arXiv subjects

Meng Sun

Publications and source records attributed to Meng Sun.

At least 37 records · Page 2Linked to original sources

arXiv2026

The effects of star-gas interactions on binary evolution in open clusters

Star-gas interactions can provide gravitational feedback that influences the dynamical evolution of stellar clusters, through processes such as dynamical friction (DF) and its non-dissipative counterpart, negative dynamical friction (NDF). Using the \texttt{PeTar} code, we perform direct $N$-body simulations of an open cluster initially containing $10^4$ stars, evolving within a gaseous medium spanning a range of ambient densities. Our results demonstrate that NDF associated with stellar outflows interacting with the surrounding gas can enhance the rate of cluster expansion, preferentially transporting stars toward the cluster outskirts. This behavior is accompanied by a more rapid decline in the number of binaries composed of a neutron star and a main-sequence star. A statistical analysis of binary orbital parameters further indicates that, compared to DF-dominated evolution, NDF tends to retain systems with larger semi-major axes and lower eccentricities. Outflow-ambient gas interactions can modify the dynamical processing of binaries in star clusters, leading to changes in the survival fraction and composition of the remaining binary population.

astro-ph.GA↗

arXiv2026

Modeling Item-Level Dynamic Variability with Residual Diffusion for Bundle Recommendation

Existing solutions for bundle recommendation (BR) have achieved remarkable effectiveness for predicting the user's preference for prebuilt bundles. However, bundle-item (B-I) affiliation will vary dynamically in real scenarios. For example, a bundle themed as 'casual outfit' may add 'hat' or remove 'watch' due to factors such as seasonal variations, changes in user preferences or inventory adjustments. Our empirical study demonstrates that the performance of mainstream BR models may fluctuate or decline under item-level variability. This paper makes the first attempt to address the above problem and proposes a novel Residual Diffusion for Bundle Recommendation(RDiffBR)asamodel-agnostic generative framework which can assist a BR model in adapting this scenario. During the initial training of the BR model, RDiffBR employs a residual diffusion model to process the item-level bundle embeddings which are generated by the BR model to represent bundle theme via a forward-reverse process. In the inference stage, RDiffBR reverses item-level bundle embeddings obtained by the well-trained bundle model under B-I variability scenarios to generate the effective item level bundle embeddings. In particular, the residual connection in our residual approximator significantly enhances BR models' ability to generate high-quality item-level bundle embeddings. Experiments on six BR models and four public datasets from different domains show that RDiffBR improves the performance of Recall and NDCG of backbone BR models by up to 23%, while only increases training time about 4%.

cs.IR↗

arXiv2025

Beyond Correctness: Exposing LLM-generated Logical Flaws in Reasoning via Multi-step Automated Theorem Proving

Large Language Models (LLMs) have demonstrated impressive reasoning capabilities, leading to their adoption in high-stakes domains such as healthcare, law, and scientific research. However, their reasoning often contains subtle logical errors masked by fluent language, posing significant risks for critical applications. While existing approaches like fact-checking, self-consistency methods, and rule-based validation provide partial solutions, they fail to detect complex logical flaws in multi-step reasoning. To overcome these challenges, we present MATP, an evaluation framework for systematically verifying LLM reasoning via Multi-step Automatic Theorem Proving. MATP translates natural language reasoning into First-Order Logic (FOL) and applies automated theorem provers to assess step-by-step logical validity. This approach identifies hidden logical errors and provides fine-grained classifications of reasoning correctness. Evaluations on a benchmark comprising 10,830 reasoning instances generated by 10 LLMs across tasks from PrOntoQA-OOD, ProofWriter, and FOLIO show that MATP surpasses prompting-based baselines by over 42 percentage points in reasoning step verification. It further reveals model-level disparities, with reasoning models generating more logically coherent outputs than general models. These results demonstrate MATP's potential to enhance the trustworthiness of LLM-generated reasoning.

