arXiv subjects

Yi Li

Publications and source records attributed to Yi Li.

At least 163 records · Page 9Linked to original sources

arXiv2025

Five-loop Anomalous Dimensions of Cubic Scalar Theory from Operator Product Expansion

In this work, we compute the anomalous dimensions of the $ϕ^Q$ operator in six-dimensional cubic scalar theory. The renormalization analysis is carried out within the framework of the Operator Product Expansion method, while the ultraviolet divergences of Feynman integrals are evaluated using the graphical function method. Inspired by the intrinsic connection between Wilson coefficients and anomalous dimensions, an algorithm was proposed recently, which provides a practical and systematic framework for calculating the anomalous dimensions of masses, fields, and composite operators, with broad potential applicability to generic quantum field theories. Notably, the HyperlogProcedures package, developed based on the graphical function method, enables the computation of two-point propagator-type integrals, derived herein for capturing ultraviolet divergences, to very high loop orders. With these advanced techniques, we have successfully computed the anomalous dimensions of the $ϕ^Q$ operator up to five loops. Furthermore, we present a large $N$ expansion of the scaling dimensions at the Wilson-Fisher fixed point, extended to the $1/N^5$ order. This computation sets a new loop-order record for the anomalous dimension of the $ϕ^Q$ operator in cubic scalar theory, while further validating the efficiency and versatility of the proposed algorithm in renormalization analyses.

hep-th↗

arXiv2025

Nonlinear oscillators at resonance with periodic forcing

In this note we unify the results of A.C. Lazer and P.O. Frederickson [3], A.C. Lazer [6], A.C. Lazer and D.E. Leach [7], J.M. Alonso and R. Ortega [1], and P. Korman and Y. Li [4] on periodic oscillations and unbounded solutions of nonlinear equations with linear part at resonance and periodic forcing. We give conditions for the existence and non-existence of periodic solutions, and obtain a rather detailed description of the dynamics for nonlinear oscillations at resonance, in case periodic solutions do not exist.

math.DS↗

arXiv2025

Observation of gauge field induced non-Hermitian helical skin effects

Synthetic gauge fields and non-Hermitian skin effects are pivotal to topological phases and non-Hermitian physics, each recently attracting great interest across diverse research fields. Realizing skin effects typically require nonreciprocal couplings or on-site gain and loss. Here, we theoretically and experimentally report that, under gauge fields, reciprocal dissipative couplings can nontrivially give rise to an unprecedented nonreciprocal skin effect, hosting pseudospin degree of freedom and featuring helical transport, dubbed as the ``helical pseudospin skin effect". Before introducing the gauge fields, this model exhibits localized pseudospin edge modes and extended bulk modes, without skin effects. As the gauge field strength is applied from $0$ to $π$, we observe the emergence of two distinct pseudospin skin effects and their topological transitions: the hybrid-order and second-order helical pseudospin skin effects. Our findings not only highlight gauge field enriched non-Hermitian topology, but also brings pseudospin-momentum locking into skin effects.

cond-mat.mes-hall↗

arXiv2025

Defects4C: Benchmarking Large Language Model Repair Capability with C/C++ Bugs

Automated Program Repair (APR) plays a critical role in enhancing the quality and reliability of software systems. While substantial progress has been made in Java-based APR, largely facilitated by benchmarks like Defects4J, there remains a significant gap in research on C/C++ program repair, despite the widespread use of C/C++ and the prevalence of associated vulnerabilities. This gap is primarily due to the lack of high-quality, open-source benchmarks tailored for C/C++. To address this issue, we introduce Defects4C, a comprehensive and executable benchmark specifically designed for C/C++ program repair. Our dataset is constructed from real-world C/C++ repositories and includes a large collection of bug-relevant commits (9M in total), 248 high-quality buggy functions, and 102 vulnerable functions, all paired with test cases for reproduction. These resources enable rigorous evaluation of repair techniques and support the retraining of learning-based approaches for enhanced performance. Using Defects4C, we conduct a comprehensive empirical study evaluating the effectiveness of 24 state-of-the-art large language models (LLMs) in repairing C/C++ faults. Our findings offer valuable insights into the strengths and limitations of current LLM-based APR techniques in this domain, highlighting both the need for more robust methods and the critical role of Defects4C in advancing future research

cs.SE↗

arXiv2025

Make Your AUV Adaptive: An Environment-Aware Reinforcement Learning Framework For Underwater Tasks

