arXiv Machine Learning July 7, 2026 By Ruilin Zhang, Louis Tao, Zhuo-Cheng Xiao

Diffusion learning reveals viable parameter manifolds and compensation geometry in biological dynamical systems

Read the original on arXiv Machine Learning →

arXiv:2607. 03671v1 Announce Type: cross Abstract: Models of complex systems often have many parameters, yet are constrained by far fewer experimentally accessible observables: similar activity can emerge from coordinated parameter changes.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

diffusion

arXiv Machine Learning

Aug 4

HyperODE: Zero-Shot Surrogate for Simulation and Inference of Dynamical Systems

arXiv:2608. 00852v1 Announce Type: new Abstract: Understanding and controlling complex dynamical systems often requires executing thousands of numerical simulations across vast parametric landscapes, which is time-consuming.

By Ajitesh Srivastava

arXiv Machine Learning

Jun 30

Scalar Representations of Neural Network Training Dynamics

arXiv:2606. 30384v1 Announce Type: new Abstract: Training in artificial neural networks can be viewed as a trajectory evolving through a high-dimensional loss landscape.

By Pedro Jim\'enez-Gonz\'alez, Miguel C. Soriano, Lucas Lacasa

rag

arXiv Machine Learning

Jun 9

Trajectory Geometry of Transformer Representations Across Layers

arXiv:2606. 09287v1 Announce Type: new Abstract: Understanding how transformer representations evolve across layers, not merely what they encode, remains an open problem in mechanistic interpretability.

By Vishal Pandey, Gopal Singh

llms rag safety

Hugging Face Trending Papers

Jun 29

Scalar Representations of Neural Network Training Dynamics

Training in artificial neural networks can be viewed as a trajectory evolving through a high-dimensional loss landscape. However, the large number of trainable parameters makes the direct analysis of these dynamics challenging.

rag

arXiv AI

Jun 10

Learning Where to Simulate: Generative Active Sampling for Online PDE Surrogate Training

arXiv:2606. 09949v1 Announce Type: cross Abstract: Data-driven PDE surrogates are trained with data produced by numerical PDE solvers.

By Pierre Cesar (DATAMOVE), Sofya Dymchenko (DATAMOVE), Abhishek Purandare (DATAMOVE), Bruno Raffin (DATAMOVE)

diffusion

arXiv Machine Learning

Jul 13

How are linear representations learned? Exact solutions to the dynamics of abstraction

arXiv:2607. 08843v1 Announce Type: new Abstract: In artificial and biological neural networks, concepts are often encoded as consistent linear directions in representation space.

By William W. Yang, Andrew M. Saxe, Peter E. Latham

llms safety

Related stories

HyperODE: Zero-Shot Surrogate for Simulation and Inference of Dynamical Systems

Scalar Representations of Neural Network Training Dynamics

Trajectory Geometry of Transformer Representations Across Layers

Scalar Representations of Neural Network Training Dynamics

Learning Where to Simulate: Generative Active Sampling for Online PDE Surrogate Training

How are linear representations learned? Exact solutions to the dynamics of abstraction