arXiv subjects

Yifan Su

Publications and source records attributed to Yifan Su.

At least 19 recordsLinked to original sources

arXiv2026

Antiferromagnetism-altered plasmon dynamics

The interaction between plasmons and magnons is a long-sought phenomenon with implications for fundamental physics and spintronics applications. In three-dimensional systems, this coupling is suppressed by the large mismatch in energy scales, but two-dimensional (2D) plasmons with gapless dispersion can overlap with magnons over a broad spectral range. Despite numerous theoretical predictions, experimental observation of magnon-plasmon interaction has remained elusive. In this work, we study a first-of-its-kind hybrid plasmon-magnon platform based on 2D materials. By deploying scattering-type scanning near-field optical microscopy (s-SNOM) with terahertz radiation, we image propagating plasmon wavepackets at a graphene/NiPS$_3$ interface and track their dynamics across the antiferromagnetic transition of NiPS$_3$. We observe a clear renormalization of the plasmon-polariton dispersion concurrent with the onset of antiferromagnetic order. With complementary Raman scattering and nano-terahertz spectroscopy, we unveil spectral weight redistribution and dielectric screening changes, potentially associated with the multi-magnon continuum, as the underlying mechanism. These results provide solid evidence of coupling between plasmon and antiferromagnetic order, marking a cornerstone for a potential platform for hybrid magnon-plasmon interactions in 2D materials, opening avenues for coherent spin-plasmon devices and tunable terahertz spintronic components.

cond-mat.str-el↗

arXiv2026

Sublattice-resolved coherent phonon dynamics in charge density waves

Phonons govern fundamental material properties and play a central role in various electronic phase transitions. Coherent driving of specific phonon modes enables on-demand phase control, motivating sublattice-resolved identification of real-space phonon motions. Yet experimentally resolving these motions remains challenging, limiting precise phonon-based control. Here, we introduce a dynamical protocol to track element-resolved phonon dynamics in the charge density wave material EuTe4, in which the dominant Te-sublattice charge order is accompanied by a previously unreported Eu-sublattice component. We leverage the elemental selectivity of time-resolved resonant X-ray scattering to reveal three coherent phonon modes with distinct sublattice character, thereby disentangling Eu- and Te-dominated lattice dynamics, in good agreement with theoretical calculations of the phonon eigenvectors. This time-domain approach, which surpasses the energy-resolution limits of conventional frequency-domain inelastic scattering, provides a broadly applicable framework for decomposing coherent phonons in multi-element materials, which is crucial for the targeted control of phases of matter.

cond-mat.mtrl-sci↗

arXiv2026

FlowDPG: Deterministic Policy Gradient on Flow Matching Policies for Real-World Manipulation

Real-world reinforcement learning for robotic manipulation remains challenging, and this difficulty is amplified for flow matching policies: applying policy gradient methods to these policies is fundamentally limited by the need to backpropagate through time(BPTT) along the multi-step ODE that maps noise to actions, which is computationally prohibitive and numerically fragile. We propose FlowDPG, a DDPG-style method specifically designed for flow matching policies that distills the critic gradient into the velocity field at training time, bypassing BPTT entirely. Intuitively, FlowDPG combines two complementary vectors: the demonstration-driven velocity that keeps the action feasible, and the critic-driven correction that steers it toward higher value. Our contributions are threefold: (1) a BPTT-free distillation framework that enables stable DDPG-style policy improvement on flow matching policies, (2) a formal connection between the FlowDPG update direction and vanilla Deterministic Policy Gradient via three explicit approximations, and (3) real-world validation on a long-horizon, multi-stage, dual-arm AirPods assembly task, where FlowDPG attains a 92% end-to-end success rate, substantially outperforming recent RL methods spanning value-conditioning, auxiliary-module adaptation, and adjoint-based critic-gradient approaches. Videos and more results are provided on the project page https://flowdpg.github.io.

cs.RO↗

arXiv2026

A Non-Iterative Algorithm for Clearing Two-Layer Energy-Sharing Markets with Voltage Constraints

Real-time hierarchical energy-sharing markets are promising to coordinate large numbers of prosumers. Still, most existing clearing methods rely on linearized or DC power-flow models and do not explicitly handle reactive power or voltage-security constraints. With AC network constraints, the problem becomes a large-scale bilevel Mathematical Program with Equilibrium Constraints (MPEC) that is difficult to solve in real time. This paper develops a non-iterative clearing algorithm for two-layer energy-sharing markets with voltage constraints. We first derive an efficient best-response function for each lower-layer energy-sharing market and reduce the equilibrium search to one dimension by exploiting the pricing-coupling structure. We then embed this function into the upper-layer network-constrained problem and reformulate the bilevel MPEC as a single-level mixed-integer second-order cone program (MISOCP), which is computationally tractable. Case studies on the IEEE 123-bus system with 12,300 prosumers show that the proposed method preserves nodal voltages within prescribed limits and delivers solutions with maximum errors below 0.01\% in 0.829 s.

