📝 Blogs & Articles — Awesome Graph Learning
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37 posts, articles, and resources from across the field.
The integration of various modalities in AI is advancing rapidly with new models and applications.
4 postsEnsuring reliability and consistency in AI models is a key focus area in current research.
2 postsInnovations in video analytics are driving the development of advanced AI agents for better search and summarization.
2 postsWhy it matters — This guide provides practical strategies for optimizing prompts in vision language models, which is crucial for enhancing their performance in image and video understanding tasks.
Why it matters — The NVIDIA AI Blueprint demonstrates how VLMs can enhance video analytics, offering insights into improving search and summarization capabilities, which are vital for real-time applications.
Why it matters — This post outlines the development of real-time multimodal applications, showcasing how VLMs can be integrated into XR environments, which is significant for interactive technology research.
Why it matters — GRASP introduces a novel approach to long-horizon planning in learned dynamics, which is essential for improving the efficiency and effectiveness of world models in robotics.
Why it matters — NVIDIA Cosmos Reason offers a customizable VLM for physical AI, which can significantly enhance reasoning capabilities in robotics, making it a valuable tool for researchers in this field.
Why it matters — This benchmarking series provides a framework for evaluating LLM and VLM performance in gaming, which is important for understanding model capabilities in complex interactive environments.
Why it matters — The exploration of RL-finetuned VLMs for visual reasoning tasks highlights a critical advancement in improving model robustness and consistency, which is key for real-world applications.
Why it matters — The BAIR Graduate Showcase highlights emerging researchers and their contributions, which can inspire new ideas and collaborations in the field of AI and graph learning.
Why it matters — Gemma 4 12B represents a significant advancement in multimodal models, offering a unified approach that could streamline various applications in graph learning and AI.
Why it matters — Nemotron 3.5 focuses on customizable safety in multimodal AI, addressing a critical concern for deploying AI systems in sensitive environments, which is relevant for ethical AI research.
Apple is presenting new research at the annual IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), which takes place in person in Denver at the Colorado Convention Center from June 3 to June 7. We are proud to sponsor the conference, which brings together the scientific and industrial research communities in computer vision and pattern recognition. Below is an overview of Apple’s participation at CVPR 2026.
Why it matters — VSAS-Bench provides a framework for real-time evaluation of visual streaming assistant models, which is crucial for optimizing the performance of graph-based visual reasoning systems in dynamic environments.
Why it matters — BalCapRL presents a balanced approach to reinforcement learning for image captioning, which can improve the integration of graph-based representations in multimodal tasks, enhancing the understanding of visual content.
Why it matters — The introduction of Nemotron 3 Nano Omni emphasizes long-context capabilities in multimodal intelligence, which is essential for processing complex data across various formats.
Why it matters — Nano Banana 2 showcases advancements in image generation that could influence the development of faster, more efficient models in graph learning applications.
Why it matters — This guide on Edge AI outlines the integration of LLMs and VLMs in robotics, which is crucial for developing efficient, real-time AI systems in constrained environments.
Why it matters — The upcoming livestream on fine-tuning NVIDIA Cosmos Reason presents an opportunity for researchers to learn practical skills for developing visual AI agents, which is directly applicable to their work.
Why it matters — The integration of generative AI with computer vision pipelines marks a shift in video analytics, providing new methodologies for extracting insights from raw video data.
Why it matters — The PEVA model for predicting ego-centric video frames based on human actions presents a novel approach to understanding human behavior in video sequences, which is valuable for behavioral analysis.
Why it matters — This post discusses efficient extraction methods for multimodal documents, which is critical for researchers looking to optimize data processing workflows in AI applications.
Why it matters — The discussion on AGI and its multimodal capabilities challenges existing paradigms in AI research, prompting critical reflection on the nature of intelligence and its representation.
Why it matters — PLAID's approach to protein generation using latent diffusion models highlights the intersection of biology and AI, which is significant for researchers in computational biology and drug discovery.
