arXiv subjects

Tingting Dai

Publications and source records attributed to Tingting Dai.

4 recordsLinked to original sources

arXiv2026

CTBench: Evaluating Troubleshooting Capabilities of AI Agents in Realistic Telecom Network Operations

Agents are increasingly considered for automating network operations and maintenance, where engineers must diagnose network faults, optimize configurations to enhance services, and reduce operational costs while acting under strict constraints. However, existing evaluations fail to accurately model real network characteristics or assess agents under partially observable telecom environments with diverse vendors, devices, protocols, and interfaces. In this paper, we introduce CTBench, a public benchmark for assessing whether an agent behaves like a competent telecom troubleshooting engineer. CTBench focuses on root cause analysis and path restoration. Each task is constructed by experts and annotated with rich task metadata, including golden evidence steps. CTBench uses expert-grounded metrics that evaluate both final answers and the diagnostic evidence. Experiments with representative harness-model combinations show that state-of-the-art agents perform very well at identifying endpoints in path-restoration tasks but, more generally, underperform in root cause analysis. In particular, agents struggle with interface state, link-layer, service-management, and other operational faults. Most importantly, even when agents produce plausible or correct final answers, they often fail to provide the evidence-grounded diagnoses required in operational practice. Our results further show that path restoration is generally more resource expensive, yet larger resource usage does not necessarily translate into better diagnosis.

cs.AI↗

arXiv2025

Exploiting Inter-Session Information with Frequency-enhanced Dual-Path Networks for Sequential Recommendation

Sequential recommendation (SR) aims to predict a user's next item preference by modeling historical interaction sequences. Recent advances often integrate frequency-domain modules to compensate for self-attention's low-pass nature by restoring the high-frequency signals critical for personalized recommendations. Nevertheless, existing frequency-aware solutions process each session in isolation and optimize exclusively with time-domain objectives. Consequently, they overlook cross-session spectral dependencies and fail to enforce alignment between predicted and actual spectral signatures, leaving valuable frequency information under-exploited. To this end, we propose FreqRec, a Frequency-Enhanced Dual-Path Network for sequential Recommendation that jointly captures inter-session and intra-session behaviors via a learnable Frequency-domain Multi-layer Perceptrons. Moreover, FreqRec is optimized under a composite objective that combines cross entropy with a frequency-domain consistency loss, explicitly aligning predicted and true spectral signatures. Extensive experiments on three benchmarks show that FreqRec surpasses strong baselines and remains robust under data sparsity and noisy-log conditions.

cs.IR↗

arXiv2024

The Boundary Effect of QGP Droplet and Self-similarity Effect of Hadrons on QGP-hadron Phase Transition

We investigate the boundary effect of QGP droplet and self-similarity effect of hadrons on QGP-hadron phase transition. In intermediate or low energy collisions, when the transverse momentum is below QCD scale, QGP cannot be produced. However, if the transverse momentum fluctuates to a relatively large value, small scale QGP droplet is produced. The modified MIT bag model with multiple reflection expansion method is employed to study the QGP droplet with the curved boundary effect. It is found that the energy density, entropy density and pressure of QGP with the influence are smaller than those without the influence. In hadron phase, we propose Two-Body Fractal Model (TBFM) to study the self-similarity structure, arising from the resonance, quantum correlation and interaction effects. It is observed that energy density, entropy density and pressure increase due to the self-similarity structure. We calculate the transverse momentum spectra of pions with the self-similarity structure influence, showing a good agreement with the experimental data. Considering both the boundary effect and self-similarity structure influence, our model predicts an increase in the transition temperature compared to scenarios without these two effects in HIAF energy region $2.2\sim 4.5 \,\text{GeV}$.

hep-ph↗

arXiv2023

The Spectrum of Low-$p_T$ $J/ψ$ in Heavy-Ion Collisions in a Statistical Two-Body Fractal Model

We establish a statistical two-body fractal (STF) model to study the spectrum of $J/ψ$. $J/ψ$ serves as a reliable probe in heavy-ion collisions. The distribution of $J/ψ$ in hadron gas is influenced by flow, quantum and strong interaction effects. Previous models have predominantly focused on one or two of these effects while neglecting the others, resulting in the inclusion of unconsidered effects in the fitted parameters. Here, we study the issue from a new point of view by analyzing the fact that all three effects induce a self-similarity structure, involving a $J/ψ$-$π$ two-meson state and a $J/ψ$, $π$ two-quark state, respectively. We introduce modification factor $q_{TBS}$ and $q_2$ into the probability and entropy of charmonium. $q_{TBS}$ denotes the modification of self-similarity on $J/ψ$, $q_2$ denotes that of self-similarity and strong interaction between \emph{c }and $\bar{c}$ on quarks. By solving the probability and entropy equations, we derive the values of $q_{TBS}$ and $q_2$ at various collision energies and centralities. Substituting the value of $q_{TBS}$ into distribution function, we successfully obtain the transverse momentum spectrum of low-$p_T$ $J/ψ$, which demonstrates good agreement with experimental data. The STF model can be employed to investigate other mesons and resonance states.

hep-ph↗