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Cheng Liu

Publications and source records attributed to Cheng Liu.

At least 19 recordsLinked to original sources
arXiv2026

Wuying-Browser-Agent: Real-World Centric Fundamental Long-Horizon Browser Agents

Browser agents perform well on short, clean demonstrations, but real deployment is fundamentally different: agents must sustain dozens of decisions on live websites while recovering from mistakes and navigating complex UIs. We argue that closing this gap requires alignment at every level of the pipeline, including execution, supervision, optimization, and evaluation, rather than scale alone. We present Wuying-Browser-Agent, a unified framework that addresses each of these levels. A structured browser harness provides stable execution primitives and decision-oriented context management. Reflection and UI-specialized Curriculum SFT (RUIC-SFT) explicitly trains on recovery trajectories and complex-UI interactions. Divergence-Aware Online GRPO (DAO-GRPO) improves long-horizon credit assignment through potential-based reward shaping and divergence-aware step weighting. Finally, we introduce BrowserBench, a bilingual real-web benchmark of 350 tasks averaging 37.9 steps, because most existing benchmarks are too short to expose long-horizon failure modes. Wuying-Browser-Agent-27B achieves 80.6\% on WebVoyager, 66.7\% on Online-Mind2Web, and 65.1\% on BrowserBench, establishing a new open-source state of the art on browser-use benchmarks. The same pipeline also transfers beyond browser use, demonstrating strong general agentic ability and reaching an average score of 73.8 on Tau2-Bench, Claw-Eval, and BFCL-v4.

cs.AI↗
arXiv2026

Multi-Granularity Position Embedding of Graphs via Granular-Ball for Link Prediction

Link prediction aims to identify potential or future connections within a given graph structure. Position information is essential for link prediction, as it distinguishes homogeneous nodes through their relative relationships, facilitating the accurate capture of structural patterns and implicit connections. Previous studies derive node positional information as distances to single-granularity landmarks, defined as the centers of homophilic regions, while neglecting the multi-granularity nature of homophilic structures and their hierarchical interrelations. We propose the Multi-Granularity Position Embedding of Graphs via Granular-Ball for Link Prediction (MGLP) method to obtain multi-granularity position embedding of graphs. Specifically, MGLP introduces an Adaptive Granular-Ball Graph Refinement mechanism to adaptively refine the graph into homophilic subdomains with optimal levels of granularity. The central nodes within subdomains are treated as landmarks, which form a Hierarchical Central Graph. Moreover, a novel Multi-granularity Hierarchical Distance encoding mechanism is proposed to capture both the homophilic structures within a graph and their hierarchical correlations, improving the discriminative power of nodes. Experimental results demonstrate that the multi-granularity position embedding generated by our method exhibits excellent performance and strong competitiveness compared to baseline algorithms for link prediction. Our codes are available in https://anonymous.4open.science/r/MGLP-D3C5/.

cs.SI↗
arXiv2026

Location-Aware NAS Timer Optimization in NTN-TN Integrated Networks

Efficient Non-Access Stratum (NAS) timer configuration is critical for reliable and energy-efficient Fifth Generation (5G) registration in Non-Terrestrial Network (NTN)-Terrestrial Network (TN) integrated systems, where Low Earth Orbit (LEO) satellite access introduces large registration bursts, heterogeneous propagation paths, and multi-hop satellite routing. Existing 3GPP NAS timers use fixed values, while prior closed-form timer models compute a global timer under network-level assumptions; both fail to capture user equipment (UE)-level differences in propagation delay, Access and Mobility Management Function (AMF) arrival position, and path reliability. In this paper, we propose a location-aware, UE-specific NAS timer optimization method for LEO NTN-TN integrated networks. The proposed method models the path-delay component using service-link geometry, ground-station distance, and Inter-Satellite Link (ISL) hop count, and adapts the endpoint-delay component according to each UE's expected AMF queue exposure and path reliability. Simulation results show that our method reduces registration latency, UE energy consumption, and avoidable registration attempts compared with fixed and global timer configurations, especially when timer over-provisioning causes unnecessary waiting.