cs.SE↗

arXiv2025

Exciton-polariton condensate in the van der Waals magnet CrSBr

Van der Waals magnets are an emergent material class of paramount interest for fundamental studies in coupling light with matter excitations, which are uniquely linked to their underlying magnetic properties. Among these materials, the semiconducting magnet CrSBr is possibly a first playground where we can study simultaneously the interaction of photons, magnons, and excitons at the quantum level. Here we demonstrate a coherent macroscopic quantum phase, the bosonic condensation of exciton-polaritons, emerging in a CrSBr flake embedded in a fully tunable cryogenic open optical cavity. The Bose condensate is characterized by a highly non-linear threshold-like behavior, and coherence manifests distinctly via its first and second order quantum correlations. We find that the condensate's non-linearity is highly susceptible to the magnetic order in CrSBr. Specially, it can encounter a sign change from attractive to repulsive interactions when the intrinsic antiferromagnetic order transforms to the forced ferromagnetic order. Our findings open a route towards magnetically controllable quantum fluids of light, and optomagnonic devices where spin magnetism is coupled to on-chip Bose-Einstein condensates.

cond-mat.mtrl-sci↗

arXiv2025

IGR J17091-3624: Newly Formed Periodic Dips and Multiwavelength Activity During the 2025 Outburst

The black hole low-mass X-ray binary (LMXB) candidate IGR J17091-3624 experienced a hard-state-only outburst in 2025. In this paper, we show that IXPE detected a series of intermittent X-ray dips, spanning a total interval of ~1 day. Subsequent observations with NICER, EP, NuSTAR, and Swift reveal that these dips recur with a period of 2.83$\pm$0.07 days and are accompanied by an increase in spectral hardness. This is the first time such quasi-periodic dipping behavior has been observed in this target since discovery. Our spectral analysis shows that the dips can be explained by obscuration from an ionized absorber characterized by an ionization parameter of $logξ$ ~1-3 erg cm s$^{-1}$ and an equivalent hydrogen column density of $N^{\rm zxipcf}_{\rm H}$~(1-30)$\times10^{22}$ cm$^{-2}$. The periodic reappearance of the absorber is likely caused by obscuring material located in the outer accretion disk, modulated by the binary orbital period. If confirmed, this period would suggest that the donor star in IGR J17091-3624 has deviated from the standard main-sequence evolutionary path and is likely a (partially) stripped giant. In the optical band, no significant periodicity or correlation with the X-ray dips was detected, whereas the radio counterpart exhibited a flat to steep spectrum, in contrast to the inverted spectrum typically observed during the hard state of LMXBs.

astro-ph.HE↗

arXiv2025

Tides in Massive Binaries: Numerical Solutions and Semi-Analytical Comparisons

We present a systematic comparison between the tidal secular evolution timescales predicted by the direct numerical method and those given by the commonly used semi-analytic prescriptions implemented in 1-D hydrostatic binary evolution codes. Our study focuses on binary systems with intermediate- to high-mass primaries ($M_1 = 5$-$50\,M_\odot$), companion masses between $1.4\,M_\odot$ and $10\,M_\odot$, and orbital periods ranging from 0.5 to 50 days. Before mass transfer, both approaches predict synchronization and orbital decay timescales that agree within $\sim$2 orders of magnitude and typically exceed the stellar main sequence lifetime, implying negligible tidal impact on secular orbital evolution. However, the implied dissipation channels differ, and the differences become more pronounced once mass transfer begins. To test the theoretical predictions against observations, we apply both approaches to the well-characterized PSR J0045--7319 system, which has an orbital decay timescale of 0.5 Myr. The numerical solution reveals strong resonances with internal gravity waves, bringing the predicted orbital period change rate close to the observed value. In contrast, the semi-analytic prescriptions predict orbital decay timescales longer than the Hubble time. These results suggest that for population studies, modestly calibrated parameterized equations may suffice, but for individual systems, reliable interpretation requires direct numerical approaches.

