arXiv subjects

Meng Sun

Publications and source records attributed to Meng Sun.

At least 55 records · Page 3Linked to original sources

arXiv2025

Preference-CFR$\:$ Beyond Nash Equilibrium for Better Game Strategies

Artificial intelligence (AI) has surpassed top human players in a variety of games. In imperfect information games, these achievements have primarily been driven by Counterfactual Regret Minimization (CFR) and its variants for computing Nash equilibrium. However, most existing research has focused on maximizing payoff, while largely neglecting the importance of strategic diversity and the need for varied play styles, thereby limiting AI's adaptability to different user preferences. To address this gap, we propose Preference-CFR (Pref-CFR), a novel method that incorporates two key parameters: preference degree and vulnerability degree. These parameters enable the AI to adjust its strategic distribution within an acceptable performance loss threshold, thereby enhancing its adaptability to a wider range of strategic demands. In our experiments with Texas Hold'em, Pref-CFR successfully trained Aggressive and Loose Passive styles that not only match original CFR-based strategies in performance but also display clearly distinct behavioral patterns. Notably, for certain hand scenarios, Pref-CFR produces strategies that diverge significantly from both conventional expert heuristics and original CFR outputs, potentially offering novel insights for professional players.

cs.GT↗

arXiv2025

Bogolon-mediated electromagnetic wave absorption in multicomponent Bose-Einstein condensates

We investigate the electromagnetic wave absorption process in a coherently coupled two-component Bose-Einstein condensate model in different dimensionality at zero temperature. As the analogue of phonon in the solid state physics, the elementary excitation of the Bose-Einstein condensate is described by Bogoliubov quasiparticle or bogolon for short. Due to the small magnitude of the sound velocity of the bogolon, the absorption process is prohibited by the conservation of energy and momentum. To surmount this depression, the additional degree of freedom must be considered inside of the simple Bose gas model. In this article, we develop a microscopical theory for electromagnetic wave absorption by a two-component Bose-Einstein condensate and investigate the absorption rate dependence in different dimensions. Our calculation shows the possibility of manipulating the absorption property by tuning the parameters of the condensates.

cond-mat.quant-gas↗

arXiv2025

Operational Semantics for Crystality: A Smart Contract Language for Parallel EVMs

The increasing demand for scalable blockchain has driven research into parallel execution models for smart contracts. Crystality is a novel smart contract programming language designed for parallel Ethereum Virtual Machines (EVMs), enabling fine-grained concurrency through Programmable Contract Scopes and Asynchronous Functional Relay. This paper presents the first formal structural operational semantics for Crystality, providing a rigorous framework to reason about its execution. We mechanize the syntax and semantics of Crystality in the theorem-proving assistant Coq, enabling formal verification of correctness properties. As a case study, we verify a simplified token transfer function, demonstrating the applicability of our semantics in ensuring smart contract correctness. Our work lays the foundation for formally verified parallel smart contracts, contributing to the security and scalability of blockchain systems.

cs.PL↗

arXiv2025

Runtime Backdoor Detection for Federated Learning via Representational Dissimilarity Analysis

Federated learning (FL), as a powerful learning paradigm, trains a shared model by aggregating model updates from distributed clients. However, the decoupling of model learning from local data makes FL highly vulnerable to backdoor attacks, where a single compromised client can poison the shared model. While recent progress has been made in backdoor detection, existing methods face challenges with detection accuracy and runtime effectiveness, particularly when dealing with complex model architectures. In this work, we propose a novel approach to detecting malicious clients in an accurate, stable, and efficient manner. Our method utilizes a sampling-based network representation method to quantify dissimilarities between clients, identifying model deviations caused by backdoor injections. We also propose an iterative algorithm to progressively detect and exclude malicious clients as outliers based on these dissimilarity measurements. Evaluations across a range of benchmark tasks demonstrate that our approach outperforms state-of-the-art methods in detection accuracy and defense effectiveness. When deployed for runtime protection, our approach effectively eliminates backdoor injections with marginal overheads.

