arXiv:2601.02440v1 Announce Type: new Abstract: Anomaly detection is crucial in industrial applications for identifying rare and unseen patterns to ensure system reliability. Traditional models, trained on a single class of normal data, struggle with real-world distributions where normal data exhibit diverse patterns, leading to class imbalance and long-tailed anomaly score distributions (LTD). This imbalance skews model training and degrades detection performance, especially for minority instances. To address this issue, we propose a novel importance-weighted loss designed specifically for […]

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arXiv:2601.00094v1 Announce Type: new Abstract: The problem of finding the longest simple cycle in a directed graph is NP-hard, with critical applications in computational biology, scheduling, and network analysis. While polynomial-time approximation algorithms exist for restricted graph classes, general bounds remain loose or computationally expensive. In this paper, we exploit optimum cycle means (minimum and maximum cycle means), which are computable in strongly polynomial time, to derive both strict algebraic bounds and heuristic approximations for the weight and […]

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arXiv:2601.04195v1 Announce Type: new Abstract: We present MedPI, a high-dimensional benchmark for evaluating large language models (LLMs) in patient-clinician conversations. Unlike single-turn question-answer (QA) benchmarks, MedPI evaluates the medical dialogue across 105 dimensions comprising the medical process, treatment safety, treatment outcomes and doctor-patient communication across a granular, accreditation-aligned rubric. MedPI comprises five layers: (1) Patient Packets (synthetic EHR-like ground truth); (2) an AI Patient instantiated through an LLM with memory and affect; (3) a Task Matrix spanning encounter […]

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arXiv:2601.04281v1 Announce Type: new Abstract: The disclosure of the Log4Shell vulnerability in December 2021 led to an unprecedented wave of global scanning and exploitation activity. A recent study provided important initial insights, but was largely limited in duration and geography, focusing primarily on European and U.S. network telescope deployments and covering the immediate aftermath of disclosure. As a result, the longer-term evolution of exploitation behavior and its regional characteristics has remained insufficiently understood. In this paper, we present […]

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Recent advances in reinforcement learning for large language models have produced powerful agent frameworks that achieve strong performance on multi-turn tool use, interactive search, and complex reasoning. However, existing reinforcement learning frameworks for large language model agents face three critical limitations: difficulty in handling dynamic user interactions owing to reliance on pre-scripted queries, limited scalability across varying interaction horizons with fixed scaling schedules, and substantial reward engineering overhead requiring domain-specific manual tuning. We introduce Progressive Multi-Turn Reinforcement Learning […]

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Graph Prompt Feature (GPF) learning has been widely used in adapting pre-trained GNN model on the downstream task. GPFs first introduce some prompt atoms and then learns the optimal prompt vector for each graph node using the linear combination of prompt atoms. However, existing GPFs generally conduct prompting over node’s all feature dimensions which is obviously redundant and also be sensitive to node feature noise. To overcome this issue, for the first time, this paper proposes learning sparse […]

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