astro-ph.SR↗

arXiv2025

Numerical Non-Adiabatic Tidal Calculations with GYRE-tides: The WASP-12 Test Case

We revisit the tidal evolution of the WASP-12 system using direct numerical calculations with the GYRE-tides code. WASP-12b is a hot Jupiter on a 1.1-day orbit around a slightly evolved F-type star. Its observed orbital decay rate, $|\dot{P}_{\rm orb}/P_{\rm orb}| \approx 3.2\,\mathrm{Myr}^{-1}$, provides a strong constraint on stellar tidal dissipation. We confirm that linear tides with radiative damping and convective damping, as currently implemented, are not sufficient to reproduce the observed inspiral timescale. Nevertheless, our calculations, based on fully non-adiabatic forced oscillations in MESA stellar models with convective envelopes, yield dissipation rates that are consistent with previous semi-analytic and adiabatic estimates, confirming the robustness of our numerical framework. As the only open-source, actively maintained tool capable of computing orbital evolution in exoplanet systems, GYRE-tides provides a benchmark calculation for WASP-12 and future applications. Our results validate GYRE-tides as a tool for analyzing combined radiative and convective damping, and indicate that the observed decay rate requires tidal dissipation operating in or near the fully damped regime, which may be achieved through nonlinear damping. These contributions could also be evaluated by computing the wave luminosity at the radiative-convective boundary using our tool. GYRE-tides offers an open-source framework for computing tidal dissipation in short-period exoplanet systems, including the many systems expected to show orbital decay in upcoming Roman surveys.

astro-ph.SR↗

arXiv2025

Automata-Based Steering of Large Language Models for Diverse Structured Generation

Large language models (LLMs) are increasingly tasked with generating structured outputs. While structured generation methods ensure validity, they often lack output diversity, a critical limitation that we confirm in our preliminary study. We propose a novel method to enhance diversity in automaton-based structured generation. Our approach utilizes automata traversal history to steer LLMs towards novel structural patterns. Evaluations show our method significantly improves structural and content diversity while maintaining comparable generation efficiency. Furthermore, we conduct a case study showcasing the effectiveness of our method in generating diverse test cases for testing open-source libraries.

cs.CL↗

arXiv2025

Revisiting Hallucination Detection with Effective Rank-based Uncertainty

Detecting hallucinations in large language models (LLMs) remains a fundamental challenge for their trustworthy deployment. Going beyond basic uncertainty-driven hallucination detection frameworks, we propose a simple yet powerful method that quantifies uncertainty by measuring the effective rank of hidden states derived from multiple model outputs and different layers. Grounded in the spectral analysis of representations, our approach provides interpretable insights into the model's internal reasoning process through semantic variations, while requiring no extra knowledge or additional modules, thus offering a combination of theoretical elegance and practical efficiency. Meanwhile, we theoretically demonstrate the necessity of quantifying uncertainty both internally (representations of a single response) and externally (different responses), providing a justification for using representations among different layers and responses from LLMs to detect hallucinations. Extensive experiments demonstrate that our method effectively detects hallucinations and generalizes robustly across various scenarios, contributing to a new paradigm of hallucination detection for LLM truthfulness.

cs.AI↗

arXiv2025

Dynamic Orthogonal Continual Fine-tuning for Mitigating Catastrophic Forgettings

Catastrophic forgetting remains a critical challenge in continual learning for large language models (LLMs), where models struggle to retain performance on historical tasks when fine-tuning on new sequential data without access to past datasets. In this paper, we first reveal that the drift of functional directions during the fine-tuning process is a key reason why existing regularization-based methods fail in long-term LLM continual learning. To address this, we propose Dynamic Orthogonal Continual (DOC) fine-tuning, a novel approach that tracks the drift of these functional directions and dynamically updates them during the fine-tuning process. Furthermore, by adjusting the gradients of new task parameters to be orthogonal to the tracked historical function directions, our method mitigates interference between new and old tasks. Extensive experiments on various LLM continual learning benchmarks demonstrate that this approach outperforms prior methods, effectively reducing catastrophic forgetting and providing a robust tool for continuous LLM fine-tuning. Our code is available at https://github.com/meloxxxxxx/DOC.

