How to Respond When Reviewers Claim Your Paper Is Just a Simple Combination of Existing Techniques

❌ Not recommended response

Many authors might reply:

Although we used existing techniques, we achieved SOTA performance, so the work is valuable.

This answer emphasizes first‑time use and performance gain but fails to explain:

Where the core innovation actually lies

Whether the modules are merely added together

Why the combination solves a new problem

What gap the paper fills compared with existing frameworks

Therefore, simply stating SOTA is insufficient to refute the “simple combination” objection.

✅ Recommended response

A better reply acknowledges the use of existing techniques while emphasizing the new collaborative mechanism, deep coupling, and systematic solution to the target problem.

Thank you for the feedback. We would like to clarify that the core conceptual innovation of this paper is XXX , which fills the previous gap in YYY . Although the paper incorporates retrieval, memory, and summarization modules, they are not merely concatenated; they serve the core innovative mechanism as tightly coupled components. For example, the combination of module A and module B addresses the previously unsolvable ZZZ problem. This deep‑coupled system enables significant performance gains in AAA scenarios. We will further highlight the core innovation and add a description of the inter‑module collaboration in the revised version to avoid the impression of a simple combination.

🎯 Core answer

When faced with the “simple combination” objection, the key is not to dodge the existing techniques but to explain how they are reorganized around the core problem to form a new collaborative mechanism.

Shift from listing “which existing techniques are used” to explaining “why this combination, how the deep coupling works, and what previously unsolvable issues it resolves.”

📌 One‑sentence summary

Recommend a direct response that emphasizes the paper’s core innovation, collaborative mechanism, deep coupling, and concrete contribution to the field.

GitHub address: https://github.com/MLNLP-World/Paper-Rebuttal-Tips