Papers

We study single-item lot sizing with piecewise-concave production and holding–backlog costs over $T$ periods. Production has $m$ common positive finite breakpoints, and stock costs and domains have $K$ common finite boundary levels. For fixed $m,K$, we give an exact deterministic algorithm using $\OmK(T^{m+2}\alpha(T+2))$ arithmetic operations and a Las Vegas algorithm using $\OmK(T^{m+2})$ expected operations, where $\alpha$ is the inverse Ackermann function. The algorithms retain nonlinear concave cost pieces and cover backlogging, common-grid stock limits, and explicit terminal-stock policies. A structural decomposition at stock boundary levels yields breakpoint-only prefix and suffix tables. Batching transitions by a designated production period produces implicit matrices that are Monge double staircases on each production piece. Established matrix searches, merged state orders, and backward evaluation provide the bounds. A simpler rectangle/SMAWK variant requires $\OmK(T^{m+2}\log(T+2))$ operations. On 300 no-backlogging benchmark cases, its objectives agree exactly with the predecessor dynamic program and an inventory-state oracle. Geometric-mean paired time ratios are \GeoUncap\ for uncapacitated cases and \GeoCap\ for capacitated cases, with ratios above one favoring matrix searching. A 640-case base-model validation suite and a separate 624-case generalized-model suite pass. The timing study does not benchmark the generalized models or the stronger staircase routines. All bounds assume exact arithmetic, constant-time cost queries, and explicit closed-domain concavity conditions.

Video saliency prediction is inherently harder to model than static image saliency due to the additional temporal dimension. Video saliency benchmarks rest on the premise that predicting gaze on video requires utilizing temporal activity distributed across frames. Prior work has challenged this, showing that static baselines recover a significant fraction of the explainable gaze information on LEDOV, a popular video saliency dataset, and that video saliency models fail in the same places as this static baseline. We verify that this diagnosis still stands: under a more capable gold standard than the original analysis, and an updated panel of recent architectures, the strongest temporal architecture in the panel still does not substantially improve over a fine-tuned static baseline. However, it remains unclear whether the marginal gain reflects limitations of current temporal architectures or a lack of temporal patterns in the benchmark itself. We introduce SalTempto, a video saliency benchmark with greater dynamism: 224 clips of highly dynamic content, sourced from the HACS-Segments dataset so that each clip contains an event together with its lead-up and aftermath, with gaze recordings from up to 16 subjects and a training split for adapting pretrained models. On SalTempto, the static baseline recovers only about 13% of the headroom above the centerbias, against more than half on LEDOV. A fine-tuned temporal architecture shows a substantial gain in performance over the static baseline, indicating that it does capture meaningfully more temporal information, which LEDOV fails to measure. Yet, even this SoTA model still leaves nearly half of SalTempto's headroom unexplained, indicating room for improvement in video saliency modelling. Examination of SalTempto also lets us describe human tendencies that models miss. SalTempto link: https://huggingface.co/datasets/bethgelab/video_saliency.

Power grids are beginning to host AI data centers whose demand can change abruptly and in a correlated way, and operators and planners must decide whether existing controllers can absorb the resulting transients and where new flexibility is worth installing. We cast this as a question in stochastic control: how far from optimal is an implementable controller? For controlled diffusions with affine, state-independent actuation, additive and possibly degenerate noise and quadratic control cost, any Hamilton-Jacobi-Bellman (HJB) subsolution bounds the optimal cost from below and simulation bounds the deployed cost from above, so their gap certifies the permissible suboptimality. We construct subsolutions from path-integral control by completing the control geometry: enlarging the control Gramian until it matches the physical noise makes the problem linearly solvable, and its Feynman-Kac value is an automatic lower bound whose HJB residual is exactly the energy of the fictitious control. A dual noise-deflation construction can be tighter but requires a curvature condition. The geometry yields planning rules: price control authority in proportion to local noise variance, and use shadow values to guide sparse reinforcement. For nonlinear stochastic swing dynamics of the IEEE 118-bus system after a severe load loss, a simple generator controller is certified within 2.8% of optimal under homogeneous forcing; under heterogeneous forcing the gap is 39% with uniform prices and 1.2% once the same total authority is repriced, before any hardware is added.

