Link plots/figures in NeurIPS rebuttal [R]

The perennial challenge of effectively communicating research findings in rebuttal statements has resurfaced, as evidenced by the recent OpenReview post regarding linking figures and plots in NeurIPS rebuttals Editing NeurIPS Rebuttal. The author’s frustration is understandable; dense tables often fail to convey the nuance and impact of experimental results as effectively as a well-crafted visualization. While the official guidelines disallow direct linking, the question of whether to circumvent this rule—and the potential consequences—is a complex one, reflective of a larger tension between rigid procedural adherence and the need for clear, impactful scientific communication. This situation highlights a significant limitation within the current OpenReview platform, and underscores the need for more modern markdown support that allows for seamless figure embedding, a point echoed by the original poster. The desire to present data in the most accessible format is a universally shared aspiration within the research community.

The risk, of course, is that bending the rules, even with good intentions, could be perceived negatively by reviewers. The potential repercussions range from a minor note on the rebuttal to outright rejection, a possibility that understandably weighs heavily on authors. It's a gamble predicated on the assumption that reviewers will prioritize the clarity of the information over strict adherence to guidelines. This mirrors broader conversations within the AI research community about the evolving nature of scientific presentation and the increasing emphasis on visual communication. The concern isn’t unique to NeurIPS; many researchers face similar hurdles when trying to effectively showcase their work within the constraints of various submission platforms. The related post Made a small model that extracts text from a white background illustrates the ingenuity researchers employ to overcome technical limitations, a sentiment resonant with the current debate.

Ultimately, the decision rests on a careful assessment of the reviewers and the specific context of the rebuttal. A risk-averse approach might involve converting the figures into static images and including them as supplementary material, though this sacrifices the interactive element and potentially adds unnecessary complexity. A more daring strategy would be to include the links, accompanied by a brief explanation acknowledging the technicality of the approach and emphasizing the importance of visual clarity for understanding the results. A proactive disclosure of this choice—framing it as a deliberate attempt to enhance comprehension—could mitigate potential negative perceptions. The future of AI research increasingly demands a focus on explainability and interpretability, and presenting data in a clear and accessible manner is crucial to achieving this goal. The discussion around NeurIPS 2026 AI-generated reviews further highlights a shift toward more streamlined and efficient review processes, potentially including more sophisticated methods for data presentation.

The core issue isn’t simply about linking figures; it’s about the broader need for platforms like OpenReview to evolve alongside the changing landscape of scientific communication. The current limitations feel increasingly anachronistic in an era where data visualization is a fundamental tool for research dissemination. Moving forward, it’s worth considering how platforms can proactively support more interactive and visually engaging presentation formats while maintaining the integrity of the review process. Will we see OpenReview, and others like it, embrace more modern markdown standards, or will researchers continue to navigate this tension between procedural compliance and effective communication?