Autonomous large language model (LLM) agents have shown strong potential for open-ended information-seeking tasks. However, existing agent frameworks primarily rely on sequential execution, making them vulnerable to error accumulation in long-horizon reasoning. Recent plan-and-execute approaches partially alleviate this issue, but their fixed planning strategies still struggle to recover from intermediate failures. We propose Logic Tree, a non-linear agentic reasoning framework formulated as a Directed Acyclic Graph (DAG). Instead of following a fixed plan with sequential execution, Logic Tree models complex task solving as a dynamically evolving and recoverable graph search process. The framework decomposes queries into parallel execution branches and incorporates a two-level self-correction mechanism. Local backtracking performs targeted rollback to repair failed reasoning states, while global replanning revises high-level execution strategies and preserves verified intermediate conclusions through a knowledge sanitation process. Experiments on BrowseComp, GAIA, and XBench show that Logic Tree consistently outperforms both sequential and plan-and-execute baselines. Further ablation studies demonstrate that combining DAG-based parallel execution with dynamic error recovery substantially improves robustness in long-horizon research tasks.

To tackle the problem of domain-specific knowledge scarcity within large language models (LLMs), knowledge graph-retrievalaugmented method has been proven to be an effective and efficient technique for knowledge infusion. However, existing approaches face two primary challenges: knowledge mismatch between public available knowledge graphs and the specific domain of the task at hand, and poor information compliance of LLMs with knowledge graphs. In this paper, we leverage a small set of labeled samples and a large-scale corpus to efficiently construct domain-specific knowledge graphs by an LLM, addressing the issue of knowledge mismatch. Additionally, we propose a three-stage KG-LLM alignment strategy to enhance the LLM’s capability to utilize information from knowledge graphs. We conduct experiments with a limited-sample setting on two biomedical question-answering datasets, and the results demonstrate that our approach outperforms existing baselines.