arXiv:2510. 21889v2 Announce Type: replace-cross Abstract: Causal inference identifies cause-and-effect relationships between variables.
By Marios Andreou, Nan Chen
arXiv:2609.01048v1 Announce Type: cross
Abstract: Across the full Pythia suite (160M-12B, eight checkpoints, four task families), a linear probe can read a target variable from the residual stream as...
By Xining Xun
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: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
arXiv:2609.01108v1 Announce Type: new
Abstract: TRACE (Math & Lienhart, arXiv:2602.01135) reads causal graphs over event types out of a pretrained autoregressive sequence model by thresholding a per-...
By Alex Chadyuk, Alicia Zhang, Roy Kucukates
The paper presents RCBNB-MB, a causal discovery algorithm that relaxes the assumption of a single, time‑consistent causal structure in time series. It identifies latent causal regimes—subsets of time points where a stable causal graph holds—and iteratively segments the series to recover both regime transitions and the corresponding causal graphs using Markov blankets. The authors provide theoretical guarantees and demonstrate through simulations and real IT monitoring data that RCBNB-MB outperforms baseline methods in detecting regime changes and their causal structures.
By Lei Zan, Charles K. Assaad, Emilie Devijver, Eric Gaussier