The rapid development of Diffusion Language Models (DLMs) raises concerns about watermarking for DLM-generated detection. However, existing sequential LLM watermarking cannot be directly applied to DLMs, as DLMs’ generation order is arbitrary. While emerging studies adapt biased LLM watermarking to DLMs by temporarily predicting the watermark prefix, they suffer from degraded quality and unstable watermarking due to bias accumulation and prediction errors. Besides, they cannot carry multi-bit watermarks. In this paper, we propose unbiased multi-bit watermarking for DLMs. We introduce a stability-aware constraint that allows watermarking only in stable contexts and a bit-controlled, unbiased modulation to preserve the original DLM output distribution, achieving stable watermarking with minimal quality impact. To enhance detection robustness, we design a Regret-based Remasking, which grants a “second chance” for unwatermarked tokens to be regenerated. It can seamlessly integrate into DLM inference with no added diffusion steps and latency. Experiments across DLMs and various tasks show that our scheme is effective, achieving superior generation quality compared to baselines while maintaining high detection accuracy and multi-bit capacity. Our code is available here https://github.com/iieSKLCSDsg/UMR.

Recent advances in AI and wearable devices, such as augmented-reality glasses, have made it possible to augment human memory by retrieving personal experiences in response to natural language queries. However, existing egocentric video datasets fall short in supporting the personalization and long-context reasoning required for episodic memory retrieval. To address these limitations, we introduce EgoMemory, a benchmark derived from Ego4D, enriched with 165,795 user-specific object annotations over 245 videos from 45 participants, yielding 639 distinct, human-curated, and evaluated queries for rich and individualized episodic memory retrieval. Leveraging this resource, we present EgoRetriever, a novel, training-free retrieval framework that combines Multimodal Large Language Models with reflective Chain-of-Thought prompting. Our approach enables interpretive inference of user intent and generates detailed target video descriptions by leveraging contextualized personal memory for video retrieval. Extensive experiments on three benchmarks, including EgoMemory, EgoCVR, and EgoLife, demonstrate that EgoRetriever consistently and substantially outperforms state-of-the-art baselines, highlighting its strong generalizability and practical potential for personalized, long-context egocentric video retrieval.

Retrieval-Augmented Generation (RAG) enhances the factual accuracy of Large Language Model (LLM) outputs based on external knowledge bases. These knowledge bases often carry significant intellectual property (IP) value, raising the urgent need for robust watermarking techniques to protect IP. However, existing RAG watermarking methods remain in their infancy, facing challenges such as limited encoding capacity and potential degradation of RAG performance or knowledge quality. In this paper, we propose knowledge-infused and multi-bit watermarking (KMW) for RAG knowledge bases. It generates watermark text to infuse the knowledge base by benign knowledge completion and a tailored generative watermarking algorithm. Each generated text can carry a multi-bit watermark segment. For effective detection, we design a Watermark Text Indexer that optimizes queries for steady retrieval of watermarked texts. Experiments on multiple datasets and LLMs show KMW reliably extracts watermarks from adversarial RAGs. It is robust against knowledge selection, alteration, expansion, and RAG setting restrictions, while remaining stealthy and secure. This highlights that KMW ensures effective IP protection for RAG systems. Our code is available here https://github.com/iieSKLCSDsg/KMW.

The widespread dissemination of toxic content on social media poses a serious threat to both online environments and public discourse, highlighting the urgent need for detoxification methods that effectively remove toxicity while preserving the original semantics.However, existing approaches often struggle to simultaneously achieve strong detoxification performance, semantic preservation, and robustness to out-of-distribution data. Moreover, they typically rely on costly, manually annotated parallel corpora while showing poor data efficiency.To address these challenges, we propose GEM, a two-stage training framework that jointly optimizes Model Generalization, Data Efficiency, and Semantic Preservation.We first perform supervised fine-tuning on a small set of high-quality, filtered parallel data to establish a strong initialization. Then, we leverage unlabeled toxic inputs and a custom-designed reward model to train the LLM using Group Relative Policy Optimization.Experimental results demonstrate that our method effectively mitigates the trade-offs faced by previous work, achieving state-of-the-art performance with improved generalization and significantly reduced dependence on annotated data. Our code is available at https://github.com/allacnobug/Detoxification-of-Text.

Pre-trained language models like BERT achieve superior performances in various NLP tasks without explicit consideration of syntactic information. Meanwhile, syntactic information has been proved to be crucial for the success of NLP applications. However, how to incorporate the syntax trees effectively and efficiently into pre-trained Transformers is still unsettled. In this paper, we address this problem by proposing a novel framework named Syntax-BERT. This framework works in a plug-and-play mode and is applicable to an arbitrary pre-trained checkpoint based on Transformer architecture. Experiments on various datasets of natural language understanding verify the effectiveness of syntax trees and achieve consistent improvement over multiple pre-trained models, including BERT, RoBERTa, and T5.

We propose a novel Bi-directional Cognitive Knowledge Framework (BCKF) for reading comprehension from the perspective of complementary learning systems theory. It aims to simulate two ways of thinking in the brain to answer questions, including reverse thinking and inertial thinking. To validate the effectiveness of our framework, we design a corresponding Bi-directional Cognitive Thinking Network (BCTN) to encode the passage and generate a question (answer) given an answer (question) and decouple the bi-directional knowledge. The model has the ability to reverse reasoning questions which can assist inertial thinking to generate more accurate answers. Competitive improvement is observed in DuReader dataset, confirming our hypothesis that bi-directional knowledge helps the QA task. The novel framework shows an interesting perspective on machine reading comprehension and cognitive science.