cs.LG↗

arXiv2025

Reliable Unlearning Harmful Information in LLMs with Metamorphosis Representation Projection

While Large Language Models (LLMs) have demonstrated impressive performance in various domains and tasks, concerns about their safety are becoming increasingly severe. In particular, since models may store unsafe knowledge internally, machine unlearning has emerged as a representative paradigm to ensure model safety. Existing approaches employ various training techniques, such as gradient ascent and negative preference optimization, in attempts to eliminate the influence of undesired data on target models. However, these methods merely suppress the activation of undesired data through parametric training without completely eradicating its informational traces within the model. This fundamental limitation makes it difficult to achieve effective continuous unlearning, rendering these methods vulnerable to relearning attacks. To overcome these challenges, we propose a Metamorphosis Representation Projection (MRP) approach that pioneers the application of irreversible projection properties to machine unlearning. By implementing projective transformations in the hidden state space of specific network layers, our method effectively eliminates harmful information while preserving useful knowledge. Experimental results demonstrate that our approach enables effective continuous unlearning and successfully defends against relearning attacks, achieving state-of-the-art performance in unlearning effectiveness while preserving natural performance. Our code is available in https://github.com/ChengcanWu/MRP.

cs.LG↗

arXiv2025

POSYDON Version 2: Population Synthesis with Detailed Binary-Evolution Simulations across a Cosmological Range of Metallicities

Whether considering rare astrophysical events on cosmological scales or unresolved stellar populations, accurate models must account for the integrated contribution from the entire history of star formation upon which that population is built. Here, we describe the second version of POSYDON, an open-source binary population synthesis code based on extensive grids of detailed binary evolution models computed using the MESA code, which follows both stars' structures as a binary system evolves through its complete evolution from the zero-age main sequence, through multiple phases of mass transfer and supernovae, to their death as compact objects. To generate synthetic binary populations, POSYDON uses advanced methods to interpolate between our large, densely spaced grids of simulated binaries. In our updated version of POSYDON, we account for the evolution of stellar binaries across a cosmological range of metallicities, extending from $10^{-4}$ $Z_{\odot}$ to 2 $Z_{\odot}$, including grids specifically focused on the Small and Large Magellanic Clouds (0.2 $Z_{\odot}$ and 0.45 $Z_{\odot}$). In addition to describing our model grids and detailing our methodology, we outline several improvements to POSYDON. These include the incorporation of single stars in stellar populations, a treatment for stellar mergers, and a careful modeling of "reverse-mass transferring" binaries in which a once-accreting star later becomes a donor star. Our simulations are focused on binaries with at least one high-mass component, such as those that host neutron stars and black holes, and we provide post-processing methods to account for the cosmological evolution of metallicity and star formation as well as rate calculations for transient events.

