arXiv:2607.15893v2 Announce Type: replace
Abstract: While the internal mechanisms of autoregressive (AR) transformers have been studied extensively, much less is known about diffusion language models (DLMs), an emerging alternative that generates text by iterative denoising. In this work, we study how DLMs implement induction, a mechanism behind in-context learning in which the model finds a repeated context and copies the token that followed it. Our analysis compares attention-only AR models and absorbing-mask DLMs with matched architectures. We find that DLMs learn a bidirectional induction circuit, where previous-token and next-token heads write local context into the residual stream and later induction heads use it to find and copy the answer from the matching source position. The circuit is direction-symmetric, working whether the source appears in the past or in the future. When only left context is visible, matching what an AR model sees, the DLM does not outperform its AR counterpart in induction capabilities. However, we observe it has stronger induction when both sides of the masked token are visible, pointing to bidirectional context access rather than a stronger one-sided mechanism. Beyond induction, we provide causal evidence that DLMs compute the global fraction of masked tokens and use it as an implicit timestep, even though they are given no explicit timestep embedding.
Science Journals
arXiv:2607.16015v2 Announce Type: replace
Abstract: 6D pose estimation remains a key challenge in robotics and computer vision, particularly in industrial environments. The deployment of currently available data-driven methods is often limited by resource-intensive data pipelines, reliance on textured 3D models, and sensitivity to geometric deviations caused by damages or assembly defects. We present PIXIE, a zero-shot framework that estimates the 6D pose of an object from an RGB image using only an untextured 3D model. Synthetic depth and normal maps are rendered from sampled reference viewpoints and matched to the query image via a pretrained cross-modality feature matcher. Matched keypoints are back-projected to obtain 2D--3D correspondences for PnP-based pose estimation. Relying exclusively on geometry makes the method inherently robust to lighting and texture variation, while correspondence filtering handles geometric deviations between the model and physical object. We evaluate on widely-used public benchmarks, reporting state-of-the-art results on texture-less objects without object-specific training, and introduce a novel dataset with assembly defects, texture variations, and occlusion to demonstrate real-world applicability.
arXiv:2110.13814v4 Announce Type: replace-cross
Abstract: We study actual bidding behavior when a new auction format gets introduced into the marketplace. More specifically, we investigate this question using a novel dataset on internet display advertising auctions that exploits a staggered adoption by different publishers (sellers) of first-price auctions (FPAs), instead of the traditional second-price auctions (SPAs). We analyze the auction format change using difference-in-differences regressions and a synthetic difference-in-differences estimator, which better handles pre-trends. The results show that revenue per sold impression (price) jumps considerably for treated publishers relative to control publishers, with increases ranging from 25% to 70% of the pre-treatment price level of the treated group. Moreover, for later auction format changes, the increase in price levels under FPAs relative to those under SPAs tends to dissipate over time, reminiscent of the revenue equivalence theorem, although the extent of this reversion depends on the specification. We view these results as suggestive of initially insufficient bid shading following the format change, as opposed to an immediate transition to a new Bayesian Nash equilibrium, with prices tending to decline in several specifications in a manner consistent with gradual adjustment in bidding behavior as bidders learn to shade their bids. Our work constitutes one of the first field studies on bidders'responses to auction format changes, providing an important complement to theoretical model predictions. As such, it provides valuable information to auction designers when considering the implementation of different formats.
