Despite substantial advancements in aligning LLMs with human values, current safety mechanisms remain susceptible to jailbreak attacks. We attribute this vulnerability to the distributional discrepancies between alignment-oriented prompts and malicious prompts. To investigate this, and drawing inspiration from logic-driven NLP tasks, we introduce LogiBreak, a universal black-box jailbreak method that utilizes logical expression translation to bypass LLM safety mechanisms. By converting harmful natural language prompts into formal logical expressions, LogiBreak exploits the distributional gap between alignment data and logic-expressed inputs, preserving the underlying semantic intent and readability while evading safety constraints. Furthermore, to fill the gap of existing benchmarks that lack systematic resources specifically targeting logical expression-based attacks against LLM robustness, we construct a novel multilingual logical expression jailbreak dataset for evaluation. Our evaluations of LogiBreak in five languages demonstrate its effectiveness and generalizability in various linguistic contexts. The code is available at https://github.com/Applied-Machine-Learning-Lab/ACL2026_Logibreak.

Chain-of-Thought (CoT) reasoning is widely used to improve LLM performance, and recent foundation recommender models adopt it by generating textual reasoning before predicting target items represented by Semantic IDs (SIDs). However, we observe that enabling thinking mode in models such as OpenOneRec can degrade recommendation quality by up to 25%. We investigate this failure and identify Linguistic Inertia: when a textual CoT segment is inserted before SID generation, the model relies more on natural-language context and less on historical SID evidence. Further analyses show that this effect is amplified by reduced access to historical information and longer CoT lengths. To mitigate it, we propose Linguistic-Inertia-Calibrated Decoding (LICD), a training-free framework that combines Reasoning-Chain Compression and Bias-Subtracted Contrastive Inference. Experiments on three large-scale benchmarks show that LICD consistently outperforms both no-thinking and original-thinking baselines. Our code is available at https://github.com/USTC-StarTeam/LICD.

Retrieval-Augmented Generation (RAG) technology effectively addresses the issues of knowledge update lag and hallucinations in large language models (LLMs) by integrating internal and external knowledge. Existing query augmentation methods improve RAG’s performance in handling complex queries but face two key challenges: (1) the separation of query augmentation and encoding tasks, which hinders information sharing and introduces cumulative errors, and (2) the difficulty of selecting the optimal augmentation strategy for different scenarios. In this work, we propose UniRAG, a unified framework for query understanding in RAG. UniRAG employs a decoder-only LLM to jointly perform query augmentation and encoding, eliminating task separation. To facilitate adaptive query augmentation, we categorize existing techniques into query paraphrasing, query expansion, and query abstraction. Our model learns to select the optimal augmentation strategy based on user queries, leveraging retrieval and generation outputs as feedback. Experimental results show that UniRAG significantly outperforms traditional query augmentation methods in five knowledge-intensive benchmark tasks in both closed and open domain question answering.