arXiv subjects

Cheng Liu

Publications and source records attributed to Cheng Liu.

At least 37 records · Page 2Linked to original sources

arXiv2026

Dynamics and Radiative Signatures of Accretion Flows onto a Kerr-like Wormhole

Wormholes are a hypothetical object that connects disparate points in spacetime. It is a theoretically well-motivated black hole alternative and offers a potential observationally testable arena for probing strong-field gravity with horizon-scale images. We perform general relativistic magnetohydrodynamic (GRMHD) simulations and general relativistic radiative transfer (GRRT) calculations of accretion flows onto a Kerr-like wormhole. Adopting a Kerr black-bounce metric with a fixed throat parameter $\ell = 2.5\,\rm M$, we explore the effects of spin using both two- and three-dimensional simulations. The accretion flow is initialized as a magnetized geometrically thick torus near one mouth of the wormhole, while the opposite mouth is initially gas-free. We find that the spin parameter influences the dynamical properties on both sides of the wormhole through the frame-dragging effects. Based on the GRMHD results, we compute ray-traced images at $230\,\mathrm{GHz}$ using \texttt{RAPTOR}, and analyze the horizon-scale image structure through higher-order photon trajectories. Our GRRT calculations show that emissions originating from the immediate vicinity of the throat can dominate, in contrast to the case of a Kerr black hole. It provides the variable component of the signal and imprints a clear quasi-periodic modulation in the light curves. These properties would be useful to either confirm or rule out such exotic compact objects through horizon-scale observations.

astro-ph.HE↗

arXiv2026

Signature of iron line profile from a Kerr-like wormhole

Broad, skewed iron K$α$ emission lines in the X-ray spectra of accreting black holes encode key information about the spacetime geometry of the innermost disk. While the Kerr metric is standard for spin measurements, horizonless alternatives like traversable "Kerr-like" wormholes can mimic many black hole signatures, challenging current data interpretations. We develop a relativistic reflection framework incorporating Kerr-like wormhole geometries to predict iron line distortions and assess the feasibility of distinguishing event horizons from wormhole throats.Using a custom ray-tracing subroutine, we implement two \textsc{XSPEC} modules: \texttt{kwline} for $δ$-function profiles and \texttt{kwconv} for full reflection spectra, parameterized by spin, throat radius, and shape-function coefficients. We compute a dense grid of line profiles and generate synthetic \textit{NuSTAR} spectra with realistic response matrices. By fitting these simulations with canonical Kerr models, we quantify deviations attributable to wormhole geometries.We find that Kerr-like wormholes produce narrower Fe K$α$ lines with suppressed red wings as the throat parameter $λ$ increases. In 50 ks \textit{NuSTAR} simulations ($λ=0.9, a_*=0.998$), simple convolutional models (\texttt{kerrconv}) can mimic the wormhole spectrum. However, self-consistent models like \texttt{relxillCp} result in statistical failure, yielding structured residuals and unphysical parameter pegging (e.g., emissivity $q_{\rm in} \to 10$). We conclude that large-throat wormholes are detectable in high-quality X-ray spectra if analyzed with fully consistent reflection models rather than post-processing approximations.

astro-ph.HE↗

arXiv2026

Adaptive Soft Error Protection for Neural Network Processing

Previous research on selective protection for neural network components typically exploits only static vulnerability differences. Although these methods improve upon classical modular redundancy, they still incur substantial overhead for neural network workloads that are both memory-intensive and compute-intensive. In this work, we observe that neural network vulnerability is also input-dependent and varies dynamically at runtime. With this observation, we propose an adaptive, vulnerability-aware fault tolerance framework. At its core, a lightweight graph neural network (GNN) model dynamically predicts soft error vulnerabilities across inputs and neural network components, enabling real-time adaptation of fault tolerance policies. This design offers a complementary and more efficient protection scheme compared to traditional approaches. Experimental results demonstrate that the GNN predictor achieves over 95% accuracy in identifying critical inputs and components. Moreover, our adaptive scheme reduces computational overhead by an average of 42.12% while preserving model accuracy, significantly outperforming static selective protection methods.

cs.LG↗

arXiv2026

TrajGuard: Streaming Hidden-state Trajectory Detection for Decoding-time Jailbreak Defense

