The AI story has largely been dominated by model performance on key industry benchmarks. But as the field matures and companies seek to derive real value from AI advances, research is also occurring in parallel into technologies that can help bring AI applications into production.
At VentureBeat, we track AI research that helps us understand where the technology is heading. We expect breakthroughs in how we design systems around models, not just the raw intelligence of a single model. As we approach 2026, here are four trends that represent the 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 new information and skills without destroying existing knowledge (known as “catastrophic forgetting”).
Traditionally, there are two ways to solve this. One is to retrain the model using a combination of old and new information, but this is expensive, time-consuming, and very complex. This makes the model inaccessible to most companies using it.
Another workaround is to provide in-context information to the model through techniques such as RAG. However, these techniques do not update the model's internal knowledge, which can lead to problems when you move away from the limits of the model's knowledge and facts start to contradict what was true when the model was trained. It also requires a lot of engineering and is limited by the model's context window.
Continuous learning allows the model to update its internal knowledge without the need for retraining. Google has been working on this with several new model architectures. One of them is Titans, which suggests another primitive. This is a learned long-term memory module that allows the system to incorporate historical context when making inferences. Intuitively, it moves some of the “learning” from offline weight updates to online memory processes, closer to the way teams already think about caches, indexes, and logs.
Nested learning pushes the same theme from a different angle. We treat the model as a set of nested optimization problems, each with its own internal workflow, and use that framework to deal with catastrophic forgetting.
Standard transformer-based language models have a dense layer that stores long-term memory acquired during pre-training, and an attention layer that preserves the immediate context. Nested learning introduces a “continuous memory system” where memory is viewed as a spectrum of modules that are updated at different frequencies. This creates a memory system that is better adapted to continuous learning.
Continuous learning complements the work being done to give agents short-term memory through context engineering. As it matures, companies can expect generations of models that adapt to changes in the environment and dynamically decide which new information to internalize and which to store in short-term memory.
world model
World models promise to enable AI systems to understand their environments without the need for human-labeled data or human-written text. Using world models, AI systems better respond to unpredictable, out-of-distribution events and become more robust to real-world uncertainties.
More importantly, world models pave the way for AI systems that can go beyond text and solve tasks that involve the physical environment. World models attempt to learn the regularities of the physical world directly from observation and interaction.
There are various approaches to creating world models. DeepMind is building Genie, a family of generative end-to-end models that simulate environments so that agents can predict how they will evolve and how their actions will change them. It captures images or prompts along with user actions and produces a series of video frames that reflect changes in the world. Genie can create interactive environments that can be used for a variety of purposes, including training robots and self-driving cars.
World Labs, a startup founded by AI pioneer Fei-Fei Li, takes a slightly different approach. World Labs' first AI system, Marble, uses generative AI to create 3D models from images or prompts, which can then be used in physics and 3D engines to render and simulate interactive environments used to train robots.
Another approach is the Joint Embedding Predictive Architecture (JEPA), proposed by Turing Award winner and former Meta AI chief Yann LeCun. JEPA models learn latent representations from raw data, allowing the system to predict what will happen next without having to generate every 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-constrained devices. The video version of the architecture, V-JEPA, is pre-trained on unlabeled internet-scale videos to learn world models through observation. Next, add a small amount of interaction data from the robot's trajectory to support planning. This combination suggests a path for enterprises to leverage the richness of passive video (training, inspections, in-vehicle cameras, retail) and add exclusive, high-value interaction data where control is needed.
In November, LeCun confirmed he was leaving Meta to start a new AI startup pursuing “systems that understand the physical world, have persistent memory, can reason, and can plan complex courses of action.”
orchestration
Frontier LLM continues to make progress on extremely difficult benchmarks, often outperforming human experts. However, even the most powerful models fail when it comes to real-world tasks and multi-step agent workflows. Context is lost, tools are called with the wrong parameters, and small mistakes compound.
Orchestration treats these failures as system problems that can be addressed with proper scaffolding and engineering. For example, routers choose between small models for faster speeds, larger models for more difficult steps, search for grounding, and deterministic tools for action.
There are currently multiple frameworks that create orchestration layers that improve the efficiency and accuracy of AI agents, especially when using external tools. Stanford University's OctoTools is an open source framework that allows you to integrate multiple tools without having to tweak or adjust your models. OctoTools uses a modular approach to plan solutions, select tools, and pass subtasks to different agents. OctoTools can use any general purpose LLM as its backbone.
Another approach is to train specialized orchestrator models that can divide the work between the various components of the AI system. One such example is Nvidia's Orchestrator. It is an 8 billion parameter model that coordinates various tools and LLMs to solve complex problems. Orchestrator was trained through a special reinforcement learning technique designed for model orchestration. You can decide when to use tools, when to delegate tasks to smaller specialized models, and when to use the inference capabilities and knowledge of larger 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 see advances in frontier models, we can expect to see orchestration frameworks evolve to enable enterprises to build robust and resource-efficient agent applications.
refinement
Refinement techniques turn “one answer” into a controlled process of suggestion, critique, revision, and validation. It frames the workflow as using the same model to generate initial output, generate feedback on it, and iteratively improve it without additional training.
Self-refinement techniques have been around for several years, but we may be at the point where we see them bring about a significant change in agent applications. This is on full display in the results of the ARC Awards, which named 2025 the “Year of the Refinement Loop” and noted that “from an information theory perspective, sophistication is intelligence.”
ARC tests models based on complex, abstract reasoning puzzles. According to ARC's own analysis, the top validated refinement solution built on the Frontier model and developed by Poetiq reached 54% on ARC-AGI-2, outperforming the runner-up Gemini 3 Deep Think (45%) at half the price.
Poetiq's solution is a recursive, self-improving system that does not rely on LLM. It is designed to leverage the inference capabilities and knowledge of the underlying model to reflect and refine your own solutions, and to invoke tools such as code interpreters as needed.
As your model becomes more powerful, adding self-refinement layers will allow you to get more out of it. Poetiq is already working with partners to adapt its metasystem to “address complex real-world problems that frontier models have difficulty solving.”
How to track AI research in 2026
A practical way to read next year's research results is to observe what new technologies will help companies move agent applications from proof-of-concept to scalable systems.
With continuous learning, rigor shifts to the origin and retention of memory. World models move it toward robust simulation and prediction of real-world events. Orchestration allows you to make better use of your resources. Once refined, move on to smart reflection and modification of your answers.
Winners not only select powerful models, but also build control planes that keep those models accurate, up-to-date, and cost-effective.