Why it matters — The latest addition to Microsoft Phi SLMs trained on NVIDIA GPUs emphasizes the practical implications of large models, which is crucial for researchers focusing on scalable AI solutions.
Why it matters — The featured sessions at NVIDIA GTC 2025 provide insights into the latest advancements in computer vision and video analytics, which are essential for staying updated in the fast-evolving field.
Why it matters — The use of AI in conservation tech highlights the potential for AI applications in environmental protection, which is an important area for researchers interested in ethical and impactful AI.
Why it matters — The focus on open research for safer AI underscores the need for transparency and collaboration in AI development, which is critical for addressing safety concerns in advanced AI systems.
Why it matters — The Segment Anything model aims to create a foundational model for image segmentation, which is a key area of research for improving object recognition and scene understanding in AI.
Vision Language Models (VLMs) offer the exciting possibility of processing text as rendered images, bypassing the need for tokenizing the text into long token sequences. Since VLM image encoders map fixed-size images to a fixed number of visual tokens, varying rendering resolution provides a fine-grained compression knob. However, accuracy deteriorates quickly as compression increases: characters shrink below the vision encoder’s effective resolution, making them indistinguishable. To address this, we propose LensVLM, an inference framework and post-training recipe that enables VLMs to scan…
NVIDIA XR AI is now available in public beta, giving developers a framework for building multimodal AI agents for AR glasses and XR devices.  
True spatial intelligence for multimodal agents transcends low-level geometric perception, evolving from knowing where things are to understanding what they are for. While existing benchmarks, such as VSI-Bench, effectively evaluate this foundational geometric stage, they fall short of probing the higher-order cognitive abilities essential for grounded intelligence. To bridge this gap, we introduce the Spatial-Functional Intelligence Benchmark (SFI-Bench), a video-based benchmark with over 1700 questions derived from diverse, egocentric indoor video scans. SFI-Bench is designed to…
AI agent systems today juggle separate models for vision, speech and language — losing time and context as they pass data from one model to the other. Unveiled today, NVIDIA Nemotron 3 Nano Omni is an open multimodal model that brings these capabilities together into one system, enabling agents to deliver faster, smarter responses with […]
Training Diffusion Models with Reinforcement Learning We deployed 100 reinforcement learning (RL)-controlled cars into rush-hour highway traffic to smooth congestion and reduce fuel consumption for everyone. Our goal is to tackle "stop-and-go" waves, those frustrating slowdowns and speedups that usually have no clear cause but lead to congestion and significant energy waste. To train efficient flow-smoothing controllers, we built fast, data-driven simulations that RL agents interact with, learning to maximize energy efficiency while maintaining throughput and operating safely around human drivers. Overall, a small proportion of well-controlled autonomous vehicles (AVs) is enough to significantly improve traffic flow and fuel efficiency for all drivers on the road. Moreover, the trained controllers are designed to be deployable on most modern vehicles, operating in a decentralized manner and relying on standard radar sensors. In our latest paper, we explore the challenges of deploying RL controllers on a large-scale, from simulation to the field, during this 100-car experiment. The challenges of phantom jams A stop-and-go wave moving backwards through highway traffic. If you drive, you’ve surely experienced the frustration of stop-and-go waves, those seemingly inexplicable traffic slowdowns that appear out of nowhere and then suddenly clear up. These waves are often caused by small fluctuations in our driving behavior that get amplified through the flow of traffic. We naturally adjust our speed based on the vehicle in front of us. If the gap opens, we speed up to keep up. If they brake, we also slow down. But due to our nonzero reaction time, we might brake just a bit harder than the vehicle in front. The next driver behind us does the same, and this keeps amplifying. Over time, what started as an insignificant slowdown turns into a full stop further back in traffic. These waves move backward through the traffic stream, leading to significant drops in energy efficiency due to frequent accelerations, accompanied by increased CO2 emissions and accident risk. And this isn’t an isolated phenomenon! These waves are ubiquitous on busy roads when the traffic density exceeds a critical threshold. So how can we address this problem? Traditional