Multi-chapter novel generation requires models to balance two quality dimensions simultaneously: chapter-level reader engagement and global narrative rhythm. Existing approaches have mainly focused on planning, memory, and execution, while paying limited attention to the mismatch between training signals and reader experience. We propose **ChapterRL**, which reformulates multi-chapter generation as a reward design problem. Specifically, ChapterRL introduces two complementary reward signals. **Reading Depth Residual** (RDR) derives debiased chapter-level quality signals from reader continuation and completion behavior through genre–position stratified normalization. **Dynamic Rhythm Trajectory** (DRT) uses an arousal estimator to impose a threshold-gated penalty on systematic rhythm collapse in the later part of a story. The two signals correspond to chapter attractiveness and global narrative momentum, and are jointly integrated into reinforcement learning training. We evaluate the full ChapterRL policy on three automatic benchmarks and separately validate its RDR reward model on task-aligned narrative domains of the Chinese WritingPreferenceBench. Results show that ChapterRL improves chapter quality and rhythm stability over supervised fine-tuning and single-reward variants, while RDR exhibits strong narrative preference alignment as an efficient scalar reward model.Code is available at [ChapterRL](https://github.com/SorBetQ/ChapterRL).

With the rapid development of large language models (LLMs) in math reasoning, the accuracy of models on existing math benchmarks has gradually approached 90% or even higher. More challenging math benchmarks are hence urgently in need to satisfy the increasing evaluation demands. To bridge this gap, we propose HighMATH. Problems in HighMATH are collected according to 3 criteria: problem complexity, knowledge domain diversity and fine-grained annotations. We collect 5,293 problems from Chinese senior high school mathematics exams published in 2024, covering 8 subjects and 7 levels of difficulty, with each problem involving an average of more than 2.4 knowledge points. We conduct a thorough evaluation of latest LLMs on the curated HighMATH, including o1-like models. Evaluation results demonstrate that the accuracy of advanced LLMs on HighMATH is significantly lower than that on previous math reasoning benchmarks. This gap even exceeds 30%. Our results also suggest that properly trained smaller LLMs may have great potential in math reasoning. Our data is available at https://github.com/tjunlp-lab/HighMATH.

Aspect-based sentiment analysis (ABSA) tasks aim to extract sentiment tuples from a sentence. Recent generative methods such as Seq2Seq models have achieved good performance by formulating the output as a sequence of sentiment tuples. However, the orders between the sentiment tuples do not naturally exist and the generation of the current tuple should not condition on the previous ones. In this paper, we propose Seq2Path to generate sentiment tuples as paths of a tree. A tree can represent “1-to-n” relations (e.g., an aspect term may correspond to multiple opinion terms) and the paths of a tree are independent and do not have orders. For training, we treat each path as an independent target, and we calculate the average loss of the ordinary Seq2Seq model over paths. For inference, we apply beam search with constrained decoding. By introducing an additional discriminative token and applying a data augmentation technique, valid paths can be automatically selected. We conduct experiments on five tasks including AOPE, ASTE, TASD, UABSA, ACOS. We evaluate our method on four common benchmark datasets including Laptop14, Rest14, Rest15, Rest16. Our proposed method achieves state-of-the-art results in almost all cases.