国家预印本平台

Non-reciprocal couplings can make added dissipation raise the barrier that protects an attractor against rare escape — a protective reversal impossible at equilibrium. We ask, and bound, how strong this effect can be per unit of entropy production. For a linear stochastic system x=Lx+ε ξ, ⟨ξξ⊤⟩=Q, with Hurwitz L=−A+N (A=A⊤≻0, N=−N⊤), the barrier’s sensitivity to a dissipation increment M≽0 is the linear form ⟨M,K⟩ of a response operator K built from the stationary Gramian, and a reversal occurs iff λmaxK>0. Under isotropic noise Q=I we conjecture the universal bound supNλmaxK+/Si≤1/128 a13, a1=λminA, and prove it — sharp constant included — in the following cases: the value 1/128a13 and the saturating 7:1 geometry in the small-coupling limit in every dimension (an explicit copositivity certificate; unconditional for d≤4, otherwise conditional on a rank-two reduction we verify numerically), and the full two-mode case for all couplings. An exact Schur complement lowers the governing matrix inequality by one dimension, and we show the isotropy hypothesis is load-bearing — for general Q the ratio is unbounded, so Q=I is not a normalisation. A free-coordinate reduction recasts the general statement as a matrix inequality on a compact box and reduces d=3 to a matrix sum-of-squares certificate (whose existence the Hol–Scherer theorem guarantees where the matrix is strictly positive). The residual — d≥3 at all couplings — remains open; it is numerically unbroken over >107 configurations up to d=12. Ensemble simulations reproduce every prediction from trajectory data on circuit, optical, mechanical, and quantum realisations.

LLMs are increasingly used to brainstorm research ideas, but existing evaluations mostly judge individual ideas by novelty, feasibility, or expert preference. We instead ask: how far are current LLM-generated ideas from human researchers? To characterize this gap, we build a large-scale evaluation framework for ideation from high-quality human research papers. For each paper, we reverse-engineer a small set of closely related prior works that likely inspired its core idea. LLMs are then prompted to generate a new idea from the set of paper titles and summaries. We introduce a two-axis research-taste taxonomy to profile each idea by its opportunity pattern and research paradigm, and use it to quantify the divergence between human and LLM ideas. Across idea sets generated by different LLMs, we observe a consistent distributional gap: LLM ideas are disproportionately concentrated around bridge-like opportunities and synthesis methods, whereas the human paper reference distribution spreads more broadly across ways of framing gaps and constructing contributions. This result suggests that strong LLMs can produce a range of reasonable ideas, but that range remains narrower than, and systematically shifted relative to, human research taste.

Suppose we want to assign a certainty equivalent--one number--to a multivariate risk. Which such assignments are law-invariant, monotone with respect to vector stochastic dominance, and invariant to independent background risk? I show that every such certainty equivalent is a positive mixture of scalar entropic certainty equivalents applied to positive projections of the vector risk. The same representation yields a robust-order characterization: unanimity across such certainty equivalents is equivalent, up to closure, to dominance after adding independent multidimensional background risk. In a social-welfare specialization, the corresponding shadow valuations are welfare weights.

When two neutron stars collide, they eject material containing heavy nuclei formed by the rapid neutron capture process ($r$-process). As these nuclei decay, they power a bright optical/near-infrared transient known as a kilonova (KN). Modeling KN emission is a complex problem involving atomic opacities, radiation transport, and heating powered by the thermalization of radioactive decay products like $γ$-rays, $α$-particles, and $β$-particles. For heating by $γ$-rays, many KN modeling codes do full radiation transport calculations. However, heating by $α$- and $β$-particles relies on simplified descriptions of collisions and transport, and remains an important source of uncertainty in KN models. In this paper, we study the thermalization and transport of $β$-particles. To study thermalization, we use evaluated atomic physics data to estimate per-species contributions to energy deposition, scattering, and electron impact ionization, which we make available online. To include non-local effects, we develop a fully relativistic framework for charged particle transport in a spherically symmetric, homologously expanding ejecta, considering two limiting magnetic-field geometries. Non-local energy deposition and escape reduce thermalization efficiency, especially in the innermost and outermost ejecta, lowering the ejecta temperature and ionization state compared to local deposition models. Coulomb scattering partially offsets these effects by trapping particles at intermediate times. Ionization by secondary electrons significantly enhances the overall ionization rate. We provide analytic prescriptions for the spatially dependent thermalization efficiency for use in future light-curve calculations. Our results demonstrate that evaluated atomic data and charged-particle transport should be incorporated into the next generation of KN models.

Recent 3D generative models can synthesize high-quality geometry but often struggle to reproduce intricate textures from reference images, largely due to the scarcity of large-scale 3D training data with rich surface appearance. In contrast, visual generative models are trained on datasets several orders of magnitude larger and excel at modeling complex visual patterns. Motivated by this gap, we introduce Ink3D, a framework that bridges 3D generation with large-scale video generative models to synthesize extremely complex textures. Ink3D first reconstructs a white-mesh geometry using an off-the-shelf 3D generation model. It then employs OrbitPainter, a conditional video generative model, to produce dense orbit-scan videos capturing object appearance across viewpoints. To convert these views into coherent textures, we introduce TextureOptimizer, a neural baking module that integrates dense multi-view observations while mitigating geometry inconsistencies arising from video generation. By decoupling geometry and texture synthesis and leveraging large-scale pretrained video priors, Ink3D enables significantly richer and more faithful texture generation than prior approaches.