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Researcher profile

Ning Liu

Ning Liu contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Artificial Intelligenceastro-ph.COastro-ph.GAcond-mat.quant-gasCryptography and Securitygr-qchep-phMachine Learningmath.COquant-ph

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Published work

6 published item(s)

preprint2026arXiv

Detecting Axion-like Particles with Plasmon in Reactor-based Experiment

Axion and axion-like particles (ALPs), predicted by various extensions of the Standard Model, can be copiously produced in nuclear reactors via the Primakoff process. In this work, we explore the detection of such relativistic ALPs through the plasmon effect in silicon detectors located near reactors. Utilizing the data from the Connie and Atucha-II experiments, we set the 90\% confidence level upper limits on the ALP-photon coupling $g_{aγ}$ over the mass range $0.1< m_a <100$ keV. Furthermore, we present that the projected sensitivity of the Oscura experiment, with an exposure of 30 kg$\cdot$ yr, will surpass the current reach of the NEON experiment by approximately one order of magnitude in the same mass range. This improvement would substantially expand the explored region of the QCD axion and ALP parameter space.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

FedSDR: Federated Self-Distillation with Rectification

Federated fine-tuning of Large Language Models faces severe statistical heterogeneity. However, existing model-level defenses often overlook the root cause: intrinsic data distribution mismatches. In this work, we first establish Federated Self-Distillation (FedSD) as a fundamental and potent strategy. By projecting client representations into a smoothed ``model-understanding space,'' FedSD alone serves as a universal booster, demonstrating superior performance over conventional algorithms. Despite its success, we identify a subtle trade-off termed the Rewrite Paradox -- unconstrained self-distillation can inadvertently increase hallucinations and redundancy. To refine this paradigm, we further propose FedSDR (Federated Self-Distillation with Rectification), the ultimate reinforced framework. It augments FedSD with a dual-stream mechanism: a local LoRA-S (Smoothing) branch to implicitly absorb heterogeneity via distilled data, and a parallel global LoRA-R (Rectification) branch anchored to raw data to enforce factual correctness. By selectively aggregating only LoRA-R, FedSDR yields a globally aligned and faithful model. Extensive experiments verify its superior performance.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Ground State and Collective Modes of Bose-Einstein Condensates in Newtonian and MOND-inspired gravitational potentials

We analytically and numerically study the ground state and collective dynamics of Bose-Einstein condensates in two traps: a Newtonian potential and a logarithmic potential inspired by Modified Newtonian Dynamics (MOND). In the ground state, the MOND potential supports bound states only in the deep-MOND regime, where the condensate becomes significantly larger than its Newtonian counterpart. The size increases with repulsive coupling parameter $β$ in both potentials. A clear scaling law of the size with $β^{1/3}$ emerges in the MOND case and is confirmed numerically over a wide parameter range, while for the Newtonian potential no simple scaling law exists as the Thomas-Fermi approximation ceases to be valid. For the dynamics, we derive and solve equations for the monopole collective mode. The larger MOND-bound condensate oscillates at a lower frequency, which scales as $β^{-1/3}$ in the strong-interaction limit. These scaling laws provide insights for quantum-simulation experiments aiming to probe modified-gravity scenarios with cold atoms.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

M3MAD-Bench: Are Multi-Agent Debates Really Effective Across Domains and Modalities?

As an agent-level reasoning and coordination paradigm, Multi-Agent Debate (MAD) orchestrates multiple agents through structured debate to improve answer quality and support complex reasoning. However, existing research on MAD suffers from two fundamental limitations: evaluations are conducted under fragmented and inconsistent settings, hindering fair comparison, and are largely restricted to single-modality scenarios that rely on textual inputs only. To address these gaps, we introduce M3MAD-Bench, a unified and extensible benchmark for evaluating MAD methods across Multi-domain tasks, Multi-modal inputs, and Multi-dimensional metrics. M3MAD-Bench establishes standardized protocols over five core task domains: Knowledge, Mathematics, Medicine, Natural Sciences, and Complex Reasoning, and systematically covers both pure text and vision-language datasets, enabling controlled cross-modality comparison. We evaluate MAD methods on nine base models spanning different architectures, scales, and modality capabilities. Beyond accuracy, M3MAD-Bench incorporates efficiency-oriented metrics such as token consumption and inference time, providing a holistic view of performance--cost trade-offs. Extensive experiments yield systematic insights into the effectiveness, robustness, and efficiency of MAD across text-only and multimodal scenarios. We believe M3MAD-Bench offers a reliable foundation for future research on standardized MAD evaluation. The code is available at http://github.com/liaolea/M3MAD-Bench.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

SpatialJB: How Text Distribution Art Becomes the &#34;Jailbreak Key&#34; for LLM Guardrails

While Large Language Models (LLMs) have powerful capabilities, they remain vulnerable to jailbreak attacks, which is a critical barrier to their safe web real-time application. Current commercial LLM providers deploy output guardrails to filter harmful outputs, yet these defenses are not impenetrable. Due to LLMs&#39; reliance on autoregressive, token-by-token inference, their semantic representations lack robustness to spatially structured perturbations, such as redistributing tokens across different rows, columns, or diagonals. Exploiting the Transformer&#39;s spatial weakness, we propose SpatialJB to disrupt the model&#39;s output generation process, allowing harmful content to bypass guardrails without detection. Comprehensive experiments conducted on leading LLMs get nearly 100% ASR, demonstrating the high effectiveness of SpatialJB. Even after adding advanced output guardrails, like the OpenAI Moderation API, SpatialJB consistently maintains a success rate exceeding 75%, outperforming current jailbreak techniques by a significant margin. The proposal of SpatialJB exposes a key weakness in current guardrails and emphasizes the importance of spatial semantics, offering new insights to advance LLM safety research. To prevent potential misuse, we also present baseline defense strategies against SpatialJB and evaluate their effectiveness in mitigating such attacks. The code for the attack, baseline defenses, and a demo are available at https://anonymous.4open.science/r/SpatialJailbreak-8E63.

0 citations0 reviewsNo code linkedOpen access
preprint2024arXiv

Plethystic Murnaghan-Nakayama rule via vertex operators

Based on the vertex operator realization of the Schur functions, a determinant-type plethystic Murnaghan--Nakayama rule is obtained and utilized to derive a general formula of the expansion coefficients of $s_ν$ in the plethysm product $(p_{n}\circ h_{k})s_μ$. Meanwhile, the equivalence between our algebraic rule and the combinatorial one is also established. As an application, we provide a simple way to compute the generalized Waring formula.

0 citations0 reviewsNo code linkedOpen access