Multi-modal Large Language Models (MLLMs) have demonstrated remarkable capabilities across various domains, propelling their evolution into multi-modal agents for human assistance. GUI automation agents for PCs face particularly daunting challenges compared to smartphone counterparts. PC environments present significantly more complex interactive elements with dense, diverse icons and widgets often lacking textual labels, leading to perception difficulties. Even advanced models like Claude-3.5 achieve only 24.0% accuracy in GUI grounding tasks. Also, PC productivity tasks involve intricate workflows spanning multiple applications with lengthy operation sequences and inter-subtask dependencies, causing dramatic performance declines where GPT-4o’s success rate drops from 41.8% at subtask level to just 8% for complete instructions.

Previous approaches have developed frameworks to address PC task complexity with varying strategies. UFO implements a dual-agent architecture separating application selection from specific control interactions. Meanwhile, AgentS augments planning capabilities by combining online search with local memory. However, these methods demonstrate significant limitations in fine-grained perception and operation of on-screen text—a critical requirement for productivity scenarios like document editing. In addition, they generally fail to address the complex dependencies between subtasks, resulting in poor performance when handling realistic intra- and inter-app workflows that characterize everyday PC usage.

Researchers from MAIS, Institute of Automation, Chinese Academy of Sciences, China, School of Artificial Intelligence, University of Chinese Academy of Sciences, Alibaba Group, Beijing Jiaotong University, and School of Information Science and Technology, ShanghaiTech University introduce PC-Agent framework to address complex PC scenarios through three innovative designs. First, the Active Perception Module enhances fine-grained interaction by extracting locations and meanings of interactive elements via accessibility trees, while using MLLM-driven intention understanding and OCR for precise text localization. Second, Hierarchical Multi-agent Collaboration implements a three-level decision process (Instruction-Subtask-Action) where a Manager Agent decomposes instructions into parameterized subtasks and manages dependencies, a Progress Agent tracks operation history, and a Decision Agent executes steps with perception and progress information. Third, Reflection-based Dynamic Decision-making introduces a Reflection Agent that assesses execution correctness and provides feedback, enabling top-down task decomposition with bottom-up precision feedback across all four collaborating agents.

PC-Agent’s architecture addresses GUI interaction through a formalized approach where an agent ρ processes user instructions I, observations O, and history H to determine actions A. The Active Perception Module enhances element recognition using pywinauto to extract accessibility trees for interactive elements while employing MLLM-driven intention understanding with OCR for precise text localization. For complex workflows, PC-Agent implements Hierarchical Multi-agent Collaboration across three levels: the Manager Agent decomposes instructions into parameterized subtasks and manages dependencies; the Progress Agent tracks operation progress within subtasks; and the Decision Agent executes step-by-step actions based on environmental perception and progress information. This hierarchical division effectively reduces decision-making complexity by breaking complex tasks into manageable components with clear interdependencies.

Experimental results demonstrate PC-Agent’s superior performance compared to both single and multi-agent alternatives. Single MLLM-based agents (GPT-4o, Gemini-2.0, Claude3.5, Qwen2.5-VL) consistently fail on complex instructions, with even the best performer achieving only 12% success rate, confirming that single-agent approaches struggle with lengthy operational sequences and complex dependencies. Multi-agent frameworks like UFO and AgentS show modest improvements but remain limited by perception deficiencies and dependency management issues. They struggle with fine-grained operations such as text editing in Word or proper data entry in Excel, and often fail to utilize information from previous subtasks. In contrast, PC-Agent significantly outperforms all previous methods, surpassing UFO by 44% and AgentS by 32% in success rate through its Active Perception Module and hierarchical multi-agent collaboration.

This study introduces PC-Agent framework, a significant advancement in handling complex PC-based tasks through three key innovations. The Active Perception Module provides refined perception and operation capabilities, enabling precise interaction with GUI elements and text. The hierarchical multi-agent collaboration architecture effectively decomposes decision-making across instruction, subtask, and action levels, while reflection-based dynamic decision-making allows for real-time error detection and correction. Validation through the newly created PC-Eval benchmark with realistic, complex instructions confirms PC-Agent’s superior performance compared to previous methods, demonstrating its effectiveness in navigating the intricate workflows and interactive environments characteristic of PC productivity scenarios.

Check out the Paper and GitHub Page. All credit for this research goes to the researchers of this project. Also, feel free to follow us on Twitter and don’t forget to join our 80k+ ML SubReddit.

Asjad is an intern consultant at Marktechpost. He is persuing B.Tech in mechanical engineering at the Indian Institute of Technology, Kharagpur. Asjad is a Machine learning and deep learning enthusiast who is always researching the applications of machine learning in healthcare.

In this tutorial, we demonstrate how to build an AI-powered PDF interaction system in Google Colab using Gemini Flash 1.5, PyMuPDF, and the Google Generative AI API. By leveraging these tools, we can seamlessly upload a PDF, extract its text, and interactively ask questions, receiving intelligent responses from Google’s latest Gemini Flash 1.5 model.