arXiv:2209.15130v3 Announce Type: replace-cross
Abstract: We study a general matrix optimization problem with a fixed-rank positive semidefinite (PSD) constraint. We perform the Burer-Monteiro factorization and consider a particular Riemannian quotient geometry in a search space that has a total space equipped with the Euclidean metric. When the original objective f satisfies standard restricted strong convexity and smoothness properties, we characterize the global landscape of the factorized objective under the Riemannian quotient geometry. We show the entire search space can be divided into three regions: (R1) the region near the target parameter of interest, where the factorized objective is geodesically strongly convex and smooth; (R2) the region containing neighborhoods of all strict saddle points; (R3) the remaining regions, where the factorized objective has a large gradient. To our best knowledge, this is the first global landscape analysis of the Burer-Monteiro factorized objective under the Riemannian quotient geometry. Our results provide a fully geometric explanation for the superior performance of vanilla gradient descent under the Burer-Monteiro factorization. When f satisfies a weaker restricted strict convexity property, we show there exists a neighborhood near local minimizers such that the factorized objective is geodesically convex. To prove our results, we provide a comprehensive landscape analysis of a matrix factorization problem with a least squares objective, which serves as a critical bridge. Our conclusions are also based on a result of independent interest stating that the geodesic ball centered at Y with a radius 1/3 of the least singular value of Y is a geodesically convex set under the Riemannian quotient geometry, which as a corollary, also implies a quantitative bound of the convexity radius in the Bures-Wasserstein space. The convexity radius obtained is sharp up to constants.
arXiv:2406.10930v2 Announce Type: replace-cross
Abstract: In our earlier paper, "2 CSPs all are approximable within a constant differential factor" (ISCO 2018, LNCS 10856), we introduced a family of combinatorial designs called 'alphabet reduction pairs of arrays' (ARPAs). These designs are parameterized by three integers $q,p,k$, with $p\leq q$ and $k\leq p$: $q$ is the size of the alphabet from which the arrays draw their entries; $p$ is the maximum number of distinct symbols allowed in a row of the second array; $k$ is the largest integer for which the two arrays coincide -- up to row permutations -- on any $k$-element subset of their columns. The first array must contain at least one occurrence of the word $0\ 1 \cdots\ q-1$ as a row. The idea is to cover as many occurrences of this word as possible using as few words as possible, each containing at most $p$ distinct symbols.
ARPAs are related to the approximability of constraint satisfaction problems with bounded constraint arity ($k$-CSPs). In this context, we are particularly interested in ARPAs that maximize the frequency of the word $0\ 1 \cdots\ q-1$. We call such ARPAs 'optimal' and study them in this paper. To this end, we introduce a simpler family of combinatorial designs called 'Cover pairs of arrays' (CPAs), which can be viewed as partially defined ARPAs with Boolean entries. We prove that ARPAs and CPAs are equivalent with respect to maximizing the frequency of their target word. As a corollary of our proof, computing the frequency of the target word in optimal ARPAs reduces to solving a linear program in $q + p + 1$ continuous variables and $k + 1$ constraints. We also prove the optimality of previously known ARPAs for $p=k$ and provide optimal ARPAs for $k=1$ and $k=2$.
arXiv:2410.16005v4 Announce Type: replace-cross
Abstract: Metastatic prostate cancer is one of the leading causes of cancer-related morbidity and mortality worldwide. It is characterized by a high mortality rate and a poor prognosis. In this work, we explore how a clinical oncologist can apply a Stackelberg game-theoretic framework to prolong metastatic prostate cancer survival, or even make it chronic in duration. We utilize a Bayesian optimization approach to identify the optimal adaptive chemotherapeutic treatment policy for a single drug (Abiraterone) to maximize the time before the patient begins to show symptoms. We show that, with precise adaptive optimization of drug delivery, it is possible to significantly prolong the cancer suppression period, potentially converting metastatic prostate cancer from a terminal disease to a chronic disease for most patients, as supported by clinical and analytical evidence. We suggest that clinicians might explore the possibility of implementing a high-level tight control (HLTC) treatment, in which the trigger signals (i.e. biomarker levels) for drug administration and cessation are both high and close together, typically yield the best outcomes, as demonstrated through both computation and theoretical analysis. This simple insight could serve as a valuable guide for improving current adaptive chemotherapy treatments in other hormone-sensitive cancers.
arXiv:2412.18613v2 Announce Type: replace-cross
Abstract: Illusions are entertaining, but they are also a useful diagnostic tool in cognitive science, philosophy, and neuroscience. A typical illusion shows a gap between how something `really is' and how something `appears to be', and this gap helps us understand the mental processing that led to how something appears to be. Illusions are also useful for investigating artificial systems, and much research has examined whether computational models of perception fall prey to the same illusions as people. Here, I invert the standard use of perceptual illusions to examine basic processing errors in current vision language models. I present these models with illusory-illusions, neighbors of common illusions that should not elicit processing errors. These include such things as perfectly reasonable ducks, crooked lines that truly are crooked, circles that seem to have different sizes because they are, in fact, of different sizes, and so on. I show that many current vision language systems mistakenly see these illusion-illusions as illusions. I suggest that such failures are part of broader failures already discussed in the literature.
