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Researcher profile

Chen Luo

Chen Luo contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
DatabasesArtificial IntelligenceComputation and LanguageSocial and Information NetworksComputer Visioncond-mat.mes-hallcond-mat.mtrl-scieess.SPHardware ArchitectureInformation RetrievalMachine LearningMultiagent SystemsPerformancephysics.soc-phPopulations and EvolutionSoftware Engineering

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Published work

11 published item(s)

preprint2026arXiv

Firefly: Illuminating Large-Scale Verified Tool-Call Data Generation from Real APIs

Training tool-calling agents requires large-scale trajectory data with verifiable labels, yet existing approaches either synthesize environments that diverge from real API behavior or generate tasks without ground-truth outcomes for verification. We present FireFly, a pipeline for generating verified tool-call data from real-world MCP servers. Our key insight is to invert the standard synthesis pipeline: rather than generating tasks and hoping they are solvable, we first let a strong LLM explore real APIs along graph-guided DAG structures, then synthesize tasks backward from observed outcomes, guaranteeing label correctness by construction. To handle the scale of real-world tool spaces (${\sim}$1,000 tools), we build a pairwise tool graph and sample sub-DAGs to focus exploration on semantically coherent workflows. To address environment drift in live APIs, we construct a retrieval-augmented simulator that caches all exploration results and replays them during training and evaluation, enabling fully offline and reproducible RL. Applying this pipeline yields 5,144 verified tasks spanning 240 servers and 993 tools. A 4B-parameter model trained with GRPO on FireFly matches Claude Sonnet 4.6 on our held-out test set and shows improvements on multiple tool-calling benchmarks including Tau2-Bench, MCPMark, and MCP-Atlas.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Intention Knowledge Graph Construction for User Intention Relation Modeling

Understanding user intentions is challenging for online platforms. Recent work on intention knowledge graphs addresses this but often lacks focus on connecting intentions, which is crucial for modeling user behavior and predicting future actions. This paper introduces a framework to automatically generate an intention knowledge graph, capturing connections between user intentions. Using the Amazon m2 dataset, we construct an intention graph with 351 million edges, demonstrating high plausibility and acceptance. Our model effectively predicts new session intentions and enhances product recommendations, outperforming previous state-of-the-art methods and showcasing the approach's practical utility.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Subspace Alignment for Vision-Language Model Test-time Adaptation

Vision-language models (VLMs), despite their extraordinary zero-shot capabilities, are vulnerable to distribution shifts. Test-time adaptation (TTA) emerges as a predominant strategy to adapt VLMs to unlabeled test data on the fly. However, existing TTA methods heavily rely on zero-shot predictions as pseudo-labels for self-training, which can be unreliable under distribution shifts and misguide adaptation due to two fundamental limitations. First (Modality Gap), distribution shifts induce gaps between visual and textual modalities, making cross-modal relations inaccurate. Second (Visual Nuisance), visual embeddings encode rich but task-irrelevant noise that often overwhelms task-specific semantics under distribution shifts. To address these limitations, we propose SubTTA, which aligns the semantic subspaces of both modalities to enhance zero-shot predictions to better guide the TTA process. To bridge the modality gap, SubTTA extracts the principal subspaces of both modalities and aligns the visual manifold to the textual semantic anchor by minimizing their chordal distance. To eliminate visual nuisance, SubTTA projects the aligned visual features onto the task-specific textual subspace, which filters out task-irrelevant noise by constraining visual embeddings within the valid semantic span, and standard TTA is further performed on the purified space to refine the decision boundaries. Extensive experiments on various benchmarks and VLM architectures demonstrate the effectiveness of SubTTA, yielding an average improvement of 2.24% over state-of-the-art TTA methods.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

CERES: Pretraining of Graph-Conditioned Transformer for Semi-Structured Session Data

