Superposition Yields Robust Neural Scaling → https://neurips.cc/virtual/2025/loc/san-diego/poster/116346
Controlling superposition in toy models and checking real LLMs, researchers show that strong superposition naturally creates the familiar “bigger model = lower loss” power laws, explaining when scaling laws work and when they might fail

Why Diffusion Models Don’t Memorize: The Role of Implicit Dynamical Regularization in Training → https://neurips.cc/virtual/2025/loc/san-diego/poster/119372
Shows diffusion models hit an early “good samples” phase and a later memorization phase. Larger datasets widen the "generalization window," avoiding overfitting much longer and revealing implicit regularization

Does RL Really Incentivize Reasoning Capacity in LLMs Beyond the Base Model? → https://neurips.cc/virtual/2025/loc/san-diego/poster/119944
Explains that while RLVR makes models better at finding correct answers efficiently, it doesn’t create really new reasoning abilities. RLVR models mostly reuse patterns already present in the base model, highlighting the need for better RL to unlock reasoning gains

1000 Layer Networks for Self-Supervised RL: Scaling Depth Can Enable New Goal-Reaching Capabilities → https://neurips.cc/virtual/2025/loc/san-diego/poster/115731
Simply making RL models way deeper, up to 1024 layers, can massively improve self-supervised RL, letting agents learn far better behaviors from scratch and boosting performance by 2-50× on locomotion and manipulation tasks

Titans + MIRAS: Helping AI have long-term memory → https://research.google/blog/titans-miras-helping-ai-have-long-term-memory/
Titans is a new architecture with a deep MLP memory that updates itself during inference using a “surprise” signal, letting the model keep important info, forget noise, and handle million-token contexts with RNN-like speed and Transformer-like accuracy

Generative Data Augmentation via Diffusion Distillation, Adversarial Alignment, and Importance Reweighting → https://neurips.cc/virtual/2025/loc/san-diego/poster/116854
Introduces DAR-GDA, which distills diffusion models into a fast one-step generator, aligns them with real data via adversarial training, and reweights synthetic samples to remove bias

Slow Transition to Low-Dimensional Chaos in Heavy-Tailed RNNs → https://arxiv.org/abs/2505.09816
Shows that RNNs with brain-like heavy-tailed weights don’t behave like Gaussian ones. They shift and widen the edge-of-chaos transition but reduce the system’s effective dimensionality.

Evaluating multiple models using labeled and unlabeled data → https://arxiv.org/abs/2501.11866
Introduces Semi-Supervised Model Evaluation (SSME), a way to evaluate classifiers using both labeled and unlabeled data by modeling how predictions relate to true labels, giving far more accurate performance estimates when labeled data is limited

Riemannian Consistency Model → https://arxiv.org/abs/2510.00983
Extends consistency models to curved spaces, enabling few-step generation that stays on the manifold, using exponential maps and covariant derivatives, and works well on spheres, tori, and 3D rotations

BioReason: Incentivizing Multimodal Biological Reasoning within a DNA-LLM Model → https://arxiv.org/abs/2505.23579
BioReason links a DNA model with an LLM so the LLM can reason over genomic data, yielding clear biological explanations and strong accuracy gains on pathway and variant prediction tasks

NFL-BA: Near-Field Light Bundle Adjustment for SLAM in Dynamic Lighting → https://asdunnbe.github.io/NFL-BA/NeurIPS2025_NFL_BA.pdf
Introduces NFL-BA, a SLAM loss that models near-field lighting so systems work better in settings like endoscopy or dark indoor scenes, yielding large improvements in camera tracking and mapping