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MARG: Multi-Agent Review Generation for Scientific Papers

Mike D'Arcy, Tom Hope, Larry Birnbaum, Doug Downey

arXiv:2401.04259v1cs.CL

TL;DR

Generating actionable feedback for scientific papers is challenging when papers exceed LLM input limits and require technical understanding. MARG distributes paper text among communicating agents and specializes agents for comment types. MARG-S generated 3.7 good comments per paper in the user study, versus 1.7 for a single-agent baseline and 0.3 for Liang et al. (2023).

  • Problem

    LLMs have limited effective context and scientific papers are long and technical, making full-paper review generation a challenging task.

    Source: §1 · p. 1

  • Method

    MARG splits paper text among communicating GPT-4 agents and uses specialized experts to help generate comments on experiments, clarity, and impact.

    Source: §1 · p. 1, §4 · p. 3

  • Results

    3.7 good comments per paper were rated for MARG-S, versus 1.7 for a single-agent baseline and 0.3 for Liang et al. (2023).

    Source: §1 · p. 1

  • Takeaways & Limitations

    MARG is presented as a way to generate peer-review feedback even for papers longer than the base model’s context size.

    Source: §1 · p. 2

    The model input omits figures and tables, and many equations are garbled or incomplete because of parsing limitations.

    Source: §3 · p. 3

Abstract

from arXiv · show

We study the ability of LLMs to generate feedback for scientific papers and develop MARG, a feedback generation approach using multiple LLM instances that engage in internal discussion. By distributing paper text across agents, MARG can consume the full text of papers beyond the input length limitations of the base LLM, and by specializing agents and incorporating sub-tasks tailored to different comment types (experiments, clarity, impact) it improves the helpfulness and specificity of feedback. In a user study, baseline methods using GPT-4 were rated as producing generic or very generic comments more than half the time, and only 1.7 comments per paper were rated as good overall in the best baseline. Our system substantially improves the ability of GPT-4 to generate specific and helpful feedback, reducing the rate of generic comments from 60% to 29% and generating 3.7 good comments per paper (a 2.2x improvement).

1 Introduction

The paper addresses the difficulty of generating actionable scientific-paper feedback when papers are long and technically demanding. It proposes MARG and reports more specific feedback and more good comments than baselines.

1 Introduction

  • The task requires understanding a work’s significance, methods, and evidence, then identifying shortcomings and suggesting improvements.The paper focuses on actionable criticism or suggestions rather than praise or simple grammatical and stylistic corrections.Source: §1 · p. 1
  • Scientific papers can exceed LLM input limits, and models may not effectively use all of even their supported context capacity.The paper notes that scientific papers may span thousands or tens of thousands of tokens.Source: §1 · p. 1
  • MARG distributes paper portions among GPT-4 agents that communicate, with specialized experts for experiments, clarity, and impact.The paper describes MARG as able to generate feedback even for papers longer than the base model’s context size.Source: §1 · pp. 1–2
  • 3.7 good comments per paper were rated for MARG-S, versus 1.7 for a single-agent baseline and 0.3 for Liang et al. (2023); 71% of MARG-S comments were rated specific.The authors also report that the method preserves accuracy while generating more specific comments.Source: §1 · pp. 1–2
  • MARG outperformed the strongest baseline by 6.1 recall points in automated evaluation and generated 2.2x as many helpful comments per review in the user study.Source: §1 · p. 2

2 Related work

Prior work addressed narrower review tasks, review generation, multi-agent LLM interaction, and context management. The paper positions MARG as combining full-paper coverage with specialized feedback and natural-language agent communication.

2.1 Review generation

  • Prior tools target specific aspects such as statistical tests or plagiarism, while automatic review generation has used small models, templates, or checklist verification with limited comment variety.Small-model and template approaches are described in Yuan and Liu (2022) and Wang et al. (2020a); checklist verification is described in Liu and Shah (2023).Source: §2.1 · p. 2
  • Impressona supports personalized feedback but not scientific-review prompt design or LM-LM interaction; Liang et al. (2023) truncate long papers and use a single prompt.The paper reports that Liang et al.’s approach is more efficient, while MARG produces more helpful comments.Source: §2.1 · p. 2

2.2 Multi-agent modeling

2.3 LLM context management

  • Architecture changes for longer inputs can require retraining or be unavailable through fixed APIs, while summarization and retrieval may discard nuanced details needed to identify paper shortcomings.The paper therefore divides the full text among agents that collectively retain it throughout the task.Source: §2.3 · p. 3

3 Task definition

The task is to generate substantive, actionable feedback that could help authors improve a scientific paper. The evaluation input omits figures and tables and may contain incomplete equations.

