Reinforcement learning from unlabeled prompts offers a promising path to improving LLM reasoning without curated supervision or verifiable rewards. However, existing label-free methods typically rely on either intrinsic self-certainty or extrinsic answer agreement, both of which can become unreliable in hard reasoning regimes: confident trajectories can be wrong, while majority answers can drift toward stable yet incorrect attractors. We propose CCAE, a verifier-free and parameter-free advantage estimator that formulates label-free GRPO as a multi-signal advantage estimation problem. CCAE decomposes the advantage into a base self-certainty term, an intrinsic evidence factor, and an extrinsic agreement factor, with their interaction adaptively calibrated by a group-level proxy for agreement reliability, without introducing any reward model, verifier, or trainable parameters. On MATH-Train, CCAE improves over the strongest unlabeled baseline by 5.0 and 4.3 points on Qwen3-4B/8B-Base, respectively, matching supervised GRPO. On the harder DAPO-14k-MATH, CCAE remains stable while prior unlabeled methods collapse, outperforming their best pre-collapse checkpoints by up to 11.1 points. Results on Natural Reasoning further suggest that CCAE remains effective beyond math-only training. Code is available at: [https://github.com/tifing/CCAE](https://github.com/tifing/CCAE).

With the rapid growth of scientific papers, understanding the changes and trends in a research area is rather time-consuming. The first challenge is to find related and comparable articles for the research. Comparative citations compare co-cited papers in a citation sentence and can serve as good guidance for researchers to track a research area. We thus go through comparative citations to find comparable objects and build a comparative scientific summarization corpus (CSSC). And then, we propose the comparative graph-based summarization (CGSUM) method to create comparative summaries using citations as guidance. The comparative graph is constructed using sentences as nodes and three different relationships of sentences as edges. The relationship that sentences occur in the same paper is used to calculate the salience of sentences, the relationship that sentences occur in two different papers is used to calculate the difference between sentences, and the relationship that sentences are related to citations is used to calculate the commonality of sentences. Experiments show that CGSUM outperforms comparative baselines on CSSC and performs well on DUC2006 and DUC2007.