!pip install -q -U google-generativeai PyMuPDF python-dotenv

First we install the necessary dependencies for building an AI-powered PDF Q&A system in Google Colab. google-generativeai provides access to Gemini Flash 1.5, enabling natural language interactions, while PyMuPDF (also known as Fitz) allows efficient text extraction from PDFs. Also, python-dotenv helps manage environment variables, such as API keys, securely within the notebook.

from google.colab import files
uploaded = files.upload()

We upload files from your local device to Google Colab. When executed, it opens a file selection dialog, allowing you to choose a file (e.g., a PDF) to upload. The uploaded file is stored in a dictionary-like object (uploaded), where keys represent file names and values contain the file’s binary data. This step is essential for directly processing documents, datasets, or model weights in a Colab environment.

import fitz


def extract_pdf_text(pdf_path):
    doc = fitz.open(pdf_path)
    full_text = ""
    for page in doc:
        full_text += page.get_text()
    return full_text


pdf_file_path="/content/Paper.pdf"
document_text = extract_pdf_text(pdf_path=pdf_file_path)
print("Document text extracted!")
print(document_text[:1000]) 

We use PyMuPDF (fitz) to extract text from a PDF file in Google Colab. The function extract_pdf_text(pdf_path) reads the PDF, iterates through its pages, and retrieves the text content. The extracted text is then stored in document_text, with the first 1000 characters printed to preview the content. This step is crucial for enabling text-based analysis and AI-driven question answering from PDFs.

import os
os.environ["GOOGLE_API_KEY"] = 'Use your own API key here'

We set the Google API key as an environment variable in Google Colab. The API key is required to authenticate requests to Google Generative AI, allowing access to Gemini Flash 1.5 for AI-powered text processing. Replacing ‘Use your own API key here’ with a valid key ensures that the model can generate responses securely within the notebook.

import google.generativeai as genai


genai.configure(api_key=os.environ["GOOGLE_API_KEY"])


model_name = "models/gemini-1.5-flash-001"


def query_gemini_flash(question, context):
    model = genai.GenerativeModel(model_name=model_name)
    prompt = f"""
Context: {context[:20000]}


Question: {question}


Answer:
"""
    response = model.generate_content(prompt)
    return response.text


pdf_text = extract_pdf_text("/content/Paper.pdf")


question = "Summarize the key findings of this document."
answer = query_gemini_flash(question, pdf_text)
print("Gemini Flash Answer:")
print(answer)

Finally, we configure and query Gemini Flash 1.5 using a PDF document for AI-powered text generation. It initializes the genai library with the API key and loads the Gemini Flash 1.5 model (gemini-1.5-flash-001). The query_gemini_flash() function takes a question and extracted PDF text as input, formulates a structured prompt, and retrieves an AI-generated response. This setup enables automated document summarization and intelligent Q&A from PDFs.

In conclusion, following this tutorial, we have successfully built an interactive PDF-based interaction system in Google Colab using Gemini Flash 1.5, PyMuPDF, and the Google Generative AI API. This solution enables users to extract information from PDFs and interactively query them easily. The combination of Google’s cutting-edge AI models and Colab’s cloud-based environment provides a powerful and accessible way to process large documents without requiring heavy computational resources.

Here is the Colab Notebook. Also, don’t forget to follow us on Twitter and join our Telegram Channel and LinkedIn Group. Don’t Forget to join our 80k+ ML SubReddit.

Even if you’re not a tech-savvy person, you’re probably using your smartphone and computer for work. We all browse the Internet, and it’s impossible to avoid keywords and articles on AI. Artificial intelligence models are getting smarter thanks to machine learning algorithms. And while many hate them and consider them unfair to the current business environment, numerous generative AI applications will make our lives better.

Generative AI is supposed to reshape app development. Companies are already tapping into the power of AI for their apps, which are supposed to become better and more intuitive. In this article, we’ll break down the ways AI will reshape apps and app development sooner than expected.

A Variety of AI Services

Many apps are already using various AI services. The implementation is already on its way. For example, online casino apps are using AI bots as part of their customer support service. Casino developers are using these services for customer support too, and using AI models to speed up app development. This mostly focuses on slot and casino game theme innovations, as well as math models.

Casino apps are quite popular these days, and playing slots and apps will become even more intuitive in the future. AI is supposed to drive this model forward, which likely means more games and tailored experiences.

It’s not just casino apps. GenAI is used in all kinds of enterprise apps, such as Microsoft’s 365 product suite. Google Workspaces also introduced a variety of AI services while Snapchat introduced an AI-powered chatbot named myAI.

Writing platforms such as Grammarly have moved to an AI model too. This powerful app and plugin improves the writing for all kinds of content, and with its AI approach in the past year, it’s becoming more and more powerful. In just a short time, grammar mistakes could be a thing of the past, and it’s all thanks to artificial intelligence.

AI-Powered vs. Traditional App Development

There’s a great difference between traditional app development and that powered by AI. Development cycles have gotten a great boost. Up until now, long development cycles meant extensive testing and debugging, as well as patching all kinds of apps. This is lengthy and expensive, as it requires a team of testers, which costs companies money.

AI embraces agile development instead. It may or may not integrate continuous machine learning, but either way, the whole process is much faster. That’s because AI can spot any problems before they materialize, resulting in a much better. Plus, AI models are getting better thanks to constant training and improvement, meaning the customers get a better and more polished experience overall.

