Top 4 Papers of NeurIPS 2025 That You Must Read

NeurIPS dropped its list of the best research papers for the year 2025, and the list does more than name-drop impressive work. It provides a map for navigating the problems the field now cares about. This article would shed some light to what those papers are, and how they were able to contribute to AI. We’ve also included links to the full papers, incase you were curious.

The Selection Criteria

The best paper award committees were tasked with selecting a handful of highly impactful papers from the Main Track and the Datasets & Benchmark Track of the conference. They came up with 4 papers as the winners.

The Winners!

Artificial Hivemind: The Open-Ended Homogeneity of Language Models (and Beyond)

Diversity is something that large language models had lacked since their genesis. Elaborate efforts have been made to help distinguish one model’s output from the others, but the efforts have been in vain. 

Homogeneity in the response of LLMs across architectures and companies, consistently, highlights the lack of creativity in LLMs. We are slowly approaching the point where a model response would be indistinguishable from the other. 

The paper outlines the problem that lies with traditional benchmarks. Most benchmarks use narrow, task-like queries (math, trivia, code). But real users ask messy, creative, subjective things. And those are exactly where models collapse into similar outputs. The paper proposes a dataset that systematically probes this territory.

These two concepts that lie at the heart of the paper:

• Intra-model repetition: A single model repeats itself across different prompts or different runs.
• Inter-model homogeneity: Different models produce shockingly similar answers.

The second part is the concerning one, as if Anthropic, Google, Meta all have different models parroting the same response, then what’s the whole point of these diverse developments?

The Solution: Infinity-Chat

Infinity-Chat, the dataset proposed as a solution to this problem, comes with more than 30,000 human annotations, giving each prompt twenty-five independent ratings. That density makes it possible to study how people’s tastes diverge, not just where they agree. When the authors compared those human judgments with model outputs, reward models, and automated LLM evaluators, they found a clear pattern: systems look well-calibrated when preferences are uniform, but they slip as soon as responses trigger genuine disagreement. That’s the real value of Infinity-Chat!

Authors: Liwei Jiang, Yuanjun Chai, Margaret Li, Mickel Liu, Raymond Fok, Nouha Dziri, Yulia Tsvetkov, Maarten Sap, Yejin Choi

Full Paper: https://openreview.net/forum?id=saDOrrnNTz

Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention Sink Free

Transformers have been around long enough that people assume the attention mechanism is a settled design. Turns out it’s not! Even with all the architectural tricks added over the years, attention still comes with cost of instability, massive activations, and the well-known attention sink that keeps models focused on irrelevant tokens.

The authors of this research took a simple question and pushed it hard: what happens if you add a gate after the attention calculation, and nothing more. They run more than thirty experiments on dense models and MoE (Mixture of Experts) models trained on trillions of tokens. The surprising part is how consistently this small tweak helps across settings.

There are two ideas that explains why gating works so well: 

• Non-linearity and sparsity: Head specific sigmoid gates add a fresh non-linearity after attention, letting the model control what information flows forward.
• Small change, big impact: The modification is tiny but consistently boosts performance across model sizes.

The Solution: Output Gating

The paper recommends a straightforward modification: apply a gate to the attention output on a per head basis. Nothing more. The experiments show that this fix consistently improves performance across model sizes. Because the mechanism is simple, the broader community is expected to adopt it without friction. The work highlights how even mature architectures still have room for meaningful improvement.

Authors: Zihan Qiu, Zekun Wang, Bo Zheng, Zeyu Huang, Kaiyue Wen, Songlin Yang, Rui Men, Le Yu, Fei Huang, Suozhi Huang, Dayiheng Liu, Jingren Zhou, Junyang Lin

Full Paper: https://openreview.net/forum?id=1b7whO4SfY

With these two out of the way, the other 2 papers don’t necessarily provide a solution, rather suggests some pointers that could be followed.

1000 Layer Networks for Self Supervised RL: Scaling Depth Can Enable New Goal Reaching Capabilities

Reinforcement learning has long been stuck with shallow models because the training signal is too weak to guide very deep networks. This paper pushes back on that assumption and shows that depth isn’t a liability. It’s a capability unlock.

The authors train networks with up to one thousand layers in a goal conditioned, self supervised setup. No rewards. No demonstrations. The agent learns by exploring and predicting how to reach commanded goals. Deeper models don’t just improve success rates. They learn behaviors that shallow models never discover.

Two ideas sit at the core of why depth works here:

• Contrastive self supervision: The agent learns by comparing states and goals, which produces a stable, dense learning signal.
• Batch size and stability: Training very deep networks only works when batch size grows with depth. Larger batches keep the contrastive updates stable and prevent collapse.

Authors: Kevin Wang, Ishaan Javali, Michał Bortkiewicz, Tomasz Trzcinski, Benjamin Eysenbach
Full Paper: https://openreview.net/forum?id=s0JVsx3bx1

Why Diffusion Models Don’t Memorize: The Role of Implicit Dynamical Regularization in Training

Diffusion models rarely memorize their training data, even when heavily parameterised. This paper digs into the training process to explain why that happens.

The authors identify two training timescales. One marks when the model starts producing high quality samples. The second marks when memorization begins. The key point is that the generalization time stays the same regardless of dataset size, while the memorization time grows as the dataset grows. That creates a widening window where the model generalizes without overfitting.

Two ideas sit at the core of why memorization stays suppressed:

• Training timescales: Generalization emerges early in training. Memorization only appears if training continues far past that point.
• Implicit dynamical regularization: The update dynamics naturally steer the model toward broad structure rather than specific samples.

This paper doesn’t introduce a model or a method. It gives a clear explanation for a behavior people had observed but couldn’t fully justify. It clarifies why diffusion models generalize so well and why they don’t run into the memorization problems seen in other generative models.

Authors: Tony Bonnaire, Raphaël Urfin, Giulio Biroli, Marc Mezard
Full Paper: https://openreview.net/forum?id=BSZqpqgqM0

Conclusion

The four papers set a clear tone for where research is headed. Instead of chasing bigger models for the sake of it, the focus is shifting toward understanding their limits, fixing long standing bottlenecks, and exposing the places where models quietly fall short. Whether it’s the creeping homogenization of LLM outputs, the overlooked weakness in attention mechanisms, the untapped potential of depth in RL, or the hidden dynamics that keep diffusion models from memorizing, each paper pushes the field toward a more grounded view of how these systems actually behave. It’s a reminder that real progress comes from clarity, not just scale.

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