NeurIPS 2025: Scale, Benchmarks, and the Signals We Should Be Paying Attention To
NeurIPS 2025: Scale, Benchmarks, and the Signals We Should Be Paying Attention To -By Tanay Baswa, Director of Solutions and Founding AI Researcher and Engineer, Enkrypt AI
NeurIPS 2025 was, by every measure, a landmark year. It was the largest NeurIPS ever, with approximately 29,000 registered attendees, and it felt like it. The conference did not just occupy a convention center; it temporarily occupied the city. Every cab driver asked if I was in town for NeurIPS. Every barista recognized the badge. Downtown felt like a distributed campus.
This kind of scale changes the character of a field. It brings energy, diversity of thought, and an influx of new participants. It also forces us to confront questions that are easy to ignore when a community is smaller and more insular. What are we optimizing for. How are we measuring progress. What problems are we choosing to prioritize.
Walking through the conference, across papers, workshops, and conversations, four themes stood out clearly.
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Operating at societal scale
NeurIPS 2025 being the largest in history is not a story about growth; it is a story about penetration. Artificial intelligence is no longer expanding into new corners of society, it is already embedded. The attendance numbers reflect that reality. The mix of people in the room reflects it even more. This was not just researchers and PhD students. It was product leaders, infrastructure engineers, founders, policy teams, operators, and security leaders, all navigating the same questions from different angles.
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That matters because it changes the nature of the conversation. The discussions are no longer about whether AI will be adopted; they are about how it is already being deployed, governed, constrained, and integrated into real systems. The center of gravity has moved from possibility to consequence.
The surrounding infrastructure behaved as if this was expected. Venues were prepared, staff were fluent in the context, side events were everywhere. The ecosystem did not bend around NeurIPS; it absorbed it. That is what maturity looks like.
At this point, AI is not a sector. It is infrastructure.
More people means more papers, and that is not a bad thing
With scale comes volume. NeurIPS 2025 had an enormous number of accepted papers. Predictably, this led to some commentary about quality dilution. That critique is not entirely unfounded, but it is incomplete.
Every major scientific field that grows rapidly goes through this phase. When more people enter, variance increases. Some work will be exploratory. Some will be derivative. Some will be exceptional. That is the cost of growth, and it is also the engine of progress.
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More importantly, more people writing papers means more people thinking rigorously. More people learning how to frame questions, design experiments, and interpret results. That is a net positive. The alternative is a closed field that optimizes for prestige over participation.
The widespread use of AI has pulled many practitioners into research for the first time. That is healthy. We should want more engineers to think like scientists, not fewer.
The community is starting to question benchmarks, not just chase them
One of the most encouraging trends at NeurIPS 2025 was the number of papers and discussions focused on benchmarking itself. Not new benchmarks, but the quality, construction, and interpretability of existing ones.
Anyone who has built or evaluated large language models seriously knows the tension here. Benchmarks provide fast feedback. They help with iteration. They give a sense of progress. They also create distortions. Models can be over-optimized for specific datasets. Small changes can produce confusing or contradictory results. High scores do not always translate to better behavior in real systems.
Even widely used Elo-based leaderboards such as LMArena have faced justified criticism around gaming, sampling bias, and over-interpretation. This is not a fringe view. It is becoming mainstream.
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The underlying question is simple but uncomfortable. Are we optimizing for numbers, or are we optimizing for usefulness. Are we building models that perform well on public benchmarks, or systems that behave well under real-world pressure.
There were several papers this year that directly interrogated dataset quality, annotation consistency, and evaluation methodology. That is a sign of maturity. It suggests the community is no longer satisfied with surface-level metrics.
It also raises a broader point. Perhaps the next meaningful benchmarks are not just accuracy scores. Perhaps they are speed, latency, adaptability, and agentic behavior. Perhaps they measure how systems coordinate tasks, handle ambiguity, and recover from failure.
Those are harder to quantify, but far more relevant.
Security is present, but reliability is becoming the primary framing
Another noticeable pattern was the absence of dedicated security workshops, while reliability and robustness were heavily represented. This does not mean security is being ignored. There were many strong papers on attacks, defenses, jailbreaks, and evaluation methods. The work is clearly ongoing.
However, the framing is shifting. The community appears to be converging on an implicit understanding that large language models will never be perfectly secure. The attack surface grows with capability. New modalities introduce new vectors. There is no final patch.
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In that context, reliability becomes central. If systems cannot be perfectly secured, they must at least be predictable. If they cannot be made invulnerable, they must be made understandable. If they fail, they should fail in bounded and transparent ways.
This is not a retreat from security. It is an evolution in how the problem is defined. It recognizes that safety, security, and reliability are intertwined, and that long-term progress depends on engineering discipline, not wishful thinking.
As someone working deeply in AI security, I see this as a realistic and healthy shift. It reflects a community that is starting to grapple seriously with operational complexity, not just theoretical risk.
NeurIPS 2025 was crowded, and intense, but it was also thoughtful, increasingly self-critical, and grounded in real engineering problems. There was less spectacle and more substance. Less obsession with headlines and more attention to failure modes, tradeoffs, and system behavior.
That is a good direction.
Large fields tend to oscillate between hype and discipline. NeurIPS 2025 felt like a step toward discipline. Toward craft. Toward responsibility.
The scale is here. The questions are getting sharper. The work is getting harder. That is exactly what progress should look like.
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