Search

We theoretically study dynamical excitonic condensates occurring in bilayers with an imposed chemical potential difference and in photodoped semiconductors. We show that optical spectroscopy can experimentally identify phase-trapped and phase-delocalized dynamical regimes of condensation. In the weak-bias regime, the trapped dynamics of the order parameter's phase lead to an in-gap absorption line at a frequency almost independent of the bias voltage, while for larger biases, the frequency of the spectral feature increases approximately linearly with bias. In both cases there is a pronounced second harmonic response. Close to the transition between the trapped and freely oscillating states, we find a strong response upon application of a weak electric probe field and compare the results to those found in a minimal model description for the dynamics of the order parameter's phase and analyze the limitations of the latter.

Large language models (LLMs) have been increasingly applied to tasks in language understanding and interactive decision-making, with their impressive performance largely attributed to the extensive domain knowledge embedded within them. However, the depth and breadth of this knowledge can vary across domains. Many existing approaches assume that LLMs possess a comprehensive understanding of their environment, often overlooking potential gaps in their grasp of actual world dynamics. To address this, we introduce Discover, Verify, and Evolve (DiVE), a framework that discovers world dynamics from a small number of demonstrations, verifies the accuracy of these dynamics, and evolves new, advanced dynamics tailored to the current situation. Through extensive evaluations, we assess the impact of each component on performance and compare the dynamics generated by DiVE to human-annotated dynamics. Our results show that LLMs guided by DiVE make more informed decisions, achieving rewards comparable to human players in the Crafter environment and surpassing methods that require prior task-specific training in the MiniHack environment.

In ultrafast experiments, an optical pump pulse often generates transient domain walls of the order parameter in materials with spontaneous symmetry breaking, due to either a finite penetration depth of the light on a three-dimensional (3D) material, or a finite spot size on a two-dimensional (2D) material. We clarify the decaying process of such a domain wall that is caused by fluctuations of the order parameters. We study a generic system with $U(1)$-symmetric order, and those with an additional weak $Z_2$ ($U(1)$-symmetry-breaking) term, representing the charge-density-wave (CDW) orders in recent experiments. The decay process comprises two non-trivial stages. During the first stage, exponentially growing thermal fluctuations convert the domain wall into an interface with randomly distributed topological defects. In the second stage, the topological defects undergo a coarsening dynamics within the interface. For a 2D interface in the 3D system, the coarsening dynamics leads to a diffusive growth of the correlation length. For a one-dimensional (1D) interface in the 2D system with the weak $Z_2$ term, the correlation-length growth shows a crossover from diffusive to sub-diffusive behavior. Our theory provides a fundamental physical picture for the dynamics of pump-induced domain walls in ultrafast experiments., Comment: 4.1 pages, 3 figures with supplemental materials

Plasmon polaritons are formed by coupling light with delocalized electrons. The half-light and half-matter nature of plasmon polaritons endows them with unparalleled tunability via a range of parameters, such as dielectric environments and carrier density. Therefore, plasmon polaritons are expected to be tuned when in proximity to polar materials since the carrier density is tuned by an electrostatic potential; conversely, the plasmon polariton response might enable the sensing of polarization. Here, we use infrared nano-imaging and nano-photocurrent measurements to investigate heterostructures composed of graphene and twisted hexagonal boron nitride (t-BN), with alternating polarization in a triangular network of moir\'e stacking domains. We observe that the carrier density and the corresponding plasmonic response of graphene are modulated by polar domains in t-BN. In addition, we demonstrate that the nanometer-wide domain walls of graphene moir\'es superlattices, created by the polar domains of t-BN, provide momenta to assist the plasmonic excitations. Furthermore, our studies establish that the plasmon of graphene could function as a delicate sensor for polarization textures. The evolution of polarization textures in t-BN under uniform electric fields is tomographically examined via plasmonic imaging. Strikingly, no noticeable polarization switching is observed under applied electric fields up to 0.23 V/nm, at variance with transport reports. Our nano-images unambiguously reveal that t-BN with triangular domains acts like a ferrielectric, rather than ferroelectric claimed by many previous studies., Comment: 41 pages, 20 figures