astro-ph.GA↗

arXiv2025

Mass-gap Black Holes in Coalescing Neutron Star Black Hole Binaries

The existence of a mass gap of $3-5\,M_{\odot}$ between the heaviest neutron stars (NSs) and the lightest black holes (BHs), inferred from the BH mass distribution in low mass X-ray binaries (LMXBs), has been suggested for decades. The recently reported gravitational-wave source GW230529 has been confidently identified as a NSBH merger, with the BH mass falling within this lower mass gap. This detection provides strong evidence against the existence of the latter and introduces new implications for the coalescing NSBH population, including a revised BH mass distribution and an updated local merger rate. In this study, we employ POSYDON, a binary population synthesis code that integrates detailed single- and binary-star models, to investigate coalescing NSBH binaries formed through isolated binary evolution. In particular, we focus on the BH mass distribution of the intrinsic NSBH merger population. We find that, with a high common-envelope efficiency of $α_{\rm{CE}} =2 $, the BH masses in NSBH mergers concentrate in the lower mass gap, aligning more closely with observations. However, after accounting for the constraints of the selection bias against mass-gap BHs in LMXBs, which suggests that the maximum NS birth mass is below $\simeq 2\,M_{\odot}$, we find that introducing a high $α_{\rm{CE}}$ is not required to match observations. Additionally, we explore the impact of core-collapse supernova kicks. Finally, we present the property distributions of observable NSBH mergers from our simulation and find that they match well with the observations. We find that the fraction of electromagnetic counterparts in observable populations is $\approx 4-30\%$, depending on different NS equations of state. Future detections of coalescing NSBH binaries would provide invaluable insights into SN mechanisms, common envelope evolution, and NS physics.

astro-ph.HE↗

arXiv2025

The Blue Lurker WOCS 14020: A Long-Period Post-Common-Envelope Binary in M67 Originating from a Merger in a Triple System

We present Hubble Space Telescope far-ultraviolet (FUV) spectra of a blue-lurker$-$white-dwarf (BL-WD) binary system in the 4 Gyr open cluster M67. We fit the FUV spectrum of the WD, determining it is a C/O WD with a mass of $0.72^{+0.05}_{-0.04}$ M$_\odot$ and a cooling age of $\sim400$ Myr. This requires a WD progenitor of $\sim3$ M$_\odot$, significantly larger than the current cluster turnoff mass of 1.3 M$_\odot$. We suggest the WD progenitor star formed several hundred Myr ago via the merger of two stars near the turnoff of the cluster. In this scenario, the original progenitor system was a hierarchical triple consisting of a close, near-equal-mass inner binary, with a tertiary companion with an orbit of a few thousand days. The WD is descended from the merged inner binary, and the original tertiary is now the observed BL. The likely formation scenario involves a common envelope while the WD progenitor is on the AGB, and thus the observed orbital period of 359 days requires an efficient common envelope ejection. The rapid rotation of the BL indicates it accreted some material during its evolution, perhaps via a wind prior to the common envelope. This system will likely undergo a second common envelope in the future, and thus could result in a short-period double WD binary or merger of a 0.72 M$_\odot$ C/O WD and a 0.38 $M_\odot$ Helium WD, making this a potential progenitor of an interesting transient such as a sub-Chandrasekhar Type Ia supernova.

astro-ph.SR↗

arXiv2025

Discovery of a variable yellow supergiant progenitor for the Type IIb SN 2024abfo

We report the discovery of a progenitor candidate for the Type~IIb SN2024abfo using multi-epoch pre-explosion images from the Hubble Space Telescope and the Dark Energy Camera Legacy Survey. The progenitor exhibited a ~0.7 mag decline in F814W from 2001 to 2013, followed by significant brightening and color fluctuations in the g, r and z bands. This is the first time that substantial photometric variability has been found for the progenitor of a SN IIb. We suggest that the variability is caused by intrinsic changes in the progenitor star instead of varying obscuration by circumstellar dust. Our results show that the progenitor of SN2024abfo was likely a yellow supergiant star with an initial mass of 12--18 Msun for circumstellar reddening of E(B-V)_CSM < 0.2 mag. Our study underscores the critical role of multi-epoch imaging surveys in revealing the final stages of core-collapse supernovae progenitors.

astro-ph.HE↗

arXiv2025

Protecting Deep Learning Model Copyrights with Adversarial Example-Free Reuse Detection