cs.CR↗

arXiv2025

Emulators for stellar profiles in binary population modeling

Knowledge about the internal physical structure of stars is crucial to understanding their evolution. The novel binary population synthesis code POSYDON includes a module for interpolating the stellar and binary properties of any system at the end of binary MESA evolution based on a pre-computed set of models. In this work, we present a new emulation method for predicting stellar profiles, i.e., the internal stellar structure along the radial axis, using machine learning techniques. We use principal component analysis for dimensionality reduction and fully-connected feed-forward neural networks for making predictions. We find accuracy to be comparable to that of nearest neighbor approximation, with a strong advantage in terms of memory and storage efficiency. By providing a versatile framework for modeling stellar internal structure, the emulation method presented here will enable faster simulations of higher physical fidelity, offering a foundation for a wide range of large-scale population studies of stellar and binary evolution.

astro-ph.SR↗

arXiv2024

Challenges in Forming Millisecond Pulsar-Black Holes from Isolated Binaries

Binaries harboring a millisecond pulsar (MSP) and a black hole (BH) are a key observing target for current and upcoming pulsar surveys. We model the formation and evolution of such binaries in isolation at solar metallicity using the next-generation binary population synthesis code POSYDON. We examine neutron star (NS)-BH binaries where the NS forms first (labeled NSBH), as the NS must be able to spin-up to MSP rotation periods before the BH forms in these systems. We find that NSBHs are very rare and have a birth rate < 1 Myr$^{-1}$ for a Milky Way-like galaxy in our typical models. The NSBH birth rate is 2-3 orders of magnitude smaller than that for NS-BHs where the BH forms first (labeled BHNS). These rates are also sensitive to model assumptions about the supernova (SN) remnant masses, natal kicks, and common-envelope efficiency. We find that 100% of NSBHs undergo a mass ratio reversal before the first SN and up to 64% of NSBHs undergo a double common envelope phase after the mass ratio reversal occurs. Most importantly, no NSBH binaries in our populations undergo a mass transfer phase, either stable or unstable, after the first SN. This implies that there is no possibility of pulsar spin-up via accretion, and thus MSP-BH binaries cannot form. Thus, dynamical environments and processes may provide the only formation channels for such MSP-BH binaries.

astro-ph.HE↗

arXiv2024

The Fusion of Large Language Models and Formal Methods for Trustworthy AI Agents: A Roadmap

Large Language Models (LLMs) have emerged as a transformative AI paradigm, profoundly influencing daily life through their exceptional language understanding and contextual generation capabilities. Despite their remarkable performance, LLMs face a critical challenge: the propensity to produce unreliable outputs due to the inherent limitations of their learning-based nature. Formal methods (FMs), on the other hand, are a well-established computation paradigm that provides mathematically rigorous techniques for modeling, specifying, and verifying the correctness of systems. FMs have been extensively applied in mission-critical software engineering, embedded systems, and cybersecurity. However, the primary challenge impeding the deployment of FMs in real-world settings lies in their steep learning curves, the absence of user-friendly interfaces, and issues with efficiency and adaptability. This position paper outlines a roadmap for advancing the next generation of trustworthy AI systems by leveraging the mutual enhancement of LLMs and FMs. First, we illustrate how FMs, including reasoning and certification techniques, can help LLMs generate more reliable and formally certified outputs. Subsequently, we highlight how the advanced learning capabilities and adaptability of LLMs can significantly enhance the usability, efficiency, and scalability of existing FM tools. Finally, we show that unifying these two computation paradigms -- integrating the flexibility and intelligence of LLMs with the rigorous reasoning abilities of FMs -- has transformative potential for the development of trustworthy AI software systems. We acknowledge that this integration has the potential to enhance both the trustworthiness and efficiency of software engineering practices while fostering the development of intelligent FM tools capable of addressing complex yet real-world challenges.

cs.AI↗

arXiv2024

Proposal for Superconducting Photodiode

We propose a concept of a superconducting photodiode - a device that transforms the energy and `spin' of an external electromagnetic field into the rectified steady-state supercurrent and develop a microscopic theory describing its properties. For this, we consider a two-dimensional thin film cooled down below the temperature of superconducting transition with the injected dc supercurrent and exposed to an external electromagnetic field with a frequency smaller than the superconducting gap. As a result, we predict the emergence of a photoexcited quasiparticle current, and, as a consequence, oppositely oriented stationary flow of Cooper pairs. The strength and direction of this photoinduced supercurrent depend on (i) such material properties as the effective impurity scattering time and the nonequilibrium quasiparticles' energy relaxation time and (ii) such electromagnetic field properties as its frequency and polarization.