Embodied Instruction Following (EIF) is a crucial task in embodied learning, requiring agents to interact with their environment through egocentric observations to fulfill natural language instructions. Recent advancements have seen a surge in employing large language models (LLMs) within a framework-centric approach to enhance performance in embodied learning tasks, including EIF. Despite these efforts, there exists a lack of a unified understanding regarding the impact of various components-ranging from visual perception to action execution-on task performance. To address this gap, we introduce OPEx, a comprehensive framework that delineates the core components essential for solving embodied learning tasks: Observer, Planner, and Executor. Through extensive evaluations, we provide a deep analysis of how each component influences EIF task performance. Furthermore, we innovate within this space by deploying a multi-agent dialogue strategy on a TextWorld counterpart, further enhancing task performance. Our findings reveal that LLM-centric design markedly improves EIF outcomes, identify visual perception and low-level action execution as critical bottlenecks, and demonstrate that augmenting LLMs with a multi-agent framework further elevates performance.

The qualitative reliability of the dynamical mean field theory (DMFT) is investigated for systems in which either the actual carrier density or the effective carrier density is low, by comparing the exact perturbative and dynamical mean field expressions of electron scattering rates and optical conductivities. We study two interacting systems: tight binding models in which the chemical potential is near a band edge and Dirac systems in which the chemical potential is near the Dirac point. In both systems it is found that DMFT underestimates the low frequency, near-Fermi surface single particle scattering rate by a factor proportional to the particle density. The quasiparticle effective mass is qualitatively incorrect for the low density tight binding model but not necessarily for Dirac systems. The dissipative part of the optical conductivity is more subtle: in the exact calculation vertex corrections, typically neglected in DMFT calculations, suppress the low frequency optical absorption, compensating for some of the DMFT underestimate of the scattering rate. The role of vertex corrections in calculating the conductivity for Dirac systems is clarified and a systematic discussion is given of the approach to the Galilean/Lorentz invariant low density limit. Relevance to recent calculations related to Weyl metals is discussed., Comment: 16 pages, 7 figures

Bilayer materials may support interlayer excitons comprised of electrons in one layer and holes in the other. In experiments, a non-zero exciton density is typically sustained by a bias chemical potential, implemented either by optical pumping or by electrical contacts connected to the two layers. We show that if charge can tunnel between the layers, the chemical potential bias means that an exciton condensate is in the dynamical regime of ac Josephson effect. It has physical consequences such as tunneling currents and the ability to tune a condensate from bright (emitting coherent photons) to dark by experimental controlling knobs. If the system is placed in an optical cavity, coupling with cavity photons favors different dynamical states depending on the bias, realizing superradiant phases., Comment: 5 pages, 3 figures

The ponderomotive force is an effective static force that a particle feels in an oscillating field, whose static potential may be called the ponderomotive potential. We generalize this notion to periodically driven quantum many-body systems, and propose it as a convenient tool to engineer their non-equilibrium steady states beyond the single particle level. Applied to materials driven by light, the ponderomotive potential is intimately related to the equilibrium optical conductivity, which is enhanced close to resonances. We show that the ponderomotive potential from the incident light may be used to induce exciton condensates in semiconductors, to generate attractive interactions leading to superconductivity in certain electron-phonon systems, and to create additional free energy minima in systems with charge/spin/excitonic orders. These effects are presented with experimentally relevant parameters., Comment: 6 pages, 4 figures

In this study, we explore the application of Large Language Models (LLMs) in \textit{Jubensha}, a Chinese detective role-playing game and a novel area in Artificial Intelligence (AI) driven gaming. We introduce the first dataset specifically for Jubensha, including character scripts and game rules, to foster AI agent development in this complex narrative environment. Our work also presents a unique multi-agent interaction framework using LLMs, allowing AI agents to autonomously engage in this game. To evaluate the gaming performance of these AI agents, we developed novel methods measuring their mastery of case information and reasoning skills. Furthermore, we incorporated the latest advancements in in-context learning to improve the agents' performance in information gathering, murderer identification, and logical reasoning. The experimental results validate the effectiveness of our proposed methods. This work aims to offer a novel perspective on understanding LLM capabilities and establish a new benchmark for evaluating large language model-based agents.