Model reuse techniques can reduce the resource requirements for training high-performance deep neural networks (DNNs) by leveraging existing models. However, unauthorized reuse and replication of DNNs can lead to copyright infringement and economic loss to the model owner. This underscores the need to analyze the reuse relation between DNNs and develop copyright protection techniques to safeguard intellectual property rights. Existing white-box testing-based approaches cannot address the common heterogeneous reuse case where the model architecture is changed, and DNN fingerprinting approaches heavily rely on generating adversarial examples with good transferability, which is known to be challenging in the black-box setting. To bridge the gap, we propose NFARD, a Neuron Functionality Analysis-based Reuse Detector, which only requires normal test samples to detect reuse relations by measuring the models' differences on a newly proposed model characterization, i.e., neuron functionality (NF). A set of NF-based distance metrics is designed to make NFARD applicable to both white-box and black-box settings. Moreover, we devise a linear transformation method to handle heterogeneous reuse cases by constructing the optimal projection matrix for dimension consistency, significantly extending the application scope of NFARD. To the best of our knowledge, this is the first adversarial example-free method that exploits neuron functionality for DNN copyright protection. As a side contribution, we constructed a reuse detection benchmark named Reuse Zoo that covers various practical reuse techniques and popular datasets. Extensive evaluations on this comprehensive benchmark show that NFARD achieves F1 scores of 0.984 and 1.0 for detecting reuse relationships in black-box and white-box settings, respectively, while generating test suites 2 ~ 99 times faster than previous methods.

cs.CR↗

arXiv2025

When Thinking LLMs Lie: Unveiling the Strategic Deception in Representations of Reasoning Models

The honesty of large language models (LLMs) is a critical alignment challenge, especially as advanced systems with chain-of-thought (CoT) reasoning may strategically deceive humans. Unlike traditional honesty issues on LLMs, which could be possibly explained as some kind of hallucination, those models' explicit thought paths enable us to study strategic deception--goal-driven, intentional misinformation where reasoning contradicts outputs. Using representation engineering, we systematically induce, detect, and control such deception in CoT-enabled LLMs, extracting "deception vectors" via Linear Artificial Tomography (LAT) for 89% detection accuracy. Through activation steering, we achieve a 40% success rate in eliciting context-appropriate deception without explicit prompts, unveiling the specific honesty-related issue of reasoning models and providing tools for trustworthy AI alignment.

cs.AI↗

arXiv2025

A Be star-black hole binary with a wide orbit from LAMOST time-domain survey

Binary systems consisting of an early type star and a black hole (BH) are crucial for understanding various astrophysical phenomena, particularly the origins of detected gravitational wave sources. Be binary systems are expected to represent a key evolutionary stage in hosting BHs. However, while hundreds of Be X-ray binaries are known, the only confirmed BH candidate in a Be binary remains highly controversial. We report the discovery of ALS 8814, a Be star-BH binary with a moderately eccentric ($e = 0.23$) and wide orbit ($P = 176.6$ days), revealed by the radial velocity (RV) measurement of the visible Be star. Our analysis, combining flux-calibrated spectra in the Balmer discontinuity region and spectral template matching, yields a mass of $11.2^{+1.4}_{-1.2}$ $M_\odot$ for the Be star. The minimum mass of the unseen companion, assuming an edge-on inclination ($i = 90^{\circ}$), is $9.8\pm 0.7\,M_\odot$. We rule out the presence of non-degenerate companions in ALS 8814, indicating that it can only be a BH. This discovery represents a robust case of a Be-BH binary, identified purely through precise RV measurements from a single set of lines. The extremely low peculiar velocity of ALS 8814 suggests that the BH is formed via a direct core-collapse with a negligible natal kick, implying an almost perfect alignment between the Be star's spin and the orbital plane. In this context, the binary's inclination angle is estimated to be 22$^{\circ}$-49$^{\circ}$ by analyzing the shallow double-peaked profile of the H$α$ emission line. This inclination range corresponds to a BH mass estimate between $15\,M_\odot$ and $58\,M_\odot$. As the only unambiguous Be-BH binary system known to date, ALS 8814 provides valuable constraints on the BH formation in a binary system with a high-mass companion.

astro-ph.SR↗