This study presents a novel environment-aware reinforcement learning (RL) framework designed to augment the operational capabilities of autonomous underwater vehicles (AUVs) in underwater environments. Departing from traditional RL architectures, the proposed framework integrates an environment-aware network module that dynamically captures flow field data, effectively embedding this critical environmental information into the state space. This integration facilitates real-time environmental adaptation, significantly enhancing the AUV's situational awareness and decision-making capabilities. Furthermore, the framework incorporates AUV structure characteristics into the optimization process, employing a large language model (LLM)-based iterative refinement mechanism that leverages both environmental conditions and training outcomes to optimize task performance. Comprehensive experimental evaluations demonstrate the framework's superior performance, robustness and adaptability.

eess.SY↗

arXiv2025

Perspective: Magnon-magnon coupling in hybrid magnonics

The internal coupling of magnetic excitations (magnons) with themselves has created a new research sub-field in hybrid magnonics, i.e., magnon-magnon coupling, which focuses on materials discovery and engineering for probing and controlling magnons in a coherent manner. This is enabled by, one, the abundant mechanisms of introducing magnetic interactions, with examples of exchange coupling, dipolar coupling, RKKY coupling, and DMI coupling, and two, the vast knowledge of how to control magnon band structure, including field and wavelength dependences of frequencies, for determining the degeneracy of magnon modes with different symmetries. In particular, we discuss how magnon-magnon coupling is implemented in various materials systems, with examples of magnetic bilayers, synthetic antiferromagnets, nanomagnetic arrays, layered van der Waals magnets, and (DMI SOT materials) in magnetic multilayers. We then introduce new concept of applications for these hybrid magnonic materials systems, with examples of frequency up/down conversion and magnon-exciton coupling, and discuss what properties are desired for achieving those applications.

cond-mat.mtrl-sci↗

arXiv2025

When Motion Learns to Listen: Diffusion-Prior Lyapunov Actor-Critic Framework with LLM Guidance for Stable and Robust AUV Control in Underwater Tasks

Autonomous Underwater Vehicles (AUVs) are indispensable for marine exploration; yet, their control is hindered by nonlinear hydrodynamics, time-varying disturbances, and localization uncertainty. Traditional controllers provide only limited adaptability, while Reinforcement Learning (RL), though promising, suffers from sample inefficiency, weak long-term planning, and lacks stability guarantees, leading to unreliable behavior. To address these challenges, we propose a diffusion-prior Lyapunov actor-critic framework that unifies exploration, stability, and semantic adaptability. Specifically, a diffusion model generates smooth, multimodal, and disturbance-resilient candidate actions; a Lyapunov critic further imposes dual constraints that ensure stability; and a Large Language Model (LLM)-driven outer loop adaptively selects and refines Lyapunov functions based on task semantics and training feedback. This "generation-filtering-optimization" mechanism not only enhances sample efficiency and planning capability but also aligns stability guarantees with diverse mission requirements in the multi-objective optimization task. Extensive simulations under complex ocean dynamics demonstrate that the proposed framework achieves more accurate trajectory tracking, higher task completion rates, improved energy efficiency, faster convergence, and improved robustness compared with conventional RL and diffusion-augmented baselines.

eess.SY↗

arXiv2025

MemIntelli: A Generic End-to-End Simulation Framework for Memristive Intelligent Computing