math.OC↗

arXiv2026

Room-temperature multistage metastability in a moir\'e superstructure

Metastability is fundamental not only to phase ordering and transitions, but also to a broad range of modern technologies, from memory devices to metallic glasses. In condensed-matter physics, charge density waves (CDWs) offer versatile platforms for accessing metastable states due to their sensitivity to external stimuli. However, most metastable CDW states are stabilized only at low temperatures, limiting their practical utility. In this study, we report the observation of electrically driven, room-temperature, nonvolatile metastable states in the bulk form of EuTe$_4$, a recently discovered compound that hosts an innate moir\'e superlattice characterized by the stacking of incommensurate monolayer and bilayer CDWs. Systematic transport measurements reveal discrete resistivity plateaus and strong electric-field sensitivity, with a large number of metastable states readily induced across a wide temperature window within a giant hysteresis loop, making them well-suited for high-temperature, multi-bit memory applications. By integrating photoemission spectroscopy, diffraction, and in-situ transport measurements, we uncover that these metastable states do not stem from conventional mechanisms such as the emergence of new ordered phases or changes in incommensurate periodicity. Instead, they are characterized by a suppression of the original CDW amplitude and a reduction in correlation length, pointing to a unique electric-field-induced switching of out-of-plane CDW phases in the moir\'e superstructure. Our findings not only provide critical insights into metastable phenomena in moir\'e systems with stacked electronic orders but also establish EuTe$_4$ as a promising platform for developing room-temperature, multi-bit memory devices.

cond-mat.str-el↗

arXiv2026

Atomically Resolved Acoustic Dynamics Coupled with Magnetic Order in a van der Waals Antiferromagnet

Magnetoelastic coupling in van der Waals (vdW) magnetic materials enables a unique interplay between the spin and lattice degrees of freedom. Characterizing the elastic responses with atomic and femtosecond resolution across the magnetic transition is essential for guiding the design of magnetically tunable actuators and strain-mediated spintronic devices. Here, ultrafast x-ray diffraction employed at a free-electron laser reveals that the atomic displacements, wave vectors, and dispersion relations of acoustic phonon modes in a vdW antiferromagnet FePS$_3$ are coupled with the magnetic order, by tracking both in-plane and out-of-plane Bragg peaks upon optical excitation across the N\'eel temperature (T$_N$). One transverse mode shows that a quasi-out-of-plane atomic displacement undergoes a significant directional change across T$_N$. Its quasi-in-plane wave vector is derived by the comparison between the measured sound velocity and the first-principles calculations. The other transverse mode is an interlayer shear acoustic mode whose amplitude is strongly enhanced in the antiferromagnetic phase, exhibiting eight times stronger amplitude than the longitudinal acoustic mode below T$_N$. The atomically resolved characterization of acoustic phonon dynamics that couple with magnetic ordering opens opportunities for harnessing unique magnetoelastic coupling in vdW magnets on ultrafast timescales.

cond-mat.mtrl-sci↗

arXiv2025

Probing the Critical Point (CritPt) of AI Reasoning: a Frontier Physics Research Benchmark

While large language models (LLMs) with reasoning capabilities are progressing rapidly on high-school math competitions and coding, can they reason effectively through complex, open-ended challenges found in frontier physics research? And crucially, what kinds of reasoning tasks do physicists want LLMs to assist with? To address these questions, we present the CritPt (Complex Research using Integrated Thinking - Physics Test, pronounced "critical point"), the first benchmark designed to test LLMs on unpublished, research-level reasoning tasks that broadly covers modern physics research areas, including condensed matter, quantum physics, atomic, molecular & optical physics, astrophysics, high energy physics, mathematical physics, statistical physics, nuclear physics, nonlinear dynamics, fluid dynamics and biophysics. CritPt consists of 71 composite research challenges designed to simulate full-scale research projects at the entry level, which are also decomposed to 190 simpler checkpoint tasks for more fine-grained insights. All problems are newly created by 50+ active physics researchers based on their own research. Every problem is hand-curated to admit a guess-resistant and machine-verifiable answer and is evaluated by an automated grading pipeline heavily customized for advanced physics-specific output formats. We find that while current state-of-the-art LLMs show early promise on isolated checkpoints, they remain far from being able to reliably solve full research-scale challenges: the best average accuracy among base models is only 5.7%, achieved by GPT-5 (high), moderately rising to around 10% when equipped with coding tools. Through the realistic yet standardized evaluation offered by CritPt, we highlight a large disconnect between current model capabilities and realistic physics research demands, offering a foundation to guide the development of scientifically grounded AI tools.