Topological semimetals with massless Dirac and Weyl fermions represent the forefront of quantum materials research. In two dimensions (2D), a peculiar class of fermions that are massless in one direction and massive in the perpendicular direction was predicted sixteen years ago. These highly exotic quasiparticles - the semi-Dirac fermions - ignited intense theoretical and experimental interest but remain undetected. Using magneto-optical spectroscopy, we demonstrate the defining feature of semi-Dirac fermions - $B^{2/3}$ scaling of Landau levels - in a prototypical nodal-line metal ZrSiS. In topological metals, including ZrSiS, nodal-lines extend the band degeneracies from isolated points to lines, loops or even chains in the momentum space. With $\textit{ab initio}$ calculations and theoretical modeling, we pinpoint the observed semi-Dirac spectrum to the crossing points of nodal-lines in ZrSiS. Crossing nodal-lines exhibit a continuum absorption spectrum but with singularities that scale as $B^{2/3}$ at the crossing. Our work sheds light on the hidden quasiparticles emerging from the intricate topology of crossing nodal-lines and highlights the potential to explore quantum geometry with linear optical responses.

Ferroelectricity, a spontaneous and reversible electric polarization, is found in certain classes of van der Waals (vdW) material heterostructures. The discovery of ferroelectricity in twisted vdW layers provides new opportunities to engineer spatially dependent electric and optical properties associated with the configuration of moir\'e superlattice domains and the network of domain walls. Here, we employ near-field infrared nano-imaging and nano-photocurrent measurements to study ferroelectricity in minimally twisted WSe2. The ferroelectric domains are visualized through the imaging of the plasmonic response in a graphene monolayer adjacent to the moir\'e WSe2 bilayers. Specifically, we find that the ferroelectric polarization in moir\'e domains is imprinted on the plasmonic response of the graphene. Complementary nano-photocurrent measurements demonstrate that the optoelectronic properties of graphene are also modulated by the proximal ferroelectric domains. Our approach represents an alternative strategy for studying moir\'e ferroelectricity at native length scales and opens promising prospects for (opto)electronic devices., Comment: 19 pages, 3 figures

We derive exciton-spin interactions from a microscopic correlated model that captures important aspects of the physics of charge-transfer (CT) insulators to address magnetism associated with exciton creation. We present a minimal model consisting of coupled clusters of transition metal d and ligand p orbitals that captures the essential features of the local atomic and electronic structure. First, we identify the lowest-energy state and optically allowed excited states within a cluster by applying the molecular orbital picture to the ligand p orbitals. Then, we derive the effective interactions between two clusters mediated by intercluster hoppings, which include exciton-spin couplings. The interplay of the correlations and the spatial structure of the CT exciton leads to strong magnetic exchange couplings with spatial anisotropy. Finally, we calculate an optical excitation spectrum in our effective model to obtain insights into magnetic sidebands optically observed in magnetic materials. We demonstrate that the spin-flip excitation due to the strongly enhanced local spin interactions around the exciton gives rise to the magnetic sidebands., Comment: 13 pages, 8 figures

We present the development of SpeCamX, a mobile application that transforms any unmodified smartphone into a powerful multispectral imager capable of capturing multispectral information. Our application includes an augmented bilirubinometer, enabling accurate prediction of blood bilirubin levels (BBL). In a clinical study involving 320 patients with liver diseases, we used SpeCamX to image the bulbar conjunctiva region, and we employed a hybrid machine learning prediction model to predict BBL. We observed a high correlation with blood test results, demonstrating the efficacy of our approach. Furthermore, we compared our method, which uses spectrally augmented learning (SAL), with traditional learning based on RGB photographs (RGBL), and our results clearly indicate that SpeCamX outperforms RGBL in terms of prediction accuracy, efficiency, and stability. This study highlights the potential of SpeCamX to improve the prediction of bio-chromophores, and its ability to transform an ordinary smartphone into a powerful medical tool without the need for additional investments or expertise. This makes it suitable for widespread use, particularly in areas where medical resources are scarce.

