arXiv subjects

Zhenghong Yu

Publications and source records attributed to Zhenghong Yu.

6 recordsLinked to original sources

arXiv2026

FlowLog: Re-thinking Datalog for Fast and Extensible Static Analysis

Datalog is widely used to build static analyzers, yet existing engines often force a tradeoff between efficiency and extensibility. In practice, static analyses are not run once and forgotten: users edit facts, tune rules, diagnose bottlenecks, and often need semantics beyond standard Datalog, leaving these tasks to ad hoc tooling or invasive engine rewrites. We demonstrate FlowLog, a Datalog compiler that turns Souffl\'e-style programs into Differential Dataflow executables for efficient and extensible static analysis. Across 24 benchmarks derived from real-world workloads, FlowLog consistently outperforms state-of-the-art engines in runtime while remaining memory-efficient and scaling better. The demonstration uses a DOOP points-to analysis. Attendees run it, switching the same program from one-shot to incremental evaluation that retracts a fact and updates results in milliseconds; tune it, inspecting per-operator costs in a browser-based profiler and repairing a bad join order; and extend it with a k-core example beyond standard Datalog.

cs.PL↗

arXiv2025

FlowLog: Efficient and Extensible Datalog via Incrementality

Datalog-based languages are regaining popularity as a powerful abstraction for expressing recursive computations in domains such as program analysis and graph processing. However, existing systems often face a trade-off between efficiency and extensibility. Engines like Souffle achieve high efficiency through domain-specific designs, but lack general-purpose flexibility. Others, like RecStep, offer modularity by layering Datalog on traditional databases, but struggle to integrate Datalog-specific optimizations. This paper bridges this gap by presenting FlowLog, a new Datalog engine that uses an explicit relational IR per-rule to cleanly separate recursive control (e.g., semi-naive execution) from each rule's logical plan. This boundary lets us retain fine-grained, Datalog-aware optimizations at the logical layer, but also reuse off-the-shelf database primitives at execution. At the logical level (i.e. IR), we apply proven SQL optimizations, such as logic fusion and subplan reuse. To address high volatility in recursive workloads, we adopt a robustness-first approach that pairs a structural optimizer (avoiding worst-case joins) with sideways information passing (early filtering). Built atop Differential Dataflow--a mature framework for streaming analytics--FlowLog supports both batch and incremental Datalog and adds novel recursion-aware optimizations called Boolean (or algebraic) specialization. Our evaluation shows that FlowLog outperforms state-of-the-art Datalog engines and modern databases across a broad range of recursive workloads, achieving superior scalability while preserving a simple and extensible architecture.

cs.DB↗

arXiv2025

Accelerating Focal Search in Multi-Agent Path Finding with Tighter Lower Bounds

Multi-Agent Path Finding (MAPF) involves finding collision-free paths for multiple agents while minimizing a cost function--an NP-hard problem. Bounded suboptimal methods like Enhanced Conflict-Based Search (ECBS) and Explicit Estimation CBS (EECBS) balance solution quality with computational efficiency using focal search mechanisms. While effective, traditional focal search faces a limitation: the lower bound (LB) value determining which nodes enter the FOCAL list often increases slowly in early search stages, resulting in a constrained search space that delays finding valid solutions. In this paper, we propose a novel bounded suboptimal algorithm, double-ECBS (DECBS), to address this issue by first determining the maximum LB value and then employing a best-first search guided by this LB to find a collision-free path. Experimental results demonstrate that DECBS outperforms ECBS in most test cases and is compatible with existing optimization techniques. DECBS can reduce nearly 30% high-level CT nodes and 50% low-level focal search nodes. When agent density is moderate to high, DECBS achieves a 23.5% average runtime improvement over ECBS with identical suboptimality bounds and optimizations.

cs.MA↗

arXiv2025

Enhancing Lifelong Multi-Agent Path Finding with Cache Mechanism

Multi-Agent Path Finding (MAPF), which focuses on finding collision-free paths for multiple robots, is crucial in autonomous warehouse operations. Lifelong MAPF (L-MAPF), where agents are continuously reassigned new targets upon completing their current tasks, offers a more realistic approximation of real-world warehouse scenarios. While cache storage systems can enhance efficiency and reduce operational costs, existing approaches primarily rely on expectations and mathematical models, often without adequately addressing the challenges of multi-robot planning and execution. In this paper, we introduce a novel mechanism called Lifelong MAPF with Cache Mechanism (L-MAPF-CM), which integrates high-level cache storage with low-level path planning. We have involved a new type of map grid called cache for temporary item storage. Additionally, we involved a task assigner (TA) with a locking mechanism to bridge the gap between the new cache grid and L-MAPF algorithm. The TA dynamically allocates target locations to agents based on their status in various scenarios. We evaluated L-MAPF-CM using different cache replacement policies and task distributions. L-MAPF-CM has demonstrated performance improvements particularly with high cache hit rates and smooth traffic conditions.

cs.RO↗

arXiv2024

Caching-Augmented Lifelong Multi-Agent Path Finding

Multi-Agent Path Finding (MAPF), which involves finding collision-free paths for multiple robots, is crucial in various applications. Lifelong MAPF, where targets are reassigned to agents as soon as they complete their initial targets, offers a more accurate approximation of real-world warehouse planning. In this paper, we present a novel mechanism named Caching-Augmented Lifelong MAPF (CAL-MAPF), designed to improve the performance of Lifelong MAPF. We have developed a new type of map grid called cache for temporary item storage and replacement, and created a locking mechanism to improve the planning solution's stability. A task assigner (TA) is designed for CAL-MAPF to allocate target locations to agents and control agent status in different situations. CAL-MAPF has been evaluated using various cache replacement policies and input task distributions. We have identified three main factors significantly impacting CAL-MAPF performance through experimentation: suitable input task distribution, high cache hit rate, and smooth traffic. In general, CAL-MAPF has demonstrated potential for performance improvements in certain task distributions, map and agent configurations.

cs.RO↗

arXiv2023

Rethinking the editing of generative adversarial networks: a method to estimate editing vectors based on dimension reduction

While Generative Adversarial Networks (GANs) have recently found applications in image editing, most previous GAN-based image editing methods require largescale datasets with semantic segmentation annotations for training, only provide high level control, or merely interpolate between different images. Previous researchers have proposed EditGAN for high-quality, high-precision semantic image editing with limited semantic annotations by finding `editing vectors'. However, it is noticed that there are many features that are not highly associated with semantics, and EditGAN may fail on them. Based on the orthogonality of latent space observed by EditGAN, we propose a method to estimate editing vectors that do not rely on semantic segmentation nor differentiable feature estimation network. Our method assumes that there is a correlation between the intensity distribution of features and the distribution of hidden vectors, and estimates the relationship between the above distributions by sampling the feature intensity of the image corresponding to several hidden vectors. We modified Linear Discriminant Analysis (LDA) to deal with both binary feature editing and continuous feature editing. We then found that this method has a good effect in processing features such as clothing type and texture, skin color and hair.

cs.CV↗