Data handling is also getting much better. In traditional app development cycles, real-time processing of structured data is a problem. AI models are much better at handling all kinds of data. While they still need time to introduce real-time analytics, this particular feature of app development is much better at handling sensitive data.

Decision-making is about to get much better. Companies are already using AI models to rely on user-focused decision-making instead of static algorithms used in traditional app development. The current techniques have limited adaptability, but with the help of AI, they will soon become a thing of the past. Decision-making with AI relies on self-learning algorithms, which means predictive decisions.

One notable example of this decision-making AI implementation is Google Maps’ optimized routes. They’ve already been phased out for some time, giving you better and more fuel-efficient routes when you enter the correct data. Some may not like it, but thanks to AI, we’re looking at much better routes in the future.

With superior app scaling, app development will become even better in the future. So will maintenance, which has already been in use. Extensive redevelopment is clogging things up in app development, and digital-native app development doesn’t utilize dynamic scaling methods. Unlike them, AI-powered app development is inherently adaptable and scalable. Netflix is already using it as part of the responsive content delivery system, and many other apps will soon start using it too.

In the maintenance department, AI-powered app development will speed things up in over-the-air updates. This has already been implemented by many companies, including Tesla. AI can scan for in-app delivery errors or updates much faster and more precisely. Self-improving machine learning algorithms will make maintenance and evolution much better. With an updated software update map, users will enjoy a much better experience.

User experience will also be more personalized and highly adaptive in the future. App developers can use it to deliver a more custom-tailored experience. For example, casino apps can recommend games most suitable to player preferences. Spotify has already adopted such a model in its ever-evolving music recommendations.

This is also notable in streaming apps such as Netflix and HBO, as well as dating apps and similar alternatives.

Why is AI Integral in Modern App Development?

There are several reasons why artificial intelligence is crucial for modern app development. First and foremost, automated processes are streamlining development lifecycles. It means less strain on developers, as AI models and machine learning algorithms are more precise with their predictions.

Adaptive learning is another factor that makes AI integral for future app development. AI-powered apps are adjusting to user feedback and implementing changes faster than ever before. Social media algorithms are getting the most out of these models at the moment. They deliver much more precise recommendations to a level we haven’t experienced before.

The predictive capabilities of AI app development are out of this world. AI doesn’t just predict changes – it anticipates user needs and updates features proactively. Thanks to enhanced personalization, we’ll soon be getting apps that offer a custom-tailored experience, which mainly applies to gaming apps and retail shopping apps as well.

Resource optimization is another factor where AI app development excels. It enhances app performance and reduces operational costs. Some employees in certain departments may not like it, but the future is already here, and we need to adapt to it.

Stereo depth estimation plays a crucial role in computer vision by allowing machines to infer depth from two images. This capability is vital for autonomous driving, robotics, and augmented reality applications. Despite advancements in deep learning, many existing stereo-matching models require domain-specific fine-tuning to achieve high accuracy. The challenge lies in developing a model that can be generalized across different environments without additional training.

One of the key problems in stereo depth estimation is the domain gap between training and real-world data. Many current approaches depend on small, specific datasets that fail to capture the complexity of natural environments. This limitation results in models that perform well on controlled benchmarks but fail in diverse scenarios. Furthermore, fine-tuning these models for new domains is computationally expensive and impractical for real-time applications. Overcoming these challenges requires a more robust approach that eliminates the need for domain-specific training.

Traditional stereo depth estimation methods rely on constructing cost volumes, which encode the disparity between image pairs. These methods utilize 3D convolutional neural networks (CNNs) for cost filtering but struggle with generalization beyond their training data. Iterative refinement techniques attempt to enhance accuracy by progressively improving disparity predictions. However, these approaches are limited by their reliance on recurrent modules, which increase computational costs. Some recent methods have explored transformer-based architectures but have faced challenges in effectively handling the disparity search space while maintaining efficiency.

Researchers at NVIDIA introduced FoundationStereo, a foundation model designed to address these limitations and achieve strong zero-shot generalization. To build this model, the research team created a large-scale synthetic training dataset containing one million stereo-image pairs with high photorealism and diverse scenarios. An automated self-curation pipeline was developed to filter out ambiguous samples, ensuring high-quality training data. Further, the model incorporates a side-tuning feature backbone, which leverages monocular priors from existing vision foundation models. This adaptation bridges the gap between synthetic and real-world data, improving generalization without requiring per-domain fine-tuning.

The methodology behind FoundationStereo integrates several innovative components. The Attentive Hybrid Cost Volume (AHCF) module is a key element that enhances disparity estimation by combining 3D Axial-Planar Convolution and a Disparity Transformer. The 3D Axial-Planar Convolution refines cost volume filtering by separating spatial and disparity information, leading to improved feature aggregation. Meanwhile, the Disparity Transformer introduces long-range context reasoning, allowing the model to process complex depth structures effectively. Moreover, FoundationStereo employs a hybrid approach, integrating a CNN with a Vision Transformer (ViT) to adapt monocular depth priors into the stereo framework. Combining these techniques ensures a more precise initial disparity estimation, which is further refined through iterative processing.