arXiv:2501.16801v2 Announce Type: replace-cross
Abstract: We present an effective theory for describing electron dynamics driven by an optical external field in a Schr\"{o}dinger's cat state. We show that the reduced electron density matrix evolves as an average over trajectories $\{\rho_\alpha\}$ weighted by the Sudarshan--Glauber $P$ distribution $P(\alpha)$ in the weak light--matter coupling regime. Each trajectory obeys an equation of motion, $\mathrm{i} \partial_t\rho_\alpha=\mathcal{H}_{\alpha} \rho_\alpha-\rho_\alpha\mathcal{H}_{\alpha}$, where an effective Hamiltonian $\mathcal{H}_{\alpha}$ becomes non-Hermitian due to quantum interference of light. The optical quantum interference is transferred to electrons through the asymmetric action between the ket and bra state vectors in $\rho_{\alpha}$. This non-Hermitian dynamics differs from the conventional one observed in open quantum systems, described by $\mathrm{i} \partial_t\rho=\mathcal{H}\rho-\rho \mathcal{H}^\dagger$, which has complex conjugation in the second term. We confirm that the reduced, trajectory-resolved effective theory agrees with full electron-photon simulations for the few-electron Dicke model, thereby validating the interferential non-Hermitian description in the weak-coupling regime.
arXiv:2501.17936v2 Announce Type: replace-cross
Abstract: Rare events of large-scale spatially-correlated exponential random fields are studied. The influence of spatial correlations on clustering and non-sphericity is investigated. The size of the performed simulations permits to study beyond-$7.5$-sigma events (one in $10^{13}$). As an application, this allows to resolve individual Hubble patches which fulfil the condition for primordial black hole formation. It is argued that their mass spectrum is drastically altered due to co-collapse of clustered overdensities as well as the mutual threshold-lowering through the latter. Furthermore, the corresponding non-sphericities may imply possibly large changes in the initial black hole spin distribution.
arXiv:2502.12984v5 Announce Type: replace-cross
Abstract: In this paper, we propose a general approach for approximate simulation and analysis of delay differential equations (DDEs) with distributed time delays based on methods for ordinary differential equations (ODEs). The key innovation is that we 1) propose an Erlang mixture approximation of the kernel in the DDEs and 2) use the linear chain trick to transform the resulting approximate DDEs to ODEs. We refer to this as the Erlang ODE approximation of the DDEs, and we prove that the Erlang mixture approximation converges for continuous and bounded kernels if the number of terms increases sufficiently fast. Furthermore, we show that if the kernel is also exponentially bounded, the Erlang ODE approximation can be used to assess the stability of the steady states of the original DDEs and that the solution to the ODE approximation converges. Additionally, we propose an approach based on bisection and least-squares estimation for determining optimal parameter values in the approximation. Finally, we present numerical examples that demonstrate the accuracy and convergence rates of the approximations and the efficacy of the proposed approach for bifurcation analysis and Monte Carlo simulation. The numerical examples involve a modified logistic equation, chemotherapy-induced myelosuppression, and a point reactor kinetics model of a molten salt nuclear fission reactor.
arXiv:2505.10811v2 Announce Type: replace-cross
Abstract: We consider the $\Set{H}_2$-optimal feedback control problem, for the case in which the plant is passive with bounded $\Set{L}_2$ gain, and the feedback law is constrained to be output-strictly passive. We show that this problem distills to a convex, infinite-dimensional optimal control problem, in which the optimization domain is the Youla parameter for the closed-loop system. We devise truncated, finite-dimensional optimizations to find sub-optimal controllers, and lower bounds on the optimal objective. Furthermore we show that both these optimizations converge to the optimal objective of the original infinite-dimensional problem as their respective domains are increased. The idea is demonstrated on a simple vibration suppression example.
