Reward modeling aligns language models with human preferences, yet coding tasks, requiring strict logic and syntax, pose challenges due to their domain diversity. In this paper, we propose RewardCode, a generalist code reward model trained via a two-stage pipeline, enabling scalable and precise evaluation through principle-based scoring and unit test generation. Specifically, the first stage combines Structured Fine-Tuning and Rejective Fine-Tuning to distill cross-domain summarization and code evaluation capabilities from advanced LLMs. The second stage introduces Pairwise-GRPO, a reinforcement learning method that uses preference pairs to improve solution discrimination, while ensuring the generation of consistent and verifiable unit tests. Further, to train a cross-domain code reward model, we construct CodePair-18K, a dataset of verifiable code preference pairs and executable unit tests. Experiments on multiple benchmarks show that RewardCode outperforms strong baselines in evaluation accuracy, verifying its effectiveness in advancing general-purpose code LLMs.

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.

Survey paper plays a crucial role in scientific research, especially given the rapid growth of research publications. Recently, researchers have begun using LLMs to automate survey generation for better efficiency. However, the quality gap between LLM-generated surveys and those written by human remains significant, particularly in terms of outline quality and citation accuracy. To close these gaps, we introduce SURVEYFORGE, which first generates the outline by analyzing the logical structure of human-written outlines and referring to the retrieved domain-related articles. Subsequently, leveraging high-quality papers retrieved from memory by our scholar navigation agent, SURVEYFORGE can automatically generate and refine the content of the generated article. Moreover, to achieve a comprehensive evaluation, we construct SurveyBench, which includes 100 human-written survey papers for win-rate comparison and assesses AI-generated survey papers across three dimensions: reference, outline, and content quality. Experiments demonstrate that SURVEYFORGEcan outperform previous works such as AutoSurvey.