arXiv subjects

Tao Duan

Publications and source records attributed to Tao Duan.

4 recordsLinked to original sources

arXiv2026

The validity of the Stokes-Einstein relation in ionic liquids

Ionic liquids (ILs) exhibit supercooled liquids behavior even above room temperature and the Stokes-Einstein (SE) relation is often considered to be invalid in ILs as that in supercooled liquids. However, the conclusion is usually drawn based on some variants of SE relation. In this work, we have systematically investigated the validity of the Stokes-Einstein relation in ILs by performing molecular dynamics simulations of coarse-grained IL within the temperature range of 400-800 K. Both the original SE formulation, and two commonly employed variants, were examined to assess their applicability and consistency. These three formulas yield distinctly different results, indicating that the two variants are not reliable substitutes for the original SE relation. The inconsistency suggests the fact that the r in Stokes law varies with conditions. The breakdown arises from the ion correlations introduced by the strong electrostatic interactions. This proposition is further confirmed by simulations.

cond-mat.soft↗

arXiv2026

Right Family, Wrong Skill: Benchmarking Risk Exposure in Agent Skill Retrieval

Agent skill libraries are becoming routable software assets: a retrieved skill can contribute instructions, scripts, resource bindings, and execution assumptions to an agent. This makes retrieval failures more specific than broad irrelevance. A system can find the right capability family yet expose the wrong same-capability representative. We study this failure as same-capability risk-exposure retrieval. Each benchmark unit pairs a helpful skill with a query-specific risky sibling that shares the capability family but differs on an execution-controlling contract, such as the required resource, precondition, procedure, or artifact. We introduce SameCapRisk-Bench, an auditable benchmark with 1,190 skill-risk units and 1,686 evaluation query cases: 694 marked-sibling units under public library pressure and 496 hard role-flip units where the same two skills swap helpful/risky roles across paired queries. The release records admission evidence, cue/leakage checks, source hashes, family relations, and fixed candidate pools. The benchmark reports helpful ranking together with harmful sibling rate (HSR@K), the top-K exposure of the marked risky sibling. On this benchmark, public SkillRouter, SkillRet, and R3-Skill retrieve helpful skills at high Recall@3 (0.848--0.888) but also expose marked risky siblings frequently (HSR@3 0.346--0.372). A fully public score-and-cluster pipeline lowers HSR@3 to 0.128--0.182, with Recall@3 of 0.713--0.776. Under a benchmark-trained reference scorer, public text-cluster and controlled resolvers reach HSR@3 0.012 and 0.007; the latter attains Recall@3 0.833. Skill retrieval should therefore report both capability matching and same-family risk exposure, with HSR serving as a targeted exposure certificate for fixed skill libraries.

cs.IR↗

arXiv2025

BSODiag: A Global Diagnosis Framework for Batch Servers Outage in Large-scale Cloud Infrastructure Systems

Cloud infrastructure is the collective term for all physical devices within cloud systems. Failures within the cloud infrastructure system can severely compromise the stability and availability of cloud services. Particularly, batch servers outage, which is the most fatal failure, could result in the complete unavailability of all upstream services. In this work, we focus on the batch servers outage diagnosis problem, aiming to accurately and promptly analyze the root cause of outages to facilitate troubleshooting. However, our empirical study conducted in a real industrial system indicates that it is a challenging task. Firstly, the collected single-modal coarse-grained failure monitoring data (i.e., alert, incident, or change) in the cloud infrastructure system is insufficient for a comprehensive failure profiling. Secondly, due to the intricate dependencies among devices, outages are often the cumulative result of multiple failures, but correlations between failures are difficult to ascertain. To address these problems, we propose BSODiag, an unsupervised and lightweight diagnosis framework for batch servers outage. BSODiag provides a global analytical perspective, thoroughly explores failure information from multi-source monitoring data, models the spatio-temporal correlations among failures, and delivers accurate and interpretable diagnostic results. Experiments conducted on the Alibaba Cloud infrastructure system show that BSODiag achieves 87.5% PR@3 and 46.3% PCR, outperforming baseline methods by 10.2% and 3.7%, respectively.

cs.DC↗

arXiv2024

Representation Learning of Tangled Key-Value Sequence Data for Early Classification

Key-value sequence data has become ubiquitous and naturally appears in a variety of real-world applications, ranging from the user-product purchasing sequences in e-commerce, to network packet sequences forwarded by routers in networking. Classifying these key-value sequences is important in many scenarios such as user profiling and malicious applications identification. In many time-sensitive scenarios, besides the requirement of classifying a key-value sequence accurately, it is also desired to classify a key-value sequence early, in order to respond fast. However, these two goals are conflicting in nature, and it is challenging to achieve them simultaneously. In this work, we formulate a novel tangled key-value sequence early classification problem, where a tangled key-value sequence is a mixture of several concurrent key-value sequences with different keys. The goal is to classify each individual key-value sequence sharing a same key both accurately and early. To address this problem, we propose a novel method, i.e., Key-Value sequence Early Co-classification (KVEC), which leverages both inner- and inter-correlations of items in a tangled key-value sequence through key correlation and value correlation to learn a better sequence representation. Meanwhile, a time-aware halting policy decides when to stop the ongoing key-value sequence and classify it based on current sequence representation. Experiments on both real-world and synthetic datasets demonstrate that our method outperforms the state-of-the-art baselines significantly. KVEC improves the prediction accuracy by up to $4.7 - 17.5\%$ under the same prediction earliness condition, and improves the harmonic mean of accuracy and earliness by up to $3.7 - 14.0\%$.

cs.LG↗