We explore the microscopic origin of THz-driven lattice dynamics in methylammonium lead bromide CH$_3$NH$_3$PbBr$_3$ using two-dimensional THz Kerr-effect spectroscopy. The analysis of the experiment enables discrimination between competing nonlinear excitation mechanisms and reveals that the dominant pathway is phonon-mediated rather than purely photonic. In particular, we argue that the commonly observed 1.15~THz Raman-active $A_g$ mode is not excited directly by the THz electric field. Instead, it emerges through cubic anharmonic coupling to an infrared-active phonon at 0.68~THz. Furthermore, a satellite feature at 1.36~THz is identified as a two-phonon Raman response of the same infrared-active mode. The observation of strong quadratic coupling involving the infrared-active phonon suggests an unconventional form of electron-phonon dressing present in lead-halide perovskites.

Reinforcement learning (RL) for learning path recommendation (LPR) faces two coupled obstacles. First, the policy must commit to a sequence of L concepts without intermediate feedback, producing a combinatorial search space that grows super-exponentially with L and provides reward only at the final step. Second, expert learning paths would be the natural cure for sparse-reward RL, but they do not exist in educational data, because student logs record what learners did, not what they should have done. We address both obstacles by importing a recipe from simulator-based demonstration learning in robotics: the knowledge tracing simulator is used both to synthesize per-learner expert demonstrations through evolutionary search and to train a deployment-free policy that distills these demonstrations into a feed-forward learner. Our framework, EVOL, instantiates this pipeline with an asymmetric actor-critic where the actor commits to deployment-realistic blind planning while the critic exploits the privileged simulator state during training. Across three datasets (ASSIST15, Junyi, and EdNet; 39-189 concepts) and path lengths L = 5, 10, and 20, EVOL surpasses 8 baselines spanning heuristic, sequential, RL, graph-enhanced RL, and LLM-enhanced methods. We further compare three imitation strategies (BC, AWR, and DAPG) and show that final performance is governed by the quality of evolutionary experts rather than by the particular imitation objective.

A $4$-general set of PG(n,q) is a point set with no four coplanar,and Mb{n}{q} is the largest such size. The climb problem asks, at each $n$, whether the constructions of Pavese (2025) can be improved by one point. We show that the answer is governed by the code tables: $PG(M-1,q)$ contains an $N$-point $4$-general set exactly when a projective $[N,N-M,\ge 5]_q$ code exists. Over $F_4$ this settles the first two rungs: Mb{3}{4}=5 and Mb{4}{4}=11, each with two independent proofs, so Pavese's constructions are optimal at both. Over $F_5$ the same reduction gives Mb{4}{5}=12, one more than the near-MDS lower bound. At $n=5$ the tables fall silent; we state the verified position $21\le Mb{5}{4}\le 30$ rather than the tabulated $\le 29$, which rests on a private communication. Any $22$-point $4$-general set of $PG(5,4)$, if one exists, shares at most $12$ points with Pavese's extremal $21$-point set; the proof of this rigidity statement is a finite computation, with certificates in Appendix~A.

Gaussian Boson Sampling (GBS) has provided strong evidence of quantum advantage, motivating the search for real-world applications. Such applications rely on mapping physical problems to the native GBS output: photon-number patterns sampled from a highly non-trivial probability distribution. Here we derive a GBS-compatible mapping for the rate constant $k_\mathrm{IC}$ of internal conversion (IC), incorporating two important physical effects – Duschinsky rotations and Herzberg–Teller contribution. We validate this framework using sampling simulations of dibenzoterrylene, reproducing reported values of $k_\mathrm{IC}$. Beyond $k_\mathrm{IC}$ reconstruction, we show that the photon-number patterns of GBS encode the vibrational mode combinations responsible for the IC process, which we refer to as non-radiative pathways. This pathway-resolved data can then be grouped in different ways to extract multiple physically motivated observables. In particular, it enables $k_\mathrm{IC}$ analysis at several levels, from individual modes and mode family contributions to recurring vibrational configurations shared by multiple pathways. We find that Duschinsky rotations increase the fraction of $k_\mathrm{IC}$ associated with correlated vibrational pathways from 46% to 86%, and identify the mode combinations with the largest rate contributions. These pathway-resolved insights can guide targeted molecular modifications, including selective deuteration and bond substitution, to control non-radiative decay.

