The AI narrative has been dominated by model performance on key industry benchmarks. But as the field matures and enterprises look to derive real value from advances in AI, we are seeing parallel research into technologies that help produce AI applications.
At VentureBeat, we’re keeping an eye on AI research that can help us understand where the practical implementation of the technology is headed. We are looking forward to breakthroughs that are not just about the raw intelligence of a model, but also about how we engineer the systems around them. As we approach 2026, here are four trends that could offer a blueprint for the next generation of robust, scalable enterprise applications.
continuous learning
Continuous learning addresses one of the key challenges of current AI models: teaching them new information and skills without destroying their existing knowledge (often referred to as “catastrophic forgetting”).
Traditionally, there are two ways to solve this. One is to retrain the model with a mixture of old and new information, which is expensive, time-consuming, and extremely complex. This makes it inaccessible to most companies that use the model.
Another solution is to provide contextual information to models through techniques such as RAG. However, these techniques do not update the model’s internal knowledge, which can prove problematic as you move away from the model’s knowledge cutoff and facts begin to contradict what was true at the time of training the model. They also require a lot of engineering and are limited by the reference windows of the models.
Continuous learning enables models to update their internal knowledge without the need for retraining. Google is working on this with several new model architectures. One of them is Titans, which proposes a different primitive: a learned long-term memory module that lets the system incorporate historical context at an estimated time. Intuitively, this translates some of the “learning” from offline weight updates to the online memory process, similar to how teams already think about caches, indexes, and logs.
Nested learning approaches the same topic from another angle. It treats a model as a set of nested optimization problems, each with its own internal workflow, and uses that framing to address catastrophic forgetting.
Standard transformer-based language models have dense layers that store long-term memory acquired during pre-training, and attention layers that hold immediate context. Nested learning introduces a “continuum memory system”, where memory is viewed as a spectrum of modules that update at different frequencies. This creates a memory system that is more suitable for continuous learning.
Continuous learning complements work being done on giving agents short-term memory through context engineering. As it matures, enterprises can expect the generation of models that adapt to changing environments, dynamically deciding which new information to internalize and which to preserve in short-term memory.
world model
World models promise to give AI systems the ability to understand their environments without the need for human-labeled data or human-generated text. With world models, AI systems can better respond to unexpected and out-of-distribution events and become more robust against real-world uncertainty.
More importantly, world models open the way for AI systems that can move beyond text and solve tasks involving physical environments. World models attempt to learn the regularities of the physical world from direct observation and interactions.
There are different approaches to creating a world model. DeepMind is building Genie, a family of generative end-to-end models that simulate an environment so that an agent can predict how the environment will evolve and how actions will change it. It takes an image or signal along with the user’s actions and generates a sequence of video frames that shows how the world changes. Genie can create interactive environments that can be used for a variety of purposes, including training robots and self-driving cars.
World Labs, a new startup founded by AI pioneer Fei-Fei Li, takes a slightly different approach. Marble, World Labs’ first AI system, uses generative AI to create a 3D model from an image or prompt, which can be used by physics and 3D engines to render and simulate interactive environments used to train robots.
Another approach is the Joint Embedding Predictive Architecture (JEPA), championed by Turing Award winner and former Meta AI chief Yann LeCun. JEPA models learn latent representations from raw data so that the system can predict what will happen next without generating each pixel.
JEPA models are much more efficient than generative models, making them suitable for fast-paced real-time AI applications that need to run on resource limited devices. V-JEPA, the video version of the architecture, is pre-trained on unlabeled Internet-scale videos to learn world models through observation. It then adds small amounts of interaction data from the robot trajectory to support the plan. This combination points to a path where enterprises leverage abundant passive video (training, inspection, dashcam, retail) and add limited, high-value interaction data where they need control.
In November, LeCun confirmed he would be leaving Meta and starting a new AI startup that would “advance systems that understand the physical world, have persistent memory, can reason, and plan complex task sequences.”
instrument space
Frontier LLMs continue to advance on very challenging benchmarks, often outperforming human experts. But when it comes to real-world tasks and multi-step agentic workflows, even robust models fail: they lose context, call tools with the wrong parameters, and small mistakes add up.
Orchestration treats failures as system problems that can be addressed with the right scaffolding and engineering. For example, a router chooses between a faster smaller model, a larger model for difficult steps, recovery for grounding, and a deterministic device for tasks.
There are now several frameworks that create orchestration layers to improve the efficiency and accuracy of AI agents, especially when using external tools. Stanford’s OctoTools is an open-source framework that can organize multiple tools without the need to fine-tune or adjust models. OctoTools uses a modular approach that plans the solution, selects tools, and dispatches subtasks to different agents. OctoTools can use any general purpose LLM as its backbone.
Another approach is to train a specialized orchestrator model that can divide labor among different components of the AI system. One such example is Nvidia’s Orchestrator, an 8-billion-parameter model that coordinates various devices and LLMs to solve complex problems. The orchestrator was trained through a special reinforcement learning technique designed for model orchestration. It can tell when to use tools, when to delegate tasks to smaller specialized models, and when to use the reasoning capabilities and knowledge of larger generalist models.
A characteristic of these and other similar frameworks is that they can benefit from advances in underlying models. So as we continue to see advancements in the frontier model, we can expect orchestration frameworks to evolve and help enterprises build robust and resource-efficient agentic applications.
Refinement
Refinement techniques turn “an answer” into a controlled process: proposing, criticizing, revising, and verifying. It frames the workflow using the same model to generate the initial output, generate feedback on it, and improve it iteratively without additional training.
While self-cleaning technologies have been around for a few years, we may be at a point where we may see them provide a step change in agentic applications. This was put on full display in the results of the ARC Prize, which dubbed 2025 “the year of the refinement loop” and wrote, “From an information theory perspective, refinement is intelligence.”
ARC tests models on complex abstract logic puzzles. ARC’s own analysis shows that the top verified refinement solution, built on the Frontier model and developed by Poetic, reached 54% on ARC-AGI-2, beating runner-up Gemini 3 DeepThink (45%) at half the price.
Poetic’s solution is an iterative, self-improving system that is LLM-agnostic. It is designed to leverage the reasoning capabilities and knowledge of the underlying model to reflect and refine one’s own solution and apply tools such as code interpreters when needed.
As models become stronger, adding self-refining layers will make it possible to get more out of them. Poetic is already working with partners to adapt its meta-system to “handle complex real-world problems that frontier models struggle to solve.”
How to track AI research in 2026
A practical way to read the research in the coming year is to see what new technologies can help enterprises move agentic applications from proof-of-concept to scalable systems.
Continuous learning leads to rigidity in memory formation and retention. World models shift this towards stronger simulation and prediction of real-world events. Orchestration shifts this towards better utilization of resources. Refinement shifts it towards smart reflection and improvement of answers.
Winners will not only choose robust models, they will build control planes that will keep those models accurate, operational, and cost-efficient.
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