Performance evaluation of FoundationStereo demonstrates its superiority over existing methods. To assess its zero-shot generalization capabilities, the model was tested on multiple datasets, including Middlebury, KITTI, and ETH3D. When trained solely on Scene Flow, FoundationStereo significantly reduced error rates compared to previous models. For instance, the Middlebury dataset recorded a BP-2 error of 4.4%, outperforming prior state-of-the-art methods. On ETH3D, it achieved a BP-1 error of 1.1%, further establishing its robustness. In KITTI-15, the model attained a D1 error rate of 2.3%, marking a significant improvement over previous benchmarks. Qualitative comparisons of in-the-wild images revealed its ability to handle challenging scenarios, including reflections, textureless surfaces, and complex lighting conditions. These results highlight the effectiveness of FoundationStereo’s architecture in achieving reliable depth estimation without fine-tuning.

The research presents a major advancement in stereo-depth estimation by addressing generalization challenges and computational efficiency. By leveraging a large-scale synthetic dataset and integrating monocular priors with innovative cost-filtering techniques, FoundationStereo eliminates the need for domain-specific training while maintaining high accuracy across different environments. The findings demonstrate how the proposed methodology sets a new benchmark for zero-shot stereo-matching models and paves the way for more versatile applications in real-world settings.

Check out the Paper and GitHub Page. All credit for this research goes to the researchers of this project. Also, feel free to follow us on Twitter and don’t forget to join our 80k+ ML SubReddit.

Nikhil is an intern consultant at Marktechpost. He is pursuing an integrated dual degree in Materials at the Indian Institute of Technology, Kharagpur. Nikhil is an AI/ML enthusiast who is always researching applications in fields like biomaterials and biomedical science. With a strong background in Material Science, he is exploring new advancements and creating opportunities to contribute.

Reasoning capabilities have become essential for LLMs, but analyzing these complex processes poses a significant challenge. While LLMs can generate detailed text reasoning output, the lack of process visualization creates barriers to understanding, evaluating, and improving. This limitation manifests in three critical ways: increased cognitive load for users attempting to parse complex reasoning paths; difficulty detecting logical fallacies, circular reasoning, and missing steps that remain obscured in lengthy text outputs; and restrictions on downstream applications due to the absence of standardized visualization frameworks. So, there is a need for unified visualization solutions that can effectively illustrate diverse reasoning methodologies across the growing ecosystem of LLM providers and models.

Existing methods like sequential reasoning show step-by-step problem decomposition and have evolved through several variants. Tree-based approaches like Tree-of-Thoughts enable state-based branching for parallel path exploration, while Beam Search reasoning evaluates solution paths based on scoring mechanisms. Further, current visualization approaches fall into two categories: model behavior analysis and reasoning process illustration. Tools like BertViz and Transformers Interpret provide detailed visualizations of attention mechanisms but are limited to low-level model behaviors. Frameworks such as LangGraph offer basic flow visualization without supporting diverse reasoning methodologies, while general-purpose tools like Graphviz and Mermaid lack specific adaptations for LLM reasoning analysis.

Researchers from the University of Cambridge and Monash University have proposed ReasonGraph, a web-based platform for visualizing and analyzing LLM reasoning processes. It supports sequential and tree-based reasoning methods while seamlessly integrating with major LLM providers and over fifty state-of-the-art models. ReasonGraph incorporates an intuitive UI with meta reasoning method selection, configurable visualization parameters, and a modular framework that facilitates efficient extension. By providing a unified visualization framework, ReasonGraph effectively reduces cognitive load in analyzing complex reasoning paths, improves error detection in logical processes, and enables more effective development of LLM-based applications.

ReasonGraph utilizes a modular framework that provides extensible reasoning visualization through the clear separation of components. The front-end tier handles visualization logic and user participation handling, implementing an asynchronous event handling module where user interactions with method selection and parameter configuration trigger corresponding state updates. The backend framework is organized around three core modules implemented in Flask: a Configuration Manager for state updates, an API Factory for LLM integration, and a Reasoning Methods module for reasoning approach encapsulation. Framework modularity exists at both API and reasoning method levels, with the API Factory providing a unified interface for multiple LLM providers through the BaseAPI class.

The evaluation of ReasonGraph shows the platform’s robustness in three key aspects. In parsing reliability, the rule-based XML parsing approach achieves nearly 100% accuracy in extracting and visualizing reasoning paths from properly formatted LLM outputs. For processing efficiency, the Mermaid-based visualization generation time is negligible compared to the LLM’s reasoning time, maintaining consistent performance across all six reasoning methods implemented in the platform. Regarding platform usability, preliminary feedback from open-source platform users shows that approximately 90% of users successfully used the platform without assistance, though these metrics continue to evolve as the user base expands and the platform undergoes regular updates.

In this paper, researchers introduced ReasonGraph, a web-based platform that enables visualization and analysis of LLM reasoning processes across six mainstream methods and over 50 models. It achieves high usability across diverse applications in academia, education, and development through its modular framework and real-time visualization capabilities. Future work includes (a) using the open-source community to integrate additional reasoning methods and expand model API support, (b) developing the platform based on community feedback and user suggestions, (c) exploring downstream applications such as reasoning evaluation, educational tutorials, etc, and (d) implementing editable nodes in the visualization flowcharts to enable direct modification of reasoning processes.

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Check out the Paper. All credit for this research goes to the researchers of this project. Also, feel free to follow us on Twitter and don’t forget to join our 80k+ ML SubReddit.