cond-mat.supr-con↗

arXiv2024

Mass Transfer in Eccentric Orbits with Self-consistent Stellar Evolution

We investigate Roche lobe overflow mass transfer (MT) in eccentric binary systems between stars and compact objects (COs), modeling the coupled evolution of both the star and the orbit due to eccentric MT (eMT) in a self-consistent framework. We implement the analytic expressions for secular rates of change of the orbital semi-major axis and eccentricity, assuming a delta function MT at periapse, into the binary stellar evolution code MESA. Two scenarios are examined: (1) a simplified model isolating the effects of eMT on stellar and orbital evolution, and (2) realistic binary configurations that include angular momentum exchange (e.g., tides, mass loss, spin-orbit coupling, and gravitational wave radiation). Unlike the ad hoc approach of instant circularization that is often employed, explicit modeling of eMT reveals a large fraction of binaries can remain eccentric post-MT. Even binaries which naturally circularize during eMT have different properties (donor mass and orbital size) compared to predictions from instant circularization, with some showing fundamentally different evolutionary outcomes (e.g., stable versus unstable MT). We demonstrate that a binary's initial mass ratio and eccentricity are predictive of whether it will remain eccentric or circularize after eMT. These findings underscore the importance of eMT in understanding CO-hosting binary populations, including X-ray binaries, gravitational wave sources, and other high-energy transients.

astro-ph.SR↗

arXiv2024

Irregularly Sampled Time Series Interpolation for Detailed Binary Evolution Simulations

Modeling of large populations of binary stellar systems is an intergral part of a many areas of astrophysics, from radio pulsars and supernovae to X-ray binaries, gamma-ray bursts, and gravitational-wave mergers. Binary population synthesis codes that employ self-consistently the most advanced physics treatment available for stellar interiors and their evolution and are at the same time computationally tractable have started to emerge only recently. One element that is still missing from these codes is the ability to generate the complete time evolution of binaries with arbitrary initial conditions using pre-computed three-dimensional grids of binary sequences. Here we present a highly interpretable method, from binary evolution track interpolation. Our method implements simulation generation from irregularly sampled time series. Our results indicate that this method is appropriate for applications within binary population synthesis and computational astrophysics with time-dependent simulations in general. Furthermore we point out and offer solutions to the difficulty surrounding evaluating performance of signals exhibiting extreme morphologies akin to discontinuities.

astro-ph.SR↗

arXiv2024

Adversarial Representation Engineering: A General Model Editing Framework for Large Language Models

Since the rapid development of Large Language Models (LLMs) has achieved remarkable success, understanding and rectifying their internal complex mechanisms has become an urgent issue. Recent research has attempted to interpret their behaviors through the lens of inner representation. However, developing practical and efficient methods for applying these representations for general and flexible model editing remains challenging. In this work, we explore how to leverage insights from representation engineering to guide the editing of LLMs by deploying a representation sensor as an editing oracle. We first identify the importance of a robust and reliable sensor during editing, then propose an Adversarial Representation Engineering (ARE) framework to provide a unified and interpretable approach for conceptual model editing without compromising baseline performance. Experiments on multiple tasks demonstrate the effectiveness of ARE in various model editing scenarios. Our code and data are available at https://github.com/Zhang-Yihao/Adversarial-Representation-Engineering.

cs.LG↗

arXiv2024

The Formation of Black Holes in Non-interacting, Isolated Binaries. Gaia Black Holes as Calibrators of Stellar Winds From Massive Stars

Context. The black holes discovered using Gaia, especially Gaia BH1 and BH2, have low mass companions of solar-like metallicity in wide orbits. For standard isolated binary evolution formation channels including interactions such an extreme mass ratio is unexpected; especially in orbits of hundreds to thousands of days. Aims. Here, we investigate a non-interacting formation path for isolated binaries to explain the formation of Gaia BH1 and BH2. Methods. We use single star models computed with MESA to constrain the main characteristics of possible progenitors of long-period black hole binaries like Gaia BH1 and BH2. Then, we incorporate these model grids into the binary population synthesis code POSYDON, to explore whether the formation of the observed binaries at solar metallicity is indeed possible. Results. We find that winds of massive stars ($\gtrsim 80\,M_\odot$), especially during the Wolf-Rayet phase, tend to cause a plateau in the initial stellar mass to final black hole mass relation (at about $13\,M_\odot$ in our default wind prescription). However, stellar winds at earlier evolutionary phases are also important at high metallicity, as they prevent the most massive stars from expanding ($<100\,R_\odot$) and filling their Roche lobe. Consequently, the strength of the applied winds affects the range of the final black hole masses in non-interacting binaries, making it possible to form systems similar to Gaia BH1 and BH2. Conclusions. We deduce that wide binaries with a black hole and a low mass companion can form at high metallicity without binary interactions. There could be hundreds of such systems in the Milky Way. The mass of the black hole in binaries evolved through the non-interacting channel can potentially provide insights into the wind strength during the progenitors evolution.