Memristive in-memory computing (IMC) has emerged as a promising solution for addressing the bottleneck in the Von Neumann architecture. However, the couplingbetweenthecircuitandalgorithm in IMC makes computing reliability susceptible to non-ideal effects in devices and peripheral circuits. In this respect, efficient softwarehardwareco-simulationtoolsarehighlydesiredtoembedthedevice and circuit models into the algorithms. In this paper, for the first time, we proposed an end-to-end simulation framework supporting flexible variable-precision computing, named MemIntelli, to realize the pre-verification of diverse intelligent applications on memristive devices. At the device and circuit level, mathematical functions are employed to abstract the devices and circuits through meticulous equivalent circuit modeling. On the architecture level, MemIntelli achieves flexible variable-precision IMC supporting integer and floating data representation with bit-slicing. Moreover, MemIntelli is compatible with NumPy and PyTorch for seamless integration with applications. To demonstrate its capabilities, diverse intelligent algorithms, such as equation solving, data clustering, wavelet transformation, and neural network training and inference, were employed to showcase the robust processing ability of MemIntelli. This research presents a comprehensive simulation tool that facilitates the co-design of the IMC system, spanning from device to application.

cs.AR↗

arXiv2025

Quantum Jump Approach for Photosynthetic Energy Transfer with Chemical Reaction and Fluorescence Loss

Recently, the coherent modified Redfield theory (CMRT) has been widely used to simulate the excitation-energy-transfer (EET) processes in photosynthetic systems. However, the numerical simulation of the CMRT is computationally expensive when dealing with large-scale systems, e.g. photosystem I (PSI) and II (PSII). On the other hand, the chemical reaction and fluorescence loss traditionally treated by the non-Hermitian Hamiltonian approach may result in significantly error in a wide range of parameters. To address these issues, we introduce a quantum jump approach (QJA) based on the CMRT to simulate the evolution of photosynthetic complexes including both the chemical reaction and fluorescence loss. The QJA shows higher accuracy and efficiency in simulating the EET processes. The QJA-CMRT approach may provide a powerful tool to design and optimize artificial photosynthetic systems, which benefits future innovation in the field of energy.

physics.chem-ph↗

arXiv2025

Spontaneous $π$ flux trapping in granular rings of unconventional superconductors

We study Josephson couplings in unconventional superconductors and generalize the Sigrist-Rice formula by incorporating symmetry constraints and interface orientation disorder. Applying this framework to granular superconducting rings, we establish a no-go result that single-band chiral superconductors cannot spontaneously trap a magnetic flux. This rules out chiral $p$-wave pairing in $β$-Bi$_2$Pd, in light of the half-quantum flux observed in recent Little-Parks experiments. Incorporating the full crystalline and time-reversal symmetries, we show that a fully gapped helical equal-spin pairing state, naturally stabilized by spin-orbit coupling arising from local inversion-symmetry breaking, is instead favored. We further find that granular rings of such superconductors can trap a spontaneous $π$ flux in a manner robust against interface disorder.

cond-mat.supr-con↗

arXiv2025

Robust and Efficient Communication in Multi-Agent Reinforcement Learning

Multi-agent reinforcement learning (MARL) has made significant strides in enabling coordinated behaviors among autonomous agents. However, most existing approaches assume that communication is instantaneous, reliable, and has unlimited bandwidth; these conditions are rarely met in real-world deployments. This survey systematically reviews recent advances in robust and efficient communication strategies for MARL under realistic constraints, including message perturbations, transmission delays, and limited bandwidth. Furthermore, because the challenges of low-latency reliability, bandwidth-intensive data sharing, and communication-privacy trade-offs are central to practical MARL systems, we focus on three applications involving cooperative autonomous driving, distributed simultaneous localization and mapping, and federated learning. Finally, we identify key open challenges and future research directions, advocating a unified approach that co-designs communication, learning, and robustness to bridge the gap between theoretical MARL models and practical implementations.

