NeurIPS, short for Neural Information Processing Systems Conference, is the world’s most influential AI research conference. This year saw around 15,000 researchers descend on San Diego for six days of papers, posters, workshops and corporate pitches. With 5,290 papers accepted from over 21,500 submissions, NeurIPS serves as an annual barometer of where AI research is heading and what problems the community considers worth focusing on. Or at least it used to.
Last year’s conference was memorable for Ilya Sutskever’s provocative keynote declaring the end of pre-training as we know it. This year, the Turing Award winner Richard Sutton argued AI has “lost its way” in its rush to commercialisation, calling for a return to fundamentals: “We need agents that learn continually. We need world models and planning.” Sociologist Zeynep Tufekci warned that “Artificial Good-Enough Intelligence can unleash chaos and destruction” long before AGI arrives, while Yejin Choi highlighted the puzzle of “jagged intelligence”, the concept we visualised last week where models ace benchmarks yet fail unpredictably on many simple tasks. The exhibition halls and corridors also doubled as a hiring fair, with labs and startups competing for talent in what remains a brutally competitive market.
The main conference floor is a maze of stands and concepts presented in the form of posters, with researchers jostling to photograph the work and speak to the authors. Google’s poster on “Nested Learning” drew crowds several deep. The paper proposes that architecture and optimisation are not separate concepts but different levels of the same underlying process, offering a potential path toward systems that learn continuously without forgetting.
Each year several papers win NeurIPS awards. This year’s seven winners cluster around familiar challenges. One tackles the “Artificial Hivemind” effect; after testing over 70 models, the researchers found they all generate eerily similar responses. Adjusting temperature settings or combining multiple models does not actually create diversity. Another shows that 1000-layer networks dramatically improve self-supervised reinforcement learning.
One of the Best Paper awards went to a team from Alibaba’s Qwen group for a deceptively simple modification that adds a small filter into the “attention” mechanism in LLM models (attention is how models decide which parts of an input to focus on). The technique helps solve a known quirk where models waste capacity by fixating on the first word in a sequence regardless of its importance. The NeurIPS Selection Committee praised Alibaba for sharing industrial-scale research; “This paper represents a substantial amount of work that is possible only with access to industrial scale computing resources, and the authors’ sharing of the results, which will advance the community’s understanding of attention in large language models, is highly commendable.” The technique is already deployed in Qwen3-Next, demonstrating how quickly research can move from conference poster to commercial product. (We also saw DeepSeek release a model this week with an attention efficiency breakthrough which we describe below.)
But despite vibrant community events like NeurIPS, research is increasingly happening in secret. “I cannot imagine us putting out the transformer papers for general use now,” one current Google DeepMind researcher told the Financial Times. Another former scientist was blunter: “The company has shifted to one that cares more about product and less about getting research results out for the general public good.” This is not unique to Google. Miles Brundage, formerly of OpenAI, cited publishing constraints as one reason for his departure. Meta’s new Superintelligence Lab has reportedly discussed moving away from open releases for its most capable models. The State of AI Report 2025 documented a broad decline in publicly available AI research, with the drop particularly steep for Google. If the frontier labs are holding back their most significant work, what appears at conferences increasingly represents incremental improvements, or research from academia and smaller players who lack the compute required for breakthrough work.
Takeaways: The general research agenda is clearly focused on immediate and pressing problems: memory, continuous learning, reasoning reliability, and efficiency. Progress in these areas is likely to remain rapid regardless, even without dramatic breakthroughs, given the diversity and intensity of research. But when the next major breakthrough arrives, something akin to OpenAI’s o1 reasoning concept from this time last year, it will likely emerge in production first. The conference remains invaluable for understanding the science, but the frontier has moved behind closed doors.