astro-ph.SR↗

arXiv2024

The coevolution of migrating planets and their pulsating stars through episodic resonance locking

Hot Jupiters are expected to form far from their host star and move toward close-in, circular orbits via a smooth, monotonic decay due to mild and constant tidal dissipation. Yet, three systems have recently been found exhibiting planet-induced stellar pulsations suggesting unexpectedly strong tidal interactions. Here we combine stellar evolution and tide models to show that dynamical tides raised by eccentric gas giants can give rise to chains of resonance locks with multiple modes, enriching the dynamics seen in single-mode resonance locking of circularized systems. These series of resonance locks yield orders-of-magnitude larger changes in eccentricity and harmonic pulsations relative to those expected from a single episode of resonance locking or nonresonant tidal interactions. Resonances become more frequent as a star evolves off the main sequence providing an alternative explanation to the origin of some stellar pulsators and yielding the concept of "dormant migrating giants". Evolution trajectories are characterized by competing episodes of inward/outward migration and spin-up/-down of the star which are sensitive to the system parameters, revealing a new challenge in modeling migration paths and in contextualizing the observed populations of giant exoplanets and stellar binaries. This sensitivity however offers a new window to constrain the stellar properties of planetary hosts via tidal asteroseismology.

astro-ph.EP↗

arXiv2024

MILE: A Mutation Testing Framework of In-Context Learning Systems

In-context Learning (ICL) has achieved notable success in the applications of large language models (LLMs). By adding only a few input-output pairs that demonstrate a new task, the LLM can efficiently learn the task during inference without modifying the model parameters. Such mysterious ability of LLMs has attracted great research interests in understanding, formatting, and improving the in-context demonstrations, while still suffering from drawbacks like black-box mechanisms and sensitivity against the selection of examples. In this work, inspired by the foundations of adopting testing techniques in machine learning (ML) systems, we propose a mutation testing framework designed to characterize the quality and effectiveness of test data for ICL systems. First, we propose several mutation operators specialized for ICL demonstrations, as well as corresponding mutation scores for ICL test sets. With comprehensive experiments, we showcase the effectiveness of our framework in evaluating the reliability and quality of ICL test suites. Our code is available at https://github.com/weizeming/MILE.

cs.SE↗

arXiv2024

Cross Fusion RGB-T Tracking with Bi-directional Adapter

Many state-of-the-art RGB-T trackers have achieved remarkable results through modality fusion. However, these trackers often either overlook temporal information or fail to fully utilize it, resulting in an ineffective balance between multi-modal and temporal information. To address this issue, we propose a novel Cross Fusion RGB-T Tracking architecture (CFBT) that ensures the full participation of multiple modalities in tracking while dynamically fusing temporal information. The effectiveness of CFBT relies on three newly designed cross spatio-temporal information fusion modules: Cross Spatio-Temporal Augmentation Fusion (CSTAF), Cross Spatio-Temporal Complementarity Fusion (CSTCF), and Dual-Stream Spatio-Temporal Adapter (DSTA). CSTAF employs a cross-attention mechanism to enhance the feature representation of the template comprehensively. CSTCF utilizes complementary information between different branches to enhance target features and suppress background features. DSTA adopts the adapter concept to adaptively fuse complementary information from multiple branches within the transformer layer, using the RGB modality as a medium. These ingenious fusions of multiple perspectives introduce only less than 0.3\% of the total modal parameters, but they indeed enable an efficient balance between multi-modal and temporal information. Extensive experiments on three popular RGB-T tracking benchmarks demonstrate that our method achieves new state-of-the-art performance.

