arXiv:2607. 05175v1 Announce Type: new Abstract: We characterize observability in representation learning through Platonic Projection Structures (PPS), an operator-theoretic framework for analyzing representation accessibility under partial observation.
By Kazuo Ishii, Bishnu Prasad Gautam, Jieling Wu, Javaid Saher
arXiv:2601. 18930v4 Announce Type: replace-cross Abstract: We are interested in enabling autonomous agents to learn and reason about systems with hidden states, such as locking mechanisms.
By Seiji Shaw, Travis Manderson, Chad Kessens, Nicholas Roy
arXiv:2607. 17327v1 Announce Type: cross Abstract: Quantum machine learning models define probabilistic input--output maps through coherent quantum evolution and measurement.
By Johannes Fankhauser, Lukas J. Fiderer, Hans J. Briegel
arXiv:2605. 15995v2 Announce Type: replace-cross Abstract: Learning latent representations from complex data is central to modern machine learning, spanning temporal, multimodal, and partially observed systems.
By Gwenol\'e Quellec
arXiv:2606. 27378v1 Announce Type: cross Abstract: We introduce an axiomatic evaluation framework for latent thought representations in LLMs, comprising metrics that are independent of downstream benchmark scores and reveal representational failures that benchmark accuracy masks.
By Fahd Seddik, Fatemeh Fard
arXiv:2602. 01177v3 Announce Type: replace-cross Abstract: We develop an information-theoretic framework connecting stability, privacy, and generalization for quantum learning algorithms.
By Ayanava Dasgupta, Naqueeb Ahmad Warsi, Masahito Hayashi