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Problem of exploration finite undirected graphs by a collective of agents is considered in this work. Two agents-researchers simultaneously move on graph, they read and change marks of graph elements, transfer the information to the agent-experimenter (it builds explored graph representation). It was constructed an algorithm linear (from amount of the graph’s nodes) time complexity, quadratic space complexity and communication complexity, that is equal to O(n²·log(n)). Two agents (which move on graph) need two different colors (in total three colors) for graph exploration. An algorithm is based on depth-first traversal method.

The study presented in the paper is devoted to the problem of finite graph exploration using a collective of agents. Finite non-oriented graphs without loops and multiple edges are considered in this paper. The collective of agents consists of two agents-researchers, who have a finite memory independent of the number of nodes of the graph studied by them and use two colors each (three colors are used in the aggregate) and one agentexperimental, who has a finite, unlimitedly growing internal memory. Agents-researches can simultaneously traverse the graph, read and change labels of graph elements, and also transmit the necessary information to a third agent — the agent-experimenter. An agent-experimenter is a non-moving agent in whose memory the result of the functioning of agents-researchers at each step is recorded and, also, a representation of the investigated graph (initially unknown to agents) is gradually built up with a list of edges and a list of nodes.

The work includes detail describes of the operating modes of agents-researchers with an indication of the priority of their activation. The commands exchanged between agents-researchers and an agent-experimenter during the execution of procedures are considered. Problematic situations arising in the work of agentsresearchers are also studied in detail, for example, staining a white vertex, when two agents simultaneously fall into the same node, or marking and examining the isthmus (edges connecting subgraphs examined by different agents-researchers), etc. The full algorithm of the agent-experimenter is presented with a detailed description of the processing of messages received from agents-researchers, on the basis of which a representation of the studied graph is built. In addition, a complete analysis of the time, space, and communication complexities of the constructed algorithm was performed.

The presented graph exploration algorithm has a quadratic (with respect to the number of nodes of the studied graph) time complexity, quadratic space complexity, and quadratic communication complexity. The graph exploration algorithm is based on the depth-first traversal method.

Disassembly of stripped x86 binaries is an important yet non-trivial task. Disassembly is difficult to perform correctly without debug information, especially on x86 architecture, which has variablesized instructions interleaved with data. Moreover, the presence of indirect jumps in binary code adds another layer of complexity. Indirect jumps impede the ability of recursive traversal, a common disassembly technique, to successfully identify all instructions within the code. Consequently, disassembling such code becomes even more intricate and demanding, further highlighting the challenges faced in this field. Many tools, including commercial ones such as IDA Pro, struggle with accurate x86 disassembly. As such, there has been some interest in developing a better solution using machine learning (ML) techniques. ML can potentially capture underlying compiler-independent patterns inherent for the compiler-generated assembly. Researchers in this area have shown that it is possible for ML approaches to outperform the classical tools. They also can be less timeconsuming to develop compared to manual heuristics, shifting most of the burden onto collecting a big representative dataset of executables with debug information. Following this line of work, we propose an improvement of an existing RGCN-based architecture, which builds control and flow graph on superset disassembly. The enhancement comes from augmenting the graph with data flow information. In particular, in the embedding we add Jump Control Flow and Register Dependency edges, inspired by Probabilistic Disassembly. We also create an open-source x86 instruction identification dataset, based on a combination of ByteWeight dataset and a selection open-source Debian packages. Compared to IDA Pro, a state of the art commercial tool, our approach yields better accuracy, while maintaining great performance on our benchmarks. It also fares well against existing machine learning approaches such as DeepDi.