With the release of OpenAI’s o1 model, reasoning models that adopt slow-thinking strategies have become increasingly common. Their outputs often contain complex reasoning, intermediate steps, and self-reflection, making existing evaluation methods and reward models inadequate. In particular, they struggle to judge answer equivalence and to reliably extract final answers from long, complex responses. To address this challenge, we propose xVerify, an efficient answer verifier for evaluating reasoning models. xVerify shows strong equivalence judgment capabilities, enabling accurate comparison between model outputs and reference answers across diverse question types. To train and evaluate xVerify, we construct the VAR dataset, consisting of multi-round annotated QA pairs generated by multiple LLMs across challenging reasoning tasks. Experimental results on both test and generalization sets show that all xVerify variants achieve over 95% F1 score and accuracy. Notably, the smallest model, xVerify-0.5B-I, outperforms all evaluation methods except GPT-4o, while xVerify-3B-Ib surpasses GPT-4o in overall performance. In addition, reinforcement learning experiments using xVerify as the reward model yield an 18.4% improvement for Qwen2.5-7B compared with direct generation, exceeding the gains achieved with Math Verify as the reward. These results demonstrate the effectiveness and generalizability of xVerify. All xVerify resources are available on [GitHub](https://github.com/IAAR-Shanghai/xVerify).

Memory systems are essential for enabling long-term learning and sustained interaction in AI systems such as LLMs and agents. However, memory storage and retrieval often suffer from hallucinations, including fabrication, errors, conflicts, and omissions. Existing evaluations mainly rely on end-to-end question answering, making it difficult to identify which memory operation causes hallucinations. To address this, we propose the Hallucination in Memory Benchmark (HaluMem), the first operation-level benchmark for evaluating hallucinations in memory systems. HaluMem defines three tasks (memory extraction, memory updating, and memory question answering) to comprehensively reveal hallucination behaviors across different operational stages of interaction. To support evaluation, we construct user-centric, multi-turn human-AI interaction datasets, HaluMem-Medium and HaluMem-Long, containing about 15k memory points and 3.5k questions. The datasets feature long dialogues, with average lengths of 1.5k and 2.6k turns and context sizes exceeding 1M tokens, enabling evaluation across varying context scales and task complexities. Experiments on HaluMem show that current memory systems tend to introduce and accumulate hallucinations during extraction and updating, which then propagate to question answering. These findings highlight the need for interpretable and constrained memory operation mechanisms to improve memory reliability. All resources are available at [GitHub](https://github.com/MemTensor/HaluMem).