The paper investigates whether causal softmax attention can realize policy mirror descent (PMD) as a repeated controller rather than a one‑step algebraic identity. It constructs a fixed causal‑softmax actor–environment–one‑step‑critic protocol, detailing actor, routing, sampling, and normalization residuals, and shows that a frozen one‑step audit model closely approximates PMD. Empirical results demonstrate that the learned actor with an exact one‑step critic achieves median policy loss only about 5% higher than the exact PMD oracle across multiple control settings.
By Yuhe Sui, Yingzhi Tang, Shufang Chen
arXiv:2607. 11796v1 Announce Type: new Abstract: Selective state-space models such as Mamba route information through a bank of first-order modes whose input coupling is set by a learned selection mechanism.
By Raktim Bhattacharya
arXiv:2609.18961v1 Announce Type: new
Abstract: Mechanistic interpretability identifies sparse subsets of heads and MLP blocks that carry specific behaviors. We ask whether such causal signals can gu...
By Son Ha Xuan, Phat T. Tran-Truong, Xuan-Bach Le
arXiv:2607. 10203v2 Announce Type: replace-cross Abstract: Adaptive-compute world models -- early-exit or mixture-of-depths predictors that spend variable depth per step -- assume depth buys better predictions and can be routed adaptively.
By Achyuthan Sivasankar
The paper introduces Untied Self-Conditioning, a sampler that corrects a train–inference mismatch in flow‑matching language models. By dampening redundant directions in the self‑conditioning input and approximating a step‑average prediction from history, the method improves generation quality without retraining. On LangFlow and ELF‑B datasets, it dramatically lowers perplexity and is preferred in the majority of pairwise comparisons.
By Bocheng Li, Linli Xu
arXiv:2609. 12259v1 Announce Type: new Abstract: Matrix-valued memories make rank the natural budget of a learned representation: the number of independent directions a state spans bounds what it can bind, compose, and track.
By Samuel Larson