Domain-specific Named Entity Recognition (NER) often requires data augmentation due to the scarcity of annotated corpora. Guidance Data Augmentation (GDA), a method utilizing Large Language Models (LLMs) to decompose sentences into abstract components, can lead to over-abstraction, resulting in undefined entity tags and sentences lacking domain-specific vocabulary. In this work, we propose Reflective GDA (R-GDA), a framework that introduces a multi-agent feedback loop to enhance augmentation quality. R-GDA incorporates two distinct agents: a Guidance Refiner (GR), which assesses the initial abstraction to prevent over-generalization, and an Augmentation Calibrator (AC), which validates the final generated sample for domain-fidelity and tag integrity. On the SciERC and NCBI-disease datasets, R-GDA improves F1-Score, validating its effectiveness. Concurrently, it achieves low BERTScore in most cases, indicating greater sentence diversity. For the FIN dataset, it achieves performance comparable to the GDA baseline. R-GDA consistently prevents errors regarding domain-specific tags, demonstrating that the reflective feedback mechanism enhances data fidelity by mitigating critical generation errors.

Peer review is the primary mechanism for evaluating scientific contributions, yet prior studies have mostly examined paper features or external metadata in isolation. The emergence of open platforms such as OpenReview has transformed peer review into a transparent and interactive process, recording not only scores and comments but also rebuttals, reviewer–author exchanges, and meta-reviewer decisions. In this paper, we present a large-scale empirical study of ICLR 2017–2025, encompassing over 28,000 submissions, integrating four dimensions: paper structure and language quality, submission strategies and external metadata, author–reviewer interaction dynamics, and reviewer disagreement patterns. Our results show that factors beyond scientific novelty significantly shape acceptance outcomes. In particular, the rebuttal stage emerges as a decisive phase: timely, substantive author–reviewer communication strongly increases acceptance likelihood, often outweighing initial reviewer skepticism. Clearer writing, earlier submission, and effective disagreement resolution also correlate with higher acceptance probabilities. Based on these findings, we propose data-driven guidelines for authors, reviewers, and meta-reviewers to enhance transparency and fairness in peer review.