Jiang Zhou

Jiang Zhou, Yunhao Wang, Xing Wu et al.

Synthetic data rephrasing has emerged as a powerful technique for enhancing knowledge acquisition during large language model (LLM) pretraining. However, existing approaches operate at the single-document level, rewriting individual web pages in isolation. This confines synthesized examples to intra-document knowledge, missing cross-document relationships and leaving facts with limited associative context. We propose WRAP++ (Web discoveRy Amplified Pretraining), which amplifies the associative context of factual knowledge by discovering cross-document relationships from web hyperlinks and synthesizing joint QA over each discovered document pair. Concretely, WRAP++ discovers high-confidence relational motifs including dual-links and co-mentions, and synthesizes QA that requires reasoning across both documents. This produces relational knowledge absent from either source document alone, creating diverse entry points to the same facts. Because the number of valid entity pairs grows combinatorially, this discovery-driven synthesis also amplifies data scale far beyond single-document rewriting. Instantiating WRAP++ on Wikipedia, we amplify ~8.4B tokens of raw text into 80B tokens of cross-document QA data. On SimpleQA, OLMo-based models at both 7B and 32B scales trained with WRAP++ substantially outperform single-document approaches and exhibit sustained scaling gains, underscoring the advantage of cross-document knowledge discovery and amplification.

Bingzheng Wang, Xiaoyan Gu, Hongbo Xu et al.

Diffusion models have been widely deployed in AIGC services; however, their reliance on opaque training data and procedures exposes a broad attack surface for backdoor injection. In practical auditing scenarios, due to the protection of intellectual property and commercial confidentiality, auditors are typically unable to access model parameters, rendering existing white-box or query-intensive detection methods impractical. More importantly, even after the backdoor is detected, existing detoxification approaches are often trapped in a dilemma between detoxification effectiveness and generation quality. In this work, we identify a previously unreported phenomenon called temporal noise unconsistency, where the noise predictions between adjacent diffusion timesteps is disrupted in specific temporal segments when the input is triggered, while remaining stable under clean inputs. Leveraging this finding, we propose Temporal Noise Consistency Defense (TNC-Defense), a unified framework for backdoor detection and detoxification. The framework first uses the adjacent timestep noise consistency to design a gray-box detection module, for identifying and locating anomalous diffusion timesteps. Furthermore, the framework uses the identified anomalous timesteps to construct a trigger-agnostic, timestep-aware detoxification module, which directly corrects the backdoor generation path. This effectively suppresses backdoor behavior while significantly reducing detoxification costs. We evaluate the proposed method under five representative backdoor attack scenarios and compare it with state-of-the-art defenses. The results show that TNC-Defense improves the average detection accuracy by $11\%$ with negligible additional overhead, and invalidates an average of $98.5\%$ of triggered samples with only a mild degradation in generation quality.