Realist, no-collapse interpretations of quantum mechanics, such as Everett's, face the probability problem: how to justify the norm-squared (Born) rule from the wavefunction alone. While any basis-independent measure can only be norm-squared (due to the Gleason-Busch Theorem) this fact conflicts with various popular, non-wavefunction-based phenomenological measures - such as observer, outcome or world counting - that are frequently demanded of Everettians. These alternatives conflict, however, with the wavefunction realism upon which Everett's approach rests, which seems to call for an objective, basis-independent measure based only on wavefunction amplitudes. The ability of quantum probabilities to destructively interfere with each other, however, makes it difficult to see how probabilities can be derived solely from amplitudes in an intuitively appealing way. I argue that the use of algorithmic probability can solve this problem, since the objective, single-case probability measure that wavefunction realism demands is exactly what algorithmic information theory was designed to provide. The result is an intuitive account of complex-valued amplitudes, as coefficients in an optimal lossy data compression, such that changes in algorithmic information content (entropy deltas) are associated with phenomenal transitions.
Compression as a universal principle of animal behavior
Universitat Politècnica de Catalunya（加泰罗尼亚理工大学）；Universitat de Barcelona（巴塞罗那大学）；University of Aberdeen（阿伯丁大学）；Tajen University（大仁科技大学）；University of Roehampton（罗汉普顿大学）
Same Compression Principle, Different Geometry: Rate-Distortion Signatures Dissociate Biological and Artificial Visual Systems
Windreich Department of AI；Human Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA；Department of Computing, Imperial College London, London, UK；Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA；Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA；Berkman Klein Center for Internet \& Society, Harvard University, Cambridge, MA, USA
Efficient Learned Data Compression via Dual-Stream Feature Decoupling
College of Computer Science, TMCC, SysNet, DISSec, GTIISC, Nankai University（南开大学计算机学院，TMCC，SysNet，DISSec，GTIISC）；College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算与数据科学学院）；Beijing Institute of Technology（北京理工大学）
Understanding LLM Behaviors via Compression: Data Generation, Knowledge Acquisition and Scaling Laws
Institute for Interdisciplinary Information Sciences, Tsinghua University（清华大学交叉信息研究院）；School of Computer Science and Engineering, Beihang University（北航计算机科学与工程学院）
