Building Worlds for AI: Nex-N1 and the Future of Agentic Systems

Author: Denis Avetisyan

Researchers have unveiled a new infrastructure and model, Nex-N1, designed to automatically generate complex environments and empower more capable autonomous agents.

Nex-N1 demonstrates competitive performance against established models across both agent and coding benchmarks, suggesting its capacity to navigate complex tasks while exhibiting proficiency in algorithmic reasoning.

This paper introduces a unified ecosystem for large-scale environment construction and demonstrates the performance of Nex-N1 across diverse agentic tasks and frameworks.

The pursuit of increasingly autonomous agents necessitates a shift from static imitation to incentive-driven learning, yet scalable infrastructure for generating the requisite high-quality interaction signals remains a critical bottleneck. This paper introduces a unified ecosystem for large-scale environment construction, detailed in ‘Nex-N1: Agentic Models Trained via a Unified Ecosystem for Large-Scale Environment Construction’, addressing this challenge through automated environment diversity, complexity, and fidelity. We demonstrate that training the Nex-N1 model within this infrastructure yields robust performance exceeding state-of-the-art open-source alternatives and approaching that of proprietary models on complex agentic tasks. Will this approach to scalable agentic learning unlock truly generalizable autonomous systems?

The Fragility of Swift Thought: Limits of Current AI

Many contemporary AI agents are built upon the architecture of Large Language Models, which predominantly operate using what’s known as ‘System 1 Thinking’. This cognitive style, mirroring human intuition, prioritizes rapid assessment and immediate responses, allowing agents to quickly process information and generate outputs. However, this reliance on swift, associative reasoning can introduce vulnerabilities; decisions, while efficient, may be based on superficial patterns or incomplete analysis. Consequently, agents employing this approach are prone to overlooking crucial details, failing to consider long-term consequences, or succumbing to biases present in their training data – ultimately leading to solutions that, while seemingly plausible, are fundamentally flawed or lack robustness.

The prevalent reliance on rapid, intuitive processing within current AI agents often leads to decisions characterized by shortsightedness – a phenomenon known as myopic decision-making. These agents, mirroring System 1 thinking in humans, prioritize immediate responses over comprehensive analysis, effectively optimizing for the present at the expense of future outcomes. This limitation proves particularly detrimental when tackling complex problems demanding strategic foresight and long-term planning; an agent focused solely on the immediate step may overlook crucial contextual information or fail to anticipate downstream consequences. Consequently, while proficient in responding to simple queries, these agents struggle with tasks requiring nuanced understanding, iterative refinement, and the ability to balance competing priorities over extended periods, highlighting a significant bottleneck in their capacity for true intelligence.

Current agent frameworks, despite advancements in artificial intelligence, often demonstrate a significant limitation in their ability to generalize beyond the specific data they were trained on. These systems frequently require substantial, task-specific datasets to achieve even moderate performance, hindering their adaptability to novel situations or unforeseen challenges. This dependence on extensive training isn’t simply a matter of computational cost; it reflects a fundamental struggle to extract underlying principles and apply them flexibly. Consequently, an agent proficient in one domain may falter dramatically when confronted with a slightly altered task, necessitating a costly and time-consuming retraining process. The pursuit of truly intelligent agents, therefore, necessitates research into methods that promote robust generalization – allowing systems to learn from limited data and apply knowledge across a broader spectrum of scenarios.

Human evaluators assessed the agent's coding abilities. — Human evaluators assessed the agent’s coding abilities.

Data as Seed: Scaling Robustness Through Generation

Agentic Scaling addresses the data scarcity problem in complex AI training by programmatically generating large datasets. This method moves beyond static datasets by dynamically creating varied environments and scenarios for agents to interact with. The core principle involves automating the creation of diverse situations, effectively increasing the volume and breadth of training examples without manual annotation. This automated generation process allows for exploration of edge cases and unusual circumstances that may not be adequately represented in existing, curated datasets, ultimately aiming to improve the robustness and generalization capabilities of trained AI models.

NexGAP functions as a generalized pipeline for generating training data by integrating external tools and diverse information sources. The system employs a modular design allowing for the incorporation of APIs, databases, and web scraping capabilities to dynamically create varied scenarios and inputs. Data is ingested from these real-world resources, processed through a series of transformations, and then formatted into a standardized structure suitable for agent training. This fusion of data types – including text, images, and structured data – aims to improve the robustness and generalization capabilities of trained agents by exposing them to a broader range of realistic conditions and information modalities.