Sajjad Ansari is a final year undergraduate from IIT Kharagpur. As a Tech enthusiast, he delves into the practical applications of AI with a focus on understanding the impact of AI technologies and their real-world implications. He aims to articulate complex AI concepts in a clear and accessible manner.

Parlant: Build Reliable AI Customer Facing Agents with LLMs 💬 ✅ (Promoted)

AI-generated videos from text descriptions or images hold immense potential for content creation, media production, and entertainment. Recent advancements in deep learning, particularly in transformer-based architectures and diffusion models, have propelled this progress. However, training these models remains resource-intensive, requiring large datasets, extensive computing power, and significant financial investment. These challenges limit access to cutting-edge video generation technologies, making them primarily available to well-funded research groups and organizations.

Training AI video models is expensive and computationally demanding. High-performance models require millions of training samples and powerful GPU clusters, making them difficult to develop without significant funding. Large-scale models, such as OpenAI’s Sora, push video generation quality to new heights but demand enormous computational resources. The high cost of training restricts access to advanced AI-driven video synthesis, limiting innovation to a few major organizations. Addressing these financial and technical barriers is essential to making AI video generation more widely available and encouraging broader adoption.

Different approaches have been developed to handle the computational demands of AI video generation. Proprietary models like Runway Gen-3 Alpha feature highly optimized architectures but are closed-source, restricting broader research contributions. Open-source models like HunyuanVideo and Step-Video-T2V offer transparency but require significant computing power. Many rely on extensive datasets, autoencoder-based compression, and hierarchical diffusion techniques to enhance video quality. However, each approach comes with trade-offs between efficiency and performance. While some models focus on high-resolution output and motion accuracy, others prioritize lower computational costs, resulting in varying performance levels across evaluation metrics. Researchers continue to seek an optimal balance that preserves video quality while reducing financial and computational burdens.

HPC-AI Tech researchers introduce Open-Sora 2.0, a commercial-level AI video generation model that achieves state-of-the-art performance while significantly reducing training costs. This model was developed with an investment of only $200,000, making it five to ten times more cost-efficient than competing models such as MovieGen and Step-Video-T2V. Open-Sora 2.0 is designed to democratize AI video generation by making high-performance technology accessible to a wider audience. Unlike previous high-cost models, this approach integrates multiple efficiency-driven innovations, including improved data curation, an advanced autoencoder, a novel hybrid transformer framework, and highly optimized training methodologies.

The research team implemented a hierarchical data filtering system that refines video datasets into progressively higher-quality subsets, ensuring optimal training efficiency. A significant breakthrough was the introduction of the Video DC-AE autoencoder, which improves video compression while reducing the number of tokens required for representation. The model’s architecture incorporates full attention mechanisms, multi-stream processing, and a hybrid diffusion transformer approach to enhance video quality and motion accuracy. Training efficiency was maximized through a three-stage pipeline: text-to-video learning on low-resolution data, image-to-video adaptation for improved motion dynamics, and high-resolution fine-tuning. This structured approach allows the model to understand complex motion patterns and spatial consistency while maintaining computational efficiency.

The model was tested across multiple dimensions: visual quality, prompt adherence, and motion realism. Human preference evaluations showed that Open-Sora 2.0 outperforms proprietary and open-source competitors in at least two categories. In VBench evaluations, the performance gap between Open-Sora and OpenAI’s Sora was reduced from 4.52% to just 0.69%, demonstrating substantial improvements. Open-Sora 2.0 also achieved a higher VBench score than HunyuanVideo and CogVideo, establishing itself as a strong contender among current open-source models. Also, the model integrates advanced training optimizations such as parallelized processing, activation checkpointing, and automated failure recovery, ensuring continuous operation and maximizing GPU efficiency.

Key takeaways from the research on Open-Sora 2.0 include :

1. Open-Sora 2.0 was trained for only $200,000, making it five to ten times more cost-efficient than comparable models.
2. The hierarchical data filtering system refines video datasets through multiple stages, improving training efficiency.
3. The Video DC-AE autoencoder significantly reduces token counts while maintaining high reconstruction fidelity.
4. The three-stage training pipeline optimizes learning from low-resolution data to high-resolution fine-tuning.
5. Human preference evaluations indicate that Open-Sora 2.0 outperforms leading proprietary and open-source models in at least two performance categories.
6. The model reduced the performance gap with OpenAI’s Sora from 4.52% to 0.69% in VBench evaluations.
7. Advanced system optimizations, such as activation checkpointing and parallelized training, maximize GPU efficiency and reduce hardware overhead.
8. Open-Sora 2.0 demonstrates that high-performance AI video generation can be achieved with controlled costs, making the technology more accessible to researchers and developers worldwide.

Check out the Paper and GitHub Page. All credit for this research goes to the researchers of this project. Also, feel free to follow us on Twitter and don’t forget to join our 80k+ ML SubReddit.

Aswin AK is a consulting intern at MarkTechPost. He is pursuing his Dual Degree at the Indian Institute of Technology, Kharagpur. He is passionate about data science and machine learning, bringing a strong academic background and hands-on experience in solving real-life cross-domain challenges.

