Deep Research Agents (DR-Agents) extend LLMs with multi-step retrieval, tool use, and intermediate computation to solve complex problems. However, their execution logs often contain redundant context, noisy evidence, and brittle dependencies among intermediate decisions. As a result, final answers can be sensitive to irrelevant or weakly verified information. We propose DualResearch, a log-based post-processing framework for answer reconstruction. Given a query and a completed DR-Agent log, DualResearch normalizes the log into event records and constructs two evidence graphs: a breadth semantic graph for declarative evidence and a depth causal graph for procedural provenance. It then retrieves breadth nodes and depth paths, assigns higher weights to retrieval channels with more concentrated retained candidate distributions, and jointly considers relevance and cross-view agreement to select evidence for reconstruction; the concentration measure reflects retrieval confidence rather than factual correctness. Experiments on HLE, GAIA, and GPQA show consistent accuracy improvements over underlying DR-Agents and controlled log-reuse baselines.

Deep research is an inherently challenging task that demands both breadth and depth of thinking. It involves navigating diverse knowledge spaces and reasoning over complex, multi-step dependencies, which presents substantial challenges for agentic systems. To address this, we propose FlowSearch, a multi-agent framework that actively constructs and evolves a dynamic structured knowledge flow to drive subtask execution and reasoning. FlowSearch is capable of strategically planning and expanding the knowledge flow to enable parallel exploration and hierarchical task decomposition, while also adjusting the knowledge flow in real time based on feedback from intermediate reasoning outcomes and insights. FlowSearch achieves competitive performance on both general and scientific benchmarks, including GAIA, HLE, GPQA and TRQA, demonstrating its effectiveness in multi-disciplinary research scenarios and its potential to advance scientific discovery. The code will be available.