cs.NI↗
arXiv2026

Robust logical Bell nonlocality based on quantum error correction codes

Quantum nonlocality based on the violation of Bell-like inequalities constitutes a fundamental feature of quantum physics and drives the development of device-independent (DI) quantum information technologies. Existing studies of Bell nonlocality have mainly focused on physical qubit systems, where the observed nonlocal correlations are directly encoded in physical degrees of freedom. The decoherence sensitivity of Bell nonlocality largely limits the performance and security of its DI applications. Here, we investigate the robust logical Bell nonlocality based on quantum error correction codes. We construct the general logical Bell inequality in the stabilizer coding subspace and prove its violation indicates the global nonlocal feature of the logical system. Then, we indicate that the logical Bell nonlocality is robust against decoherence. Comparing with the physical qubit system, the fidelity thresholds for the logical Bell inequality violation based on the [[3,1,1]] and [[7,1,1]] repetition codes under the bit-flip error model can be reduced from 82.8% to 73.10% and 66.35%, increasing DI QKD's bit-flip noise threshold from 10.64% to 14.42% and 23.36%, respectively. Such stabilizer-based framework can be also used to characterize the multipartite logical Bell nonlocality in principle. Finally, a logical Bell test implementation circuit based on the [[3,1,1]] repetition code is presented. This work provides a feasible avenue for unlocking robust Bell nonlocality in scalable logical quantum systems and facilitates its applications in future scalable quantum network.

quant-ph↗
arXiv2026

Harvesting AI Computation at the Edge via Generic Approximation

With the widespread adoption of AI in various IoT scenarios such as smart sensing and processing, AI chips have become a common component at the edge. These chips are typically specialized for structured neural network (NN) processing and are designed to meet peak workload demands. However, they are often underutilized and suffer from considerable computational waste due to temporal or spatial redundancy in processing. Conversely, general-purpose processing engines at the edge may struggle with compute-intensive tasks such as signal processing and complex numerical operations because of stringent resource constraints. To address this imbalance, we propose a framework that harvests unused AI computation resources using general-purpose approximation techniques. The core idea is to automatically convert traditional computing tasks into neural network models via a representative neural architecture search (NAS) method. These approximate versions of general-purpose tasks are then deployed on AI engines during their idle periods. Specifically, we introduce a runtime scheduler that offloads these tasks to AI chips without compromising the performance of primary AI workloads, thereby alleviating the burden on general-purpose processors. Experiments on a representative AIoT processor show that our proposed AI computation harvesting strategy delivers substantial performance improvements across a set of edge processing tasks.

cs.AR↗
arXiv2026

VeriPilot: An LLM-Powered Verilog Debugging Framework

Verilog debugging remains one of the most time-consuming stages in digital circuit design. Recent advances in Large Language Models (LLMs) have enabled automated debugging; however, most existing approaches rely solely on test outputs and compiler feedback in an end-to-end manner, limiting their effectiveness on complex bugs. A key challenge is that the root cause of an error may be far removed from its observable outputs, making it difficult for LLMs to trace long dependency chains in code. This challenge is further exacerbated in large codebases, where long context lengths hinder efficient reasoning. To address these limitations, we propose VeriPilot, an LLM-powered debugging framework that leverages golden reference models to enable fine-grained bug localization and repair. VeriPilot goes beyond output-level comparison by aligning internal variable semantics between the Verilog design and its corresponding golden model through LLM-based analysis. It then performs step-by-step signal tracing using Control-Data-Flow Graphs (CDFGs) derived from static analysis, identifying a minimal set of suspicious code regions along with their correct counterparts from the golden model. These structured insights are subsequently provided to the LLM to guide reasoning and automated code repair. Experimental results on the Comprehensive Verilog Design Problems (CVDP) benchmark from NVIDIA demonstrate that VeriPilot improves the repair success rate of GPT-4o from 54.3\% to 85.71\%, significantly enhancing both bug localization accuracy and repair effectiveness for complex Verilog designs. The source code and benchmark are publicly available at Github https://github.com/YihanWn/VeriPilot.git.

