Although existing automated merging tools have made efforts in merging displayed text and syntactic conflicts, the deep logical and semantic conflicts that do not cause compilation errors still require human review to fully resolve. In order to make the merged results more reliable and reduce the workload of human review, we propose DEEPGRAPHMERGE, a reliable merge conflict resolution system that specifically detects and resolves deep logical and semantic conflicts in collaborative development. Our method combines hierarchical directed graph neural networks (HD-GNN) with static analysis to identify and reconcile complex, deep semantic and logical conflicts across files. The key innovation lies in our hierarchical dependency graph, which explicitly explores the underlying program logic, enabling precise detection of deep semantic and logical conflicts. Experimental results across four languages (Java, C#, JavaScript, TypeScript) demonstrate superior performance: 91.2% accuracy in resolving challenging semantic and logical conflicts. The method’s ability to understand and harmonize deep semantic and logical differences represents a significant advance over current merge technologies.

LLM-agent systems often decompose high-level objectives into subtask dependency graphs, assuming that each subtask’s output is reliable and conditionally independent of others given its parent responses. However, this assumption frequently breaks during execution, as ground-truth responses are inaccessible, leading to inter-agent misalignment—failures caused by inconsistencies and coordination breakdowns among agents. To address this, we propose SeqCV, a dynamic framework for reliable execution under violated conditional independence. SeqCV executes subtasks sequentially, each conditioned on all prior verified responses, and performs consistency checks immediately after agents generate short token sequences. At each checkpoint, a token sequence is accepted only if it represents shared knowledge consistently supported across diverse LLM models; otherwise, it is discarded, triggering recursive subtask decomposition for finer-grained reasoning. Despite its sequential nature, SeqCV avoids repeated corrections on the same misalignment and achieves higher effective throughput than parallel pipelines. Across multiple reasoning and coordination tasks, SeqCV improves accuracy by up to 30\% over existing LLM-agent systems. Code is available at https://github.com/tmllab/2025_NeurIPS_SeqCV.

In collaborative software development, developers generally make code changes and commit the changes to the repositories. Among others, "making small, single-purpose commits" is considered the best practice for making commits, allowing the team to quickly understand the code changes. Rather than following best practices, developers often make tangled commits, which wrap code changes that implement different purposes. Such commits make it difficult for other developers to understand the code changes when conducting subsequent development. Early works on untangling code changes rely on human-specified heuristic rules or features, do not consider context, and are labor intensive. Recent works model the local context of code changes as a graph at the statement level, with statements as nodes and code dependencies as edges, and then cluster the changed statements. However, recent works ignore the hidden dependencies in the global context, e.g. a pair of tangled code changes may have no code dependency, and a pair of untangled code changes may have obvious code dependency. To solve this problem, we focus on detecting hidden dependencies among code changes. We model the global context of code changes as graphs at finer-grained, hierarchical levels, i.e., at both entity and statement levels. Then we propose a Heterogeneous Directed Graph Neural Network (HD-GNN) to detect hidden dependencies among code changes by aggregating the global context in both connected or disconnected entity-level subgraphs that intersected with the code changes. Evaluation of common C # and Java datasets with 1,612 and 14k tangled commits and manually validated datasets (MVD) with 600 commits shows that HD-GNN achieves an average enhancement of effectiveness of 25% and 19.2% compared to existing approaches and far superior to existing approaches in MVD, without sacrificing time efficiency.

In face recognition, searching a person's face in the whole picture is generally too time‐consuming to ensure high‐detection accuracy. Objects similar to the human face or multi‐view faces in low‐resolution images may result in the failure of face recognition. To alleviate the above problems, a real‐time face recognition method based on pre‐identification and multi‐scale classification is proposed in this study. The face area is segmented based on the proportion of human faces in the pedestrian area to reduce the search range, and faces can be robustly detected in complicated scenarios such as heads moving frequently or with large angles. To accurately recognise small‐scale faces, the authors propose the multi‐scale and multi‐channel shallow convolution network, which combines a multi‐scale mechanism on the feature map with a multi‐channel convolution network for real‐time face recognition. It performs face matching only in the pre‐identified face areas instead of the whole image, therefore it is more efficient. Experimental results showed that the proposed real‐time face recognition method detects and recognises faces correctly, and outperforms the existing methods in terms of effectiveness and efficiency.

It plays an important role to accurately track multiple vehicles in intelligent transportation, especially in intelligent vehicles. Due to complicated traffic environments it is difficult to track multiple vehicles accurately and robustly, especially when there are occlusions among vehicles. To alleviate these problems, a new approach is proposed to track multiple vehicles with the combination of robust detection and two classifiers. An improved ViBe algorithm is proposed for robust and accurate detection of multiple vehicles. It uses the gray-scale spatial information to build dictionary of pixel life length to make ghost shadows and object's residual shadows quickly blended into the samples of the background. The improved algorithm takes good post-processing method to restrain dynamic noise. In this paper, we also design a method using two classifiers to further attack the problem of failure to track vehicles with occlusions and interference. It classifies tracking rectangles with confidence values between two thresholds through combining local binary pattern with support vector machine (SVM) classifier and then using a convolutional neural network (CNN) classifier for the second time to remove the interference areas between vehicles and other moving objects. The two classifiers method has both time efficiency advantage of SVM and high accuracy advantage of CNN. Comparing with several existing methods, the qualitative and quantitative analysis of our experiment results showed that the proposed method not only effectively removed the ghost shadows, and improved the detection accuracy and real-time performance, but also was robust to deal with the occlusion of multiple vehicles in various traffic scenes.