cs.AI↗

arXiv2025

Bidirectional ultrafast control of charge density waves via phase competition

The intricate competition between coexisting charge density waves (CDWs) can lead to rich phenomena, offering unique opportunities for phase manipulation through electromagnetic stimuli. Leveraging time-resolved X-ray diffraction, we demonstrate ultrafast control of a CDW in EuTe$_4$ upon optical excitation. At low excitation intensities, the amplitude of one of the coexisting CDW orders increases at the expense of the competing CDW, whereas at high intensities, it exhibits a nonmonotonic temporal evolution characterized by both enhancement and reduction. This transient bidirectional controllability, tunable by adjusting photo-excitation intensity, arises from the interplay between optical quenching and phase-competition-induced enhancement. Our findings, supported by phenomenological time-dependent Ginzburg-Landau theory simulations, not only clarify the relationship between the two CDWs in EuTe$_4$, but also highlight the versatility of optical control over order parameters enabled by phase competition.

cond-mat.str-el↗

arXiv2025

Dynamics of a jointly commensurate moir\'e charge density wave

The advent of two-dimensional moir\'e systems has revolutionized the exploration of phenomena arising from strong correlations and nontrivial band topology. Recently, a moir\'e superstructure formed by two coexisting charge density waves (CDWs) with slightly mismatched wavevectors has been realized. These incommensurate CDWs can collectively exhibit commensurability, resulting in the jointly commensurate CDW (JC-CDW) and establishing a new paradigm for controlling moir\'e potential and periodicity. Achieving such functionality, however, hinges on a key open question: how do the amplitude, phase coherence, and periodicity of this order respond to external perturbations? Here, we address this question using a suite of time- and momentum-resolved diffraction and spectroscopic techniques to probe light-induced CDW dynamics in EuTe$_4$. Our time-resolved diffraction measurements distinguish the instantaneous quenching of the JC-CDW amplitude, as verified by time-resolved photoemission spectroscopy, from the much slower evolution of phase fluctuations. Furthermore, while the JC-CDW wavevector remains locked along the CDW direction upon photoexcitation, indicating a preserved moir\'e periodicity, the correlation length of JC-CDW shows an exclusive reduction perpendicular to its wavevector, unveiling the formation of previously unexplored shear-type defects. Together, this multimodal methodology reconstructs the spatiotemporal evolution of the JC-CDW upon excitation. These findings not only highlight the remarkable robustness of JC-CDWs out of equilibrium, but also provide insight into optical manipulation and engineering of moir\'e quantum materials through defect control.

cond-mat.str-el↗

arXiv2025

ParaThinker: Native Parallel Thinking as a New Paradigm to Scale LLM Test-time Compute

Recent advances in Large Language Models (LLMs) have been driven by test-time compute scaling - a strategy that improves reasoning by generating longer, sequential thought processes. While effective, this approach encounters a significant bottleneck as computation increases, where further computation offers only marginal performance gains. We argue this ceiling is not an inherent limit of the model's capability but a flaw in the scaling strategy itself, a phenomenon we term "Tunnel Vision", where a model's imperfect initial steps lock it into a suboptimal reasoning path. To overcome this, we introduce a new scaling paradigm: native thought parallelism. We present ParaThinker, an end-to-end framework that trains an LLM to generate multiple, diverse reasoning paths in parallel and synthesize them into a superior final answer. By exploring different lines of thoughts simultaneously, ParaThinker effectively sidesteps the Tunnel Vision issue and unlocks the model's latent reasoning potential. Our approach demonstrates that scaling compute in parallel (width) is a more effective and efficient way to superior reasoning than simply scaling sequentially (depth). On challenging reasoning benchmarks, ParaThinker achieves substantial accuracy improvements over sequential LLMs (12.3% for 1.5B and 7.5% for 7B models on average with 8 parallel paths), while adding only negligible latency overhead (7.1%). This enables smaller models to surpass much larger counterparts and establishes parallel thinking as a critical, efficient dimension for scaling future LLMs.