How AI is Revolutionizing Video Content Creation

Introduction

The world of video content creation has been evolving at a rapid pace, especially with the rise of digital media platforms. Whether it’s a YouTube vlog, a promotional video, or even corporate training materials, video content is everywhere. As the demand for high-quality videos grows, creators are turning to technology for assistance, and AI video generators are playing a pivotal role.

In this article, we will dive deep into how AI is transforming the video creation process, from AI in personalized video content to simplifying the editing process and revolutionizing the way we create videos. With AI making these tasks more accessible, creators from all backgrounds are able to elevate their content creation game, no matter their technical expertise. Let’s explore how AI is shaping the future of video content.

The Role of AI in Video Production

AI has made video production more efficient and accessible to a broader range of creators. Gone are the days when video production required expensive equipment and specialized skills. With the rise of AI video generators, anyone can produce high-quality videos quickly.

AI tools are now used to automate many aspects of the video creation process. For instance, AI in video editing enables quick scene transitions, automatic cropping, and even the addition of special effects. This automation allows creators to focus more on their message and creativity instead of worrying about the technicalities.

AI can also assist in video stabilization, which helps smooth out shaky footage. Whether you’re filming a shaky vlog or a moving object, AI tools can ensure that your video looks stable and professional. This technological advantage is a game-changer for beginners and seasoned creators alike.

The AI-driven workflow is much faster and cost-efficient, significantly reducing production time. Whether it’s generating video from a script or automatically trimming footage, AI in video creation helps get the job done faster.

AI-Powered Script Writing and Storyboarding

While AI has been widely acknowledged for its abilities in video editing, it’s also making strides in the pre-production phase. Writing a script and creating a storyboard can be time-consuming, but AI is stepping in to assist.

With AI in personalized video content, creators can input topics, keywords, or themes, and AI-powered tools generate scripts or ideas for videos. These tools can create a rough draft of the script, which the creator can then refine, making the writing process significantly faster.

Storyboarding, a crucial aspect of video planning, is also being enhanced by AI. AI-driven tools can automatically create storyboards based on the script, helping creators visualize the scenes before filming. This visual representation helps save time during production and ensures the video follows a logical and creative flow.

For creators who might not have experience with writing scripts or creating detailed storyboards, AI video generators and other tools are essential for easing the burden of these tasks.

Video Editing and Post-Production

Post-production is where much of the magic happens. However, editing videos can be daunting, especially for beginners. AI has made great strides in improving this aspect of video content creation.

With AI video editing tools, creators can automate much of the editing process. For example, AI can automatically suggest scene transitions, effects, and even background music that best suits the content. This means creators can focus on refining the final output rather than spending hours editing individual frames.

AI-driven color grading and correction tools can adjust the hues and lighting of the video to make it visually stunning, without requiring advanced knowledge of post-production software. Additionally, AI in audio enhancement tools can clean up background noise, adjust the volume of voices, and ensure audio consistency across the video.

For those working with motion graphics, AI can streamline the creation of animations and visual effects. Whether it’s adding animated text or implementing 3D elements, AI helps speed up the process while ensuring professional-quality results.

These AI tools are also helping in audio mixing by automating tasks like leveling out voice volume and eliminating background noises. This AI-assisted audio enhancement saves creators from spending excessive time tweaking their soundtracks.

Enhancing Personalization and Audience Engagement

One of the most exciting aspects of AI’s role in video content creation is its ability to personalize videos for the audience. Thanks to AI’s ability to analyze user behavior and preferences, creators can deliver personalized video content that resonates with their viewers.

For instance, AI can help content creators generate video content tailored to specific demographics. By analyzing past engagement, AI can suggest content topics or even personalize scripts to better cater to a specific audience’s interests.

AI is also enhancing audience interaction within videos. AI chatbots for interactive videos allow users to engage directly with content, making the experience more immersive. Viewers can now make choices that affect the outcome of the video, creating a more personalized and engaging experience.

Moreover, AI in personalized video content can assist in segmenting content for diverse audiences. Creators can use AI tools to optimize content length, language, and even themes to ensure they connect with their target audience on a deeper level.

The Future of AI in Video Content Creation

The future of AI in video creation looks incredibly promising. As machine learning and deep learning algorithms evolve, AI will only become more proficient at automating various aspects of video production.

AI video generators will continue to improve, with the ability to create videos from a broader range of inputs, such as text-based content. Imagine typing a script and having an entire video automatically generated, complete with visuals, voiceovers, and music—this could soon be a reality.

AI will also make videos even more interactive and immersive. Integrating AI with emerging technologies like augmented reality (AR) and virtual reality (VR) will open new doors for creators to produce fully immersive video experiences. AI in personalized video content could lead to even more dynamic, audience-responsive videos, where the content evolves in real-time based on viewer preferences.

The integration of AI video editing tools will be more seamless, allowing creators to tweak everything from sound design to visual effects with minimal effort. AI’s predictive capabilities will also help creators stay ahead of trends by analyzing data and suggesting content ideas that are likely to engage viewers.

Ethical Considerations in AI-Powered Video Content

As AI becomes more embedded in the video content creation process, there are important ethical considerations to keep in mind. One of the biggest concerns is the potential for deepfakes—videos that use AI to create realistic but fake content. While this technology can be fun and creative, it also raises serious concerns about misinformation and manipulation.

