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新论文 · v1arXiv:2611.13049

The Gene Is Not the Gate: Conceptualizing the Semantic Genome of Quantum Software

Jiongchi Yu, Yue Duan, Ziming Zhao, Xiaofei Yue, Zhaoxuan Li

原文摘要

Quantum software spans applications, program representations, compilers, runtimes, and hardware-facing control. Existing abstractions describe these artifacts and layers, but they do not capture the semantic responsibilities that must remain valid as a computation is transformed across the stack. This limits systematic reasoning about cross-layer correctness, evolution, and reuse. We present QGenome, a semantic foundation for quantum software genomics. We define a quantum software gene as a typed, reusable responsibility characterized by an explicit contract, execution context, concrete expressions, and supporting evidence. QGenome organizes genes across five software strata and ten responsibility families grouped into computational semantics, coordination, realization, and assurance. Typed genes compose into pathways and genomes through dependency, refinement, provenance, and phenotype relations, enabling responsibilities to be traced across alternative implementations and execution contexts. We illustrate the taxonomy using representative public artifacts across the quantum software stack and define principles for contextual validity, genome evolution, environmental adaptation, and capability expression. Rather than presenting a complete taxonomy or an automated extraction technique, QGenome establishes a falsifiable framework and a concrete research agenda for gene mining, lineage recovery, cross-layer regression testing and repair, and evidence-grounded quantum software agents.

cs.SE

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2505.24030

From Images to Signals: Are Large Vision Models Useful for Time Series Analysis?

Ziming Zhao, ChengAo Shen, Hanghang Tong, Dongjin Song, Zhigang Deng, Qingsong Wen, Jingchao Ni

原文摘要

Transformer-based models have gained increasing attention in time series research, driving interest in Large Language Models (LLMs) and foundation models for time series analysis. As the field moves toward multi-modality, Large Vision Models (LVMs) are emerging as a promising direction. In the past, the effectiveness of Transformer and LLMs in time series has been debated. When it comes to LVMs, a similar question arises: are LVMs truely useful for time series analysis? To address it, we design and conduct the first principled study involving 4 LVMs, 8 imaging methods, 18 datasets and 26 baselines across both high-level (classification) and low-level (forecasting) tasks, with extensive ablation analysis. Our findings indicate LVMs are indeed useful for time series classification but face challenges in forecasting. Although effective, the contemporary best LVM forecasters are limited to specific types of LVMs and imaging methods, exhibit a bias toward forecasting periods, and have limited ability to utilize long look-back windows. We hope our findings could serve as a cornerstone for future research on LVM- and multimodal-based solutions to different time series tasks.

cs.LG · cs.AI · cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06294

A.S.E 2.0: Understanding How Repository Context Impacts the Security of LLM-Generated Code

Jiongchi Yu, Keke Lian, Junqing Wang, Zhikang Fan, Zhaoxuan Li, Yongheng Huang, Ziming Zhao, Dong Zhang

原文摘要

Large language model (LLM) coding assistants and agents construct a repository context before generating code. For security-sensitive code, this context decides whether the model sees the project's safeguards (e.g., authorization checks) or insecure code to imitate. However, context construction is designed almost exclusively for functional correctness, and secure-code benchmarks treat the context as fixed, so how repository context affects the security of generated code remains unclear. We construct CASE-Bench, a benchmark of 145 repository-level tasks from real CVEs in 51 projects. Each task runs in Docker, has functional tests and a proof-of-concept exploit validated in both directions, and is annotated with the repository code that enforces or undermines its security. Using CASE-Bench, we compare 13 context construction methods and six controls with six LLMs, edit single context elements, and analyze coding-agent trajectories. We find that (1) functionality-oriented context raises the functional pass rate by up to 8.2 percentage points (pp) but the secure pass rate by at most 3.7 pp, (2) withholding a safeguard lowers the secure pass rate by up to 8.1 pp, while one inserted insecure exemplar raises correct-but-exploitable code by 6.3 pp, and (3) coding agents observe the relevant safeguard in only 27-36% of trajectories. These findings inform security-aware context construction and secure-code evaluation.

cs.CR · cs.AI · cs.SE

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.35043

Steering for Safety, Hijacking for Profit: Trojan Threats in the LLM Activation Steering Supply Chain

Yu Yan, Jiahao Chen, Siqi Lu, Yongjuan Wang, Ziming Zhao, Zhaoxuan Li, Qingjun Yuan

原文摘要

As large language models (LLMs) are increasingly integrated into Web search, online recommendation, and intelligent decision-making, the third-party plugin ecosystem centered on model customization continues to grow. Among these, Steering Vectors guide model behavior by intervening in internal representations. However, the open plugin ecosystem also introduces new supply-chain security risks: malicious plugins can disguise themselves as having legitimate functionality and covertly manipulate outputs without significantly affecting model performance. Although this risk poses a real threat, the security issues of Steering plugins in the LLM supply-chain ecosystem still lack systematic research. This paper audits eight mainstream open-source Steering plugin libraries and reveals three structural vulnerabilities: insufficient distribution integrity verification, excessive plugin privileges, and missing runtime monitoring, which offers an attack surface for attackers. To investigate this threat, we propose a malicious plugin, IDH. Specifically, IDH designs a norm-gated mutually exclusive routing mechanism and proposes an information-decay inflection point localization method to automatically select the injection layer, thereby simultaneously achieving benign and malicious functions. To counter such threats, we propose a runtime defense framework, by suppressing anomalous activation interventions and orthogonalizing inter-layer trajectory. We evaluate IDH across five mainstream LLMs, using safety-refusal steering as the legitimate functionality and commercial recommendation manipulation as the hijack objective. Experiments covering six brands across three commercial domains demonstrate that IDH achieves an attack success rate (ASR) of up to 97.8% while preserving high refusal rates on harmful requests.

cs.CR

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arXiv ↗PDF ↗