arXiv subjects

Tong Liu

Publications and source records attributed to Tong Liu.

At least 91 records · Page 5Linked to original sources

arXiv2026

Quantum Computing -- Strategic Recommendations for the Industry

This whitepaper surveys the current landscape and short- to mid-term prospects for quantum-enabled optimization and machine learning use cases in industrial settings. Grounded in the QCHALLenge program, it synthesizes hardware trajectories from different quantum architectures and providers, and assesses their maturity and potential for real-world use cases under a standardized traffic-light evaluation framework. We provide a concise summary of relevant hardware roadmaps, distinguishing superconducting and ion-trap technologies, their current states, modalities, and projected scaling trajectories. The core of the presented work are the use case evaluations in the domains of optimization problems and machine learning applications. For the conducted experiments, we apply a consistent set of evaluation criteria (model formulation, scalability, solution quality, runtime, and transferability) which are assessed in a shared system of three categories, ranging from optimistic (solutions produced by quantum computers are competitive with classical methods and/or a clear path to a quantum advantage is shown) to pessimistic (significant hurdles prevent practical application of quantum solutions now and potentially in the future). The resulting verdicts illuminate where quantum approaches currently offer promise, where hybrid classical-quantum strategies are most viable, and where classical methods are expected to remain superior.

quant-ph↗

arXiv2026

Statistical analysis of multi-band plateaus in gamma-ray burst afterglows

Plateau features are frequently observed in the afterglows of gamma-ray bursts (GRBs), yet their physical origins remain under debate. In this work, we compile a sample of 124 GRBs with known redshifts and simultaneous X-ray and optical afterglow observations. We categorize them into four subsets based on the existence of plateaus and the bands in which they appear. Namely, Dataset 1: plateaus are detected simultaneously in both X-ray and optical bands (75 bursts); Dataset 2: plateaus are only in X-rays (15 bursts); Dataset 3: plateaus appear only in the optical (17 bursts); Dataset 4: no plateaus in either band (17 bursts). We employ these datasets to test the applicability of the energy-injection model by examining whether the temporal decay index $α$ and the spectral index $β$ of GRB afterglows simultaneously satisfy the closure relations in X-ray and optical bands. We find that 47 bursts of Dataset 1 simultaneously obey the closure relations in both bands under the conditions of the electron spectral index $p>2$ and the injection parameter $q\in (0, 0.5)$, and 69 of the dataset for $p>1$ and $q\in (0, 0.8)$, providing a strong support for the energy-injection interpretation. However, for Datasets 2 and 3, although $α$ and $β$ of the plateaus mostly satisfy the closure relations, those in the other band show significant deviations, which implies that bursts with a single-band plateau are inconsistent with the interpretation of energy injection. Furthermore, we also compare the isotropic X-ray energy of plateaus with the rotational energy budget of millisecond magnetars.

astro-ph.HE↗

arXiv2025

Integral filtered Sen theory and applications

We study Nygaard-, conjugate-, and Hodge filtrations on the many variants of Breuil--Kisin modules associated to integral semi-stable Galois representations. This leads to an integral Sen operator satisfying certain ``$1$-degree shrinking" on the increasing conjugate filtration, and (in special cases) a mod $p$ Sen operator satisfying certain ``$p$-degree shrinking". These constructions are related with prismatic $F$-crystals, Hodge--Tate crystals and $F$-gauges, and have explicit relations with classical (non-prismatic) operators. As applications, we obtain vanishing and torsion bound results on graded of the integral Hodge filtration; our explicit methods also recover results of Gee--Kisin and Bhatt--Gee--Kisin concerning the mod $p$ Hodge filtrations and Frobenius structures.

math.NT↗

arXiv2025

Bleeding Pathways: Vanishing Discriminability in LLM Hidden States Fuels Jailbreak Attacks