cs.CL↗

arXiv2025

BTPG-max: Achieving Local Maximal Bidirectional Pairs for Bidirectional Temporal Plan Graphs

Multi-Agent Path Finding (MAPF) requires computing collision-free paths for multiple agents in shared environment. Most MAPF planners assume that each agent reaches a specific location at a specific timestep, but this is infeasible to directly follow on real systems where delays often occur. To address collisions caused by agents deviating due to delays, the Temporal Plan Graph (TPG) was proposed, which converts a MAPF time dependent solution into a time independent set of inter-agent dependencies. Recently, a Bidirectional TPG (BTPG) was proposed which relaxed some dependencies into ``bidirectional pairs" and improved efficiency of agents executing their MAPF solution with delays. Our work improves upon this prior work by designing an algorithm, BPTG-max, that finds more bidirectional pairs. Our main theoretical contribution is in designing the BTPG-max algorithm is locally optimal, i.e. which constructs a BTPG where no additional bidirectional pairs can be added. We also show how in practice BTPG-max leads to BTPGs with significantly more bidirectional edges, superior anytime behavior, and improves robustness to delays.

cs.MA↗

arXiv2025

On Decision-Dependent Uncertainties in Power Systems with High-Share Renewables

The continuously increasing renewable energy sources (RES) and demand response (DR) are becoming important sources of system flexibility. As a consequence, decision-dependent uncertainties (DDUs), interchangeably referred to as endogenous uncertainties, impose new characteristics to power system dispatch. The DDUs faced by system operators originate from uncertain dispatchable resources such as RES units or DR, while reserve providers encounter DDUs arising from the uncertain reserve deployment. This paper presents a systematic framework for addressing robust dispatch problems with DDUs. The main contributions include i) the robust characterization of DDUs with a dependency decomposition structure; ii) a generic DDU coping mechanism, manifested as the bilateral matching between uncertainty and flexibility; iii) analyses of the influence of DDU incorporation on the convexity/non-convexity of robust dispatch problems; and iv) generic solution algorithms adaptive for DDUs. Under this framework, the inherent distinctions and correlations between DDUs and DIUs are revealed, providing a fundamental theoretical basis for the economic and reliable operation of RES-dominated power systems. Applications in the source and demand sides illustrate the importance of considering DDUs and verify the effectiveness of proposed algorithms for robust dispatch with DDUs.

eess.SY↗

arXiv2025

Extreme Scenario Characterization for High Renewable Energy Penetrated Power Systems over Long Time Scales

Power systems with high renewable energy penetration are highly influenced by weather conditions, often facing significant challenges such as persistent power shortages and severe power fluctuations over long time scales. This paper addresses the critical need for effective characterization of extreme scenarios under these situations. First, novel risk indices are proposed to quantify the severity of continuous power shortages and substantial power fluctuations over long-term operations. These indices are independent of specific scheduling strategies and incorporate the system's resource regulation capabilities. By employing a filtering-based approach, the proposed indices focus on retaining key characteristics of continuous power shortages and fluctuation events, enabling the identification of extreme scenarios on long time scales. Secondly, an extreme scenario generation method is developed using Gaussian mixture models and sequential Monte Carlo simulation. Especially, this method periodically evaluates the severity of generated scenarios based on the defined risk indices, retaining extreme scenarios while discarding less critical ones. Finally, case studies based on real-world data demonstrate the efficacy of the proposed method. The results confirm that integrating the identified extreme scenarios significantly enhances the system's ability to ensure long-term security and reliability under high renewable energy penetration.

eess.SY↗

arXiv2025

Compositional and Equilibrium-Free Conditions for Power System Stability -- Part II: Method and Application

This two-part paper proposes a compositional and equilibrium-free approach to analyzing power system stability. In Part I, we have established the stability theory and proposed stability conditions based on the delta dissipativity. In Part II, we focus on methods for applying our theory to complex power grids. We first propose a method to verify the local condition, i.e., delta dissipativity, for heterogeneous devices in power systems. Then, we propose a method to verify the coupling condition based on Alternating Direction Method of Multipliers (ADMM). Finally, we investigate three applications of our theory including stability assessment toward multiple equilibria, stability assessment under varying operating conditions, and a distributed computing framework. Case studies on modified IEEE 9-bus, 39-bus, and 118-bus benchmarks well verified our theory and methods.