Distinguishing and controlling the extent of Mottness is important for materials where the energy scales of the onsite Coulomb repulsion U and the bandwidth W are comparable. Here we report the ultrafast electronic dynamics of 1T-TaS$_2$ by ultrafast time- and angle-resolved photoemission spectroscopy. A comparison of the electron dynamics for the newly-discovered intermediate phase (I-phase) as well as the low-temperature commensurate charge density wave (C-CDW) phase shows distinctive dynamics. While the I-phase is characterized by an instantaneous response and nearly time-resolution-limited fast relaxation (~200 fs), the C-CDW phase shows a delayed response and a slower relaxation (a few ps). Such distinctive dynamics refect the different relaxation mechanisms and provide nonequilibrium signatures to distinguish the Mott insulating I-phase from the C-CDW band insulating phase. Moreover, a light-induced bandwidth reduction is observed in the C-CDW phase, pushing it toward the Mott insulating phase. Our work demonstrates the power of ultrafast light-matter interaction in both distinguishing and controlling the extent of Mottness on the ultrafast timescale.

The blockchain technology has been used for recording state transitions of smart contracts - decentralized applications that can be invoked through external transactions. Smart contracts gained popularity and accrued hundreds of billions of dollars in market capitalization in recent years. Unfortunately, like all other programs, smart contracts are prone to security vulnerabilities that have incurred multimillion-dollar damages over the past decade. As a result, many automated threat mitigation solutions have been proposed to counter the security issues of smart contracts. These threat mitigation solutions include various tools and methods that are challenging to compare. This survey develops a comprehensive classification taxonomy of smart contract threat mitigation solutions within five orthogonal dimensions: defense modality, core method, targeted contracts, input-output data mapping, and threat model. We classify 133 existing threat mitigation solutions using our taxonomy and confirm that the proposed five dimensions allow us to concisely and accurately describe any smart contract threat mitigation solution. In addition to learning what the threat mitigation solutions do, we also show how these solutions work by synthesizing their actual designs into a set of uniform workflows corresponding to the eight existing defense core methods. We further create an integrated coverage map for the known smart contract vulnerabilities by the existing threat mitigation solutions. Finally, we perform the evidence-based evolutionary analysis, in which we identify trends and future perspectives of threat mitigation in smart contracts and pinpoint major weaknesses of the existing methodologies. For the convenience of smart contract security developers, auditors, users, and researchers, we deploy a regularly updated comprehensive open-source online registry of threat mitigation solutions., Comment: ACM Computing Surveys (CSUR)

Plasmon polaritons in van der Waals (vdW) materials hold promise for next-generation photonics. The ability to deterministically imprint spatial patterns of high carrier density in cavities and circuitry with nanoscale features underlies future progress in nonlinear nanophotonics and strong light-matter interactions. Here, we demonstrate a general strategy to atomically imprint low-loss graphene plasmonic structures using oxidation-activated charge transfer (OCT). We cover graphene with a monolayer of WSe$_2$, which is subsequently oxidized into high work-function WOx to activate charge transfer. Nano-infrared imaging reveals low-loss plasmon polaritons at the WOx/graphene interface. We insert WSe$_2$ spacers to precisely control the OCT-induced carrier density and achieve a near-intrinsic quality factor of plasmons. Finally, we imprint canonical plasmonic cavities exhibiting laterally abrupt doping profiles with single-digit nanoscale precision via programmable OCT. Specifically, we demonstrate technologically appealing but elusive plasmonic whispering-gallery resonators based on free-standing graphene encapsulated in WOx. Our results open avenues for novel quantum photonic architectures incorporating two-dimensional materials., Comment: 17 pages, 4 figures

Metals are canonical plasmonic media at infrared and optical wavelengths, allowing one to guide and manipulate light at the nano-scale. A special form of optical waveguiding is afforded by highly anisotropic crystals revealing the opposite signs of the dielectric functions along orthogonal directions. These media are classified as hyperbolic and include crystalline insulators, semiconductors and artificial metamaterials. Layered anisotropic metals are also anticipated to support hyperbolic waveguiding. Yet this behavior remains elusive, primarily because interband losses arrest the propagation of infrared modes. Here, we report on the observation of propagating hyperbolic waves in a prototypical layered nodal-line semimetal ZrSiSe. The observed waveguiding originates from polaritonic hybridization between near-infrared light and nodal-line plasmons. Unique nodal electronic structures simultaneously suppress interband loss and boost the plasmonic response, ultimately enabling the propagation of infrared modes through the bulk of the crystal.