arXiv:2505.23460v5 Announce Type: replace-cross
Abstract: Chirality-induced spin selectivity (CISS) refers to phenomena where molecular chirality governs spin polarization. While symmetry simply requires chiral molecules to support spin-vector correlations, we show that CISS is fundamentally a conditioned measurement of these correlations. We illustrate this principle for spin-resolved one-photon ionization of a randomly oriented ensemble of chiral molecules. We introduce and quantify the phenomenon of enantio-sensitive locking of the photoelectron current to its spin, thereby providing a complete description of spin-conditioned photoelectron currents in one-photon ionization.
arXiv:2506.00452v5 Announce Type: replace-cross
Abstract: In orthogonal frequency division multiplexing (OFDM), accurate channel estimation is crucial. Classical signal processing-based approaches, such as linear minimum mean-squared error (LMMSE) estimation, often require second-order statistics that are difficult to obtain in practice. Recent deep neural network (DNN)-based methods have been introduced to address this, but they often suffer from high inference complexity. This paper proposes an Attention-aided MMSE (A-MMSE), a model-based DNN framework that learns the linear MMSE filter via the Attention Transformer. Once trained, the A-MMSE performs channel estimation through a single linear operation, eliminating nonlinear activations during inference and thus reducing computational complexity. To improve the learning efficiency of the A-MMSE, we develop a two-stage Attention encoder that captures the frequency and temporal correlation structure of OFDM channels. We also introduce a rank-adaptive extension that adjusts the filter rank at deployment time, enabling efficient operation under resource-constrained receivers. Numerical simulations show that A-MMSE consistently outperforms baseline methods across a wide range of signal-to-noise ratio (SNR) conditions. In particular, the A-MMSE and its rank-adaptive extension provide an improved performance-complexity trade-off.
arXiv:2509.05044v3 Announce Type: replace-cross
Abstract: This paper provides a complete classification of all subvarieties of pointed Abelian lattice-ordered groups (l-groups), as well as all subquasivarieties that are generated by their totally ordered members. We present two complementary approaches to achieve this classification.
First, using purely l-group-theoretic methods, we analyze the structure of lexicographic products and values to identify all join-irreducible members of the lattice of subvarieties of positively pointed Abelian l-groups. We provide a novel equational basis for each of these subvarieties, leading to a complete description of the entire subvariety lattice. As a direct application, our l-group-theoretic classification yields an alternative, self-contained proof of Komori's classification of subvarieties of MV-algebras.
Second, we explore the connection to MV-algebras via an extended version of Mundici's functor. We prove that this functor preserves universal classes, a result of independent model-theoretic interest. This allows us to lift the classification of universal classes of totally ordered MV-algebras, due to Gispert, to a complete classification of universal classes of totally ordered pointed Abelian l-groups. As a direct consequence, we obtain a complete structural description of the lattice of subquasivarieties that are generated by their totally ordered members.
arXiv:2509.09371v2 Announce Type: replace-cross
Abstract: Distributionally robust optimization (DRO) protects statistical learning against distributional shifts by optimizing the worst-case performance over a set of perturbed distributions. However, standard DRO formulations often treat all feature perturbations equally. This can be unnecessarily conservative when external knowledge suggests that the predictive signal is embedded in a low-dimensional representation of covariates. We propose REpresentation-Aware Distributionally robust estimation (READ), a Wasserstein DRO framework that uses external representations to guide the geometry of robustness. Rather than uniformly perturbing all covariate directions, READ increases the transport cost of perturbations that change representation coordinates, thereby reshaping the dual regularization toward the representation subspace. Meanwhile, it preserves protection against variations orthogonal to the representation. We study READ in two regimes. First, for inference on the current target, we characterize our estimator asymptotically and develop a Wasserstein profile inference approach to construct representation-aligned confidence regions while enabling automatic hyperparameter tuning. Second, for deployment to future populations that differ from the current target but are generated from the same representation-invariant random-coefficient model, we show that the resulting regions achieve higher coverage of future model parameters than standard methods. Simulations and a single-cell multi-omics application demonstrate the advantages of READ in multi-source and multitask transfer learning settings.