Existing jailbreak defense paradigms primarily rely on static detection of prompts, outputs, or internal states, often neglecting the dynamic evolution of risk during decoding. This oversight leaves risk signals embedded in decoding trajectories underutilized, constituting a critical blind spot in current defense systems. In this work, we empirically demonstrate that hidden states in critical layers during the decoding phase carry stronger and more stable risk signals than input jailbreak prompts. Specifically, the hidden representations of tokens generated during jailbreak attempts progressively approach high-risk regions in the latent space. Based on this observation, we propose TrajGuard, a training-free, decoding-time defense framework. TrajGuard aggregates hidden-state trajectories via a sliding window to quantify risk in real time, triggering a lightweight semantic adjudication only when risk within a local window persistently exceeds a threshold. This mechanism enables the immediate interruption or constraint of subsequent decoding. Extensive experiments across 12 jailbreak attacks and various open-source LLMs show that TrajGuard achieves an average defense rate of 95%. Furthermore, it reduces detection latency to 5.2 ms/token while maintaining a false positive rate below 1.5%. These results confirm that hidden-state trajectories during decoding can effectively support real-time jailbreak detection, highlighting a promising direction for defenses without model modification.

cs.CR↗

arXiv2026

Towards Exploratory and Focused Manipulation with Bimanual Active Perception: A New Problem, Benchmark and Strategy

Recently, active vision has reemerged as an important concept for manipulation, since visual occlusion occurs more frequently when main cameras are mounted on the robot heads. We reflect on the visual occlusion issue and identify its essence as the absence of information useful for task completion. Inspired by this, we come up with the more fundamental problem of Exploratory and Focused Manipulation (EFM). The proposed problem is about actively collecting information to complete challenging manipulation tasks that require exploration or focus. As an initial attempt to address this problem, we establish the EFM-10 benchmark that consists of 4 categories of tasks that align with our definition (10 tasks in total). We further come up with a Bimanual Active Perception (BAP) strategy, which leverages one arm to provide active vision and another arm to provide force sensing while manipulating. Based on this idea, we collect a dataset named BAPData for the tasks in EFM-10. With the dataset, we successfully verify the effectiveness of the BAP strategy in an imitation learning manner. We hope that the EFM-10 benchmark along with the BAP strategy can become a cornerstone that facilitates future research towards this direction. Project website: EFManipulation.github.io.

cs.RO↗

arXiv2026

On the Vulnerability of FHE Computation to Silent Data Corruption

Fully Homomorphic Encryption (FHE) is rapidly emerging as a promising foundation for privacy-preserving cloud services, enabling computation directly on encrypted data. As FHE implementations mature and begin moving toward practical deployment in domains such as secure finance, biomedical analytics, and privacy-preserving AI, a critical question remains insufficiently explored: how reliable is FHE computation on real hardware? This question is especially important because, compared with plaintext computation, FHE incurs much higher computational overhead, making it more susceptible to transient hardware faults. Moreover, data corruptions are likely to remain silent: the FHE service has no access to the underlying plaintext, causing unawareness even though the corresponding decrypted result has already been corrupted. To this end, we conduct a comprehensive evaluation of SDCs in FHE ciphertext computation. Through large-scale fault-injection experiments, we characterize the vulnerability of FHE to transient faults, and through a theoretical analysis of error-propagation behaviors, we gain deeper algorithmic insight into the mechanisms underlying this vulnerability. We further assess the effectiveness of different fault-tolerance mechanisms for mitigating these faults.

cs.CR↗

arXiv2026

Newly discovered Luminous blue variable candidates in M31 & M33

This study presents an investigation of nearly two dozen candidate Luminous Blue Variables (cLBVs) in the galaxies M31 and M33. Eight stars have been studied in detail, while an additional sixteen objects are briefly mentioned. Multi-epoch spectra of confirmed cLBVs from LAMOST and previous literature show broad hydrogen, He I lines, abundant Fe II and [Fe II] emission lines, and discernible spectral variability, consistent with the characteristics of known LBVs. Low outflow velocities inferred from P Cygni profiles are also incorporated into the classification criteria. Moreover, key stellar properties, including temperature and luminosity, are determined using the Spectral Energy Distribution (SED) fitting and spectral modeling. By comparison with stellar evolutionary tracks on the temperature luminosity diagram, the initial masses are estimated to be in the range of approximately 32 to 60 $M_{\odot}$. Except for J013401 and J013411, other stars locate within the typical LBV region between the S Doradus instability strip and their outburst phase. More importantly, our sample, except for the binary system, are all positioned in the LBVs region rather than that of B[e]SGs in the near-infrared color-color diagram. Based on all available information, one of the eight sources is confirmed as an LBV, four stars are designated as high-probability cLBVs, and the remaining three stars await further photometric observations to secure their classification. Given the current scarcity of known cLBVs, our study has the potential to make a significant increase in the number of LBVs in M31 and M33.