User sessions empower many search and recommendation tasks on a daily basis. Such session data are semi-structured, which encode heterogeneous relations between queries and products, and each item is described by the unstructured text. Despite recent advances in self-supervised learning for text or graphs, there lack of self-supervised learning models that can effectively capture both intra-item semantics and inter-item interactions for semi-structured sessions. To fill this gap, we propose CERES, a graph-based transformer model for semi-structured session data. CERES learns representations that capture both inter- and intra-item semantics with (1) a graph-conditioned masked language pretraining task that jointly learns from item text and item-item relations; and (2) a graph-conditioned transformer architecture that propagates inter-item contexts to item-level representations. We pretrained CERES using ~468 million Amazon sessions and find that CERES outperforms strong pretraining baselines by up to 9% in three session search and entity linking tasks.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Breaking Down Memory Walls: Adaptive Memory Management in LSM-based Storage Systems (Extended Version)

Log-Structured Merge-trees (LSM-trees) have been widely used in modern NoSQL systems. Due to their out-of-place update design, LSM-trees have introduced memory walls among the memory components of multiple LSM-trees and between the write memory and the buffer cache. Optimal memory allocation among these regions is non-trivial because it is highly workload-dependent. Existing LSM-tree implementations instead adopt static memory allocation schemes due to their simplicity and robustness, sacrificing performance. In this paper, we attempt to break down these memory walls in LSM-based storage systems. We first present a memory management architecture that enables adaptive memory management. We then present a partitioned memory component structure with new flush policies to better exploit the write memory to minimize the write cost. To break down the memory wall between the write memory and the buffer cache, we further introduce a memory tuner that tunes the memory allocation between these two regions. We have conducted extensive experiments in the context of Apache AsterixDB using the YCSB and TPC-C benchmarks and we present the results here.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Efficiently Reclaiming Space in a Log Structured Store

A log structured store uses a single write I/O for a number of diverse and non-contiguous pages within a large buffer instead of using a write I/O for each page separately. This requires that pages be relocated on every write, because pages are never updated in place. Instead, pages are dynamically remapped on every write. Log structuring was invented for and used initially in file systems. Today, a form of log structuring is used in SSD controllers because an SSD requires the erasure of a large block of pages before flash storage can be reused. No update-in-place requires that the storage for out-of-date pages be reclaimed (garbage collected or "cleaned"). We analyze cleaning performance and introduce a cleaning strategy that uses a new way to prioritize the order in which stale pages are garbage collected. Our cleaning strategy approximates an "optimal cleaning strategy". Simulation studies confirm the results of the analysis. This strategy is a significant improvement over previous cleaning strategies.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

On Performance Stability in LSM-based Storage Systems (Extended Version)

The Log-Structured Merge-Tree (LSM-tree) has been widely adopted for use in modern NoSQL systems for its superior write performance. Despite the popularity of LSM-trees, they have been criticized for suffering from write stalls and large performance variances due to the inherent mismatch between their fast in-memory writes and slow background I/O operations. In this paper, we use a simple yet effective two-phase experimental approach to evaluate write stalls for various LSM-tree designs. We further explore the design choices of LSM merge schedulers to minimize write stalls given an I/O bandwidth budget. We have conducted extensive experiments in the context of the Apache AsterixDB system and we present the results here.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Simulation of Real-time Routing for UAS traffic Management with Communication and Airspace Safety Considerations

Small Unmanned Aircraft Systems (sUAS) will be an important component of the smart city and intelligent transportation environments of the near future. The demand for sUAS related applications, such as commercial delivery and land surveying, is expected to grow rapidly in next few years. In general, sUAS traffic routing and management functions are needed to coordinate the launching of sUAS from different launch sites and determine their trajectories to avoid conflict while considering several other constraints such as expected arrival time, minimum flight energy, and availability of communication resources. However, as the airborne sUAS density grows in a certain area, it is difficult to foresee the potential airspace and communications resource conflicts and make immediate decisions to avoid them. To address this challenge, we present a temporal and spatial routing algorithm and simulation platform for sUAS trajectory management in a high density urban area that plans sUAS movements in a spatial and temporal maze taking into account obstacles that are either static or dynamic in time. The routing allows the sUAS to avoid static no-fly areas (i.e. static obstacles) or other in-flight sUAS and areas that have congested communication resources (i.e. dynamic obstacles). The algorithm is evaluated using an agent-based simulation platform. The simulation results show that the proposed algorithm outperforms other route management algorithms in many areas, especially in processing speed and memory efficiency. Detailed comparisons are provided for the sUAS flight time, the overall throughput, conflict rate and communication resource utilization. The results demonstrate that our proposed algorithm can be used to address the airspace and communication resource utilization needs for a next generation smart city and smart transportation.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Structural Temporal Graph Neural Networks for Anomaly Detection in Dynamic Graphs