In this article, we propose a new method to calculate the angular momentum distribution of fis- sion fragments adapted to the case of 2-center axial basis used to solve the Hartree-Fock-Bogoliubov equations in view of fission studies. As a first application, we have studied the properties of the fission fragment angular momentum distribution in the scission area of 240Pu using the Gogny D1S interaction. Here, a one dimensional asymmetric and adiabatic path (the lowest in energy), gener- ated by the newly proposed Link and Drop methods, has been considered. The major result of this study is the appearance of an odd-even angular momentum staggering in both fragments when going through scission. The odd-J components are progressively suppressed as the fragments separate, leading to an increasing dominance of even-J components. This behavior is systematically correlated with the rapid reduction of the intrinsic octupole deformation of the fragments, asso- ciated with the restoration of a more nearly reflection-symmetric shape. Finally, particle-number projection reveals the role of shell effects associated with specific fragment configurations in shaping the fragment angular momentum distributions.

Let $u^\varepsilon$ and $u$ be the convex solutions of \[ \det D^2u^\varepsilon=F(x,x/\varepsilon),\qquad \det D^2u=\overline F(x) \] on a bounded convex domain $\Omega$, with the same Dirichlet data. Here, $F$ is uniformly positive and periodic in its second variable, and $\overline F(x)=\int_{\mathbb T^n}F(x,y)\,dy$. For $m=0,1$ and $0<\alpha\leq1$, we prove \[ \|u^\varepsilon-u\|_{L^\infty(\Omega)}\leq C\varepsilon^{m+\alpha},\qquad \forall\,0<\varepsilon\leq1, \] provided that $u\in C^{m+2,\alpha}(\overline\Omega)$ and $F\in C_x^{m,\alpha}(\overline\Omega;C_y^{0,\gamma}(\mathbb T^n))$ for some $0<\gamma<1$. Both exponents are optimal in their respective regularity scales. We also establish the $O(\varepsilon^{\alpha})$ rate for locally weighted periodic Borel measures, with constants independent of the distribution of the microscopic measure.

Touché 2026 extends causality extraction to counter-causal claims: news sentences whose surface form appears causal but whose meaning denies the causation, as in "It is falsely believed that X caused Y." A system that relies on surface cues such as "caused" or "led to" will accept such a sentence as causal and give it the wrong polarity. On the Countercausal News Corpus (CCNC), the task has three subtasks: deciding whether a sentence is causal (detection), locating its cause and effect spans (extraction), and labeling its polarity as procausal, counter-causal, or uncausal. We build one model per subtask. Detection is a fine-tuned classifier with a single cross-task rule that uses the extracted spans to remove false positives. For extraction, we ensemble three RoBERTa-large BILOU+CRF taggers by averaging their token-level scores before decoding, rather than voting on the spans each tagger produces. For polarity, where labeled counter-causal examples are scarcest, we add training sentences generated by a large language model prompted with nine patterns of counter-causal expression adapted from Hagen et al., keeping only those that pass automatic structural checks. On the held-out CCNC test set, the system reaches F1 0.869 on detection and macro-F1 0.817 on polarity, and in the organizers' final causal-only evaluation of extraction it scores granularity-adjusted F1 0.728, the highest extraction score among all submissions including the organizers' baseline. The development split is used only for component selection and the ablations reported in the paper.

This article examines the Fictitious-Domain approach using Distributed Lagrange Multipliers (FD-DLM), a method that handles elliptic interface problems with separate meshes without requiring the interface to match the background mesh. It compares two stable, low-order FD-DLM discretizations when using both continuous and discontinuous multiplier spaces. The study distinguishes between satisfying the weak coupling constraint per element and the size of the discrepancy between the two discrete primal fields. Numerical experiments indicate that the continuous formulation usually yields smaller local errors and maximum mismatch values, while the discontinuous formulation shows a more regular decrease in mismatch as the mesh is refined and has smaller primal errors.

