Reflections on NeurIPS 2025: Advancing evaluation and continual learning in AI

Written by members of the Labelbox Research team:
Shahriar Golchin, Smit Modi, Stepan Tytarenko, Almas Abdibayev, and Marc Wetter

As NeurIPS 2025 comes to a close, we find it timely to reflect on the key themes and emerging research directions that stood out this year and are likely to shape the research agenda in the year ahead. Let's take a look at some of the key insights from keynotes, invited talks, oral presentations, and posters, while also pointing to promising directions for future work.

Specifically, we’ll focus on two central areas that emerged across the conference: (1) how to faithfully measure and validate the capabilities of these powerful AI systems, and (2) how to build interactive AI systems that continually learn through experience over time.

Evaluation and benchmarking take center stage

One of the strongest takeaways from NeurIPS 2025 is that evaluation and benchmarking are no longer peripheral concerns. They are now recognized as central to reliable and meaningful progress in AI.

However, faithfully evaluating current AI systems has become increasingly challenging due to several key issues:

• Data contamination: This remains a major concern in evaluating AI models. Data contamination occurs when the test set overlaps with the training data (Golchin & Surdeanu, 2024). The sheer scale of training data makes it difficult to reliably filter and moderate it to prevent such overlap (Golchin & Surdeanu, 2025). As a result, benchmark performance can be artificially inflated, casting doubt on the reliability of reported gains (Li & Flanigan, 2024).
• Pattern matching and memorization over generalization: Many evaluations remain overly sensitive to distributional similarity between training and test data. Even without direct contamination, high performance can result from template recognition, shortcut exploitation, or shallow pattern matching (Panwar et al., 2025). This limits the ability to assess out-of-distribution generalization (Mirzadeh et al., 2025).
• Evaluation faithfulness: Perhaps most fundamentally, even “standard” benchmarks may fail to capture the capabilities they aim to measure (Bean et al., 2025). Tasks may be solvable via unintended artifacts rather than the targeted capabilities (e.g., reasoning, planning, grounded understanding) (Heineman et al., 2025). This underscores the need for greater methodological rigor in task and dataset design, including constructing evaluations that serve as valid proxies for the intended capability, using high-quality data, and interpreting performance scores as supporting evidence rather than definitive endpoints.

Agents that continually learn: From static foundation models to interactive AI systems

If NeurIPS 2023 and 2024 were about foundation models everywhere, NeurIPS 2025 was about agents everywhere. Reinforcement learning re-emerged as a unifying framework connecting learning, planning, tool use, and long horizon behavior. Importantly, reinforcement learning was rarely framed as a self contained subfield. Instead, it was increasingly treated as infrastructure—a systems layer that enables experience driven improvement through interaction, feedback, and iterative refinement during inference.

There is broad agreement within the research community that continual learning represents a critical pathway toward more general and adaptive AI systems. However, despite this consensus, there is currently no widely adopted practical implementation that fully achieves continual learning in deployed systems. Much of the existing work explores partial solutions through targeted architectural modifications, such as incorporating memory modules that allow agents to retain, retrieve, and update knowledge over time. These efforts represent early but important steps toward building interactive AI systems that can learn continuously from experience.

Looking ahead: From inflection to consolidation

If NeurIPS 2025 marked an inflection point in AI research, then 2026 is poised to be a year of consolidation and refinement. Several trends suggest how the field may evolve:

• Datasets and benchmarks: There is a continued shift toward more realistic, open-ended, and task-diverse benchmarks that better reflect real-world use cases. These are designed not only to showcase model strengths but also to expose failure modes, enabling more robust evaluation of generalization and reasoning capabilities.
• Agentic reinforcement learning: Reinforcement learning is evolving beyond traditional paradigms such as reward maximization or human feedback alignment. Emerging approaches emphasize continual learning through experience, multi turn interaction, and integration with tool ecosystems, enabling agents to adapt over time and operate in complex, dynamic environments.

As a final note, a significant undercurrent we observed throughout NeurIPS 2025 was the growing prominence of a silently accepted prosaic alignment perspective within the AI community. This is the view that future Artificial General Intelligence may be achievable through existing practical machine learning techniques without the need for a fundamentally new paradigm or a transformative breakthrough in our understanding of intelligence.

How Labelbox helps support the next wave of AI research

At Labelbox, we are committed to advancing AI by supporting one of its most essential pillars: high-quality data. As AI systems become increasingly complex, the role of reliable data in training and evaluation becomes even more critical. The challenges emphasized at NeurIPS 2025, particularly around model evaluation and benchmarking, resonate deeply with our mission.

To address these needs, we have been developing novel, high-signal datasets and benchmarks that serve as meaningful proxies for evaluating specific model capabilities. These datasets are designed with a focus on faithfulness, signal strength, and task relevance, ensuring that they align closely with the underlying competencies we aim to measure.

Our efforts span two complementary strategies:

• Real-world, expert-curated datasets: We create datasets using real-world data that is carefully curated by subject-matter experts. These datasets are privately designed to be free from data contamination and are crafted to target specific model capabilities rigorously and realistically. While these datasets provide rich and domain-relevant insights, they also require significant human effort and expertise to design and annotate properly.
• Abstract stress-testing of model capabilities: In parallel, the Applied Machine Learning Research team at Labelbox has internally developed a novel evaluation methodology that abstractly and systematically stress-tests AI models through a capability-focused lens, without relying on human evaluation or manual curation. These evaluations are domain-agnostic and designed to expose generalizable failure modes (e.g., brittleness, shortcut behavior, or systematic errors) in a way that can be harder to detect with narrowly scoped, real-world benchmarks alone. Compared to traditional, domain-specific benchmarks, we found that these abstract evaluations are more scalable, cost-efficient, and higher-signal.

In short, NeurIPS 2025 highlighted a clear shift in focus across the AI community: rigorous evaluation is not just a support task, but a core research challenge. At Labelbox, we fully embrace this perspective. Our work sits at the intersection of cutting-edge AI development and foundational evaluation research, and we view high-quality data as a key enabler of scientific progress. In this spirit, we believe that research should fuel research.

Insights from the academic community directly inform how we design our datasets, benchmarks, and evaluation protocols. In turn, we develop tools and methodologies that allow researchers and practitioners to evaluate models with greater fidelity, transparency, and depth. By closing this loop, we aim to accelerate the broader AI ecosystem, using research to build better data and using better data to drive better research. If you’re working on AI projects and want to explore ways to improve data quality and evaluation, we’d love to chat about how Labelbox can help support your research and model development. Get in touch here.

Special thanks to the authors of this post: Shahriar Golchin, Smit Modi, Stepan Tytarenko, Almas Abdibayev, and Marc Wetter.