d-TreeRPO: Towards More Reliable Policy Optimization for Diffusion Language Models

Reliable reinforcement learning (RL) for diffusion large language models (dLLMs) requires both accurate advantage estimation and precise estimation of prediction probabilities. Existing RL methods for dLLMs fall short in both aspects: they rely on coarse or unverifiable reward signals, and they estimate prediction probabilities without accounting for the bias relative to the true, unbiased expected prediction probability that properly integrates over all possible decoding orders. To mitigate these issues, we propose \emph{d}-TreeRPO, a reliable RL framework for dLLMs that leverages tree-structured rollouts and bottom-up advantage computation based on verifiable outcome rewards to provide fine-grained and verifiable step-wise reward signals. When estimating the conditional transition probability from a parent node to a child node, we theoretically analyze the estimation error between the unbiased expected prediction probability and the estimate obtained via a single forward pass, and find that higher prediction confidence leads to lower estimation error. Guided by this analysis, we introduce a time-scheduled self-distillation loss during training that enhances prediction confidence in later training stages, thereby enabling more accurate probability estimation and improved convergence. Experiments show that \emph{d}-TreeRPO outperforms existing baselines and achieves significant gains on multiple reasoning benchmarks, including +86.2 on Sudoku, +51.6 on Countdown, +4.5 on GSM8K, and +5.3 on Math500. Ablation studies and computational cost analyses further demonstrate the effectiveness and practicality of our design choices.

FusionLog: Cross-System Log-based Anomaly Detection via Fusion of General and Proprietary Knowledge

Log-based anomaly detection is critical for ensuring the stability and reliability of web systems. One of the key problems in this task is the lack of sufficient labeled logs, which limits the rapid deployment in new systems. Existing works usually leverage large-scale labeled logs from a mature web system and a small amount of labeled logs from a new system, using transfer learning to extract and generalize general knowledge across both domains. However, these methods focus solely on the transfer of general knowledge and neglect the disparity and potential mismatch between such knowledge and the proprietary knowledge of target system, thus constraining performance. To address this limitation, we propose FusionLog, a novel zero-label cross-system log-based anomaly detection method that effectively achieves the fusion of general and proprietary knowledge, enabling cross-system generalization without any labeled target logs. Specifically, we first design a training-free router based on semantic similarity that dynamically partitions unlabeled target logs into 'general logs' and 'proprietary logs.' For general logs, FusionLog employs a small model based on system-agnostic representation meta-learning for direct training and inference, inheriting the general anomaly patterns shared between the source and target systems. For proprietary logs, we iteratively generate pseudo-labels and fine-tune the small model using multi-round collaborative knowledge distillation and fusion based on large language model (LLM) and small model (SM) to enhance its capability to recognize anomaly patterns specific to the target system. Experimental results on three public log datasets from different systems show that FusionLog achieves over 90% F1-score under a fully zero-label setting, significantly outperforming state-of-the-art cross-system log-based anomaly detection methods.

A Survey on Parallel Text Generation: From Parallel Decoding to Diffusion Language Models

As text generation has become a core capability of modern Large Language Models (LLMs), it underpins a wide range of downstream applications. However, most existing LLMs rely on autoregressive (AR) generation, producing one token at a time based on previously generated context-resulting in limited generation speed due to the inherently sequential nature of the process. To address this challenge, an increasing number of researchers have begun exploring parallel text generation-a broad class of techniques aimed at breaking the token-by-token generation bottleneck and improving inference efficiency. Despite growing interest, there remains a lack of comprehensive analysis on what specific techniques constitute parallel text generation and how they improve inference performance. To bridge this gap, we present a systematic survey of parallel text generation methods. We categorize existing approaches into AR-based and Non-AR-based paradigms, and provide a detailed examination of the core techniques within each category. Following this taxonomy, we assess their theoretical trade-offs in terms of speed, quality, and efficiency, and examine their potential for combination and comparison with alternative acceleration strategies. Finally, based on our findings, we highlight recent advancements, identify open challenges, and outline promising directions for future research in parallel text generation.

FAIR: A Causal Framework for Accurately Inferring Judgments Reversals

Artificial intelligence researchers have made significant advances in legal intelligence in recent years. However, the existing studies have not focused on the important value embedded in judgments reversals, which limits the improvement of the efficiency of legal intelligence. In this paper, we propose a causal Framework for Accurately Inferring case Reversals (FAIR), which models the problem of judgments reversals based on real Chinese judgments. We mine the causes of judgments reversals by causal inference methods and inject the obtained causal relationships into the neural network as a priori knowledge. And then, our framework is validated on a challenging dataset as a legal judgment prediction task. The experimental results show that our framework can tap the most critical factors in judgments reversal, and the obtained causal relationships can effectively improve the neural network's performance. In addition, we discuss the generalization ability of large language models for legal intelligence tasks using ChatGPT as an example. Our experiment has found that the generalization ability of large language models still has defects, and mining causal relationships can effectively improve the accuracy and explain ability of model predictions.