My time at NeurIPS 2025 left me energized and optimistic about the future of artificial intelligence. The conference was a vibrant hub of ideas, showcasing not just incremental progress but foundational shifts in how we approach AI. As Chief Scientific Officer at Reliant AI, it was particularly exciting to see how many emerging trends directly address the complex challenges we are tackling in the life sciences.

This year's gathering reinforced that while the foundations of AI research are advancing, many unsolved problems remain. From groundbreaking new algorithms to thought-provoking discussions on the nature of AI reasoning, NeurIPS 2025 provided a clear picture of the road ahead. It’s a future where AI will not just answer questions, but help us solve some of the most intricate puzzles in science and medicine.

Key Takeaways from the Forefront of AI Research

This year, several presentations stood out for their direct relevance to our work and the broader AI community.

New Frontiers in Algorithms and Reasoning

The intersection of Large Language Models (LLMs) and Reinforcement Learning (RL) was a major theme. Yejin Choi delivered a powerful talk on the novel techniques being developed in this space. These hybrid approaches are essential for addressing the very issues we face at Reliant AI, where models must not only process information but also learn and adapt in specialized domains.

Kyunghyun Cho’s presentation on "Learning to X" was another highlight. His work on developing algorithms that can learn to perform various tasks is a testament to the continued progress in core AI research. It’s a reminder that beyond the hype of specific applications, the fundamental science of AI is moving forward, unlocking capabilities that will have broad implications for years to come.

Melanie Mitchell’s session offered a crucial dose of introspection. She explored how high-performing LLMs produce “alien intelligence”: an intelligence that gives the right answers, but based on a rationale that no human would ever provide (and in some cases, is evidently an incorrect generalization). This reminds me of the famous “Move 37”, a play by AlphaGo that initially baffled human experts but ultimately proved brilliant. As AI becomes ever more capable of superhuman capabilities, Mitchell's presentation underscores the critical need for explainability and caution as we deploy these powerful systems.

The Unique Challenge of AI in Life Sciences

One of the most persistent themes in my conversations with colleagues was the difficulty of creating verifiable rewards for AI in the life sciences. In domains like mathematics or coding, it is relatively straightforward to verify if an AI’s output is correct. A math problem has a single right answer, and code either runs or it doesn't. This has been the path of least resistance for achieving superhuman performance, often aided by longer chains of thought.

However, how do you create a verifiable reward for a complex biological question? The answers our users need often involve nuanced interpretations of data, information outside a single document's scope, and a deep understanding of scientific context. There is no simple "correct" or "incorrect" label. This is an incredibly challenging area and one where Reliant AI is investing significant time and resources. I was encouraged to see emerging techniques at NeurIPS that will be instrumental in helping us navigate this ambiguity and build more reliable systems.

Moving Beyond Prompt Engineering

The conference also signaled a clear industry shift away from manual prompt engineering. Few researchers or founders I spoke with believe that painstakingly tweaking instructions for models is a sustainable or efficient use of time. The consensus is that this task is better suited for algorithms.

Instead, the focus is turning back to RL and RL-like algorithms to automate the process of optimizing model interactions. Automated tools like DSPy are accelerating this transition, allowing development teams to streamline their workflows and focus on higher-level problems. This move toward automated optimization will be critical for building sophisticated, scalable AI applications.

With this in mind, it was a pleasure to present Reliant AI’s first scientific contribution to AI research. In our work, we introduce a new algorithm — TOPR — for fine-tuning large language models with reinforcement learning, with far fewer data restrictions than existing techniques. We have been using TOPR for a while now to fine-tune models where a high degree of accuracy must be maintained while reducing cost and improving speed. Because it is so efficient, we believe TOPR will also play a key role in automated prompt optimization.

Building for a Specialized World

My discussions with other founders and researchers in AI for life sciences consistently returned to a central point: building applications for this sector is fundamentally different from creating a general-purpose chatbot. The complexities of scientific data, the need for precision, and the high stakes involved demand a specialized approach.

General-purpose tools may be impressive, but they lack the domain-specific knowledge and nuanced understanding required to answer complex questions in fields like drug discovery or clinical research. The insights shared at NeurIPS reinforce our commitment at Reliant AI to building purpose-driven models that are tailored to the unique demands of the life sciences community.

An Exciting Future Ahead

Leaving NeurIPS 2025, I am more convinced than ever that we are on the cusp of major breakthroughs. The challenges are significant, but the collective ingenuity and collaborative spirit of the AI research community are more than equal to the task. The shift toward more robust, adaptable, and specialized AI systems aligns perfectly with our mission at Reliant AI. We are excited to integrate these new learnings into our work and continue pushing the boundaries of what is possible in applying AI to solve the world's most complex scientific problems.