaming video without needing to process the entire stream at once.## Exploring the World of Multimodal RetrievalAlright, let's shift gears and talk about **Multimodal Retrieval**, an equally *thrilling* and *rapidly evolving* area of AI research. What exactly is it? Well, guys, in our daily lives, information rarely comes in just one form. We see images with text captions, videos with audio, documents with charts, and so much more. **Multimodal retrieval** is all about building AI systems that can understand and connect information presented in *multiple formats*—like text, images, audio, and video—and retrieve the most relevant results based on queries that can also be multimodal. This is a huge leap beyond just searching for text or images separately. Imagine being able to ask a question using both text and an image, and the system understands the context from both inputs to give you a perfectly tailored answer. The papers we're looking at here demonstrate some *incredible progress* in this domain. We're seeing innovations in *agentic frameworks* that can generate complex reports, *benchmarks* for attributing sources in visual Q&A, and even applications in *cold-start recommender systems* and *autonomous driving*. The integration of *Large Language Models (LLMs)* with various data types is a recurring theme, enabling AIs to perform *structured reasoning* and understand *implicit knowledge* across modalities. This field is *absolutely critical* for creating truly intelligent agents that can interact with the world the way humans do, by synthesizing information from diverse sources. Think about a chatbot that can not only answer your questions but also show you relevant diagrams or videos, or a self-driving car that uses both visual data and real-time verbal instructions to navigate. *Enhancing fine-grained visual understanding*, tackling *positional biases in embedding models*, and developing *retrieval-augmented generation (RAG)* for complex scenarios are all key focus areas. The potential applications are vast, from enhanced e-commerce search to advanced medical diagnostics and highly capable AI assistants. These cutting-edge works truly showcase how AI is learning to speak the world's many languages, both literal and metaphorical.### Multimodal DeepResearcher: Generating Text-Chart Interleaved Reports From Scratch with Agentic FrameworkAccepted by AAAI 2026 Oral, *Multimodal DeepResearcher* is a game-changer! It's an *agentic framework* that can generate *text-chart interleaved reports from scratch*. This means AI can now produce sophisticated, data-rich reports by understanding and synthesizing information from various modalities – super impressive for automating complex research.### MAVIS: A Benchmark for Multimodal Source Attribution in Long-form Visual Question Answering*MAVIS*, accepted by AAAI 2026, introduces a vital *benchmark* for *multimodal source attribution in long-form visual question answering*. This helps researchers evaluate how well AI can pinpoint where specific information comes from in complex visual narratives, which is key for *verifiability and trust*.### MARC: Multimodal and Multi-Task Agentic Retrieval-Augmented Generation for Cold-Start Recommender SystemAccepted at RDGENAI at CIKM 2025 workshop, *MARC* leverages *multimodal and multi-task agentic RAG* to tackle *cold-start problems in recommender systems*. This means it can make great recommendations even for new users or items with very little prior data, by intelligently using diverse information sources.### Implicit-Knowledge Visual Question Answering with Structured Reasoning TracesThis paper explores *implicit-knowledge visual question answering*. It’s about how AI can answer questions that require common sense or background knowledge not explicitly stated in the visual data, by generating *structured reasoning traces*. Pretty cool for making AI smarter!### RAC3: Retrieval-Augmented Corner Case Comprehension for Autonomous Driving with Vision-Language ModelsAccepted by IEEE Transactions on Multimedia, *RAC3* is all about improving *autonomous driving*. It uses *retrieval-augmented corner case comprehension* with *vision-language models* to help self-driving cars understand and react to unusual or tricky situations that are rare but critical for safety.### APVR: Hour-Level Long Video Understanding with Adaptive Pivot Visual Information RetrievalYes, this one is so important it appears in both categories! *APVR* (accepted by AAAI 2026) highlights its *multimodal* nature by addressing *hour-level long video understanding* through *adaptive pivot visual information retrieval*. It's all about extracting key visual info from long videos, making it relevant in multimodal contexts.### Hierarchical Knowledge Graphs for Story Understanding in Visual NarrativesUpdated with the ICIDS 2025 camera-ready version, this research uses *hierarchical knowledge graphs* to enhance *story understanding in visual narratives*. By structuring knowledge, AI can better grasp the plot, characters, and events across different visual media.### VITRIX-CLIPIN: Enhancing Fine-Grained Visual Understanding in CLIP via Instruction Editing Data and Long CaptionsAccepted to NeurIPS 2025, *VITRIX-CLIPIN* aims to *enhance fine-grained visual understanding in CLIP*. It uses *instruction editing data and long captions* to make powerful models like CLIP even better at recognizing subtle details in images and videos.### GCAgent: Long-Video Understanding via Schematic and Narrative Episodic MemoryAnother cross-category star! *GCAgent* excels at *long-video understanding* by using *schematic and narrative episodic memory*. This approach is inherently multimodal as it combines visual input with narrative structure to form a comprehensive understanding.### Look As You Think: Unifying Reasoning and Visual Evidence Attribution for Verifiable Document RAG via Reinforcement LearningA poster of AAAI'2026, this paper introduces *Look As You Think*, which unifies *reasoning and visual evidence attribution* for *verifiable document RAG*. It’s all about making AI transparent, showing *why* it gives a certain answer by pointing to the exact visual evidence in documents.### A Multimodal Manufacturing Safety Chatbot: Knowledge Base Design, Benchmark Development, and Evaluation of Multiple RAG ApproachesThis fascinating work describes a *Multimodal Manufacturing Safety Chatbot*. It involves *knowledge base design, benchmark development, and evaluating multiple RAG approaches*, showing how multimodal AI can significantly improve safety in industrial settings.### MOON Embedding: Multimodal Representation Learning for E-commerce Search Advertising*MOON Embedding* delves into *multimodal representation learning* specifically for *e-commerce search advertising*. By combining different types of data (images, text, user behavior), it creates richer embeddings for better product discovery and targeted ads.### Positional Bias in Multimodal Embedding Models: Do They Favor the Beginning, the Middle, or the End?Accepted to AAAI 2026 main track, this *intriguing study* investigates *positional bias in multimodal embedding models*. It asks a fundamental question: do these models pay more attention to the beginning, middle, or end of input sequences? Understanding this bias is crucial for developing fair and robust AI.### Hindsight Distillation Reasoning with Knowledge Encouragement Preference for Knowledge-based Visual Question AnsweringThis paper focuses on *knowledge-based visual question answering*, using *hindsight distillation reasoning with knowledge encouragement preference*. It helps AI learn from its mistakes and improve its ability to answer complex questions that require external knowledge sources.### Multimodal Peer Review Simulation with Actionable To-Do Recommendations for Community-Aware Manuscript RevisionsLast but not least, this paper presents a *Multimodal Peer Review Simulation*. It offers *actionable to-do recommendations* for *community-aware manuscript revisions*, a truly innovative application of multimodal AI to streamline the academic peer-review process.Wow, what a journey through the *latest AI breakthroughs*! We've seen some truly innovative work in **Video Retrieval** and **Multimodal Retrieval** from November 2025. From making self-driving cars smarter and understanding long videos better to generating complex reports and enhancing e-commerce searches, the applications are endless. These papers from *PapowFish* and *DailyArXiv* aren't just academic exercises; they're laying the groundwork for future AI systems that will profoundly impact our daily lives. Keep an eye on these spaces, folks, because the pace of innovation is just incredible! Don't forget to check out the [Github page](https://github.com/PapowFish/DailyArXiv) for a better reading experience and even more papers. Until next time, keep learning and exploring!