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.

Reinforcement Learning from Verifiable Rewards (RLVR) is pivotal for enhancing LLM code generation, yet its efficacy is often hindered by insufficient test case coverage, leading to reward hacking and policy degradation. To address this, we propose RobustTests, a framework featuring a faulty-code-driven test case synthesis strategy. By leveraging "near-correct" faulty codes, RobustTests captures latent logical discrepancies and employs validator agents with behavioral feature clustering to filter invalid or redundant test cases. Additionally, a stepwise dense reward function based on pass rates is introduced to mitigate false negatives and enhance training robustness. Using this pipeline, we construct an augmented version of the CodeContests+ dataset with superior diagnostic utility. Experimental results show that RL fine-tuning of Qwen3-32B via RobustTests achieves a 3% absolute gain on LiveCodeBench, demonstrating its effectiveness in advancing LLM code generation proficiency. Codes and data are available at https://huggingface.co/datasets/sid6/RobustTests.