astro-ph.SR↗

arXiv2026

Reasoning Knowledge-Gap in Drone Planning via LLM-based Active Elicitation

Human-AI joint planning in Unmanned Aerial Vehicles (UAVs) typically relies on control handover when facing environmental uncertainties, which is often inefficient and cognitively demanding for non-expert operators. To address this, we propose a novel framework that shifts the collaboration paradigm from control takeover to active information elicitation. We introduce the Minimal Information Neuro-Symbolic Tree (MINT), a reasoning mechanism that explicitly structures knowledge gaps regarding obstacles and goals into a queryable format. By leveraging large language models, our system formulates optimal binary queries to resolve specific ambiguities with minimal human interaction. We demonstrate the efficacy of this approach through a comprehensive workflow integrating a vision-language model for perception, voice interfaces, and a low-level UAV control module in both high-fidelity NVIDIA Isaac simulations and real-world deployments. Experimental results show that our method achieves a significant improvement in the success rate for complex search-and-rescue tasks while significantly reducing the frequency of human interaction compared to exhaustive querying baselines.

cs.RO↗

arXiv2026

Emergent Polar Metal Phase in a Van der Waals Mott Magnet

We report the emergence of a two-dimensional (2D) polar metal phase in van der Waals compound FePSe$_3$ under moderate pressures. This layered material is a Mott insulator with antiferromagnetic order under ambient conditions. We show that FePSe$_3$ uniquely allows tuning a 2D correlated insulator into an exotic metal state where a loss of inversion symmetry leads to periodic polar displacements of ions, within a conducting phase - a polar metal. Our combined synchrotron and neutron diffraction data allow us to present a long-sought, unambiguous high-pressure structural model and show the polar displacements of this new phase. We also observe the suppression of magnetic ordering at the insulator-to-metal transition correspondent with this structural change. Our work outlines a comprehensive temperature-pressure phase diagram of FePSe$_3$, combining detailed structural, magnetic and transport data. The high-pressure phase exhibits activated semiconductor behavior at high temperatures, a $T^2$-dependence in its resistivity at lower temperatures - despite the conditions required for a `good metal' Fermi-Liquid description not being met in this case - and a low-temperature resistivity upturn which is suppressed as the system is tuned away from the concomitant transitions. The realisation of a tunable 2D polar metal state in FePSe$_3$ due to the loss of its inversion symmetry combined with pressure-induced metallicity offers a promising new platform to investigate this exotic phase at accessible pressures.

cond-mat.str-el↗

arXiv2026

Towards the Giant Radio Array for Neutrino Detection (GRAND): the GRANDProto300 and GRAND@Auger prototypes

The Giant Radio Array for Neutrino Detection (GRAND) is a proposed multi-messenger observatory of Ultra-High-Energy (UHE) particles of cosmic origin. Its main goal is to find the long-sought origin of UHE cosmic rays by detecting large numbers of them and the secondary particles created by their interactions like gamma rays and neutrinos. The GRAND Collaboration plans to achieve this using large arrays of radio antennas that look for the radio signals emitted by the air showers initiated by the interactions of the UHE particles in the atmosphere. Since 2023, three small-scale prototype GRAND arrays have been in operation: GRAND@Nançay in France, GRAND@Auger in Argentina, and GRANDProto300 in China. Together, their goal is to validate the detection principle of GRAND under prolonged field conditions, achieving efficient, autonomous radio-detection of air showers. We describe the hardware, software, layout, and operation of the GRAND prototypes. Using their data, we show a first characterization of the local electromagnetic environment of each site and a measurement of the Galactic synchrotron emission. Despite challenges, the successful operation of the prototypes confirms that the GRAND instrumentation is apt to address the goals of the experiment and lays the groundwork for its ensuing stages.

