英文论文降重2.0promax-AI-powered paraphrasing tool.

Example

Original: We propose a novel convolutional neural network that significantly improves image classification accuracy on standard benchmarks. Paraphrase (light): We introduce a new convolutional neural architecture achieving notably better image-classification performance on common benchmark datasets. Paraphrase (aggressive): In this study, we design an innovative CNN that delivers substantially higher accuracy across popular image-classification testbeds.

Scenario

When an author needs to reword a claim in the abstract or the contribution sentence to reduce textual similarity while keeping the claim intact. The user can select 'light' if they want minimal wording change (retain most technical terms) or 'aggressive' to maximize originality (change sentence structure, replace terms with abbreviations or synonyms, and re-order phrases).

Example

Original paragraph: Related work has explored CNNs for classification. Some papers use data augmentation. Others focus on architecture search. But they don't consider label noise. Rewritten paragraph: Prior work on convolutional networks has emphasized data augmentation and automated architecture search; however, comparatively little attention has been paid to training under label noise, which motivates our investigation.

Scenario

When preparing a literature review or related-work section, the tool can merge short, disjoint sentences, insert appropriate transitions, reorder sentences to build a logical narrative, and reduce surface-level overlap with cited sources. This is useful when consolidating multiple summarized studies into a cohesive paragraph while reducing repetitive phrasing that triggers similarity checks.

Example

Original: Smith et al. (2019) demonstrated that dropout improves generalization in convolutional networks, and we build on their approach. Diagnosis: detected 4-gram overlap with source text 'dropout improves generalization in'. Paraphrase (resolved): Building on Smith et al.'s (2019) finding that applying dropout boosts generalization in CNNs, we extend their training protocol to noisy-label settings.

Scenario

When a reviewer or plagiarism screening tool flags text for high similarity, the user runs the flagged passage through the similarity-aware mode. The system highlights specific n-gram matches, explains which phrases triggered the match, and provides alternatives that rephrase those spans while keeping citations intact. This supports resubmission after similarity objections and helps authors lower automated similarity scores without changing substantive meaning.