LLMs remain vulnerable to jailbreak attacks that exploit adversarial prompts to circumvent safety measures. Current safety fine-tuning approaches face two critical limitations. First, they often fail to strike a balance between security and utility, where stronger safety measures tend to over-reject harmless user requests. Second, they frequently miss malicious intent concealed within seemingly benign tasks, leaving models exposed to exploitation. Our work identifies a fundamental cause of these issues: during response generation, an LLM's capacity to differentiate harmful from safe outputs deteriorates. Experimental evidence confirms this, revealing that the separability between hidden states for safe and harmful responses diminishes as generation progresses. This weakening discrimination forces models to make compliance judgments earlier in the generation process, restricting their ability to recognize developing harmful intent and contributing to both aforementioned failures. To mitigate this vulnerability, we introduce DEEPALIGN - an inherent defense framework that enhances the safety of LLMs. By applying contrastive hidden-state steering at the midpoint of response generation, DEEPALIGN amplifies the separation between harmful and benign hidden states, enabling continuous intrinsic toxicity detection and intervention throughout the generation process. Across diverse LLMs spanning varying architectures and scales, it reduced attack success rates of nine distinct jailbreak attacks to near-zero or minimal. Crucially, it preserved model capability while reducing over-refusal. Models equipped with DEEPALIGN exhibited up to 3.5% lower error rates in rejecting challenging benign queries and maintained standard task performance with less than 1% decline. This marks a substantial advance in the safety-utility Pareto frontier.

cs.CR↗

arXiv2025

Possible quasi-periodic optical oscillations of ZTF blazars

Based on the Zwicky Transient Facility (ZTF), we selected 10 blazars as our sample sources. Among these, we found four blazars (J 0923.5+4125, J 1221.3+3010, J 1503.5+4759, and J 1652.7+4024) showing possible indications of quasi periodic oscillations (QPOs) modulation. We conducted a detailed analysis of their optical light curves (g- and r-bands) over the past five years using the root mean square (RMS)-Flux relation, flux distribution, and QPO detection methods to investigate their variability characteristics. A linear RMS-Flux relation is present in both bands, and their flux distributions follow a log-normal form. This suggests that optical variability may arise from multiplicative, nonlinear processes across different timescales and flux states. Further QPO analysis using the weighted wavelet Z-transform (WWZ), Lomb-Scargle periodogram (LSP), and autoregressive integrated moving average (ARIMA) methods identified candidate periodic signals in four blazars. J 0923.5+4125 (period $\sim$ 205 days) and J 1221.3+3010 ($\sim$ 630 days) show local significances of $\sim 3 σ$, whereas J 1503.5+4759 ($\sim$ 38.5 days) and J 1652.7+4024 ($\sim$ 48 days) reach $\sim 4 σ$. After accounting for the look-elsewhere effect, the global significances for J 1503.5+4759 in the g- and r-bands are $\sim 2.7 σ$, while for J 1652.7+4024 they are approximately $\sim 2.5 σ$ in both bands. These two blazars warrant further monitoring and investigation.

astro-ph.GA↗

arXiv2025

ChessMamba: Structure-Aware Interleaving of State Spaces for Change Detection in Remote Sensing Images

Change detection (CD) in multitemporal remote sensing imagery presents significant challenges for fine-grained recognition, owing to heterogeneity and spatiotemporal misalignment. However, existing methodologies based on vision transformers or state-space models typically disrupt local structural consistency during temporal serialization, obscuring discriminative cues under misalignment and hindering reliable change localization. To address this, we introduce ChessMamba, a structure-aware framework leveraging interleaved state-space modeling for robust CD with multi-temporal inputs. ChessMamba integrates a SpatialMamba encoder with a lightweight cross-source interaction module, featuring two key innovations: (i) Chessboard interleaving with snake scanning order, which serializes multi-temporal features into a unified sequence within a single forward pass, thereby shortening interaction paths and enabling direct comparison for accurate change localization; and (ii) Structure-aware fusion via multi-dilated convolutions, selectively capturing center-and-corner neighborhood contexts within each mono-temporal. Comprehensive evaluations on three CD tasks, including binary CD, semantic CD and multimodal building damage assessment, demonstrate that ChessMamba effectively fuses heterogeneous features and achieves substantial accuracy improvements over state-of-the-art methods.The relevant code will be available at: github.com/DingLei14/ChessMamba.

cs.CV↗

arXiv2025

Which active galaxies might be neutrino emitters?