cs.CV↗

arXiv2024

To Be or not to Be: the role of rotation in modeling Galactic Be X-ray Binaries

Be X-ray binaries (Be-XRBs) are one of the largest subclasses of high-mass X-ray binaries, comprised of a rapidly rotating Be star and neutron star companion in an eccentric orbit, intermittently accreting material from a decretion disk around the donor. Originating from binary stellar evolution, Be-XRBs are of significant interest to binary population synthesis (BPS) studies, encapsulating the physics of supernovae, common envelope, and mass transfer (MT). Using the state-of-the-art BPS code, POSYDON, which relies on pre-computed grids of detailed binary stellar evolution models, we investigate the Galactic Be-XRB population. POSYDON incorporates stellar rotation self-consistently during MT phases, enabling detailed examination of the rotational distribution of Be stars in multiple phases of evolution. Our fiducial BPS and Be-XRB model align well with the orbital properties of Galactic Be-XRBs, emphasizing the role of rotational constraints. Our modeling reveals a rapidly rotating population ($ω/ω_\mathrm{crit} \gtrsim 0.3$) of Be-XRB-like systems with a strong peak at intermediate rotation rates ($ω/ω_\mathrm{crit} \simeq 0.6$) in close alignment with observations. All Be-XRBs undergo a MT phase before the first compact object forms, with over half experiencing a second MT phase from a stripped helium companion (Case BB). Computing rotationally-limited MT efficiencies and applying them to our population, we derive a physically motivated MT efficiency distribution, finding that most Be-XRBs have undergone highly non-conservative MT ($\barβ_\mathrm{rot} \simeq 0.05$). Our study underscores the importance of detailed angular momentum modeling during MT in interpreting Be-XRB populations, emphasizing this population as a key probe for the stability and efficiency of MT in interacting binaries.

astro-ph.HE↗

arXiv2024

Formation of Wind-Fed Black Hole High-mass X-ray Binaries: The Role of Roche-lobe-Overflow Post Black-Hole Formation

The three dynamically confirmed wind-fed black hole high-mass X-ray binaries (BH-HMXBs) are suggested to all contain a highly spinning black hole (BH). However, based on the theories of efficient angular momentum transport inside the stars, we expect that the first-born BHs in binary systems should have low spins, which is consistent with gravitational-wave observations. As a result, the origin of the high BH spins measured in wind-fed BH-HMXBs remains a mystery. In this paper, we conduct a binary population synthesis study on wind-fed BH-HMXBs at solar metallicity with the use of the newly developed code POSYDON, considering three scenarios for BH accretion: Eddington-limited, moderately super-Eddington, and fully conservative accretion. Taking into account the conditions for accretion-disk formation, we find that regardless of the accretion model, these systems are more likely to have already experienced a phase of Roche-lobe overflow after the BH formation. To account for the extreme BH spins, highly conservative accretion onto BHs is required, when assuming the accreted material carries the specific angular momentum at the innermost stable orbit. Besides, in our simulations we found that the systems with donor stars within the mass range of $10-20\,M_{\odot}$ are prevalent, posing a challenge in explaining simultaneously all observed properties of the BH-HMXB in our Galaxy, Cygnus X-1, and potentially hinting that the accretion efficiency onto non-degenerate stars, before the formation of the BH, is also more conservative than assumed in our simulations.

astro-ph.HE↗

arXiv2024

Automata Extraction from Transformers

In modern machine (ML) learning systems, Transformer-based architectures have achieved milestone success across a broad spectrum of tasks, yet understanding their operational mechanisms remains an open problem. To improve the transparency of ML systems, automata extraction methods, which interpret stateful ML models as automata typically through formal languages, have proven effective for explaining the mechanism of recurrent neural networks (RNNs). However, few works have been applied to this paradigm to Transformer models. In particular, understanding their processing of formal languages and identifying their limitations in this area remains unexplored. In this paper, we propose an automata extraction algorithm specifically designed for Transformer models. Treating the Transformer model as a black-box system, we track the model through the transformation process of their internal latent representations during their operations, and then use classical pedagogical approaches like L* algorithm to interpret them as deterministic finite-state automata (DFA). Overall, our study reveals how the Transformer model comprehends the structure of formal languages, which not only enhances the interpretability of the Transformer-based ML systems but also marks a crucial step toward a deeper understanding of how ML systems process formal languages. Code and data are available at https://github.com/Zhang-Yihao/Transfomer2DFA.

cs.LG↗