arXiv AI July 22, 2026 By Jian Liu, Dong Sun

Temporal-Causal Unity as an Operational Framework for Collective Dynamics: Causal-Progress Clocks, Synchronization, and Polarization

arXiv:2607. 18620v1 Announce Type: cross Abstract: This paper develops temporal-causal unity (TCU), a framework connecting a process-philosophical thesis -- time is the ordered unfolding of causal change -- to an operational model of cognitive and social dynamics.

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

arXiv Machine Learning

Aug 5

Bridging Prediction and Attribution: Identifying Forward and Backward Causal Influence Ranges Using Assimilative Causal Inference

arXiv:2510. 21889v2 Announce Type: replace-cross Abstract: Causal inference identifies cause-and-effect relationships between variables.

By Marios Andreou, Nan Chen

arXiv AI

Jun 16

Attention is Just Another Name for Coupling?: A Fast-Slow ODE Perspective on Hierarchical Pretraining

arXiv:2606. 16730v1 Announce Type: cross Abstract: Causal self-attention is a coupling mechanism: each token's hidden state is updated by a learned mixture of preceding tokens at the same timescale.

By Zhengyuan Gao

arXiv Machine Learning

Jun 18

Attention as Frustrated Synchronization

arXiv:2606. 18694v1 Announce Type: new Abstract: A network of oscillators that synchronizes perfectly computes nothing further, so an attention architecture built from synchronization must locate its computation in structured departures from agreement.

By Joshua Nunley

llms

arXiv Machine Learning

Jul 31

Re-examining Granger Causality with Causal Bayesian Networks and Reichenbachs Principles

arXiv:2501. 02672v4 Announce Type: replace-cross Abstract: Granger causality (GC) is widely used to infer directed relationships in time-series data.

By S. A. Adedayo

benchmarks

arXiv Machine Learning

Jul 31

DoTime: A Synthetic Benchmark Generator for Interventional and Counterfactual Time Series

arXiv:2607. 27263v1 Announce Type: new Abstract: Most benchmarks for causal inference over time series are observational, small, or domain-specific, leaving interventional and counterfactual estimation under-served exactly where it matters most, such as in healthcare, policy evaluation, and climate science.

By Dennis Thumm, Billy Tim Anthony, Ying Chen

benchmarks

arXiv Machine Learning

6d ago

Orientation, not magnitude: the causal structure of task-vector interference in merged language models

arXiv:2608. 11797v1 Announce Type: new Abstract: Model merging by task arithmetic works until it doesn't, and the field diagnoses why with magnitudes: layerwise representation bias, deviations from cross-task linearity, parameter overlap.

By Chencheng Zhu

llms efficiency safety

Related stories

Bridging Prediction and Attribution: Identifying Forward and Backward Causal Influence Ranges Using Assimilative Causal Inference

Attention is Just Another Name for Coupling?: A Fast-Slow ODE Perspective on Hierarchical Pretraining

Attention as Frustrated Synchronization

Re-examining Granger Causality with Causal Bayesian Networks and Reichenbachs Principles

DoTime: A Synthetic Benchmark Generator for Interventional and Counterfactual Time Series

Orientation, not magnitude: the causal structure of task-vector interference in merged language models