This paper analytically investigates the bit error rate (BER) performance of downlink multi-rate (MR)-non-orthogonal multiple access (NOMA) over Nakagami-m fading channels for an arbitrary number of users and symbol rates. The analysis is first developed for a three-user case, then generalized to systems with more than three users, yielding an exact BER expression for the first detected user and an accurate closed-form approximations for the remaining users under the perfect-successive interference cancellation (SIC) assumption. These results are further unified into a general average-BER expression over Nakagami-m fading for arbitrary user numbers and symbol-rate configurations. A symbol-pattern-dependent signal-to-interference plus noise ratio (SINR) analysis further shows how the symbol-duration diversity enables inherent interference cancellation, explaining the BER advantage of MR-NOMA over conventional single-symbol rate (SR)-NOMA. An average BER minimization problem is also formulated and numerically solved to optimize the users' power allocation, with simulation results corroborating the analytical findings. Overall, MR-NOMA is shown to offer greater flexibility in user pairing while maintaining reliable BER performance and accommodating diverse quality-of-service (QoS) requirements.

Large-scale pre-training has improved the generalization of neural operators across diverse PDEs. However, existing PDE foundation models still struggle with heterogeneous dynamics, where shared representations may cause knowledge interference, while mixture-of-experts (MoE) architectures suffer from increasing expert redundancy. We propose SPEAR, a spectral-disentangled MoE neural operator with knowledge-guided expert aggregation for large-scale PDE pre-training. SPEAR decouples latent features into low- and high-frequency components, enabling shared modeling of transferable dynamics and specialized learning of PDE-specific patterns. To address expert redundancy, we design a knowledge-guided expert aggregation strategy that measures expert similarity from dataset-specific learned knowledge and routing preferences, enabling the identification and consolidation of similar experts. Experiments on twelve PDE datasets and multiple downstream benchmarks demonstrate superior performance in pre-training, fine-tuning, and transfer learning. Furthermore, our aggregation strategy reduces the number of experts by 50% while maintaining or improving prediction accuracy, achieving a balance between model efficiency and generalization for PDE foundation models.

Contraction analysis of dynamical systems turns a global convergence problem into a local one. If the variational system is stable in a uniform sense, then all trajectories of the original system converge toward each other. This paper develops a stochastic analogue for continuous-time stochastic systems, where convergence is measured by Wasserstein distance between probability laws. The key step is to employ mean-square differentiation to construct stochastic variational dynamics. Its uniform $L_2$ stability can then be integrated along curves of initial conditions, yielding Wasserstein contraction of the associated probability laws. This yields criteria formulated in terms of differential Lyapunov functions, and on manifolds, it leads to intrinsic Riemannian formulations. A distinctive feature of the stochastic setting is that the coupling of the noises becomes a design freedom. To exploit this freedom, we introduce an orthogonal differential coupling that extends the framework beyond synchronous coupling. The resulting criterion explicitly exposes how the curvature of the state space enters the contraction mechanism and, in the orthonormal-frame case, recovers the classical Ricci-curvature structure. Finally, we apply the framework to stochastic control on a compact manifold. By introducing stochastic control input, we overcome a deterministic obstruction to global stabilization. That is, a system that cannot be globally stabilized through smooth time-invariant deterministic feedback is rendered globally contracting in $W_2$, and its steady state distribution can be made arbitrarily concentrated around a prescribed target equilibrium.

