Chain-of-thought (CoT) reasoning is widely used in large language models (LLMs), but the resulting reasoning traces remain underexplored.We study these traces through the lens of discourse-level negation.Specifically, we distinguish between corrective negation, which rejects a prior reasoning step, and refining negation, which narrows or qualifies it, and introduce metrics to quantify their use in human- and LLM-authored reasoning traces.Across multiple benchmarks, we find that negation occurs much more frequently in intermediate reasoning traces than in final response texts.We then test whether negation-based features provide predictive and descriptive signal for correctness, model identity, and human-vs.-LLM authorship.For correctness prediction, negation-based features consistently outperform simple structural baselines and in several settings add complementary signal to embedding-based representations, although embeddings remain stronger overall.In a controlled comparison on correct human and LLM traces from the same dataset, our strongest results arise in human-vs.-LLM classification, where negation features outperform both structural and embedding baselines.Overall, these findings position discourse-level negation as an interpretable feature for reasoning-trace analysis, with especially strong utility for provenance-related classification and modest but consistent value for correctness prediction.

Despite the communicative importance of negation, its detection remains challenging. Previous approaches perform poorly in out-of-domain scenarios, and progress outside of English has been slow due to a lack of resources and robust models. To address this gap, we present D-Neg: a syntax-aware graph reasoning model based on a transformer that incorporates syntactic embeddings by attention-gating. D-Neg uses graph attention to represent syntactic structures, emulating the effectiveness of rule-based dependency approaches for negation detection. We train D-Neg using 7 English resources and their translations into 10 languages, all aligned at the annotation level. We conduct an evaluation of all these datasets in in-domain and out-of-domain settings. Our work represents a significant advance in negation detection, enabling more effective cross-lingual research.

Despite their success, LLMs are too computationally expensive to replace task- or domain-specific NLP systems. However, the variety of corpus formats makes reusing these systems difficult. This underscores the importance of maintaining an interoperable NLP landscape. We address this challenge by pursuing two objectives: standardizing corpus formats and enabling massively parallel corpus processing. We present a unified conversion framework embedded in a massively parallel, microservice-based, programming language-independent NLP architecture designed for modularity and extensibility. It allows for the integration of external NLP conversion tools and supports the addition of new components that meet basic compatibility requirements. To evaluate our dual data- and process-oriented approach to standardization, we (1) benchmark its efficiency in terms of processing speed and memory usage, (2) demonstrate the benefits of standardized corpus formats for NLP downstream tasks, and (3) illustrate the advantages of incorporating custom formats into a corpus format ecosystem.