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The AI narrative has been primarily dominated by model performance against key industry benchmarks. But as the field evolves and companies seek to derive real value from advances in AI, we are seeing parallel research into techniques that help produce AI applications.
At VentureBeat, we track AI research that can help understand where practical implementation of the technology is heading. We look forward to advances that are not just about the raw intelligence of a single model, but also about how we design the systems around them. As we approach 2026, here are four trends that can model the next generation of robust, scalable enterprise applications.
Continuous learning
Continuous learning addresses one of the main challenges of current AI models: teaching them new information and skills without destroying their existing knowledge (often called “”catastrophic forgetting“).
Traditionally, there are two ways to solve this problem. The first is to retrain the model with a mixture of old and new information, which is expensive, time-consuming, and extremely complicated. This makes it inaccessible to most businesses using templates.
Another solution is to provide models with contextual information via techniques such as RAG. However, these techniques do not update the model’s internal knowledge, which can be problematic as one moves further from the model’s knowledge threshold and the facts begin to conflict with what was true at the time the model was trained. They also require a lot of engineering and are limited by template popups.
Continuous learning allows models to update their internal knowledge without needing to retrain. Google has been working on this with several new model architectures. One of them is Titanswhich proposes a different primitive: a learned long-term memory module that allows the system to incorporate historical context at the time of inference. Intuitively, it moves some of the “learning” from offline weight updates to an online memory process, closer to how teams already think about caches, indexes, and logs.
Nested learning pushes the same theme from another angle. It treats a model as a set of nested optimization problems, each with its own internal workflow, and uses this framework to address catastrophic oversights.
Standard transformer-based language models have dense layers that store long-term memory obtained during pre-training and attention layers that contain the immediate context. Nested Learning introduces a “continuous memory system,” in which memory is viewed as a spectrum of modules that update at different frequencies. This creates a memory system more suited to lifelong learning.
Continuous learning is complementary to work carried out to give agents short-term memory through context engineering. As they mature, companies can expect a generation of models that adapt to changing environments, dynamically deciding which new information to internalize and which to retain in short-term memory.
Models of the world
Global models promise to give AI systems the ability to understand their environment without the need for human-labeled data or human-generated text. With global models, AI systems can better respond to unpredictable and out-of-distribution events and become more robust in the face of real-world uncertainty.
More importantly, global models pave the way for AI systems that can go beyond text and solve tasks involving physical environments. World models attempt to learn regularities of the physical world directly from observation and interaction.
There are different approaches to creating global models. DeepMind builds Geniusa family of end-to-end generative models that simulate an environment so that an agent can predict how the environment will evolve and how actions will modify it. It takes an image or prompt along with the user’s actions and generates the sequence of video frames that reflect the changing world. Genie can create interactive environments that can be used for different purposes, including training robots and self-driving cars.
Global Laboratoriesa new startup founded by AI pioneer Fei-Fei Li, is taking 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 then be used by a physics and 3D engine to render and simulate the interactive environment used to train the robots.
Another approach is the Joint Integration Predictive Architecture (JEPA) adopted by Yann LeCun, Turing Award winner and former head of Meta AI. JEPA models learn latent representations from raw data so the system can anticipate what comes next without generating every pixel.
JEPA models are much more efficient than generative models, making them suitable for fast real-time AI applications that need to run on resource-constrained devices. V-JEPAthe video version of the architecture, is pre-trained on unlabeled internet-scale video to learn patterns of the world through observation. It then adds a small amount of interaction data from the robots’ trajectories to aid planning. This combination portends a path in which companies leverage abundant passive video (training, inspection, dashcams, retail) and add limited, high-value interaction data where they need control.
In November, LeCun confirmed he would leave Meta and will launch a new AI startup that will pursue “systems that understand the physical world, have persistent memory, can reason, and plan complex action sequences.”
Orchestration
Frontier LLMs continue to make progress on very difficult benchmarks, often outperforming human experts. But when it comes to real-world tasks and multi-step agent workflows, even the strongest models fail: they lose context, call tools with incorrect parameters, and make small errors worse.
Orchestration treats these failures as system problems that can be resolved with the appropriate scaffolding and engineering. For example, a router chooses between a small, fast model, a larger model for more difficult steps, recovery for grounding, and deterministic tools for actions.
There are now several frameworks that create orchestration layers to improve the efficiency and accuracy of AI agents, especially when using external tools. that of Stanford OctoTools is an open source framework that can orchestrate multiple tools without the need to refine or adjust models. OctoTools uses a modular approach that plans a solution, selects tools, and passes 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 distribute work among different components of the AI system. One such example is Nvidia Orchestratoran 8 billion parameter model that coordinates different tools and LLMs to solve complex problems. Orchestrator was trained using a special reinforcement learning technique designed for model orchestration. It can indicate when to use tools, when to delegate tasks to small, specialized models, and when to use the reasoning skills and knowledge of large, generalist models.
One of the features of these and other similar frameworks is that they can benefit from advances in the underlying models. So, as we see advancements in state-of-the-art models, we can expect orchestration frameworks to evolve and help businesses build robust, resource-efficient agentic applications.
Refinement
Refinement techniques transform “an answer” into a controlled process: propose, criticize, revise and verify. It frames the workflow as using the same model to generate an initial result, produce feedback on it, and improve it iteratively, without additional training.
Although self-refinement techniques have been around for a few years, we might be at a point where we can see them bring a step change in agentic applications. This was fully highlighted in the results of the ARC Prize, which described 2025 as “Year of refinement loop” and wrote: “From the point of view of information theory, refinement is intelligence. ”
ARC tests models on complex abstract reasoning puzzles. ARC’s own analysis reports that the best verified refinement solution, built on a frontier model and developed by Poetiqachieved 54% on ARC-AGI-2, beating the runner-up, Gemini 3 Deep Think (45%), at half the price.
Poetiq’s solution is a recursive, self-improving system independent of the LLM. It is designed to leverage the reasoning capabilities and knowledge of the underlying model to reflect and refine its own solution and invoke tools such as code interpreters when necessary.
As models become more robust, adding layers of self-refinement will help get the most out of them. Poetiq 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 coming year’s research is to look at what new techniques can help companies move agent applications from proof-of-concept to scalable systems.
Continuous learning shifts rigor toward the provenance and retention of memory. Global models direct it toward robust simulation and prediction of real-world events. Orchestration directs it towards better use of resources. Refinement moves it towards intelligent thinking and correcting answers.
The winners will not only choose solid models, but they will also build the control plan that will keep those models correct, up-to-date and profitable.