Multiple-Input-Multiple-Output (MIMO) technology is considered to be a viable method for spectral efficiency and data rate enhancement in wireless communications. This article presents a two-element capsule antenna with orthogonal current distributions for polarization multiplexing in a wireless capsule endoscopy (WCE) application at 915 MHz. The proposed antenna was evaluated in a MIMO link where it served as the transmitter and an existing on-body antenna was the receiver. The performance was assessed in two distinct environments: an anatomically accurate human body model using full-wave (FW) simulations, and a homogeneous liquid tissue phantom, using both FW simulations and measurements. The former was used to evaluate the performance in a realistic environment and to demonstrate the superiority of the proposed capsule antenna against existing two-element antennas in the literature, while the latter aimed at verifying the antenna design by comparison of simulated and measured MIMO links. We studied channel capacity and eigenvalues for the comparisons. The MIMO link with the proposed antenna provided nearly doubled median channel capacity relative to a baseline single-input-single-output link, consistently in the simulations and measurements. Our results prove the suitability of the proposed antenna for polarization multiplexing and, in terms of channel capacity, establish it as the best conformal two-element capsule antenna design at 915 MHz.

As the dominant trading mechanism in decentralized finance, Automated Market Maker has been widely studied in research. However, limited research has been done with the trading fees taken into consideration. In this work, we study how much trading fee Liquidity Providers(LPs) can receive from arbitrage trading when the fee rate approaches zero. We give a closed-form formula for it and our result shows that the trading fees generated by arbitrage trading can fully offset the LVR loss when the price process is continuous. We further extend our conclusions to price processes with jumps and the theoretical analysis shows that jumps are the only cause of LP loss apart from market risks. Our results provide practical guidance for AMM designers as well as LPs.

Solving severely ill-posed imaging inverse problems requires recovering image structures that are unobservable or weakly constrained by the measurements. Diffusion models provide expressive learned priors for inferring such missing information, while posterior sampling incorporates measurement consistency along the reverse process. Standard diffusion posterior samplers, however, rely on instantaneous measurement-aware estimates, without explicitly exploiting information carried by previous posterior corrections. We introduce Consecutive Posterior Fusion Denoising Diffusion Null-Space Models (CPF-DDNM), an inference-time strategy that fuses consecutive measurement-aware estimates to improve the diffusive recovery of unobservable image structures, without requiring retraining or additional denoiser evaluations. We instantiate this principle within DDNM, whose range/null-space decomposition reveals that consecutive fusion preserves the measurement-determined component while acting exclusively on the prior-driven null-space estimate. We thus provide a geometric interpretation of CPF-DDNM and a local error analysis that characterizes the optimal time-dependent fusion coefficient, including the extrapolative regime. Experiments on sparse-view and simulated low-dose computed tomography, as well as medical image super-resolution, show consistent improvements over DDNM and competitive performance against diffusion-based inverse solvers.

Quantum computing is a promising paradigm for problems that are challenging for classical machines, but realizing that promise requires far more qubits than a single processor can offer. Distributed quantum computing (DQC) scales out by connecting multiple quantum processing units (QPUs), at the cost of making entanglement the scarce resource: every remote gate consumes a Bell pair, and inter-QPU links generate Bell pairs at finite rates orders of magnitude slower than local gates. Since quantum programs are executed repeatedly, this rate bounds how fast shots complete and thus how fast results are obtained. Existing DQC compilers emit a single implementation per circuit, so throughput is capped by its busiest link while other links stay idle. We observe that alternative compilations of the same circuit are logically equivalent yet stress different links; executing them concurrently and pooling their samples converts idle Bell pairs into additional shots. We formulate the joint selection of compilations and allocation of shots under per-link Bell-pair capacities as Candidate-Constrained Max-Shot Allocation (CMA), prove it NP-hard, and solve it with a dynamic program and its approximate variant AppDP, a compact MILP, and a greedy heuristic Effi. In simulation on six-QPU networks, multi-compilation raises throughput by 2-4.5* over the single compilation and correspondingly improves output fidelity under equal Bell-pair budgets. On hardware, it increases measured fidelity by up to 93% and reaches the single-compilation fidelity in up to 10* less time. Scaling to 36 QPUs and 144-qubit circuits, MILP improves throughput over the single compilation by 76.8% on average across 48 configurations while Effi allocates in milliseconds.