We compute the frequency dependent conductivity of the two dimensional square lattice Hubbard model at zero temperature as a function of density to second order in the interaction strength, and compare the results to the predictions of single-site dynamical mean field theory computed at the same order. We find that despite the neglect of vertex corrections, the single site dynamical mean field approximation produces semiquantitatively accurate results for most carrier concentrations, but fails qualitatively for the nearly empty or nearly filled band cases where the theory exhibits an emergent Galilean invariance. The theory also becomes qualitatively inaccurate very near half filling if nesting is important., Comment: 12 pages, 6 figures

Nonlinear responses of superconductors to intense terahertz radiation has been an active research frontier. Using terahertz pump-terahertz probe spectroscopy, we investigate the c-axis nonlinear optical response of a high-temperature superconducting cuprate. After excitation by a single-cycle terahertz pump pulse, the reflectivity of the probe pulse oscillates as the pump-probe delay is varied. Interestingly, the oscillatory central frequency scales linearly with the probe frequency, a fact widely overlooked in pump-probe experiments. By theoretically solving the nonlinear optical reflection problem on the interface, we show that our observation is well explained by the Josephson-type third-order nonlinear electrodynamics, together with the emission coefficient from inside the material into free space. The latter results in a strong enhancement of the emitted signal whose physical frequency is around the Josephson plasma edge. Our result offers a benchmark for and new insights into strong-field terahertz spectroscopy of related quantum materials., Comment: 8 pages, 4 figures

Nonequilibrium hidden states, both transient and long-lived, provide a unique window into thermally inaccessible regimes of strong coupling between microscopic degrees of freedom in quantum materials. Understanding the physical origin of these states is of both fundamental and practical significance, allowing the exploration of far-from-equilibrium thermodynamics and the development of optoelectronic devices with on-demand photoresponses. However, mapping the ultrafast formation of a long-lived hidden phase remains a long-standing challenge in physics since the initial state of the system is not recovered rapidly and conventional pump-probe methods are thus not applicable. Here, using a suite of state-of-the-art single-shot spectroscopy techniques, we present a direct ultrafast visualization of the photoinduced phase transition to both transient and long-lived hidden states in an electronic crystal, 1T-TaS2. Capturing the dynamics of this complex phase transformation in a single-shot fashion demonstrates a commonality in microscopic pathways, driven by the collapse of charge order, that the system undergoes to enter the hidden state and provides unambiguous spectral fingerprints that distinguish such state from thermally accessible phases. We present a theory of fluctuation-dominated process that explains both the dynamics and the nature of the metastable state. Our results settle the debate around the origin of this elusive metastable state and pave the way for the discovery of new quantum phases of matter.

We analyze nonlinear optics schemes for generating pairs of quantum entangled plasmons in the terahertz-infrared range in graphene. We predict that high plasmonic field concentration and strong optical nonlinearity of monolayer graphene enables pair-generation rates much higher than those of conventional photonic sources. The first scheme we study is spontaneous parametric down conversion in a graphene nanoribbon. In this second-order nonlinear process a plasmon excited by an external pump splits into a pair of plasmons, of half the original frequency each, emitted in opposite directions. The conversion is activated by applying a dc electric field that induces a density gradient or a current across the ribbon. Another scheme is degenerate four-wave mixing where the counter-propagating plasmons are emitted at the pump frequency. This third-order nonlinear process does not require a symmetry-breaking dc field. We suggest nano-optical experiments for measuring position-momentum entanglement of the emitted plasmon pairs. We estimate the critical pump fields at which the plasmon generation rates exceed their dissipation, leading to parametric instabilities., Comment: 11 pages, 8 figures

Deep learning (DL) is an emerging analysis tool across sciences and engineering. Encouraged by the successes of DL in revealing quantitative trends in massive imaging data, we applied this approach to nano-scale deeply sub-diffractional images of propagating polaritonic waves in complex materials. We developed a practical protocol for the rapid regression of images that quantifies the wavelength and the quality factor of polaritonic waves utilizing the convolutional neural network (CNN). Using simulated near-field images as training data, the CNN can be made to simultaneously extract polaritonic characteristics and materials parameters in a timescale that is at least three orders of magnitude faster than common fitting/processing procedures. The CNN-based analysis was validated by examining the experimental near-field images of charge-transfer plasmon polaritons at Graphene/{\alpha}-RuCl3 interfaces. Our work provides a general framework for extracting quantitative information from images generated with a variety of scanning probe methods.