approaches like ramp metering and variable speed limits attempt to manage traffic flow, but they often require costly infrastructure and centralized coordination. A more scalable approach is to use AVs, which can dynamically adjust their driving behavior in real-time. However, simply inserting AVs among human drivers isn’t enough: they must also drive in a smarter way that makes traffic better for everyone, which is where RL comes in. Fundamental diagram of traffic flow. The number of cars on the road (density) affects how much traffic is moving forward (flow). At low density, adding more cars increases flow because more vehicles can pass through. But beyond a critical threshold, cars start blocking each other, leading to congestion, where adding more cars actually slows down overall movement. Reinforcement learning for wave-smoothing AVs RL is a powerful control approach where an agent learns to maximize a reward signal through interactions with an environment. The agent collects experience through trial and error, learns from its mistakes, and improves over time. In our case, the environment is a mixed-autonomy traffic scenario, where AVs learn driving strategies to dampen stop-and-go waves and reduce fuel consumption for both themselves and nearby human-driven vehicles. Training these RL agents requires fast simulations with realistic traffic dynamics that can replicate highway stop-and-go behavior. To achieve this, we leveraged experimental data collected on Interstate 24 (I-24) near Nashville, Tennessee, and used it to build simulations where vehicles replay highway trajectories, creating unstable traffic that AVs driving behind them learn to smooth out. Simulation replaying a highway trajectory that exhibits several stop-and-go waves. We designed the AVs with deployment in mind, ensuring that they can operate using only basic sensor information about themselves and the vehicle in front. The observations consist of the AV’s speed, the speed of the leading vehicle, and the space gap between them. Given these inputs, the RL agent then prescribes either an instantaneous acceleration or a desired speed for the AV. The key advantage of using only these local measurements is that the RL controllers can be deployed on most modern vehicles in a decentralized way, without requiring additional infrastructure. Reward design The most challenging part is designing a reward function that, when maximized, aligns with the different objectives that we desire the AVs to achieve: Wave smoothing: Reduce stop-and-go oscillations. Energy efficiency: Lower fuel consumption for all vehicles, not just AVs. Safety: Ensure reasonable following distances and avoid abrupt braking. Driving comfort: Avoid aggressive accelerations and decelerations. Adherence to human driving norms: Ensure a “normal” driving behavior that doesn’t make surrounding drivers uncomfortable. Balancing these objectives together is difficult, as suitable coefficients for each term must be found. For instance, if minimizing fuel consumption dominates the reward, RL AVs learn to come to a stop in the middle of the highway because that is energy optimal. To prevent this, we introduced dynamic minimum and maximum gap thresholds to ensure safe and reasonable behavior while optimizing fuel efficiency. We also penalized the fuel consumption of human-driven vehicles behind the AV to discourage it from learning a selfish behavior that optimizes energy savings for the AV at the expense of surrounding traffic. Overall, we aim to strike a balance between energy savings and having a reasonable and safe driving behavior. Simulation results Illustration of the dynamic minimum and maximum gap thresholds, within which the AV can operate freely to smooth traffic as efficiently as possible. The typical behavior learned by the AVs is to maintain slightly larger gaps than human drivers, allowing them to absorb upcoming, possibly abrupt, traffic slowdowns more effectively. In simulation, this approach resulted in significant fuel savings of up to 20% across all road users in the most congested scenarios, with fewer than 5% of AVs on the road. And these AVs don’t have to be special vehicles! They can simply be standard consumer cars equipped with a smart adaptive cruise control (ACC), which is what we tested at scale. Smoothing behavior of RL AVs. Red: a human trajectory from the dataset. Blue: successive AVs in the platoon, where AV 1 is the closest behind the human trajectory. There is typically between 20 and 25 human vehicles between AVs. Each AV doesn’t slow down as much or accelerate as fast as its leader, leading to decreasing wave amplitude over time and thus energy savings. 