The IceCube Neutrino Observatory has identified several individual neutrino emitters associated with supermassive black hole accretion phenomena, including blazars, tidal disruption events, and, unexpectedly, Seyfert galaxies. A key open question is which types of active galactic nuclei (AGNs) are most likely to be neutrino emitters. Here we show that high-confidence extragalactic neutrino emitters tend not only to have higher hard X-ray fluxes but also to be more variable in mid-infrared (MIR) than other AGNs in the \textit{Swift} BAT AGN Spectroscopic Survey. MIR variations effectively trace long-term fluctuations in AGN accretion disks and/or jets. In addition to the role of X-ray flux emphasized in previous studies, we speculate that long-term central engine fluctuations may also be critical for neutrino production. This hypothesis may inform IceCube neutrino-electromagnetic counterpart association studies and provide new insights into cosmic ray acceleration sites. First, the observed neutrinos are unlikely to originate from AGN host galaxies or from interactions between large-scale (dozens of parsecs) winds/outflows and the surrounding interstellar medium. Second, if neutrinos are produced in the X-ray corona, the corona should exhibit strong magnetic turbulence dissipation or magnetic reconnection whose rate changes substantially on timescales of years. Third, the relativistic jets of blazar neutrino emitters may be intrinsically unstable over years. Finally, if neutrinos are related to interactions between small-scale winds/outflows and torus clouds, such winds/outflows must be highly episodic.

astro-ph.HE↗

arXiv2025

Hyperaccretion-driven relativistic jets from massive collapsars in active galactic nucleus disks

The observable characteristics of gamma-ray bursts (GRBs) embedded in the accretion disk of active galactic nuclei (AGNs) are mainly determined by the jet propagation within the disk. In the massive collapsar scenario, we consider that the mass and metallicity of progenitor stars can significantly affect the jet durations and luminosities, which in turn influence whether the jet can break out from AGN disks. For the cases with low metallicity, massive stars tend to keep their massive envelopes. Thus the hyperaccretion of these envelopes onto the newborn black holes (BHs) can prolong the activity duration of the central engine, thereby allowing the jets to potentially break out from the disks. For successful jets, we further study their prompt emission and afterglows for different supermassive BHs and locations and discuss the detectability of these signals by instruments such as \emph{Swift} and Einstein Probe. Future related observations will help constrain the structure, components, and evolutionary history of AGN disks and the massive stars embedded within them.

astro-ph.HE↗

arXiv2025

TrioXpert: An Automated Incident Management Framework for Microservice System

Automated incident management plays a pivotal role in large-scale microservice systems. However, many existing methods rely solely on single-modal data (e.g., metrics, logs, and traces) and struggle to simultaneously address multiple downstream tasks, including anomaly detection (AD), failure triage (FT), and root cause localization (RCL). Moreover, the lack of clear reasoning evidence in current techniques often leads to insufficient interpretability. To address these limitations, we propose TrioXpert, an end-to-end incident management framework capable of fully leveraging multimodal data. TrioXpert designs three independent data processing pipelines based on the inherent characteristics of different modalities, comprehensively characterizing the operational status of microservice systems from both numerical and textual dimensions. It employs a collaborative reasoning mechanism using large language models (LLMs) to simultaneously handle multiple tasks while providing clear reasoning evidence to ensure strong interpretability. We conducted extensive evaluations on two microservice system datasets, and the experimental results demonstrate that TrioXpert achieves outstanding performance in AD (improving by 4.7% to 57.7%), FT (improving by 2.1% to 40.6%), and RCL (improving by 1.6% to 163.1%) tasks. TrioXpert has also been deployed in Lenovo's production environment, demonstrating substantial gains in diagnostic efficiency and accuracy.

cs.SE↗

arXiv2025

A Design Science Blueprint for an Orchestrated AI Assistant in Doctoral Supervision

This study presents a design science blueprint for an orchestrated AI assistant and co-pilot in doctoral supervision that acts as a socio-technical mediator. Design requirements are derived from Stakeholder Theory and bounded by Academic Integrity. We consolidated recent evidence on supervision gaps and student wellbeing, then mapped issues to adjacent large language model capabilities using a transparent severity-mitigability triage. The artefact assembles existing capabilities into one accountable agentic AI workflow that proposes retrieval-augmented generation and temporal knowledge graphs, as well as mixture-of-experts routing as a solution stack of technologies to address existing doctoral supervision pain points. Additionally, a student context store is proposed, which introduces behaviour patches that turn tacit guidance into auditable practice and student-set thresholds that trigger progress summaries, while keeping authorship and final judgement with people. We specify a student-initiated moderation loop in which assistant outputs are routed to a supervisor for review and patching, and we analyse a reconfigured stakeholder ecosystem that makes information explicit and accountable. Risks in such a system exist, and among others, include AI over-reliance and the potential for the illusion of learning, while guardrails are proposed. The contribution is an ex ante, literature-grounded design with workflow and governance rules that institutions can implement and trial across disciplines.