NexA4A facilitates automated generation of varied agent architectures and workflows by programmatically composing different components, including perception modules, planning algorithms, and action policies. This automated synthesis extends beyond simple hyperparameter tuning; it explores fundamentally different structural configurations for agents, enabling systematic investigation of the design space. The system allows for the creation of agents with diverse capabilities and limitations, testing robustness across a wider range of scenarios than would be feasible with manual design. NexA4A’s outputs include fully functional agents, alongside metadata detailing their architectural specifications and performance characteristics, providing a comprehensive dataset for analyzing the impact of different design choices.

The NexA4A agent framework streamlines the process from initial description to the generation of high-quality trajectories.

NexAU: A Framework for Universal Agent Construction

NexAU functions as a unified development environment designed to reduce the technical barrier to creating sophisticated autonomous agents. The framework abstracts away the intricacies of agent architecture, including memory management, tool utilization, and environmental interaction, allowing developers to focus on defining agent behavior and objectives rather than low-level implementation details. This simplification is achieved through pre-built modules and standardized interfaces, enabling rapid prototyping and deployment of agents across diverse applications without requiring specialized expertise in areas such as reinforcement learning or natural language processing. Consequently, NexAU facilitates the construction of complex agent systems with a reduced codebase and accelerated development cycle.

The NexAU framework utilizes the ReAct paradigm, which combines reasoning and acting to enhance agent performance. ReAct operates by prompting the agent to generate both a thought process – detailing its reasoning – and an action to take within its environment. This iterative process allows the agent to observe the results of its actions and refine its subsequent reasoning and actions. Specifically, the agent alternates between generating a thought, then an action, and receiving observations based on that action, creating a closed-loop system for problem-solving and task completion. This approach allows agents to overcome limitations of purely reactive or purely deliberative systems by dynamically adapting to environmental feedback and improving performance over time.

NexAU utilizes the Modular Communication Protocol (MCP) to facilitate interaction with live, external servers, thereby expanding the agent’s operational scope beyond local computation. MCP enables agents to query and utilize real-time data, access specialized services, and perform actions within external systems. This integration is achieved through a standardized API allowing NexAU agents to dynamically connect to and exchange information with any server implementing the MCP protocol, regardless of its underlying technology or location. Supported functionalities include data retrieval via API calls, submission of requests for external processing, and reception of asynchronous event notifications from connected servers, all managed transparently within the NexAU framework.

NexAU allows for arbitrarily deep composition of sub-agents and standard tools, enabling flexible and complex agent architectures.

Empirical Validation: Benchmarking Nex-N1 Performance

The ‘Nex-N1’ series comprises models trained utilizing a novel approach to agent development, and validation confirms its ability to generalize effectively across multiple agent frameworks. This generalization was assessed by deploying the trained models within diverse environments and evaluating performance consistency. Specifically, ‘Nex-N1’ models were integrated with frameworks differing in their underlying architectures and communication protocols, demonstrating adaptability beyond the training environment. This robustness is critical for real-world deployment, allowing ‘Nex-N1’ to function effectively irrespective of the specific agent infrastructure in use.

Evaluation of the ‘Nex-N1’ models utilized benchmark suites including ‘GAIA 2’, ‘SWE-bench’, and ‘BFCL’ to provide a quantitative assessment of end-to-end performance and coding capabilities. ‘GAIA 2’ tests general agentic capabilities through a diverse set of tasks, while ‘SWE-bench’ specifically measures performance on software engineering problems. The ‘BFCL’ (Behavioral Function Call Learning) benchmark evaluates the agent’s ability to utilize function calls to achieve specified goals. These benchmarks were selected to cover a range of complexity and task types, enabling a comprehensive analysis of ‘Nex-N1’s’ strengths and limitations in practical application scenarios.

Empirical validation of Nex-N1 demonstrates significant performance gains across multiple benchmark tasks. Specifically, the model achieved a success rate exceeding 92.9% on the ‘SWE-bench’ coding challenge and 85.0% on the ‘BFCL’ (Function Call) benchmark when utilizing the Google Search API. Furthermore, Nex-N1 attained a score of 47.0% on the ‘Deep Research Benchmark’ and exhibited strong capabilities in webpage creation, achieving a 44.5% success rate on that particular task. These results collectively indicate substantial improvements in agentic performance across diverse application areas.

