Four AI research trends enterprise teams should watch in 2026
The AI narrative has mostly been dominated by model performance on key industry benchmarks. But as the field matures and enterprises look to draw real value from advances in AI, we’re seeing parallel research in techniques that help productionize AI applications.
At VentureBeat, we are tracking AI research that can help understand where the practical implementation of technology is heading. We are looking forward to breakthroughs that are not just about the raw intelligence of a single model, but about how we engineer the systems around them. As we approach 2026, here are four trends that can represent the blueprint for the next generation of robust, scalable enterprise applications.
Continual learning
Continual 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 mix of old and new information, which is expensive, time-consuming, and extremely complicated. This makes it inaccessible to most companies using models.
Another workaround is to provide models with in-context information 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 start conflicting with what was true at the time of the model’s training. They also require a lot of engineering and are limited by the context windows of the models.
Continual learning enables models to update their internal knowledge without the need for retraining. Google has been 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 inference time. Intuitively, it shifts some “learning” from offline weight updates into 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 that framing to address catastrophic forgetting.
Standard transformer-based language models have dense layers that store the long-term memory obtained during pretraining and attention layers that hold the immediate context. Nested Learning introduces a “continuum memory system,” where memory is seen as a spectrum of modules that update at different frequencies. This creates a memory system that is more attuned to continual learning.
Continual learning is complementary to the work being done on giving agents short-term memory through context engineering. As it matures, enterprises can expect a generation of models that adapt to changing environments, dynamically deciding which new information to internalize and which to preserve in short-term memory.
World models
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 unpredictable and out-of-distribution events and become more robust against the uncertainty of the real world.
More importantly, world models open the way for AI systems that can move beyond text and solve tasks that involve physical environments. World models try to learn the regularities of the physical world directly from observation and interaction.
There are different approaches for creating world models. DeepMind is building Genie, a family of generative end-to-end models that simulate an environment so an agent can predict how the environment will evolve and how actions will change it. It takes in an image or prompt along with user actions and generates the sequence of video frames that reflect how the world changes. Genie can create interactive environments that can be used for different 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 a prompt, which can then be used by a physics and 3D engine to render and simulate the interactive environment used to train robots.
Another approach is the Joint Embedding Predictive Architecture (JEPA) espoused by Turing Award winner and former Meta AI Chief Yann LeCun. 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, which makes them suitable for fast-paced real-time AI applications that need to run on resource constrained devices. V-JEPA, the video version of the architecture, is pre-trained on unlabeled internet-scale video to learn world models through observation. It then adds a small amount of interaction data from robot trajectories to support planning. That combination hints at a path where enterprises leverage abundant passive video (training, inspection, dashcams, retail) and add limited, high-value interaction data where they need control.
In November, LeCun confirmed that he will be leaving Meta and will be starting a new AI startup that will pursue “systems that understand the physical world, have persistent memory, can reason, and can plan complex action sequences.”
Orchestration
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 strong models fail: They lose context, call tools with the wrong parameters, and compound small mistakes.
Orchestration treats those failures as systems problems that can be addressed with the right scaffolding and engineering. For example, a router chooses between a fast small model, a bigger model for harder steps, retrieval for grounding, and deterministic tools for actions.
There are now multiple frameworks that create orchestration layers to improve efficiency and accuracy of AI agents, especially when using external tools. Stanford's OctoTools is an open-source framework that can orchestrate multiple tools without the need to fine-tune or adjust the 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 divide labor between different components of the AI system. One such example is Nvidia’s Orchestrator, an 8-billion-parameter model that coordinates different tools and LLMs to solve complex problems. 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 small specialized models, and when to use the reasoning capabilities and knowledge of large generalist models.
One of the characteristics of these and other similar frameworks is that they can benefit from advances in the underlying models. So as we continue to see advances in frontier models, we can expect orchestration frameworks to evolve and help enterprises build robust and resource-efficient agentic applications.
Refinement
Refinement techniques turn “one answer” into a controlled process: propose, critique, revise, and verify. It frames the workflow as using the same model to generate an initial output, produce feedback on it, and iteratively improve, without additional training.
While self-refinement techniques have been around for a few years, we might be at a point where we can 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 as the “Year of the Refinement Loop” and wrote, “From an information theory perspective, refinement is intelligence.”
ARC tests models on complicated abstract reasoning puzzles. ARC’s own analysis reports that the top verified refinement solution, built on a frontier model and developed by Poetiq, reached 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 that is LLM-agnostic. 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 needed.
As models become stronger, adding self-refinement layers will make it possible to get more 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 research in the coming year is to watch which new techniques can help enterprises move agentic applications from proof-of-concepts into scalable systems.
Continual learning shifts rigor toward memory provenance and retention. World models shift it toward robust simulation and prediction of real-world events. Orchestration shifts it toward better use of resources. Refinement shifts it toward smart reflection and correction of answers.
The winners will not only pick strong models, they will build the control plane that keeps those models correct, current, and cost-efficient.