cs.AR↗
arXiv2026

Relativistic Thermal Emission from Accretion Disks in Kerr-MOG Spacetimes

In Scalar-Tensor-Vector Gravity (STVG, also known as MOG), a massive vector field $\phi_\mu$ generates a repulsive fifth force that endows rotating black holes with a gravitational charge $Q \propto \sqrt{\alpha}\,M$, modifying the near-horizon geometry through a single deformation parameter $\alpha$. We investigate how this vector-field coupling imprints itself on the thermal continuum emission of geometrically thin, optically thick accretion disks in the Kerr-MOG black hole. By re-deriving the innermost stable circular orbit (ISCO), the Novikov-Thorne radiative flux, the relativistic energy shift, and the null geodesic structure for the Kerr-MOG spacetime, we compute fully relativistic disk spectra across a broad range of spins, inclinations, and fifth-force strengths using a dedicated \textsc{xspec} spectral model (\texttt{kmspec}). We find that the fifth-force charge pushes the ISCO outward, lowers the peak disk temperature, and systematically softens the thermal continuum relative to its Kerr black hole counterpart at the same spin, with the deviation amplified at high observer inclinations. The resulting spectral modification closely mimics a reduction of spin in the pure Kerr black hole framework, indicating that independent spin measurements from, e.g., iron-line reflection spectroscopy are indispensable for disentangling the vector-field contribution. All results recover the standard Kerr black hole predictions when $\alpha = 0$, and the model is validated against independent analytic and numerical benchmarks to machine precision. Application to a 69.6~ks \textit{XMM-Newton} observation of LMC~X-1 yields $\alpha < 0.044$ at 90\% confidence, consistent with the Kerr metric and general relativity.

astro-ph.HE↗
arXiv2026

ANNS-AMP: Accelerating Approximate Nearest Neighbor Search via Adaptive Mixed-Precision Computing

Approximate nearest neighbor search(ANNS) is a critical kernel in modern applications such as LLM and recommendation systems.However,its efficiency is fundamentally limited by the need to compute distances between a query and a massive number of high-dimensional vectors,most of which are non-neighbors.Existing approaches reduce redundancy via index optimization or early termination,but remain constrained by fixed-precision computation,leading to unnecessary arithmetic and memory bandwidth overhead.This paper presents ANNS-AMP,an adaptive mixed-precision framework and accelerator that adapts the precision of distance computation to the characteristics of queries and data distribution.The key insight is that different regions of the vector space require different levels of precision to preserve top-k accuracy.ANNS-AMP leverages the clustered structure of PQ-based indices and introduces a lightweight predictor to determine cluster-level precision at runtime based on features such as scale,radius,and query distance.To efficiently realize variable-precision execution,we design a bit-serial accelerator with a bit-interleaved data layout,enabling throughput to scale with reduced precision while mitigating memory bandwidth bottlenecks and load imbalance through a greedy scheduling strategy.Moreover,the runtime predictor can also reuse the bit-serial computing array for efficient runtime prediction and can be fitted to the ANNS pipeline without performance penalty.According to our experiments on representative datasets,ANNS-AMP achieves 163.76x,10.57x,and 2.06x performance speedups on average,and reduces average energy consumption by 1100.00x,39.41x,and 6.66x compared to CPU,GPU,and customized ANNS accelerator baselines,respectively,while maintaining accuracy loss below 2.7%.These results demonstrate that adaptive mixed-precision computing is a promising direction for efficient large-scale ANNS.

cs.PF↗
arXiv2026

UAT: Unified Audio-Text Diffusion for Audio Generation, Editing, and Captioning

Audio generation and audio-to-text understanding remain largely separate, with diffusion models dominating high-fidelity synthesis and autoregressive (AR) language models driving captioning and semantic prediction. Existing unified approaches typically rely on either heterogeneous modules or AR-centric modeling, which can hinder joint optimization and limit acoustic fidelity. We present UAT, to our knowledge, the first diffusion-centric framework that supports unified audio generation, editing, and captioning. UAT couples continuous latent diffusion for audio with masked discrete diffusion for text, enabling bidirectional audio-text modeling within a shared dual-stream backbone. Experiments show that UAT preserves strong audio generation and editing capabilities while achieving competitive captioning performance, demonstrating a favorable balance between acoustic synthesis and semantic prediction. Demo samples are available at https://UAT-demo.github.io.

