arXiv:2608. 15976v1 Announce Type: new Abstract: A learning system can occupy execution states that are indistinguishable under every declared present-behavior readout yet respond differently to future training.
By Qinyou Wang
A learning system can occupy execution states that are indistinguishable under every declared present-behavior readout yet respond differently to future training. We formalize this through fiber fingerprints: controlled future-learning response laws restricted to present-behavior equivalence classes.
arXiv:2607. 29496v1 Announce Type: new Abstract: We study transcript management for fixed, finite-precision causal Transformers.
By Sergey Salishev
arXiv:2607. 27132v1 Announce Type: new Abstract: An agent acting under partial observability must retain a recursively updateable statistic of history that restores the Markov property, but the smallest such statistic is generally unknown.
By Zuyuan Zhang, Yongshan Chen, Mahdi Imani, Tian Lan
arXiv:2608. 01548v3 Announce Type: replace Abstract: Test-time emergence in LLM systems has a deployment boundary: additional computation can realize decisions already supported by the deployed information--execution structure, while evidence, tools, memory, and executable semantics can change the class inherited by later computation.
By Yi Liu
The paper introduces topological necessities—mechanism‑invariant subgoals derived from the topology of successful trajectories—used to guide long‑horizon goal‑conditioned reinforcement learning. By computing homology in dimensions 0 and 1 over a transport‑weighted carrier, the authors obtain an enumerable gate set that forms a recursive topological gate hierarchy. These certified gates transfer across different embodiments (e.g., from PointMaze to Ant and Humanoid) without retraining, achieving state‑of‑the‑art performance on several benchmark tasks.
By Hao Shi, Xi Li