We release PaMIR (Public Arrival-ordered Measurement for Inference in Risk), an open benchmark for credit-default prediction when labels are scarce and arrive late. The field's reference benchmark studies use eight datasets each, only two or four of them public. PaMIR brings together 19 public datasets with binary default labels – 1.24M loans, firms and card accounts from nine countries – rebuilt from pinned source snapshots by one leakage-audited recipe and never redistributed; to our knowledge it is the one of its kind as of today. Every model is a single function, scored under a repeated i.i.d. split and a label-delayed stream in which each application is scored on arrival, with AUC reported by label budget; fleet means are withheld unless every dataset is scored. A synthetic-data harness tests generated training rows without letting a generator see held-out rows. This report describes release 0.4.0 of this living benchmark.

Scalable nonlinear causal discovery requires methods that combine flexible mechanism estimators with efficient search over large graph spaces. Several algorithmic families have been proposed to address this challenge, yet their accuracy-runtime trade-offs remain poorly understood. We empirically compare the four major approaches: differentiable structure learning, amortized structure learning, score-matching, and combinatorial search. Our results reveal complementary bottlenecks: differentiable and amortized methods scale well but exhibit an accuracy gap, score-matching methods can be accurate in low dimensions but degrade quickly for increasing feature sizes, and combinatorial methods remain accurate but are slowed by repeated and redundant local scoring. Motivated by this bottleneck, we develop SPADE, a spline-based score-evaluation scheme that compiles sufficient statistics once and reuses them throughout combinatorial search. Under bounded indegree, its Gaussian variant reduces algorithmic complexity from O(nd^3) to O(nd^2+d^3). Empirically, SPADE shifts the observed scalability-accuracy frontier by orders of magnitude: it solves 100-variable problems with 160K samples in seconds and 1600-variable problems with 2.5K samples in minutes, while retaining high structural accuracy across synthetic and real-world benchmarks. These results reveal a substantial shift in the practical scale of combinatorial search and highlight the importance of evaluating scalable causal-discovery methods along the full accuracy-runtime frontier.

Time-domain multiplexing of continuous-variable quantum states provides a scalable approach to photonic quantum information processing, in which individual temporal modes can be sequentially manipulated within a common optical path. Here we demonstrate pulse-resolved post-generation phase control of squeezed-vacuum states at a repetition rate of 93 MHz using a low-loss bulk lithium-niobate electro-optic modulator driven by a broadband RF power amplifier. The driving architecture exploits low-cost, commercially available RF components and the impedance mismatch between the RF chain and the capacitive crystal load, rather than relying on dedicated custom high-voltage electronics, to obtain large voltage excursions on the modulator while operating above the piezoelectric-resonance region of lithium niobate. The modulator is driven synchronously with the optical pulse train, enabling the phase of consecutive temporal modes to be individually controlled. An interferometric calibration yields a maximum differential phase excursion of 1.93 rad. We apply the modulator to a 1035 nm squeezed-vacuum field generated by a synchronously pumped optical parametric oscillator and characterize the output using time-resolved balanced homodyne detection. With the local-oscillator phase kept fixed, the phase of the squeezed state is varied from pulse to pulse, and the corresponding phase-dependent quadrature variance is reconstructed. The resulting quadrature scan agrees with an equivalent measurement obtained by varying the local-oscillator phase with a piezoelectric actuator. These results demonstrate post-generation quantum-state phase control at the individual-pulse level and provide an electro-optic building block for programmable manipulation of time-multiplexed quantum states in recirculating optical architectures.

We present a first evaluation of machine learning applied to patient clinical and demographic data gathered in two different countries for the purpose of tuberculosis (TB) screening to identify people who would benefit from expensive molecular testing. Experiments are based on the recently-compiled CAGE-TB dataset, which includes sub-cohorts of people with presumptive TB presenting at community health care centres in South Africa and Uganda. Three neural network architectures (logistic regression (LR), multilayer perceptrons (MLP) and convolutional neural networks (CNN)) are considered in conjunction with greedy feature selection. For the convolutional neural network, a strategy that jointly optimises feature selection and feature ordering is proposed and shown to lead to consistent development and test set improvements. For all three models, development set area under the receiver operating characteristic (AUROC) curve is improved by 2-7% using feature selection. LR after feature selection achieves an AUROC of 0.8 [0.75,0.86] (95% CI) and 0.84 [0.78,0.9] when testing on the held-out Ugandan and South African data respectively. Although outperforming LR on the development cohort, the deeper networks (MLP, CNN) show inconsistent trends on the held-out cohorts, while LR achieves performance within 1-2% of the best achieved in terms of AUROC. LR narrowly misses the WHO minimum requirements by 4-9% in sensitivity even though the network is being evaluated on a completely held-out cohort. The development of neural-network based classifiers for TB screening therefore appears viable.