cs.CY↗

arXiv2025

DeLoad: Demand-Driven Short-Video Preloading with Scalable Watch-Time Estimation

Short video streaming has become a dominant paradigm in digital media, characterized by rapid swiping interactions and diverse media content. A key technical challenge is designing an effective preloading strategy that dynamically selects and prioritizes download tasks from an evolving playlist, balancing Quality of Experience (QoE) and bandwidth efficiency under practical commercial constraints. However, real world analysis reveals critical limitations of existing approaches: (1) insufficient adaptation of download task sizes to dynamic conditions, and (2) watch time prediction models that are difficult to deploy reliably at scale. In this paper, we propose DeLoad, a novel preloading framework that addresses these issues by introducing dynamic task sizing and a practical, multi dimensional watch time estimation method. Additionally, a Deep Reinforcement Learning (DRL) enhanced agent is trained to optimize the download range decisions adaptively. Extensive evaluations conducted on an offline testing platform, leveraging massive real world network data, demonstrate that DeLoad achieves significant improvements in QoE metrics (34.4% to 87.4% gain). Furthermore, after deployment on a large scale commercial short video platform, DeLoad has increased overall user watch time by 0.09% while simultaneously reducing rebuffering events and 3.76% bandwidth consumption.

cs.MM↗

arXiv2025

PIRA: Pan-CDN Intra-video Resource Adaptation for Short Video Streaming

In large scale short video platforms, CDN resource selection plays a critical role in maintaining Quality of Experience (QoE) while controlling escalating traffic costs. To better understand this phenomenon, we conduct in the wild network measurements during video playback in a production short video system. The results reveal that CDNs delivering higher average QoE often come at greater financial cost, yet their connection quality fluctuates even within a single video underscoring a fundamental and dynamic trade off between QoE and cost. However, the problem of sustaining high QoE under cost constraints remains insufficiently investigated in the context of CDN selection for short video streaming. To address this, we propose PIRA, a dynamic resource selection algorithm that optimizes QoE and cost in real time during video playback. PIRA formally integrating QoE and cost by a mathematical model, and introduce a intra video control theoretic CDN resource selection approach which can balance QoE and cost under network dynamics. To reduce the computation overheads, PIRA employs state space pruning and adaptive parameter adjustment to efficiently solve the high dimensional optimization problem. In large scale production experiments involving 450,000 users over two weeks, PIRA outperforms the production baseline, achieving a 2.1% reduction in start up delay, 15.2% shorter rebuffering time, and 10% lower average unit traffic cost, demonstrating its effectiveness in balancing user experience and financial cost at scale.

cs.MM↗

arXiv2025

SMEC: Rethinking Matryoshka Representation Learning for Retrieval Embedding Compression

Large language models (LLMs) generate high-dimensional embeddings that capture rich semantic and syntactic information. However, high-dimensional embeddings exacerbate computational complexity and storage requirements, thereby hindering practical deployment. To address these challenges, we propose a novel training framework named Sequential Matryoshka Embedding Compression (SMEC). This framework introduces the Sequential Matryoshka Representation Learning(SMRL) method to mitigate gradient variance during training, the Adaptive Dimension Selection (ADS) module to reduce information degradation during dimension pruning, and the Selectable Cross-batch Memory (S-XBM) module to enhance unsupervised learning between high- and low-dimensional embeddings. Experiments on image, text, and multimodal datasets demonstrate that SMEC achieves significant dimensionality reduction while maintaining performance. For instance, on the BEIR dataset, our approach improves the performance of compressed LLM2Vec embeddings (256 dimensions) by 1.1 points and 2.7 points compared to the Matryoshka-Adaptor and Search-Adaptor models, respectively.

cs.CL↗

arXiv2025

Does Machine Unlearning Truly Remove Knowledge?

In recent years, Large Language Models (LLMs) have achieved remarkable advancements, drawing significant attention from the research community. Their capabilities are largely attributed to large-scale architectures, which require extensive training on massive datasets. However, such datasets often contain sensitive or copyrighted content sourced from the public internet, raising concerns about data privacy and ownership. Regulatory frameworks, such as the General Data Protection Regulation (GDPR), grant individuals the right to request the removal of such sensitive information. This has motivated the development of machine unlearning algorithms that aim to remove specific knowledge from models without the need for costly retraining. Despite these advancements, evaluating the efficacy of unlearning algorithms remains a challenge due to the inherent complexity and generative nature of LLMs. In this work, we introduce a comprehensive auditing framework for unlearning evaluation, comprising three benchmark datasets, six unlearning algorithms, and five prompt-based auditing methods. By using various auditing algorithms, we evaluate the effectiveness and robustness of different unlearning strategies. To explore alternatives beyond prompt-based auditing, we propose a novel technique that leverages intermediate activation perturbations, addressing the limitations of auditing methods that rely solely on model inputs and outputs.