cs.AI↗

arXiv2025

Doubly Debiased Test-Time Prompt Tuning for Vision-Language Models

Test-time prompt tuning for vision-language models has demonstrated impressive generalization capabilities under zero-shot settings. However, tuning the learnable prompts solely based on unlabeled test data may induce prompt optimization bias, ultimately leading to suboptimal performance on downstream tasks. In this work, we analyze the underlying causes of prompt optimization bias from both the model and data perspectives. In terms of the model, the entropy minimization objective typically focuses on reducing the entropy of model predictions while overlooking their correctness. This can result in overconfident yet incorrect outputs, thereby compromising the quality of prompt optimization. On the data side, prompts affected by optimization bias can introduce misalignment between visual and textual modalities, which further aggravates the prompt optimization bias. To this end, we propose a Doubly Debiased Test-Time Prompt Tuning method. Specifically, we first introduce a dynamic retrieval-augmented modulation module that retrieves high-confidence knowledge from a dynamic knowledge base using the test image feature as a query, and uses the retrieved knowledge to modulate the predictions. Guided by the refined predictions, we further develop a reliability-aware prompt optimization module that incorporates a confidence-based weighted ensemble and cross-modal consistency distillation to impose regularization constraints during prompt tuning. Extensive experiments across 15 benchmark datasets involving both natural distribution shifts and cross-datasets generalization demonstrate that our method outperforms baselines, validating its effectiveness in mitigating prompt optimization bias.

cs.LG↗

arXiv2025

Moire-enabled optical vortex with tunable topological charge in twisted bilayer photonic crystals

The orbital angular momentum (OAM) of light is a versatile degree of freedom with transformative impact across optical communication, imaging, and micromanipulation. These applications have motivated a growing demand for compact, reconfigurable vortex arrays with tunable topological charge, yet integrating these functionalities into nanophotonic platforms remains elusive. Among possible strategies to meet this challenge is exploiting the twist degree of freedom in layered structures, which enables both emerging moire physics and unprecedented reconfigurability of photonic and electronic properties. Here, we harness these capabilities in twisted bilayer moire photonic crystals (TBMPCs) to realize vortex array generation with tunable OAM, demonstrated both analytically and experimentally. Central to this advancement is a new class of quasi-bound state in the continuum: Bessel-type modes emerging from moire-induced interlayer coupling, which generate vortex beams with tailored spiral phase distributions. We experimentally demonstrate vortex beams spanning eight OAM orders, from -3 to 4, and achieve selective excitation of distinct topological charges at a fixed telecommunication wavelength by tuning the interlayer separation and twist angle. Furthermore, localized Bessel-type modes at AA stacking regions can be excited nonlocally across the moire superlattice, enabling vortex array generation. Our work offers new insights into moire physics and introduces an innovative approach for future multiplexing technology integrating OAM, wavelength, and spatial division.

physics.optics↗

arXiv2025

Experimental Demonstration of the Timelike Unruh Effect with a Trapped-Ion System

The Unruh effect predicts that an accelerated observer perceives the Minkowski vacuum as a thermal bath, but its direct observation requires extreme accelerations beyond current experimental reach. Foundational theory [Olson & Ralph, Phys. Rev. Lett. 106, 110404 (2011)] shows that an equivalent thermal response, known as the timelike Unruh effect, can occur for detectors following specific timelike trajectories without acceleration, enabling laboratory tests with stationary yet time-dependent detectors. Here, we report a proof-of-principle demonstration of the timelike Unruh effect in a quantum system of trapped ion, where a two-level spin serves as the detector and is temporally coupled to the ambient field encoded in the ion's vibrational motion. Specifically, we study both excitation and emission dynamics of the detector moving along a spacetime trajectory in the future/past light cone, and demonstrate the thermal response of the detector to the Minkowski vacuum that resembles the Unruh effect. This work establishes a controllable tabletop platform for exploring relativistic quantum physics under accessible laboratory conditions.

