Academic english writing generation-evaluation closed-loop system driven by large language model: adversarial training and cognitive logic verification

Current pipelines are disconnected, with generation systems generating fluent text but without strong evaluation and evaluation approaches that rely on heuristics or shallow proofreading, missing a coupling of rhetorical fluency and logical rigor. These limitations have been identified in terms of argumentative validity, domain adaptability, and alignment with the journal’s interdisciplinary impact/innovation focus. To solve those issues, a closed-loop paradigm was introduced where large language models are coupled with cognitive-logic veracity modules that operate in an adversarial training manner. In particular, the proposed approach switches between a generator, which is a transformer-based model fine-tuned on academic style, and an evaluator, which has been trained to distinguish flaws in the structure of the discourse, logical coherence, and alignment within the domain. The cognitive-logic step also operates to confirm rigorous logical inference, formal structure, and argument validity at each stage of iteration of the manuscript. Along with reinforcers as feedback, the generator optimizes to produce text that satisfies linguistic fluency and cognitive rigor. Evaluating the presented method on a corpus of text representative of student papers in a range of computing disciplines, significant 15%, 10%, and 8% positive differences were found in coherence, style, and timeliness, respectively, from generative-only baselines.