Creators need to be aware of the ethical implications of using AI in video production. Ensuring that the AI-generated content remains authentic and does not deceive the audience is crucial. There’s also the question of privacy—AI systems that analyze user data to personalize video content need to respect viewer privacy and ensure that the data is used responsibly.

Lastly, the issue of bias in AI is another key concern. AI in video content has the potential to perpetuate or amplify biases, whether in terms of gender, race, or other factors. It’s essential that creators and developers prioritize fairness and inclusivity in their use of AI.

Conclusion

AI is undoubtedly transforming the world of video content creation. From AI video generators to AI in personalized video content, these innovations have made video production more accessible, efficient, and engaging for creators of all skill levels.

As we look to the future, AI’s role in video creation will only continue to expand. With new tools and technologies on the horizon, the possibilities for video creators are virtually endless. However, with great power comes great responsibility. It’s essential that we, as creators and users, ensure AI is used ethically and responsibly.

The combination of AI and human creativity will lead to a new era of video content, one that is more dynamic, interactive, and personalized than ever before. As we embrace these advancements, we can look forward to a more exciting and innovative future for video content creation.

Like humans, large language models (LLMs) often have differing skills and strengths derived from differences in their architectures and training regimens. However, they struggle to combine specialized expertise across different domains, limiting their problem-solving capabilities compared to humans. Specialized models like MetaMath, WizardMath, and QwenMath excel at mathematical reasoning but often underperform on tasks requiring common sense or medical knowledge. Even within specific domains such as mathematics, models show nuanced variations in capability, e.g., one might excel at algebra while another masters geometry. creates a need for frameworks that can identify and select the most appropriate expert models for specific problems.

Existing approaches like Mixture-of-Experts (MoE) models distribute computation across multiple specialized components, with recent emphasis on sparse approaches that activate only the most relevant experts per input. The Sparse MoE (SMoE) method has improved efficiency across vision, language, and multimodal tasks but requires combining models in the parameter space through joint training. More recent frameworks like MoA (Mixture-of-Agents) attempt to address this by combining LLM outputs symbolically. Further, Multi-agent reasoning approaches have emerged as alternatives, such as the Student-teacher technique that distills reasoning capabilities from stronger to weaker agents, while debate frameworks allow multiple agents to refine arguments collectively.

Researchers from UNC Chapel Hill have proposed SYMBOLIC-MOE, a symbolic, text-based, and gradient-free Mixture-of-Experts framework to enable adaptive instance-level mixing of pre-trained LLM experts. It takes a fine-grained perspective by emphasizing specialized skills within broader domains like algebra within mathematics or molecular biology within biomedical reasoning. They also introduced a skill-based recruiting strategy that dynamically selects the most relevant expert LLMs for each specific reasoning task based on their demonstrated strengths. Moreover,  SYMBOLIC-MOE outperforms strong LLMs like GPT4o-mini, as well as multiagent approaches, with an absolute average improvement of 8.15% over the best multi-agent baseline.

SYMBOLIC-MOE consists of three stages: model profile creation and aggregator selection followed by expert recruitment and final answer generation, both of which take place during inference. To maximize throughput and efficiency, SYMBOLIC-MOE introduces an innovative batching strategy where all instances are first analyzed to determine which LLMs will be needed. The system then intelligently groups problem instances based on their required experts, allowing each active expert model to receive all relevant instances in a single batch and ensuring each expert is loaded only once. This solution enables efficient batched inference on a single GPU while supporting a diverse pool of 16 LLMs, with the flexibility to add more GPUs for further parallelization.

SYMBOLIC-MOE shows exceptional performance across diverse benchmarks. It consistently outperforms all baseline approaches, surpassing single-model strategies, multi-agent debates with a single model, and multi-model multi-agent frameworks like MoA and ReConcile. It exceeds the strongest multi-agent baseline (Self-MoA) by an impressive 8.15% absolute average improvement, 8.28% on MMLU-Pro, 13.45% on AIME, 4.92% on GPQA, and 6.08% on MedMCQA. SYMBOLIC-MOE achieves comparable or superior performance to larger models with 70B parameters by using four 7-8B parameter models. It outperforms Llama3.3 70B on AIME and GPQA while matching its performance on MedMCQA. Efficiency testing reveals that it operates 44% faster on a single GPU than MoA while achieving better accuracy.

In conclusion, researchers introduced SYMBOLIC-MOE, a scalable MoE framework that combines models through their symbolic output. This method identifies the skills needed for a given problem and recruits agents based on those skills to engage in a discussion about a given input. SYMBOLIC-MOE outperforms standard inference-time scaling methods as well as other debate frameworks and other mixture-of-agents methods, leading to strong performance across domains without human intervention. It’s average performance across heterogeneous tasks is in fact stronger than that of advanced proprietary models such as GPT4o-mini. However, this method has limitations: (a) It involves running multiple models, which increases inference cost, and (b) it relies on skills inferred from a small validation set to set the agent profiles.

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Check out the Paper and GitHub Page. All credit for this research goes to the researchers of this project. Also, feel free to follow us on Twitter and don’t forget to join our 80k+ ML SubReddit.






