The paper introduces Action-Contrastive Masked Transition Modeling (AC‑MTM), a method that replaces the Gaussian regularizer used in Joint‑Embedding Predictive Architectures (JEPAs) with a contrastive inverse‑dynamics head. AC‑MTM trains a forward latent‑prediction model while an auxiliary inverse‑dynamics task forces the encoder to distinguish actions from latent transitions, preventing collapse without requiring a target network or reconstruction loss. Experiments on pixel‑control and multi‑object visual tasks show that AC‑MTM matches or surpasses the performance of the Gaussian‑based SIGReg regularizer, achieving up to 20–24 point improvements on the OGBench Visual Scene benchmark.
By Jack Boylan, Chris Hokamp
The paper introduces Action-Contrastive Masked Transition Modeling (AC‑MTM), a method that stabilizes Joint‑Embedding Predictive Architectures (JEPAs) without relying on Gaussian regularization. AC‑MTM adds a training‑only inverse‑dynamics head that uses Action‑NCE to force each latent transition to identify its generating action, thereby preventing encoder collapse. Experiments on pixel‑control and multi‑object visual tasks show that AC‑MTM trains stably from scratch and matches or surpasses the performance of SIGReg, achieving up to a 24‑point improvement on the OGBench Visual Scene benchmark.
The paper argues that Large Language Models (LLMs) do not function as Solomonoff induction estimators because their training objectives—cross‑entropy, negative log‑likelihood, and next‑token prediction—optimize fit to a supplied conditional distribution rather than a program‑weighted universal mixture. It further contends that additional computation alone does not transform these models into optimal predictors without external hyper‑parameter or architectural changes. The authors suggest that neurosymbolic machine learning, exemplified by models such as Fable and Astra, represents a shift toward symbolic model synthesis, moving beyond purely statistical LLMs.
By Hector Zenil, Abicumaran Uthamacumaran, Luan Ozelim
arXiv:2601.05280v4 Announce Type: replace-cross
Abstract: On the one hand, the question of whether Large Language Models (LLMs) are Solomonoff induction estimators has become an explicit question at...
By Hector Zenil, Abicumaran Uthamacumaran, Luan Ozelim
The paper studies when joint-embedding predictive architectures (JEPAs) can recover underlying causal states from high‑dimensional observations. It introduces a latent variable model where observations arise from causal states with action‑conditioned dynamics, and proposes an information‑theoretic objective that maximizes conditional likelihood while preserving state entropy. The authors prove identifiability conditions—particularly sufficient action‑induced variation—and instantiate the objective as an action‑modulated Gaussian additive‑noise model (A‑JEPA), demonstrating theoretical and empirical success in synthetic and visual benchmarks.
By Yuhang Liu, Zhuo Huang, Javen Qinfeng Shi
arXiv:2606. 30372v1 Announce Type: new Abstract: Quantitative research across the social and behavioral sciences depends on human subject experiments that are expensive, slow, and subject to sampling bias.
By Haobo Yang
The paper introduces a Bethe Lagrangian formulation of expected free energy (EFE) that preserves a Kullback–Leibler structure, enabling message‑passing inference. By imposing an information constraint—requiring the mutual information between future observations, states, and parameters given actions to be at least the entropy of the goal prior—the authors recover the standard EFE solution at a specific Karush‑Kuhn‑Tucker multiplier. They analyze how varying this multiplier transitions the agent’s epistemic drive through inactive, interior, and saturated regimes, and benchmark the constrained Bethe agent against EFE and Q‑MDP on three tasks.
By Wouter M. Kouw
arXiv:2606. 04935v1 Announce Type: new Abstract: Active inference casts decision-making as inference, with the Expected Free Energy (EFE) unifying goal-directed and information-seeking behavior.
By Wouter W. L. Nuijten, Mykola Lukashchuk, Thijs van de Laar, Bert de Vries
arXiv:2607. 16858v1 Announce Type: cross Abstract: Across environments with mixed sources of uncertainty, unsupervised reinforcement learning requires intrinsic motivation that does not precommit to a particular direction of surprise.
By Alireza Furutanpey, Schahram Dustdar
arXiv:2606. 16076v1 Announce Type: cross Abstract: Multivariate forecasting in physical systems requires models that predict coupled temporal variables while preserving meaningful state evolution.
By Weizhi Nie, Weichao Liu, Honglin Guo, Yuting Su
arXiv:2607. 05238v1 Announce Type: new Abstract: JEPA world models predict the next latent state with a single deterministic predictor trained by latent regression.
By Zhi Song, Ximing Xing, Zhenchao Tang, hanbo Huang, Tianxu Lv, minghao Yang, Zhongzheng Niu, He Bing, Lusheng Wang, Jianhua Yao
arXiv:2605. 08446v3 Announce Type: replace Abstract: Bayesian neural networks are typically trained against the evidence lower bound (ELBO), whose Jensen gap closes only when the variational posterior is exact.
By Pavel Prochazka