arXiv subjects

Yulong Fu

Publications and source records attributed to Yulong Fu.

6 recordsLinked to original sources

arXiv2026

Benchmarking Automated Security Patch Backporting: How Far Are We?

Automated security patch backporting is critical for mitigating N-day vulnerabilities. Recent tools report success rates above 80% on their respective datasets. However, these evaluations are often confined to homogeneous environments, such as one repository or specific project versions. Consequently, it remains unclear how well these tools generalize beyond their originally targeted scenarios. We present Porting Benchmark, a curated dataset of 1,234 security patch backporting cases spanning cross-version, cross-branch, and cross-repository scenarios, paired with a common evaluation framework. Using this benchmark, we evaluate five tools spanning program analysis, LLM prompting, and LLM agents under aligned settings. Our results show that aligned evaluation changes the apparent performance landscape: PortGPT and TSBPort remain comparatively strong on the Replication Dataset, while FixMorph and Mystique degrade substantially under the common protocol. Performance degrades sharply on structurally complex patches: the best commit-level success rate falls from 85.2% on Type-I patches to 24.0% on Type-IV. We identify four root-cause categories (missing target API awareness, cross-version semantic mismatch, non-local dependency propagation failure, and patch construction or localization failure) and derive concrete directions for next-generation tool design. On a 45-case dynamically validated subset with verified test cases and constructed POCs, we further observe that reference-based benchmark scores do not fully capture real-world remediation: exact match sharply under-credits harder target adaptations, while executable validation reveals residual integration failures in the target that static reference agreement misses. Executable-feedback refinement provides limited but measurable recovery on the hardest executable cases.

cs.SE↗

arXiv2026

TactileReflex: Noise-Statistics-Driven Vision-Tactile Reflex Control for Force-Sensitive Manipulation

Manipulating fragile deformable containers, such as disposable plastic cups filled with liquid, demands real-time grip-force adaptation within an extremely narrow force margin: insufficient force causes slip, while excessive force irreversibly deforms the thin wall. Existing approaches struggle to achieve such force-sensitive manipulation tasks. We propose a noise-statistics-based calibration-driven reflex control paradigm with vision-based tactile sensing: by analyzing the sensor's intrinsic noise characteristics (via a brief static-hold-and-unload protocol), we directly derive all controller thresholds, eliminating external force calibration, trial-and-error manual tuning, or material-specific physical models. Instantiating this paradigm, we present TactileReflex, a three-channel closed-loop controller that extracts three image-level proxies, shear intensity ($S_y$), contact intensity ($F_n$), and center of pressure ($C$), from dual visuo-tactile sensors and drives prioritized reflex channels at ~12 Hz for slip suppression, weight-adaptive release, and force protection. Each channel closes the loop directly on its proxy via noise-derived thresholds. Ablation demonstrates that only the full three-channel system is able to prevent irreversible container deformation (5/5 success vs. at most 1/5 for partial configurations). In a dynamic pouring task, fixed-effort baselines fail in all 10 attempts due to pose drift, while TactileReflex achieves 9/10 success across two water volumes. As a self-contained and interpretable controller, TactileReflex can serve as a plug-and-play safety layer beneath high-level manipulation pipelines, including haptic-free VR teleoperation and vision-language-action (VLA) policies.

cs.RO↗

arXiv2025

Governable AI: Provable Safety Under Extreme Threat Models

As AI rapidly advances, the security risks posed by AI are becoming increasingly severe, especially in critical scenarios, including those posing existential risks. If AI becomes uncontrollable, manipulated, or actively evades safety mechanisms, it could trigger systemic disasters. Existing AI safety approaches-such as model enhancement, value alignment, and human intervention-suffer from fundamental, in-principle limitations when facing AI with extreme motivations and unlimited intelligence, and cannot guarantee security. To address this challenge, we propose a Governable AI (GAI) framework that shifts from traditional internal constraints to externally enforced structural compliance based on cryptographic mechanisms that are computationally infeasible to break, even for future AI, under the defined threat model and well-established cryptographic assumptions.The GAI framework is composed of a simple yet reliable, fully deterministic, powerful, flexible, and general-purpose rule enforcement module (REM); governance rules; and a governable secure super-platform (GSSP) that offers end-to-end protection against compromise or subversion by AI. The decoupling of the governance rules and the technical platform further enables a feasible and generalizable technical pathway for the safety governance of AI. REM enforces the bottom line defined by governance rules, while GSSP ensures non-bypassability, tamper-resistance, and unforgeability to eliminate all identified attack vectors. This paper also presents a rigorous formal proof of the security properties of this mechanism and demonstrates its effectiveness through a prototype implementation evaluated in representative high-stakes scenarios.