Detecting anomalies in dynamic graphs is a vital task, with numerous practical applications in areas such as security, finance, and social media. Previous network embedding based methods have been mostly focusing on learning good node representations, whereas largely ignoring the subgraph structural changes related to the target nodes in dynamic graphs. In this paper, we propose StrGNN, an end-to-end structural temporal Graph Neural Network model for detecting anomalous edges in dynamic graphs. In particular, we first extract the $h$-hop enclosing subgraph centered on the target edge and propose the node labeling function to identify the role of each node in the subgraph. Then, we leverage graph convolution operation and Sortpooling layer to extract the fixed-size feature from each snapshot/timestamp. Based on the extracted features, we utilize Gated recurrent units (GRUs) to capture the temporal information for anomaly detection. Extensive experiments on six benchmark datasets and a real enterprise security system demonstrate the effectiveness of StrGNN.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Using Reports of Own and Others' Symptoms and Diagnosis on Social Media to Predict COVID-19 Case Counts: Observational Infoveillance Study in Mainland China

Can public social media data be harnessed to predict COVID-19 case counts? We analyzed approximately 15 million COVID-19 related posts on Weibo, a popular Twitter-like social media platform in China, from November 1, 2019 to March 31, 2020. We developed a machine learning classifier to identify "sick posts," which are reports of one's own and other people's symptoms and diagnosis related to COVID-19. We then modeled the predictive power of sick posts and other COVID-19 posts on daily case counts. We found that reports of symptoms and diagnosis of COVID-19 significantly predicted daily case counts, up to 14 days ahead of official statistics. But other COVID-19 posts did not have similar predictive power. For a subset of geotagged posts (3.10% of all retrieved posts), we found that the predictive pattern held true for both Hubei province and the rest of mainland China, regardless of unequal distribution of healthcare resources and outbreak timeline. Researchers and disease control agencies should pay close attention to the social media infosphere regarding COVID-19. On top of monitoring overall search and posting activities, it is crucial to sift through the contents and efficiently identify true signals from noise.

0 citations0 reviewsNo code linkedOpen access
preprint2019arXiv

Anomalous and topological Hall effects in epitaxial thin films of the noncollinear antiferromagnet Mn$_{3}$Sn

Noncollinear antiferromagnets with a D0$_{19}$ (space group = 194, P6$_{3}$/mmc) hexagonal structure have garnered much attention for their potential applications in topological spintronics. Here, we report the deposition of continuous epitaxial thin films of such a material, Mn$_{3}$Sn, and characterize their crystal structure using a combination of x-ray diffraction and transmission electron microscopy. Growth of Mn$_{3}$Sn films with both (0001) c-axis orientation and (40$\bar{4}$3) texture is achieved. In the latter case, the thin films exhibit a small uncompensated Mn moment in the basal plane, quantified via magnetometry and x-ray magnetic circular dichroism experiments. This cannot account for the large anomalous Hall effect simultaneously observed in these films, even at room temperature, with magnitude $σ_{\mathrm{xy}}$ ($μ_{0}H$ = 0 T) = 21 $\mathrmΩ^{-1}\mathrm{cm}^{-1}$ and coercive field $μ_{0}H_{\mathrm{C}}$ = 1.3 T. We attribute the origin of this anomalous Hall effect to momentum-space Berry curvature arising from the symmetry-breaking inverse triangular spin structure of Mn$_{3}$Sn. Upon cooling through the transition to a glassy ferromagnetic state at around 50 K, a peak in the Hall resistivity close to the coercive field indicates the onset of a topological Hall effect contribution, due to the emergence of a scalar spin chirality generating a real-space Berry phase. We demonstrate that the polarity of this topological Hall effect, and hence the chiral-nature of the noncoplanar magnetic structure driving it, can be controlled using different field cooling conditions.

0 citations0 reviewsNo code linkedOpen access