Our deep research agent successfully completed the deep research benchmark.

Towards AGI: A Future Forged in Adaptability

This research marks a notable advance in the pursuit of artificial general intelligence by demonstrating a pathway toward agents capable of adapting and performing effectively across diverse and previously unseen challenges. Current AI systems often struggle with generalization, excelling in narrow tasks but failing when confronted with even slight variations; however, this work prioritizes the development of agents exhibiting enhanced robustness and adaptability. Through innovative techniques in data generation and framework design, the study showcases improvements in an agent’s ability to learn underlying principles rather than memorizing specific solutions. This shift fosters a crucial step towards creating truly intelligent systems – those that can not only solve problems but also apply learned knowledge to novel situations, ultimately paving the way for more versatile and reliable autonomous agents.

The advancement of artificial intelligence hinges on overcoming limitations in data availability and evaluation standards; therefore, a synergistic approach combining scalable data generation, universal frameworks, and rigorous benchmarking offers a viable path forward. Researchers are now capable of creating vast datasets tailored to specific tasks, circumventing the bottleneck of manual annotation and enabling more comprehensive model training. Coupled with the development of universal frameworks – adaptable architectures capable of handling diverse problems – this generated data facilitates broad generalization. Crucially, this progress is underpinned by rigorous benchmarking protocols that move beyond isolated task performance and instead assess true adaptability and robustness across a spectrum of challenges, establishing a clear blueprint for future investigations and accelerating the pursuit of increasingly capable autonomous systems.

The culmination of these developments extends beyond incremental improvements in artificial intelligence; it actively propels the field toward the ambitious horizon of Artificial General Intelligence (AGI). AGI envisions systems capable of understanding, learning, adapting, and implementing knowledge across a vast range of tasks – mirroring, and potentially exceeding, human cognitive abilities. Successfully realizing AGI promises a transformative impact on numerous sectors, from scientific discovery and complex problem-solving to personalized healthcare and creative endeavors. Unlocking the full potential of truly autonomous agents, driven by AGI, represents not merely an advancement in technology, but a fundamental shift in how intelligence is applied to address the grand challenges facing humanity, fostering innovation and progress on an unprecedented scale.

This demonstration showcases our deep research agent's capabilities. — This demonstration showcases our deep research agent’s capabilities.

The pursuit of scalable agentic systems, as detailed in this research, inherently acknowledges the transient nature of any technological solution. Nex-N1’s capacity for robust performance across varied frameworks isn’t about achieving permanence, but about establishing a resilient foundation for adaptation. Donald Davies observed, “Every abstraction carries the weight of the past,” and this rings true when considering the iterative development of agentic models. Each layer of abstraction-from the agent framework to the trajectory generation-builds upon prior work, inheriting both strengths and limitations. The emphasis on automated environment construction is, therefore, not simply about efficiency; it’s about managing that accumulated weight, ensuring the system ages gracefully rather than collapsing under its own complexity. This research effectively demonstrates a commitment to building for the long term, recognizing that true innovation lies not in eliminating decay, but in anticipating and accommodating it.

What Lies Ahead?

The architecture detailed within necessitates acknowledging the inevitable entropy of any complex system. Nex-N1, and frameworks like it, aren’t destinations, but temporary plateaus in a relentless decline toward obsolescence. The immediate challenge isn’t simply scaling to larger environments or more agents-it’s managing the accumulation of failure modes. Each automated environment constructed introduces a novel vector for unanticipated errors, a new surface for the system to erode against. The focus must shift from maximizing initial performance to minimizing the rate of degradation.

Current metrics privilege novelty – trajectory generation, tool use – but fail to adequately capture systemic resilience. A truly mature agentic framework won’t be judged by what it can do, but by how gracefully it handles what it cannot. The pursuit of general agency is, perhaps, a misdirection. Perhaps the most fruitful path lies in specializing agents for increasingly narrow, but thoroughly understood, domains, accepting limitations as a feature, not a bug.

The ultimate test won’t be whether these systems can construct environments, but whether they can reliably diagnose and repair their own emergent flaws – essentially, whether they can become self-aware of their own decay. Incidents, then, aren’t failures; they are simply steps toward maturity-provided the system possesses the capacity to learn from them before succumbing to the inevitable weight of accumulated errors.

Original article: https://arxiv.org/pdf/2512.04987.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/