We establish a criterion for finiteness of the depth 3 Chabauty–Kim set $X(\mathbb{Q}_2)_3$ for hyperelliptic curves $X$ over $\mathbb{Q}$ with a rational Weierstrass point. To illustrate its applicability, we include an example of a genus $2$ curve satisfying the criterion whose Jacobian has Néron–Severi rank $1$ and Mordell–Weil rank $4$.

Solving the high-dimensional, nonlinear partial differential equations that govern the rich physics of plasmas remains a major computational challenge. In fluid dynamics, quantum-inspired methods based on tensor-network techniques have recently emerged as a promising new paradigm, offering large degrees of data compression on conventional hardware and a natural pathway towards solving exponentially large problems on quantum computers. Early studies suggest that tensor-network methods may also help overcome key bottlenecks in computational plasma dynamics, particularly for industry-relevant scenarios where conventional simulations remain costly. In this work, we present recent progress in developing tensor-network methods compared to standard particle-in-cell codes and discuss their capability to overcome the bottlenecks of conventional approaches. We further extend the tensor-network formulation to support the insertion of electromagnetic fields at arbitrary spatial locations. This capability is demonstrated through the injection of a laser field to accelerate a plasma, providing a key step towards tensor-network-based simulation of externally driven plasma systems.

Large language models are increasingly deployed as agents that make or recommend decisions on behalf of users, often operating through interfaces that pre-fill suggested values or default options. Whether models treat such defaults as merely informational or as suggestions that systematically alter their choices remains unclear. We study default deference in two one-shot social dilemmas: a common-pool resource (CPR) extraction game and a threshold public-good (TPG) contribution game. We measure how defaults shift each model's choice distribution relative to its no-default baseline, across default values, wordings, and action-space granularities. We find that pre-filled defaults pull probability mass on the default value in both games, but the magnitude depends strongly on wording: the same model can show high pull under one formulation and near-zero pull under another. Permission-style wording reduces default pull in both games, more strongly in CPR than in TPG. Default pull is weaker in coarse action spaces, and conflict defaults attract more mass than agreement ones. These results indicate that default deference depends on the model, the wording of the interface, and the structure of available choices. For agentic systems, evaluating model behaviour without controlling the surrounding choice architecture can miss an important source of behavioural variation.

Generative models of human-object interaction are bounded by the data that exists: everyday activities involve several objects, but most captured datasets record one at a time, as multi-object capture is combinatorially expensive. Our observation is that interactions are local, so single-object captures already contain the parts of multi-object activities. We compose them: contact-consistent clips of single interactions, mirrored to balance the hands, transfer between bodies, and a language model and geometric checks admit only the pairings that are plausible, semantically and physically. Therefore, the dataset grows combinatorially with the clips rather than recording time. The COSMI dataset holds 222k sequences and 275 hours with up to five objects, nearly thirty times the largest multi-object capture, and can be extended by adding datasets or even hand-object recordings. On this data we train the COSMI method, a text-to-interaction diffusion transformer that follows how the data is built: weight-shared object slots generate a variable number of objects, predicted relative to the body parts that move them. On a benchmark with an unseen object and unseen interaction combinations, models trained on the dataset generalize to the unseen combinations. COSMI outperforms baselines in text alignment and contact accuracy, where its margin is largest on the unseen object. Code, models, and the dataset pipeline will be released on the project page: https://ptrvilya.github.io/cosmi.