astro-ph.IM↗

arXiv2026

Decision Support under Prediction-Induced Censoring

In many data-driven online decision systems, actions determine not only operational costs but also the data availability for future learning -- a phenomenon termed Prediction-Induced Censoring (PIC). This challenge is particularly acute in large-scale resource allocation for generative AI (GenAI) serving: insufficient capacity triggers shortages but hides the true demand, leaving the system with only a "greater-than" constraint. Standard decision-making approaches that rely on uncensored data suffer from selection bias, often locking the system into a self-reinforcing low-provisioning trap. To break this loop, this paper proposes an adaptive approach named PIC-Reinforcement Learning (PIC-RL), a closed-loop framework that transforms censoring from a data quality problem into a decision signal. PIC-RL integrates (1) Uncertainty-Aware Demand Prediction to manage the information-cost trade-off, (2) Pessimistic Surrogate Inference to construct decision-aligned conservative feedback from shortage events, and (3) Dual-Timescale Adaptation to stabilize online learning against distribution drift. The analysis provides theoretical guarantees that the feedback design corrects the selection bias inherent in naive learning. Experiments on production Alibaba GenAI traces demonstrate that PIC-RL consistently outperforms state-of-the-art baselines, reducing service degradation by up to 50% while maintaining cost efficiency.

eess.SY↗

arXiv2026

Understanding and Mitigating Errors of LLM-Generated RTL Code

Despite limited success in large language model (LLM)-based register-transfer-level (RTL) code generation, the root causes of errors remain poorly understood. To address this, we conduct a comprehensive error analysis, finding that most failures arise not from deficient reasoning, but from a lack of RTL programming knowledge, insufficient circuit understanding, ambiguous specifications, or misinterpreted multimodal inputs. Leveraging in-context learning, we propose targeted correction techniques: a retrieval-augmented generation (RAG) knowledge base to supply domain expertise; design description rules with rule-checking to clarify inputs; external tools to convert multimodal data into LLM-compatible formats; and an iterative simulation-debugging loop for remaining errors. Integrating these into an LLM-based framework yields significant improvement, achieving 98.1% accuracy on the VerilogEval benchmark with DeepSeek-v3.2-Speciale, demonstrating the effectiveness of our approach.

cs.AR↗

arXiv2026

AudioEval: Automatic Dual-Perspective and Multi-Dimensional Evaluation of Text-to-Audio-Generation

Text-to-audio (TTA) generation is advancing rapidly, but evaluation remains challenging because human listening studies are expensive and existing automatic metrics capture only limited aspects of perceptual quality. We introduce AudioEval, a large-scale TTA evaluation dataset with 4,200 generated audio samples (11.7 hours) from 24 systems and 126,000 ratings collected from both experts and non-experts across five dimensions: enjoyment, usefulness, complexity, quality, and text alignment. Using AudioEval, we benchmark diverse automatic evaluators to compare perspective- and dimension-level differences across model families. We also propose Qwen-DisQA as a strong reference baseline: it jointly processes prompts and generated audio to predict multi-dimensional ratings for both annotator groups, modeling rater disagreement via distributional prediction and achieving strong performance. We will release AudioEval to support future research in TTA evaluation.

cs.SD↗

arXiv2026

Lifecycle Cost-Effectiveness Modeling for Redundancy-Enhanced Multi-Chiplet Architectures

The growing demand for compute-intensive applications has made multi-chiplet architectures a promising alternative to monolithic designs, offering improved scalability and manufacturing flexibility. However, effectively managing the economic effectiveness remains challenging. Existing cost models either overlook the amortization of compute value over a chip's operational lifetime or fail to evaluate how redundancy strategies, which are widely adopted to enhance yield and fault tolerance, impact long-term cost efficiency. This paper presents a comprehensive cost-effectiveness framework for multi-chiplet architectures, introducing a novel Lifecycle Cost Effectiveness (LCE) metric that evaluates amortized compute costs by jointly optimizing manufacturing expenses and operational lifetime. Our approach uniquely integrates: (1) redundancy-aware cost modeling spanning both intra- and inter-chiplet levels, (2) reliability-driven lifetime estimation, and (3) quantitative analysis of how redundancy configurations on overall economic effectiveness. Extensive trade-off and multi-objective optimization studies demonstrate the effectiveness of the model and reveal essential co-optimization strategies between module and chiplet-level redundancy to achieve cost-efficient multi-chiplet architecture designs.

cs.AR↗

arXiv2026

The R2Pub Telescopes for Surveying: An Overview and Performance Evaluation of the System