arXiv:2601.02129v2 Announce Type: replace-cross
Abstract: Recent asteroseismic observations constitute a great challenge for rotating stellar evolution models, which predict overly fast internal rotation rates when only hydrodynamic processes are included. This suggests the absence of one or several unidentified angular momentum (AM) transport processes in these models. Transport by large-scale and strong magnetic fields in the radiative zone is a promising candidate to explain the observations. While these fields might be characterised by a fossil origin, the Tayler-Spruit dynamo constitutes a primary mechanism to form the necessary magnetic fields. Despite recent numerical studies, this mechanism remains poorly known. Motivated by this scenario, we investigated the Tayler-Spruit dynamo through a new set of 3D numerical simulations. We modelled the radiative zone as a Boussinesq stably stratified fluid whose differential rotation is maintained by a volumetric body force. Here, we report, for the first time, the coexistence of two dynamo solutions, which mainly differ by the magnetic field location (near the equator and the polar axis). While the equatorial dynamo is driven by an instability sharing both characteristics of the magnetorotational and Tayler instabilities, we focus mainly on the newly identified polar dynamo, which is driven by the standard Tayler instability. We show that this dynamo can still operate and transport AM efficiently in a strong stratification regime, with a Brunt-V\"ais\"al\"a frequency that is 130 times larger than the rotation rate. We extracted new scaling laws for the magnetic field, AM transport, and the minimum shear to trigger the dynamo. Finally, we were able to roughly constrain the signature of the generated magnetic fields on asteroseismic modes propagating in main sequence and evolved stars.
arXiv:2601.04543v2 Announce Type: replace-cross
Abstract: Using quantum key distribution (QKD) protocols, a secret key is created between two distant users (transmitter and receiver) at a particular key rate. Quantum technology can facilitate secure communication for cryptographic applications, combining QKD with one-time-pad (OTP) encryption. In order to ensure the continuous operation of QKD in real-world networks, efforts have been concentrated on optimizing the use of experimental components and effective QKD protocols to improve secret key rates and increase the transmission between multiple users. Generally, in experimental implementations, the secret key rates are limited by single-photon detectors, which are used at the receivers of QKD and create a bottleneck due to their limited detection rates (detectors with low detection efficiency and high detector dead-time). We experimentally show that secret key rates can be increased by combining the time-bin information of two such detectors on the data line of the receiver for the coherent-one-way (COW) QKD protocol with a minimal increase in quantum bit error rate (QBER, the proportion of erroneous bits). Further, we implement a point-to-multipoint COW QKD protocol, introducing an additional receiver module. The three users (one transmitter and two receivers) share the secret key in post-processing, relying on OTP encryption. Typically, the dual-receiver extension can improve the combined secret key rates of the system; however, one has to optimise the experimental parameters to achieve this within security margins. These methods are general and can be applied to any implementation of the COW protocol.
arXiv:2602.04596v2 Announce Type: replace-cross
Abstract: Bayes-filtered transformers are transformers meta-learned on sequences from a prior predictive distribution to approximate the corresponding posterior predictive distribution. They output total predictive uncertainty in a single forward pass but never explicitly represent a posterior distribution, making the standard route to separating aleatoric from epistemic uncertainty unavailable. We address this challenge through the lens of Bayesian predictive inference (BPI). Our main result is a predictive Central Limit Theorem (CLT) for supervised settings under conditions that are among the weakest known in the BPI literature. The CLT characterises the posterior of the limiting predictive distribution given an observed context as asymptotically Gaussian; the variance of this Gaussian quantifies epistemic uncertainty. We apply the framework to TabPFN, a Bayes-filtered transformer that is a state-of-the-art foundation model for tabular prediction. The resulting credible bands achieve near-nominal frequentist coverage as context length grows, and the decomposition largely matches standard desiderata: epistemic uncertainty shrinks with context length and is highest in sparsely observed regions within the span of the context data, while aleatoric uncertainty dominates near decision boundaries where classes overlap.
