ReGround: Grounding Reviewer Comments in Multimodal Evidence

AI summary

Overview

Research Area: Natural Language Processing Technical Level: Intermediate Reading Time: ~3 minutes Published: September 10, 2026

What This Paper Is About

Reviewer comments naturally relate to specific parts of the reviewed paper, yet grounding these comments to the underlying evidence is difficult due to long multimodal documents. Existing benchmarks do not capture this setting and largely focus on explicit, information-seeking queries.

Key Contributions

1. We introduce ReGround, a large-scale dataset for reviewer comment grounding that links 10,267 reviewer comments to 16,274 evidence in the original anonymous submission of 3,656 papers
2. Existing benchmarks do not capture this setting and largely focus on explicit, information-seeking queries

Main Findings

• Finding 1: Results show that retrieval over the entire paper content performs poorly, evidence-type inference is a major bottleneck, and multimodal evidence provides complementary signals that text alone misses

Methodology in Plain English

We cast grounding as a retrieval task and evaluate a wide range of retrieval methods

Why This Matters

Impact on Research: This work opens new directions for researchers in Natural Language Processing.

Real-World Applications:

• Improving AI system performance and reliability
• Developing more efficient and scalable solutions
• Addressing practical challenges in Natural Language Processing
• Enabling new capabilities in AI-powered applications

Industry Relevance: Companies working with AI can leverage these findings to enhance their products and reduce development friction.

Future Directions

• Extending the approach to related domains and use cases
• Improving scalability and efficiency of the proposed methods
• Investigating practical deployment considerations
• Exploring combinations with complementary techniques

Target Audience

• Researchers in Natural Language Processing and related fields
• Engineers building AI systems and applications
• Students studying advanced topics in artificial intelligence
• Industry practitioners seeking to apply cutting-edge research

This summary was generated from the paper abstract. For full technical details, refer to the original paper.

Authors’ abstract

Reviewer comments naturally relate to specific parts of the reviewed paper, yet grounding these comments to the underlying evidence is difficult due to long multimodal documents. Existing benchmarks do not capture this setting and largely focus on explicit, information-seeking queries. We introduce ReGround, a large-scale dataset for reviewer comment grounding that links 10,267 reviewer comments to 16,274 evidence in the original anonymous submission of 3,656 papers. We build on a simple observation: author rebuttals often include explicit references to content of the submission used to address reviewer comments, providing a high-precision annotation source. We cast grounding as a retrieval task and evaluate a wide range of retrieval methods. Results show that retrieval over the entire paper content performs poorly, evidence-type inference is a major bottleneck, and multimodal evidence provides complementary signals that text alone misses. Our dataset exposes grounding reviewer comments as a difficult and practically important problem for scientific document understanding.