eess.AS↗
arXiv2026

FT-Pilot: Automated Fault-Tolerant RTL Rewriting via Vulnerability-Guided LLMs

As integrated circuit technologies continue to scale toward advanced process nodes, the continual reduction in node capacitance and supply voltage has made digital systems increasingly vulnerable to soft errors. Although traditional full-chip hardening methods can improve reliability, they often incur unacceptable area and power overhead, making selective hardening a more practical engineering solution. However, existing approaches typically rely on time-consuming fault-injection simulation to determine hardening locations through vulnerability analysis, and still depend heavily on manual strategy selection and RTL modification during the hardening stage, making them ill-suited for efficient automated reliability optimization at early design stages. To address these challenges, this paper proposes FT-Pilot, a GNN-guided LLM framework for automatic RTL soft-error hardening. The framework first employs a GNN to identify critical vulnerable assets directly at the RTL level, and then introduces an LLM-driven rewriting engine composed of an analyzer and a rewriter, which performs RTL-level fault-tolerant code rewriting with the support of dual-knowledge-base retrieval-augmented generation and an automatic repair mechanism. Experimental results show that the proposed framework can automatically generate hardened RTL designs that are syntactically correct, functionally correct, and synthesizable across multiple benchmark circuits, while significantly reducing output error rates under soft-error scenarios. This work provides a practical automated path toward shift-left reliability optimization at the RTL level.

cs.AR↗
arXiv2026

StreamingEffect: Real-Time Human-Centric Video Effect Generation

Streaming video effect generation is highly desirable for live human-centric applications such as e-commerce streaming, entertainment, and vlogging, yet remains difficult due to the lack of suitable data and deployable editing models. Unlike generic video generation, this task requires real-time video-to-video editing that adds expressive effects while preserving human identity, background content, and temporal consistency. Existing acceleration efforts mainly focus on text-to-video generation, while efficient distillation for video editing remains largely underexplored. In this paper, we present \textbf{StreamingEffect}, a real-time human-centric streaming video effect framework. We adopt an in-context video editing architecture and train a high-quality bidirectional teacher, then distill it into a causal autoregressive student and further reduce sampling from 50 steps to 4 steps. We also introduce keyframe control, allowing reference effect frames to be injected online and propagated through the stream for interactive editing. To address the data bottleneck, we construct \textbf{VideoEffect-130K}, to our knowledge the largest human-centric video effect dataset, containing 70K effect videos and 60K editing videos across 600 effect categories curated from short-video and editing platforms. Experiments show that our method enables real-time, high-quality 720p video editing on a single H200 GPU.

cs.CV↗
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$\alpha$ 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 $\delta$-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$\alpha$ lines with suppressed red wings as the throat parameter $\lambda$ increases. In 50 ks \textit{NuSTAR} simulations ($\lambda=0.9, a_*=0.998$), 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

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

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

Knowing When to Ask: Resolving Uncertainty in Human-Robot Joint Planning via Explicit Dialogue and Implicit Intent Cues

Effective human-robot collaboration in open-world environments requires joint planning under uncertainty about the task, the environment, and the human teammate. Communication is the most direct means of resolving such uncertainty, yet most existing systems support only one-way communication: robots listen and act, treating humans as passive supervisors rather than conversational teammates capable of two-way dialogue. We propose a unified human-robot joint planning system in which the robot actively resolves uncertainty through two complementary communication channels. When uncertainty is decision-critical, an uncertainty-mitigation joint planning module engages the human in clarification dialogue: it grounds ambiguous instructions via an LLM-assisted active elicitation mechanism, enumerates traversability hypotheses through a hypothesis-augmented A* search, and computes a cost-optimal querying policy via dynamic programming, so that the robot asks only the questions whose answers actually matter for the plan. When explicit dialogue is unnecessary or impractical, a real-time intent-aware collaboration module instead reads implicit, nonverbal cues, maintaining a probabilistic belief over the human's latent task intent from spatial and directional signals to enable coordination-aware task selection without any communication overhead. We validate the proposed system in both Gazebo simulations and real-world UAV deployments, integrated with a voice dialogue interface and a Vision-Language Model (VLM)-based 3D semantic perception pipeline. Experimental results show that cost-optimal clarification dialogue cuts the interaction cost by 51.9% while maintaining a 100% task success rate, and implicit intent reading reduces the cooperative task execution time by 25.4% compared to the baselines.

cs.RO↗
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↗