100 AV field test: deploying RL at scale Our 100 cars parked at our operational center during the experiment week. Given the promising simulation results, the natural next step was to bridge the gap from simulation to the highway. We took the trained RL controllers and deployed them on 100 vehicles on the I-24 during peak traffic hours over several days. This large-scale experiment, which we called the MegaVanderTest, is the largest mixed-autonomy traffic-smoothing experiment ever conducted. Before deploying RL controllers in the field, we trained and evaluated them extensively in simulation and validated them on the hardware. Overall, the steps towards deployment involved: Training in data-driven simulations: We used highway traffic data from I-24 to create a training environment with realistic wave dynamics, then validate the trained agent’s performance and robustness in a variety of new traffic scenarios. Deployment on hardware: After being validated in robotics software, the trained controller is uploaded onto the car and is able to control the set speed of the vehicle. We operate through the vehicle’s on-board cruise control, which acts as a lower-level safety controller. Modular control framework: One key challenge during the test was not having access to the leading vehicle information sensors. To overcome this, the RL controller was integrated into a hierarchical system, the MegaController, which combines a speed planner guide that accounts for downstream traffic conditions, with the RL controller as the final decision maker. Validation on hardware: The RL agents were designed to operate in an environment where most vehicles were human-driven, requiring robust policies that adapt to unpredictable behavior. We verify this by driving the RL-controlled vehicles on the road under careful human supervision, making changes to the control based on feedback. Each of the 100 cars is connected to a Raspberry Pi, on which the RL controller (a small neural network) is deployed. The RL controller directly controls the onboard adaptive cruise control (ACC) system, setting its speed and desired following distance. Once validated, the RL controllers were deployed on 100 cars and driven on I-24 during morning rush hour. Surrounding traffic was unaware of the experiment, ensuring unbiased driver behavior. Data was collected during the experiment from dozens of overhead cameras placed along the highway, which led to the extraction of millions of individual vehicle trajectories through a computer vision pipeline. Metrics computed on these trajectories indicate a trend of reduced fuel consumption around AVs, as expected from simulation results and previous smaller validation deployments. For instance, we can observe that the closer people are driving behind our AVs, the less fuel they appear to consume on average (which is calculated using a calibrated energy model): Average fuel consumption as a function of distance behind the nearest engaged RL-controlled AV in the downstream traffic. As human drivers get further away behind AVs, their average fuel consumption increases. Another way to measure the impact is to measure the variance of the speeds and accelerations: the lower the variance, the less amplitude the waves should have, which is what we observe from the field test data. Overall, although getting precise measurements from a large amount of camera video data is complicated, we observe a trend of 15 to 20% of energy savings around our controlled cars. Data points from all vehicles on the highway over a single day of the experiment, plotted in speed-acceleration space. The cluster to the left of the red line represents congestion, while the one on the right corresponds to free flow. We observe that the congestion cluster is smaller when AVs are present, as measured by computing the area of a soft convex envelope or by fitting a Gaussian kernel. Final thoughts The 100-car field operational test was decentralized, with no explicit cooperation or communication between AVs, reflective of current autonomy deployment, and bringing us one step closer to smoother, more energy-efficient highways. Yet, there is still vast potential for improvement. Scaling up simulations to be faster and more accurate with better human-driving models is crucial for bridging the simulation-to-reality gap. Equipping AVs with additional traffic data, whether through advanced sensors or centralized planning, could further improve the performance of the controllers. For instance, while multi-agent RL is promising for improving cooperative control strategies, it remains an open question how enabling explicit communication between AVs over 5G networks could further improve stability and further mitigate stop-and-go waves. Crucially, our controllers integrate seamlessly with existing adaptive cruise control (ACC) systems, making field deployment feasible at scale. The more vehicles equipped with smart traffic-smoothing control, the fewer waves we’ll see on our roads, meaning less pollution and fuel savings for everyone! Many contributors took part in making the MegaVanderTest happen! The full list is available on the CIRCLES project page, along with more details about the project. Read more: [paper]