arXiv:2602.17923v2 Announce Type: replace-cross
Abstract: Computational models of complex physical systems often rely on simplifying assumptions which inevitably introduce model error, with consequent predictive errors. Given data on model observables, the estimation of parameterized model-error representations, along with other model parameters, would be ideally done while separating the contributions of each of the two sets of parameters, in order to ensure meaningful stand-alone model predictions. This work builds an embedded model error framework using a weight-space representation of Gaussian processes (GPs) to flexibly capture model-error spatiotemporal correlations and enable inference with GP-embedding in non-linear models. To disambiguate model and model-error/bias parameters, we extend an existing orthogonal GP method to the embedded model-error setting and derive appropriate orthogonality constraints. To address the increased dimensionality introduced by the GP representation, we employ the likelihood-informed subspace method. The construction is demonstrated on linear and non-linear examples, where it effectively corrects model predictions to match data trends. Extrapolation beyond the training data recovers the prior predictive distribution, and the orthogonality constraints lead to meaningful stand-alone model predictions and nearly uncorrelated posteriors between model and model-error parameters.
arXiv:2604.15107v2 Announce Type: replace-cross
Abstract: Shapley values provide a flexible framework for attributing feature contributions to model predictions, but they are not naturally suited for feature selection: a feature may receive a positive attribution even when it is redundant given the remaining variables. In this paper, we introduce \textbf{MinShap}, a general framework for identifying \emph{important} or \emph{non-redundant} features through conditional importance functionals $VI_j^S$. Rather than averaging feature contributions across conditioning sets, MinShap aggregates them using the \emph{minimum}, thereby testing whether a feature remains relevant under every conditioning context. We show that, under a simple \emph{null monotonicity} condition, the minimum aggregation exactly characterizes feature redundancy and yields a principled feature selection criterion. This perspective provides a unified framework for statistical feature selection and representation-based interpretability while retaining the stability advantages of Shapley-style aggregation. We develop scalable algorithms with statistical guarantees, establish connections to multiple-testing procedures, and demonstrate through theory and experiments that MinShap produces more accurate and stable feature selection than existing model-agnostic approaches.
arXiv:2606.24768v2 Announce Type: replace-cross
Abstract: This paper presents general strong duality results when testing hypotheses by betting against them. A bet is an e-variable for a composite null hypothesis $\Pcal$: a nonnegative random variable $X$ whose expected value is at most one under every $P \in \mathcal P$. Following Kelly, Breiman, Cover, Shafer, and Grunwald et al. (2024), we study a natural minimax \emph{log-optimality} criterion: given a composite alternative $\Qcal$, we characterize the ``GROW value'' $\sup_{X} \inf_{Q} \E_{Q}[\log X]$. This paper generalizes the results of Larsson et al. (2025) from (arbitrary $\mathcal P$ and) simple $\mathcal Q$ to arbitrary $\mathcal Q$. We prove that there always exists a minimizing information-projection pair between the weak-$*$ closures of the convex hulls of arbitrary $\mathcal P$ and $\mathcal Q$, and show that the GROW value for \emph{bounded} e-variables always equals their relative entropy. We also prove a similarly general strong duality for the REGROW criterion with bounded e-variables and arbitrary bounded offsets. Under various assumptions our results extend to unbounded e-variables, and examples show that without any assumptions such extensions fail. Our results are analogous to those in Larsson et al. (2026), swapping tests for bounded e-variables, minimax risk for the GROW criterion, and total variation for relative entropy.
arXiv:2607.01741v2 Announce Type: replace-cross
Abstract: Reinforcement Learning (RL) is a sequential decision-making framework in which an agent learns optimal policies through interaction with an environment by maximizing cumulative rewards. Among RL methods, Bayesian Reinforcement Learning (BRL) addresses common practical challenges related to data scarcity by leveraging prior knowledge about the environment and sequential belief updates. However, most BRL approaches require an explicit likelihood function, which is frequently inaccessible or intractable in real-world settings.
