arXiv:2608. 10678v1 Announce Type: cross Abstract: Chinese web pollution has surfaced in LLMs, motivating audits of upstream Chinese corpora.
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arXiv:2607. 22662v1 Announce Type: new Abstract: Open-web corpora curated via highly selective filters, such as FineWeb-Edu and DCLM, constitute the core of LLM pretraining data and have significantly advanced LLM performance.
arXiv:2608.29903v1 Announce Type: cross Abstract: The rapid advancement of large language models (LLMs) has made AI-generated text detection increasingly critical. Existing zero-shot detectors assume...
arXiv:2607. 15267v1 Announce Type: new Abstract: Poisoning pretraining data can introduce harmful behaviors to LMs that are difficult to detect and mitigate.
arXiv:2606. 15396v1 Announce Type: cross Abstract: Malicious content generated from large language models (LLMs) could pose severe safety risks and ethical concerns.
arXiv:2609.33676v2 Announce Type: replace Abstract: LLM agents increasingly take consequential actions through interactions with users, policies, and external tools. Auditing these agents requires au...
arXiv:2608.22137v1 Announce Type: new Abstract: Modern language models and agents increasingly require persistent memory for complete codebases, long interaction histories, and heterogeneous enterpri...