The R2Pub telescope, built by the Beijing Planetarium, is a 60 cm equatorial binocular telescope located at the Daocheng site of Yunnan Observatories in China, at an altitude of about 4700 m. This paper presents an overview of the R2Pub telescope system, including its design, instrumentation, and survey capabilities, and reports an initial evaluation of its system performance. R2Pub is a prime-focus binocular system, with each optical tube covering a field of view of approximately 18 square degrees. It is designed to detect a wide range of transient and variable sources in the local universe, such as variable stars, eclipsing binaries, supernovae, gamma-ray burst afterglows, tidal disruption events, active galactic nuclei, and other unknown transients. The observatory infrastructure, including the dome, equatorial mount, optical tubes, and associated subsystems, has been fully constructed and installed, and the system has entered the commissioning phase. Benefiting from the high-altitude location, good seeing conditions, and dark sky background at the Daocheng site, performance tests during commissioning show that the R2Pub system can reach a 5-sigma limiting magnitude of about 18.7 mag in the Pan-STARRS r' band with a 60 s exposure. Ongoing observations with R2Pub are expected to contribute to studies of variable and transient phenomena and to enhance public outreach in astronomy. The binocular design enables simultaneous dual-band observations, providing instantaneous color information for transient sources and improving the classification and physical characterization of their properties and evolution.

astro-ph.IM↗

arXiv2026

A Highly Magnetic Ultra Massive White Dwarf with a 23-minute Rotation Period

We present a physical characterization of TMTS J00063798+3104160 (J0006), a rapidly rotating,ultra-massive white dwarf (WD) identified in high-cadence light curves from the Tsinghua University-Ma Huateng Telescope for Survey (TMTS). A coherent 23-minute periodicity is detected in TMTS, TESS, and ZTF photometry. A time series of low-resolution spectra with the Keck-I 10 m telescope reveals broad, shallow hydrogen absorption features indicative of an extreme magnetic field and shows no evidence for radial-velocity variations. Atmospheric modeling yields a magnetic field strength of $\sim$ 250 MG, while Gaia astrometry and photometry imply a mass of 1.06 $\pm$ 0.01 M$_{\odot}$. A significant infrared excess is detected in the WISE W1 band and is well fitted by a 550 K blackbody, likely arising from residual material of a merger. We interpret the 23-minute photometric modulation as the rotation period of an isolated, massive WD formed likely through the merger of a double WD binary. With one of the shortest rotation periods known among candidate merger remnants and with constraints from a deep Einstein Probe X-ray nondetection, J0006 provides a rare and important observational window into the poorly explored intermediate stages of post-merger evolution.

astro-ph.SR↗

arXiv2025

Analysis of LLM Vulnerability to GPU Soft Errors: An Instruction-Level Fault Injection Study

Large language models (LLMs) are highly compute- and memory-intensive, posing significant demands on high-performance GPUs. At the same time, advances in GPU technology driven by shrinking transistor sizes and lower operating voltages have made these devices increasingly susceptible to soft errors. While prior work has examined GPU reliability, most studies have focused on general-purpose applications or conventional neural networks mostly used for vision tasks such as classification and detection. In contrast, systematic analysis of modern large-scale LLMs remains limited, despite their rapid adoption in diverse application scenarios. Given the unique characteristics of LLMs, their resilience to soft errors may differ substantially from earlier models. To bridge this gap, we conduct the first instruction-level fault injection study of LLM inference. Our approach reveals reliability characteristics from multiple perspectives, highlighting the effects of model architecture, parameter scale, and task complexity. These findings provide new insights into LLM reliability and inform the design of more effective fault tolerance mechanisms.

cs.AR↗

arXiv2025

Mitty: Diffusion-based Human-to-Robot Video Generation

Learning directly from human demonstration videos is a key milestone toward scalable and generalizable robot learning. Yet existing methods rely on intermediate representations such as keypoints or trajectories, introducing information loss and cumulative errors that harm temporal and visual consistency. We present Mitty, a Diffusion Transformer that enables video In-Context Learning for end-to-end Human2Robot video generation. Built on a pretrained video diffusion model, Mitty leverages strong visual-temporal priors to translate human demonstrations into robot-execution videos without action labels or intermediate abstractions. Demonstration videos are compressed into condition tokens and fused with robot denoising tokens through bidirectional attention during diffusion. To mitigate paired-data scarcity, we also develop an automatic synthesis pipeline that produces high-quality human-robot pairs from large egocentric datasets. Experiments on Human2Robot and EPIC-Kitchens show that Mitty delivers state-of-the-art results, strong generalization to unseen environments, and new insights for scalable robot learning from human observations.

cs.CV↗