We propose Likelihood-Free Iterated Batch Importance Sampling (LF-IBIS), a novel algorithm for BRL that updates the agent's beliefs online as new interactions become available. By combining Approximate Bayesian Computation with Iterated Batch Importance Sampling, LF-IBIS enables full Bayesian inference in settings where the environment dynamics are not described by an explicit or tractable likelihood. The method yields approximate posterior distributions over both environment parameters and optimal policies, providing a quantification of policy uncertainty useful for a Bayesian treatment of the exploration-exploitation trade-off. We test the method on a simulation study in response-adaptive randomization in clinical trials, where closed-form posteriors enable validation. Additional experiments address settings where the posterior has no closed form and illustrate online policy updating based on the posterior distribution of the optimal policy.
arXiv:2607.02947v2 Announce Type: replace-cross
Abstract: Public official-information request records contain process signals. They can support research, workflow review, and analyst-led assessment. Yet they also mix observed correspondence, platform states, inferred events, and legal outcomes. FOI-O is a reusable process-modelling method and verification infrastructure for Freedom of Information administration. It is a global model that began with the New Zealand Official Information Act and has since iterated through the Australian Commonwealth and New South Wales settings. The NZ package remains the mature reference implementation; the Australian work remains provisional pending empirical evaluation and jurisdiction-specific legal validation. FOI-O models request records, observed correspondence, controlled vocabularies, provenance, review queues, release metadata, and bounded analysis rules. Legally meaningful outcomes require certification by an authorised decision-maker. Its typed operational and semantic contracts are supported by deterministic examples, process models, fixture-only process-mining exports, quality gates, and tests. This article describes the motivation, architecture, ontology-development method, its versioned extraction and review protocol, how related repositories share data and evidence, and how the method may be adapted for Australia, validation evidence, and implementation boundaries. The project is not legal advice, is not an official government publication, and does not certify release, refusal, redaction, charging, extension, transfer, complaint, or publication outcomes.
arXiv:2607.08793v3 Announce Type: replace-cross
Abstract: Sepsis is a leading cause of mortality, yet optimal treatment policies remain contested. Existing reinforcement learning (RL) approaches learn fixed strategies for sepsis treatment, limiting adaptability to changing clinical objectives during inference. We propose EHRMPC, a framework that decouples learning patient dynamics from optimizing treatment by training a patient digital twin in the form of a generative electronic health record (EHR) model. The digital twin predicts clinical trajectories under interventions and enables model predictive control (MPC) to optimize treatments via inference-time planning over simulations. We evaluate EHR-MPC on a multicenter ICU sepsis cohort spanning 8 hospitals in the Mass General Brigham health system using both off-policy importance sampling and on-policy simulation-based evaluation. Relative to RL baselines, EHR-MPC achieves comparable off-policy performance and improved simulation performance. Unlike RL, this work frames sepsis treatment optimization as inference-time control over learned patient dynamics, establishing a general framework for decision making with generative clinical models.
arXiv:2607.11135v3 Announce Type: replace-cross
Abstract: The tryptophan residues in tubulin $\alpha\beta$-dimers form an ordered aromatic network that has been proposed to support quantum exciton transport even under physiological environmental noise. Existing studies of this system mostly assume white-noise dephasing, but the statistical properties of the protein-solvent bath coupled to tryptophan sites remain uncharacterised under physiological conditions. Here we characterise this fluctuation bath via all-atom molecular dynamics simulations of a solvated tubulin dimer at 310 K, combining high-frequency and long-time trajectories with 10 fs and 10 ps sampling intervals. The resulting autocorrelation of the site-energy fluctuations is tri-exponential, with three well-separated decay modes: sub-100-fs and picosecond fluctuations driven by water dynamics, and a nanosecond mode originating from protein conformational rearrangements. All three modes fall deep within the non-Markovian regime. We further demonstrate that the slow protein mode introduces strong quasi-static disorder, which results in Anderson localisation, while the two fast water modes frequently tune chromophore pairs through resonance, enabling environment-assisted quantum transport (ENAQT). On the full eight-site network, the coloured-noise bath confines excitons predominantly to strongly coupled proximal tryptophan pairs, in marked contrast to the more uniform delocalisation predicted by the standard white-noise Haken-Strobl model. Our workflow generalises to other pigment-protein systems with solvent-exposed chromophores.