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Current general-purpose robot reward models rely on frame-level progress labels from expert demonstrations. This approach scales poorly to large datasets, where suboptimal or failed trajectories are abundant and absolute progress is ambiguous. To address this, we introduce ROBOMETER, a scalable reward modeling framework combining intra-trajectory progress supervision with inter-trajectory preference supervision. ROBOMETER utilizes a dual objective: a frame-level loss that anchors reward magnitude to expert data, and a trajectory comparison preference loss that imposes global ordering constraints. This enables effective learning from both successful and failed trajectories. To support this formulation at scale, we curate RBM-1M, a dataset of over one million multi-embodiment trajectories containing extensive suboptimal and failure data. Across benchmarks and real-world evaluations, ROBOMETER learns highly generalizable reward functions and improves downstream robot learning performance. Code, model weights, and videos at https://robometer.github.io/. 

We show theoretically that the CISS signal may be enhanced by placing a helical molecule (DNA) into a cavity. 

We introduce and study the problem of posterior inference on tree-structured graphical models in the presence of a malicious adversary who can corrupt some observed nodes. In the well-studied broadcasting on trees model, corresponding to the ferromagnetic Ising model on a d-regular tree with zero external field, when a natural signal-to-noise ratio exceeds one (the celebrated Kesten-Stigum threshold), the posterior distribution of the root given the leaves is bounded away from Ber(1/2), and carries nontrivial information about the sign of the root. This posterior distribution can be computed exactly via dynamic programming, also known as belief propagation. We first confirm a folklore belief that a malicious adversary who can corrupt an inverse-polynomial fraction of the leaves of their choosing makes this inference impossible. Our main result is that accurate posterior inference about the root vertex given the leaves is possible when the adversary is constrained to make corruptions at a ρ-fraction of randomly-chosen leaf vertices, so long as the signal-to-noise ratio exceeds O(logd) and ρ≤cε for some universal c>0. Since inference becomes information-theoretically impossible when ρ≫ε, this amounts to an information-theoretically optimal fraction of corruptions, up to a constant multiplicative factor. Furthermore, we show that the canonical belief propagation algorithm performs this inference. 

We introduce an energy-resolved variant of quantum thermodynamics for open systems strongly coupled to their baths. The approach generalizes the Landauer-Buttiker inside-outside duality method [Phys. Rev. Lett. 120, 107701 (2018)] to interacting systems subjected to arbitrary external driving. It is consistent with the underlying dynamical quantum transport description and is capable of overcoming limitations of the only other consistent approach [New J. Phys. 12, 013013 (2010)]. We illustrate viability of the generalized inside-outside method with numerical simulations for generic junction models. 

We study the applicability of the Liouvillian exceptional points (LEPs) approach to nanoscale open quantum systems. A generic model of the driven two-level system in a thermal environment is analyzed within the nonequilibrium Green’s function (NEGF) and Bloch quantum master equation formulations. We derive the latter starting from the exact NEGF Dyson equations and highlight the qualitative limitations of the LEP treatment by examining the approximations employed in its derivation. We find that the non-Markov character of evolution in open quantum systems does not allow for the introduction of the concept of exceptional points for a description of their dynamics. Theoretical analysis is illustrated with numerical simulations. 

Abstract In computer‐based tests allowing revision and reviews, examinees' sequence of visits and answer changes to questions can be recorded. The variable‐length revision log data introduce new complexities to the collected data but, at the same time, provide additional information on examinees' test‐taking behavior, which can inform test development and instructions. In the current study, we used recently proposed statistical learning methods for sequence data to provide an exploratory analysis of item‐level revision and review log data. Based on the revision log data collected from computer‐based classroom assessments, common prototypes of revisit and review behavior were identified. The relationship between revision behavior and various item, test, and individual covariates was further explored under a Bayesian multivariate generalized linear mixed model. 

The infrared response of a system of two vibrational modes in a cavity is calculated by an effective non-Hermitian Hamiltonian derived by employing the nonequilibrium Green's function (NEGF) formalism. Degeneracies of the Hamiltonian (exceptional points, EPs) widely employed in theoretical analysis of optical cavity spectroscopies are used in an approximate treatment and compared with the full NEGF. Qualitative limitations of the EP treatment are explained by examining the approximations employed in the calculation.