eess.SY↗

arXiv2025

Evolutionary Policy Optimization

On-policy reinforcement learning (RL) algorithms are widely used for their strong asymptotic performance and training stability, but they struggle to scale with larger batch sizes, as additional parallel environments yield redundant data due to limited policy-induced diversity. In contrast, Evolutionary Algorithms (EAs) scale naturally and encourage exploration via randomized population-based search, but are often sample-inefficient. We propose Evolutionary Policy Optimization (EPO), a hybrid algorithm that combines the scalability and diversity of EAs with the performance and stability of policy gradients. EPO maintains a population of agents conditioned on latent variables, shares actor-critic network parameters for coherence and memory efficiency, and aggregates diverse experiences into a master agent. Across tasks in dexterous manipulation, legged locomotion, and classic control, EPO outperforms state-of-the-art baselines in sample efficiency, asymptotic performance, and scalability.

cs.LG↗

arXiv2025

Time-domain identification of distinct mechanisms for competing charge density waves in a rare-earth tritelluride

Understanding the origin of phase transitions and the interactions between distinct phases remains a central task in condensed matter physics. Charge density wave (CDW) systems provide an ideal platform for investigating these phenomena. While the dominant CDW phases in many materials can be explained through Fermi surface nesting or electron-phonon interactions, certain CDW phase transitions remain poorly understood, challenging conventional paradigms. One notable example is rare-earth tritelluride ErTe3, which hosts two competing CDW orders. While the dominant CDW phase fits within the electron-phonon coupling framework, the formation mechanism of the subdominant CDW remains enigmatic. In this study, we combine time-and-angle-resolved photoemission spectroscopy (trARPES) with time-dependent Ginzburg-Landau (TDGL) theory to establish a time-domain approach for probing phase transitions in solid-state systems. By analyzing the distinct recovery dynamics of the two CDW orders in ErTe3 following light excitation, we reveal a novel nucleation-like growth mechanism that likely drives the secondary CDW phase transition. This work not only uncovers a previously unknown CDW formation mechanism in rare-earth tritellurides but also introduces a non-equilibrium framework for understanding phase transitions and phase competition in quantum materials.

cond-mat.str-el↗

arXiv2025

Discovery of transient topological crystalline order in optically driven SnSe

Ultrafast optical excitation provides a powerful route for accessing emergent quantum phases far from equilibrium, enabling transient light-induced phenomena such as magnetism, ferroelectricity, and superconductivity. However, extending this approach to induce topological phases, especially in conventional semiconductors, remains challenging. Here, we report the observation of a thermally inaccessible, transient topological crystalline order in the layered semiconductor SnSe, a trivial insulator with a sizable (~ 0.8 eV) band gap, induced by femtosecond above-gap excitation. Time- and angle-resolved photoemission spectroscopy directly reveals the sub-picosecond emergence of Dirac-like linear dispersions within the band gap. Their features, including a high Fermi velocity (~ 2.5x10^5 m/s), multiple Dirac points away from high-symmetry momenta, and independence from probe photon energy, are consistent with mirror-symmetry-protected surface states of a topological crystalline insulator. The observed spectral dynamics, combined with density functional theory calculations, indicate that the femtosecond excitation transiently increases lattice symmetry, enabling topological crystalline order to emerge. Our discovery opens new avenues for ultrafast optical control of topological quantum phases in semiconductors, with potential applications in quantum and spintronic devices.

cond-mat.mtrl-sci↗

arXiv2025

Large moir\'{e} superstructure of stacked incommensurate charge density waves

Recent advances in van der Waals heterostructures have opened the new frontier of moir\'{e} physics, whereby tuning the interlayer twist angle or adjusting lattice parameter mismatch have led to a plethora of exotic phenomena such as unconventional superconductivity and fractional quantum spin Hall effect. We extend the concept of moir\'{e} engineering to materials that host incommensurate orders, where we discovered a long-period, thermally-hysteretic moir\'{e} superlattice in a layered charge density wave (CDW) compound, EuTe$_\text{4}$. Using high-momentum-resolution X-ray diffraction performed on ultrathin flakes, we found two coexisting, incommensurate CDWs with slightly mismatched in-plane wavevectors. The interaction between these two CDWs leads to their joint commensuration with the high-symmetry lattice as well as a large moir\'{e} superstructure with an in-plane period of 13.6~nm. Due to different out-of-plane orders of the incommensurate CDWs, the moir\'{e} superstructure exhibits a clear thermal hysteresis, accounting for the large hysteresis observed in electrical resistivity and numerous metastable states induced by light or electrical pulses. Our findings pave the way for a new development in moir\'{e} engineering based on an incommensurate lattice. They further highlight the important role of interlayer ordering in determining the macroscopic properties of these stacked incommensurate structures.

cond-mat.str-el↗