arXiv Machine Learning June 25, 2026 By Taiga Saito, Yu Otake, Daijiro Mizutani, Sopheakpolin Mom

What Do Language Priors Contribute to Darcy-Flow Inversion? A Mechanistic Audit

Read the original on arXiv Machine Learning →

arXiv:2606. 24967v1 Announce Type: new Abstract: In ill-posed inverse problems, the recovered solution depends as much on the prior as on the data, yet much of the engineering knowledge that could serve as that prior is recorded qualitatively rather than in formal mathematical form.

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

arXiv Machine Learning

Jun 25

Geo-Strat-RL: Learning Geological Event Reasoning from Verifiable Tasks

arXiv:2606. 25000v1 Announce Type: new Abstract: To evaluate whether vision-language models can reason about geological histories, it is necessary to construct observations for which the underlying process history is known.

By Lukas Mosser

llms reinforcement-learning multimodal

arXiv Machine Learning

Aug 3

PTP: Previous-Token Prediction based LLM Inversion for Near-Exact Prompt Reconstruction

arXiv:2607. 29378v1 Announce Type: cross Abstract: Large language models (LLMs) generate text by auto-regressively sampling the next token.

By Pirzada Suhail, Nagasai Saketh Naidu, Atanu R Sinha, Amit Sethi

llms fine-tuning

arXiv AI

Jun 2

Physics-Encoded Inverse Modeling for Arctic Snow Depth Prediction

arXiv:2601. 17074v4 Announce Type: replace-cross Abstract: Accurate estimation in time-varying inverse problems under limited and sparse observations remains a fundamental challenge across scientific domains.

By Akila Sampath, Vandana Janeja, Jianwu Wang

computer-vision

arXiv Machine Learning

Jun 26

Latent Diffusion Posterior Sampling with Surrogate Likelihood Guidance for PDE Inverse Problems

arXiv:2606. 26592v1 Announce Type: cross Abstract: We propose latent-space diffusion posterior sampling (L-DPS), an approximate Bayesian framework for high-dimensional inverse problems governed by partial differential equations (PDEs).

By Yuanzhe Wang, Alexandre M. Tartakovsky

diffusion

arXiv AI

Jul 7

InverseScope: Scalable Activation Inversion for Interpreting Large Language Models

arXiv:2506. 07406v3 Announce Type: replace-cross Abstract: Understanding the internal representations of large language models (LLMs) is a central challenge in interpretability research.

By Yifan Luo, Zhennan Zhou, Bin Dong

llms efficiency benchmarks safety

arXiv AI

Jul 28

Physics-Encoded Inverse Modeling for Arctic Snow Depth Estimation

arXiv:2601. 17074v5 Announce Type: replace-cross Abstract: Accurate estimation of unobserved quantities in time-varying inverse problems remains challenging when observations are sparse and only indirectly related to the target variable.

By Akila Sampath, Vandana P. Janeja, Jianwu Wang

computer-vision

Related stories

Geo-Strat-RL: Learning Geological Event Reasoning from Verifiable Tasks

PTP: Previous-Token Prediction based LLM Inversion for Near-Exact Prompt Reconstruction

Physics-Encoded Inverse Modeling for Arctic Snow Depth Prediction

Latent Diffusion Posterior Sampling with Surrogate Likelihood Guidance for PDE Inverse Problems

InverseScope: Scalable Activation Inversion for Interpreting Large Language Models

Physics-Encoded Inverse Modeling for Arctic Snow Depth Estimation