cs.AI↗

arXiv2025

Distributed Shared Layered Storage Quantum Simulator: A novel quantum simulation system for efficient scaling and cost optimization

Quantum simulators are essential tools for developing and testing quantum algorithms. However, the high-frequency traversal characteristic of quantum simulators represents an unprecedented demand in the history of IT, and existing distributed technologies is unable to meet this requirement, resulting in a single-node bottleneck of quantum simulator. To overcome this limitation, this paper introduces a novel Distributed Shared Layered Storage Quantum Simulator (DSLSQS). By leveraging an innovative distributed architecture in which multiple computational nodes share data storage directly, together with De-TCP/IP networking technology, DSLSQS effectively eliminates East-West data flow in distributed systems. This approach mitigates the bottleneck of distributed quantum simulation clusters and enhances the scalability. Moreover, the system employs layered storage technology, which reduces usage of expensive high-performance memory and substantially lowers simulation costs. Furthermore, this paper systematically analyzes the performance and cost constraints of distributed quantum simulator cluster, identifying distributed networking as the primary performance bottleneck and highlighting that minimizing storage costs is crucial to reducing the total cost. Finally, experimental evaluations with a 27-qubit simulation confirm the successful implementation of layered storage within the quantum simulator. DSLSQS significantly enhances simulation efficiency, yielding a performance improvement of over 350% compared to existing distributed technologies. These results underscore the superior performance and scalability of the proposed architecture in managing complex quantum computing tasks. This paper provides crucial insights for the practical deployment of quantum computing and presents an effective framework for the development of distributed quantum simulation clusters.

cs.ET↗

arXiv2025

QVecOpt: An Efficient Storage and Computing Opti-mization Framework for Large-scale Quantum State Simulation

In response to the challenges in large-scale quantum state simulation on classical computing platforms, including memory limits, frequent disk I/O, and high computational complexity, this study builds upon a previously proposed hierarchical storage-based quantum simulation system and introduces an optimization framework, the Quantum Vector Optimization Framework (QVecOpt). QVecOpt integrates four strategies: amplitude pairing, cache optimization, block storage optimization, and parallel optimization. These collectively enhance state vector storage and computational scheduling. The amplitude pairing mechanism locates relevant amplitude pairs via bitwise XOR, reducing traversal complexity of single-qubit gates from $O(2^n)$ to $O(1)$. Cache optimization pre-allocates buffers and loads only required data, cutting disk I/O. Block storage optimization partitions the state vector for on-demand loading and local updates, reducing redundant access. Parallel optimization distributes the state vector across nodes for collaborative computation, achieving near-linear speedup. Complexity analysis shows that, compared with hierarchical storage simulation, the method reduces state vector traversals for single-qubit gates from $2^n$ to 1, removing the main bottleneck. It also lowers computational and I/O complexity from $O(2^n)$ to $O(2^n/C)$ and $O(2^n/B)$. In simulations of 16-29 qubits, efficiency improves nearly tenfold, breaking the memory bottleneck of existing tools and enabling high-bit quantum circuit simulations beyond traditional methods. This work provides an efficient, scalable solution for classical simulation of large-scale quantum computation with significant academic and practical value.

cs.ET↗

arXiv2013

Proof of a Conjecture on the Genus Two Free Energy Associated to the A_n Singularity

In a recent paper [8], it is proved that the genus two free energy of an arbitrary semisimple Frobenius manifold can be represented as a sum of contributions associated with dual graphs of certain stable algebraic curves of genus two plus the so called genus two G-function, and for a certain class of Frobenius manifolds it is conjectured that the associated genus two G-function vanishes. In this paper, we prove this conjecture for the Frobenius manifolds associated with simple singularities of type A.

math-ph↗