quant-ph↗

arXiv2025

Magneto-optical spectroscopy based on pump-probe strobe light

We demonstrate a pump-probe strobe light spectroscopy for sensitive detection of magneto-optical dynamics in the context of hybrid magnonics. The technique uses a combinatorial microwave-optical pump-probe scheme, leveraging both the high-energy resolution of microwaves and the high-efficiency detection using optical photons. In contrast to conventional stroboscopy using a continuous-wave light, we apply microwave and optical pulses with varying pulse widths, and demonstrate magnetooptical detection of magnetization dynamics in Y3Fe5O12 films. The detected magneto-optical signals strongly depend on the characteristics of both the microwave and the optical pulses as well as their relative time delays. We show that good magneto-optical sensitivity and coherent stroboscopic character are maintained even at a microwave pump pulse of 1.5 ns and an optical probe pulse of 80 ps, under a 7 megahertz clock rate, corresponding to a pump-probe footprint of ~1% in one detection cycle. Our results show that time-dependent strobe light measurement of magnetization dynamics can be achieved in the gigahertz frequency range under a pump-probe detection scheme.

cond-mat.mes-hall↗

arXiv2025

Detecting Various DeFi Price Manipulations with LLM Reasoning

DeFi (Decentralized Finance) is one of the most important applications of today's cryptocurrencies and smart contracts. It manages hundreds of billions in Total Value Locked (TVL) on-chain, yet it remains susceptible to common DeFi price manipulation attacks. Despite state-of-the-art (SOTA) systems like DeFiRanger and DeFort, we found that they are less effective to non-standard price models in custom DeFi protocols, which account for 44.2% of the 95 DeFi price manipulation attacks reported over the past three years. In this paper, we introduce the first LLM-based approach, DeFiScope, for detecting DeFi price manipulation attacks in both standard and custom price models. Our insight is that large language models (LLMs) have certain intelligence to abstract price calculation from smart contract source code and infer the trend of token price changes based on the extracted price models. To further strengthen LLMs in this aspect, we leverage Foundry to synthesize on-chain data and use it to fine-tune a DeFi price-specific LLM. Together with the high-level DeFi operations recovered from low-level transaction data, DeFiScope detects various DeFi price manipulations according to systematically mined patterns. Experimental results show that DeFiScope achieves a high recall of 80% on real-world attacks, a precision of 96% on suspicious transactions, and zero false alarms on benign transactions, significantly outperforming SOTA approaches. Moreover, we evaluate DeFiScope's cost-effectiveness and demonstrate its practicality by helping our industry partner confirm 147 real-world price manipulation attacks, including discovering 81 previously unknown historical incidents.

cs.CR↗

arXiv2025

DeepTx: Real-Time Transaction Risk Analysis via Multi-Modal Features and LLM Reasoning

Phishing attacks in Web3 ecosystems are increasingly sophisticated, exploiting deceptive contract logic, malicious frontend scripts, and token approval patterns. We present DeepTx, a real-time transaction analysis system that detects such threats before user confirmation. DeepTx simulates pending transactions, extracts behavior, context, and UI features, and uses multiple large language models (LLMs) to reason about transaction intent. A consensus mechanism with self-reflection ensures robust and explainable decisions. Evaluated on our phishing dataset, DeepTx achieves high precision and recall (demo video: https://youtu.be/4OfK9KCEXUM).

cs.CR↗

arXiv2025

Designing Intent Communication for Agent-Human Collaboration

As autonomous agents, from self-driving cars to virtual assistants, become increasingly present in everyday life, safe and effective collaboration depends on human understanding of agents' intentions. Current intent communication approaches are often rigid, agent-specific, and narrowly scoped, limiting their adaptability across tasks, environments, and user preferences. A key gap remains: existing models of what to communicate are rarely linked to systematic choices of how and when to communicate, preventing the development of generalizable, multi-modal strategies. In this paper, we introduce a multidimensional design space for intent communication structured along three dimensions: Transparency (what is communicated), Abstraction (when), and Modality (how). We apply this design space to three distinct human-agent collaboration scenarios: (a) bystander interaction, (b) cooperative tasks, and (c) shared control, demonstrating its capacity to generate adaptable, scalable, and cross-domain communication strategies. By bridging the gap between intent content and communication implementation, our design space provides a foundation for designing safer, more intuitive, and more transferable agent-human interactions.

cs.HC↗