cs.LG↗

arXiv2025

Enhanced DACER Algorithm with High Diffusion Efficiency

Due to their expressive capacity, diffusion models have shown great promise in offline RL and imitation learning. Diffusion Actor-Critic with Entropy Regulator (DACER) extended this capability to online RL by using the reverse diffusion process as a policy approximator, achieving state-of-the-art performance. However, it still suffers from a core trade-off: more diffusion steps ensure high performance but reduce efficiency, while fewer steps degrade performance. This remains a major bottleneck for deploying diffusion policies in real-time online RL. To mitigate this, we propose DACERv2, which leverages a Q-gradient field objective with respect to action as an auxiliary optimization target to guide the denoising process at each diffusion step, thereby introducing intermediate supervisory signals that enhance the efficiency of single-step diffusion. Additionally, we observe that the independence of the Q-gradient field from the diffusion time step is inconsistent with the characteristics of the diffusion process. To address this issue, a temporal weighting mechanism is introduced, allowing the model to effectively eliminate large-scale noise during the early stages and refine its outputs in the later stages. Experimental results on OpenAI Gym benchmarks and multimodal tasks demonstrate that, compared with classical and diffusion-based online RL algorithms, DACERv2 achieves higher performance in most complex control environments with only five diffusion steps and shows greater multimodality.

cs.LG↗

arXiv2025

Bridging conformal field theory and parton approaches to SU(n)_k chiral spin liquids

We employ the $\mathrm{SU}(n)_k$ Wess-Zumino-Witten (WZW) model in conformal field theory to construct lattice wave functions in both one and two dimensions. The spins on all lattice sites are chosen to transform under the $\mathrm{SU}(n)$ irreducible representation with a single row and $k$ boxes in the Young tableau. It is demonstrated that the wave functions can be reinterpreted as parton states, which enables efficient conversion to matrix product states such that many physical properties can be evaluated directly. In one dimension, these wave functions describe critical spin chains whose universality classes are in one-to-one correspondence with the WZW models used in the construction. In two dimensions, our constructions yield model wave functions for chiral spin liquids, and we show how to find all topological sectors of them in a systematic way. Using the null vectors of Kac-Moody algebras, parent Hamiltonians of the $\mathrm{SU}(3)_k$ series are derived. The $\mathrm{SU}(3)_k$ chiral spin liquids are lattice analogs of non-Abelian spin-singlet fractional quantum Hall states, and the $k=2$ member hosts Fibonacci anyons.

cond-mat.str-el↗

arXiv2025

Prospects for Time-Domain and Multi-Messenger Science with eXTP

In this new era of time-domain and multi-messenger astronomy, various new transients and new phenomena are constantly being discovered thanks to the rapid advances in observations, which provide the excellent opportunity to study the physics in the extreme environments. The enhanced X-ray Timing and Polarimetry mission (eXTP), planned to be launched in 2030, has several key advantages, including advanced polarimetry, high sensitivity & large effective area, and wide energy range coverage, which make it a groundbreaking project in high-energy astrophysics. In this article, we briefly introduce the potential time-domain and multi-messenger targets for eXTP, including gravitational-wave (GW) counterparts, gamma-ray bursts (GRBs), magnetars and fast radio bursts (FRBs), tidal disruption events (TDEs), supernovae, high energy neutrinos and TeV active galactic nucleus (AGNs), and so on. We discuss the advantages of future eXTP observations for detecting these sources, their detection capabilities, the abilities to distinguish theoretical models, and their applications in gravity and cosmology.

astro-ph.HE↗

arXiv2025

An Emotion Recognition Framework via Cross-modal Alignment of EEG and Eye Movement Data

Emotion recognition is essential for applications in affective computing and behavioral prediction, but conventional systems relying on single-modality data often fail to capture the complexity of affective states. To address this limitation, we propose an emotion recognition framework that achieves accurate multimodal alignment of Electroencephalogram (EEG) and eye movement data through a hybrid architecture based on cross-modal attention mechanism. Experiments on the SEED-IV dataset demonstrate that our method achieve 90.62% accuracy. This work provides a promising foundation for leveraging multimodal data in emotion recognition

cs.MM↗