Robust Dual-Regularized Variable Selection under Outlier Contamination
本文提出了一种两阶段的稀疏中位数外积梯度(smOPG)方法，用于在存在异常值的情况下进行单指标模型的变量选择，通过结合中位数回归和局部加权来提高鲁棒性。
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Double-Scoring: Reliable Extraction of Strong Lottery Tickets
Existing methods struggle to reliably extract strong lottery ticket subnetworks from randomly initialized networks. This work proposes a dual-scoring mechanism that introduces an expanded scoring tensor space to optimize mask selection under fixed sparsity, effectively reformulating sparse structure discovery as an edge-popup optimization problem on a zero-augmented network. The approach preserves the reachability of original masks and substantially reduces sensitivity to sparsity hyperparameters by employing frozen-weight scoring during training alongside fixed-density mask optimization. Experimental results demonstrate that the method significantly outperforms fixed-density Edge-Popup, initialization-based pruning, and retrospective sparse training approaches across multiple benchmarks, while exhibiting strong robustness to variations in sparsity settings.
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Security Threats and Their Impact on Blockchain Interoperability: Identification and Countermeasures
Blockchain interoperability has led to the loss of hundreds of millions of dollars in assets due to the absence of systematic security mechanisms. This work proposes the first five-dimensional threat taxonomy specifically tailored for cross-chain interoperability, encompassing core chain attacks, network-level attacks, interoperability-specific exploits, social engineering, and smart contract vulnerabilities. By integrating systematic threat modeling, attack surface analysis, and smart contract security evaluation methodologies, the study provides a comprehensive examination of the attack surfaces associated with each threat category. Building upon this analysis, the authors establish a structured mapping framework that links identified threats to corresponding defense strategies, yielding a formalized security guideline. This framework offers both theoretical foundations and practical guidance for designing and evaluating secure and reliable blockchain interoperability solutions.
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AI-Driven Test Case Generation from Natural Language Requirements: A Survey of Techniques and Research Gaps
This study addresses the challenges of automatically generating test cases from natural language requirements—namely semantic ambiguity, weak traceability, and inconsistent evaluation—which hinder existing approaches from achieving a balance among automation, accuracy, and reliability. Through a systematic literature review conducted in accordance with the Kitchenham and Charters guidelines, the authors analyze AI- and NLP-driven research from 2000 to 2025, proposing a three-phase evolutionary framework and introducing a novel six-dimensional quality gap analysis encompassing automation, ambiguity handling, domain applicability, traceability, evaluation adequacy, and hallucination control. An examination of 21 core studies reveals that current methods fail to simultaneously satisfy all six dimensions. Based on these findings, the paper outlines four actionable research directions: hallucination mitigation, enhanced traceability, complexity-aware modeling, and compliance assurance.
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Cascading Hallucination in Agentic RAG: The CHARM Framework for Detection and Mitigation
This work addresses the challenge of cascading hallucinations in multi-step agent-based RAG systems, where early errors propagate confidently yet factually incorrect outputs, and existing approaches struggle to detect such failures effectively. The study formalizes cascading hallucination as a distinct failure mode and introduces CHARM—a lightweight, plug-and-play framework that mitigates error propagation through four mechanisms: stage-level factual verification, cross-stage consistency tracking, confidence propagation monitoring, and cascade-aware resolution triggering. Experimental results demonstrate that CHARM achieves an 89.4% detection rate for cascading errors with a low false positive rate of 5.3%, reduces error propagation by 82.1%, and incurs only a modest average latency increase of 215ms per reasoning stage across multiple multi-hop question answering benchmarks, substantially outperforming baseline methods that solely inspect final outputs.
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Robust Dual-Regularized Variable Selection under Outlier Contamination
本文提出了一种两阶段的稀疏中位数外积梯度(smOPG)方法，用于在存在异常值的情况下进行单指标模型的变量选择，通过结合中位数回归和局部加权来提高鲁棒性。
0 citationsRead paper
Double-Scoring: Reliable Extraction of Strong Lottery Tickets
Existing methods struggle to reliably extract strong lottery ticket subnetworks from randomly initialized networks. This work proposes a dual-scoring mechanism that introduces an expanded scoring tensor space to optimize mask selection under fixed sparsity, effectively reformulating sparse structure discovery as an edge-popup optimization problem on a zero-augmented network. The approach preserves the reachability of original masks and substantially reduces sensitivity to sparsity hyperparameters by employing frozen-weight scoring during training alongside fixed-density mask optimization. Experimental results demonstrate that the method significantly outperforms fixed-density Edge-Popup, initialization-based pruning, and retrospective sparse training approaches across multiple benchmarks, while exhibiting strong robustness to variations in sparsity settings.
0 citationsRead paper
Security Threats and Their Impact on Blockchain Interoperability: Identification and Countermeasures
Blockchain interoperability has led to the loss of hundreds of millions of dollars in assets due to the absence of systematic security mechanisms. This work proposes the first five-dimensional threat taxonomy specifically tailored for cross-chain interoperability, encompassing core chain attacks, network-level attacks, interoperability-specific exploits, social engineering, and smart contract vulnerabilities. By integrating systematic threat modeling, attack surface analysis, and smart contract security evaluation methodologies, the study provides a comprehensive examination of the attack surfaces associated with each threat category. Building upon this analysis, the authors establish a structured mapping framework that links identified threats to corresponding defense strategies, yielding a formalized security guideline. This framework offers both theoretical foundations and practical guidance for designing and evaluating secure and reliable blockchain interoperability solutions.
0 citationsRead paper
AI-Driven Test Case Generation from Natural Language Requirements: A Survey of Techniques and Research Gaps
This study addresses the challenges of automatically generating test cases from natural language requirements—namely semantic ambiguity, weak traceability, and inconsistent evaluation—which hinder existing approaches from achieving a balance among automation, accuracy, and reliability. Through a systematic literature review conducted in accordance with the Kitchenham and Charters guidelines, the authors analyze AI- and NLP-driven research from 2000 to 2025, proposing a three-phase evolutionary framework and introducing a novel six-dimensional quality gap analysis encompassing automation, ambiguity handling, domain applicability, traceability, evaluation adequacy, and hallucination control. An examination of 21 core studies reveals that current methods fail to simultaneously satisfy all six dimensions. Based on these findings, the paper outlines four actionable research directions: hallucination mitigation, enhanced traceability, complexity-aware modeling, and compliance assurance.
0 citationsRead paper
Cascading Hallucination in Agentic RAG: The CHARM Framework for Detection and Mitigation
This work addresses the challenge of cascading hallucinations in multi-step agent-based RAG systems, where early errors propagate confidently yet factually incorrect outputs, and existing approaches struggle to detect such failures effectively. The study formalizes cascading hallucination as a distinct failure mode and introduces CHARM—a lightweight, plug-and-play framework that mitigates error propagation through four mechanisms: stage-level factual verification, cross-stage consistency tracking, confidence propagation monitoring, and cascade-aware resolution triggering. Experimental results demonstrate that CHARM achieves an 89.4% detection rate for cascading errors with a low false positive rate of 5.3%, reduces error propagation by 82.1%, and incurs only a modest average latency increase of 215ms per reasoning stage across multiple multi-hop question answering benchmarks, substantially outperforming baseline methods that solely inspect final outputs.
0 citationsRead paper