A model trained on "The capital of X is Y" may produce "Y" after "The capital of X is" but fail after "The capital of X:." We call these different ways of eliciting the same fact request forms. To separate learning a fact from retrieving it, we train two models in two stages. In the first stage (request-form training), one model sees each fact in five forms and the other sees the same facts only as statements. In the second stage (target-fact training), both receive identical training on new facts, all as statements. Both then retrieve the new facts almost equally well from statements, but differ sharply on other request forms. Thus, a model can learn how to retrieve through a request form before it learns the facts. To understand this difference, we examine the hidden state immediately before the answer, which we call the context state. When given two different request forms for the same fact, the model trained on five forms in stage one produces more similar context states than the model trained on statements alone in that stage. Changing this state at retrieval time can enable or prevent retrieval of an already learned fact, and the same effect transfers across facts and factual relations, such as capitals and currencies. To test its role during learning, we change the context state only during target-fact training. This intervention changes later retrieval without intervention at test time. Together, these results show that later retrieval depends on earlier request-form experience and the context state during fact learning.

The role of women on corporate boards and its impact on innovation remains heavily debated. While existing literature offers conflicting perspectives on whether female board representation spurs or hinders innovaiton, most research has focused exlusively on large firms. This studt adresses this gap by investigating the causal impact of women on boards on both "hard" (technological) and "soft" (non-technological) innovation within SMEs. Using a longitudinal dataset f 2762 Italian innovative SMEs from 2015 to 2024 and a 2SLS identification strategy, we find highly nuanced patterns. For hard innovation, the instrumented model reveals a robust positive causal impacr, reversing a negative baseline coefficient and exposing a severe negative selection bias. For soft innovation, a stable positive impact is confirmed. Our findings provide crucial causal evidence for SMEs' governance, while offering tailored insights for policymakers and practitioners to incentivize women' management roles in SMES.

Deploying a pretrained controller exposes it to conditions that are absent from its training data. Sensor drift, outright sensor failure and accumulating measurement noise all induce a distribution shift that can collapse an otherwise competent policy; typically at a point in time where no expert is available to supply corrective labels. We present a method that lets a policy recover from such shifts online and without supervision. Our controller is an ensemble of recurrent networks, each of which observes a randomly masked subset of the observation vector, and whose Gaussian outputs are combined through sequential Kalman fusion so that confident members dominate the consensus action. At deployment, we treat this consensus as a self-supervised label and fine-tune each member towards it, scaling each member's contribution proportional to the complement of its squared Kalman gain. Gradients are computed using RFLO, an efficient and biologically plausible approximation of Real-Time Recurrent Learning, so that a parameter update follows every environment step and the policy reacts to a shift as it unfolds. On a range of simulated continuous control tasks, our approach recovers close to the original performance after a sensor shift, while ensembles that see the full observation are unable to recover. The same framework subsumes fully online interactive imitation learning: when an expert is present, the consensus label is replaced by the expert action and the identical update rule refines the policy during teleoperation.

Panoramic images provide a complete 360-degree field of view, enabling comprehensive scene understanding for embodied perception. However, heterogeneous embodied platforms exhibit substantial differences in observation viewpoints and spatial layouts, giving rise to cross-embodiment observation shifts that pose additional challenges to consistent and reliable panoramic perception, while systematic studies of this problem remain limited. To bridge this gap, we introduce a new task, termed Cross-Embodiment Open Panoramic Segmentation. Meanwhile, we establish EmbPASS, a multi-platform panoramic semantic segmentation benchmark spanning Vehicle, Drone, Wearable, and Quadruped platforms under a unified semantic taxonomy, providing a testbed for systematically studying cross-embodiment panoramic perception. We further propose EPONet, an open-vocabulary panoramic semantic segmentation network that integrates Relation-Aware Metric Adapter (RAMA) and Content-Adaptive Semantic Transfer (CAST) to enhance spatial modeling and semantic transfer under heterogeneous embodied observations. Extensive experiments show that EPONet achieves the best platform-balanced performance on EmbPASS with 35.82% mIoU, outperforming the strongest baseline by 1.10%, while remaining competitive on existing panoramic segmentation benchmarks. The source code and EmbPASS benchmark will be made publicly available at https://github.com/guopj1/EmbPASS.