Sajjad Ansari is a final year undergraduate from IIT Kharagpur. As a Tech enthusiast, he delves into the practical applications of AI with a focus on understanding the impact of AI technologies and their real-world implications. He aims to articulate complex AI concepts in a clear and accessible manner.

Parlant: Build Reliable AI Customer Facing Agents with LLMs 💬 ✅ (Promoted)

LLMs are widely used for conversational AI, content generation, and enterprise automation. However, balancing performance with computational efficiency is a key challenge in this field. Many state-of-the-art models require extensive hardware resources, making them impractical for smaller enterprises. The demand for cost-effective AI solutions has led researchers to develop models that deliver high performance with lower computational requirements.

Training and deploying AI models present hurdles for researchers and businesses. Large-scale models require substantial computational power, making them costly to maintain. Also, AI models must handle multilingual tasks, ensure high instruction-following accuracy, and support enterprise applications such as data analysis, automation, and coding. Current market solutions, while effective, often demand infrastructure beyond the reach of many enterprises. The challenge is to optimize AI models for processing efficiency without compromising accuracy or functionality.

Several AI models currently dominate the market, including GPT-4o and DeepSeek-V3. These models excel in natural language processing and generation but require high-end hardware, sometimes needing up to 32 GPUs to operate effectively. While they provide advanced capabilities in text generation, multilingual support, and coding, their hardware dependencies limit accessibility. Some models also struggle with enterprise-level instruction-following accuracy and tool integration. Businesses need AI solutions that maintain competitive performance while minimizing infrastructure and deployment costs. This demand has driven efforts to optimize language models to function with minimal hardware requirements.

Researchers from Cohere introduced Command A, a high-performance AI model, designed specifically for enterprise applications requiring maximum efficiency. Unlike conventional models that require large computational resources, Command A operates on just two GPUs while maintaining competitive performance. The model comprises 111 billion parameters and supports a context length of 256K, making it suitable for enterprise applications that involve long-form document processing. Its ability to efficiently handle business-critical agentic and multilingual tasks sets it apart from its predecessors. The model has been optimized to provide high-quality text generation while reducing operational costs, making it a cost-effective alternative for businesses aiming to leverage AI for various applications.

The underlying technology of Command A is structured around an optimized transformer architecture, which includes three layers of sliding window attention, each with a window size of 4096 tokens. This mechanism enhances local context modeling, allowing the model to retain important details across extended text inputs. A fourth layer incorporates global attention without positional embeddings, enabling unrestricted token interactions across the entire sequence. The model’s supervised fine-tuning and preference training further refine its ability to align responses with human expectations regarding accuracy, safety, and helpfulness. Also, Command A supports 23 languages, making it one of the most versatile AI models for businesses with global operations. Its chat capabilities are preconfigured for interactive behavior, enabling seamless conversational AI applications.

Performance evaluations indicate that Command A competes favorably with leading AI models such as GPT-4o and DeepSeek-V3 across various enterprise-focused benchmarks. The model achieves a token generation rate of 156 tokens per second, 1.75 times higher than GPT-4o and 2.4 times higher than DeepSeek-V3, making it one of the most efficient models available. Regarding cost efficiency, private deployments of Command A are up to 50% cheaper than API-based alternatives, significantly reducing the financial burden on businesses. Command A also excels in instruction-following tasks, SQL-based queries, and retrieval-augmented generation (RAG) applications. It has demonstrated high accuracy in real-world enterprise data evaluations, outperforming its competitors in multilingual business use cases.

In a direct comparison of enterprise task performance, human evaluation results show that Command A consistently outperforms its competitors in fluency, faithfulness, and response utility. The model’s enterprise-ready capabilities include robust retrieval-augmented generation with verifiable citations, advanced agentic tool use, and high-level security measures to protect sensitive business data. Its multilingual capabilities extend beyond simple translation, demonstrating superior proficiency in responding accurately in region-specific dialects. For instance, evaluations of Arabic dialects, including Egyptian, Saudi, Syrian, and Moroccan Arabic, revealed that Command A delivered more precise and contextually appropriate responses than leading AI models. These results emphasize its strong applicability in global enterprise environments where language diversity is crucial.

Several key takeaways from the research include:

1. Command A operates on just two GPUs, significantly reducing computational costs while maintaining high performance.
2. With 111 billion parameters, the model is optimized for enterprise-scale applications that require extensive text processing.
3. The model supports a 256K context length, enabling it to process longer enterprise documents more effectively than competing models.
4. Command A is trained on 23 languages, ensuring high accuracy and contextual relevance for global businesses.
5. It achieves 156 tokens per second, 1.75x higher than GPT-4o and 2.4x higher than DeepSeek-V3.
6. The model consistently outperforms competitors in real-world enterprise evaluations, excelling in SQL, agentic, and tool-based tasks.
7. Advanced RAG capabilities with verifiable citations make it highly suitable for enterprise information retrieval applications.
8. Private deployments of Command A can be up to 50% cheaper than API-based models.
9. The model includes enterprise-grade security features, ensuring safe handling of sensitive business data.
10. Demonstrates high proficiency in regional dialects, making it ideal for businesses operating in linguistically diverse regions.

Check out the Model on Hugging Face. All credit for this research goes to the researchers of this project. Also, feel free to follow us on Twitter and don’t forget to join our 80k+ ML SubReddit.