3 Task definition

  • Actionable feedback means suggestions or criticism, including questions that imply a need for clarification, rather than positive remarks.The task excludes simple grammatical or stylistic errors and focuses on substantive comments.Source: §3 · p. 3
  • Papers are split at paragraph boundaries into chunks that fit the model input, with paragraphs labeled by position and section.Source: §3 · p. 3
  • The model input excludes figures and tables, and parsing limitations leave many equations garbled or incomplete.The authors expect text alone to support many comments because main conclusions from visuals are often stated in text.Source: §3 · p. 3

4 Multi-agent review generation

MARG-S uses communicating LLM agents to review paper portions and specialized agents to generate feedback across the paper. The system organizes review comments by type, then applies a separate refinement stage to improve or remove them.

4 Multi-agent review generation

  • MARG defines leader, worker, and optional expert agents: workers receive paper chunks, while the leader coordinates communication and delegates tasks.The leader produces the final output after gathering information from the other agents.Source: §4 · p. 3

4.1 Agents

  • Special prompts distinguish agent roles; experts specialize in subtasks, while each paper chunk is assigned to a worker.The authors report that assigning a subtask to a separate expert performed better in preliminary testing than instructing the leader to handle it.Source: §4.1 · pp. 4–5

4.2 Communication

  • The leader broadcasts messages, receives agent replies into its history, and returns the final answer when it no longer seeks information.Replies are treated as responses to the leader.Source: §4.2 · p. 5

Error correction

  • The system detects repeated messages, reminds the leader to follow protocol, and prompts agents to answer messages addressed to them.These interventions target loops, missing message headers, and unrecognized addressees.Source: Error correction · p. 5

4.3 Context management

  • MARG uses GPT-4’s 8k-token version; for especially long papers, inter-agent message history can still exceed an agent’s input limit.Workers retain initial prompts and three recent messages; the leader prunes received messages but keeps its outgoing history.Source: §4.3 · p. 5

4.4 Review generation

  • Prompt development used manual iteration on ARIES papers and deliberately altered papers to expose errors that might otherwise escape detection.Examples included removing a section or adding an unfounded claim.Source: §4.4 · pp. 5–6
  • Three independent groups generate experiment, clarity, and impact comments, with task-specific instructions and experts tailored to each type.The experiments expert designs expected experiments for the paper’s claims, helping identify missing or incomplete tests; clarity and impact groups focus on reproducibility and contribution, respectively.Source: §4.4 · p. 6, Experiments and evaluation · p. 6, Clarity and reproducibility · p. 6

4.5 Refinement

  • A separate multi-agent refinement stage checks each comment for clarity, specificity, and validity, and may revise, split, or remove it.Comments are processed independently, without an expert agent.Source: §4.5 · p. 6

5 Baseline methods

The baselines compare chunked single-agent feedback with multi-agent approaches and a method that truncates the paper. They vary in prompting, coordination, and refinement.

5 Baseline methods

  • The evaluation includes three baselines: two chunk-based single-agent methods with different prompts, and Liang et al. (2023), which truncates papers.A third multi-agent baseline is also described for comparison with the prompt-tuned single-agent method.Source: §5 · p. 7, §5.3 · p. 7

5.1 Single-Agent Review Generation with Basic prompt (SARG-B)

  • SARG-B processes paper chunks independently with a simple review prompt, then combines the resulting comment lists.The prompt asks for scientific-review-style feedback and allows minor grammar comments to be skipped.Source: §5.1 · p. 7

5.2 Single-Agent Review Generation with Tuned Prompt (SARG-TP)

  • SARG-TP uses a more specific, actionable prompt for each chunk and refines the merged comments against each chunk.The refinement can remove a false claim that an experiment is missing when another chunk contains it.Source: §5.2 · p. 7

5.3 Multi-Agent Review Generation with Tuned Prompt (MARG-TP)

  • MARG-TP uses multiple agents with task language and refinement instructions made as similar as possible to SARG-TP.Its refinement is not applied separately to every chunk.Source: §5.3 · p. 7

5.4 Multi-Agent Review Generation with Specialized Agents (MARG-S)

  • MARG-S concatenates three specialized mini-reviews and is also evaluated by comment type and without refinement.The mini-reviews cover experiments, clarity, and impact.Source: §5.4 · p. 7

5.5 Liang et al. (2023) baseline (LiZCa)

  • Liang et al. (2023)’s LiZCa baseline truncates the paper, includes figure and table captions, and extracts criticisms from an outline-style review.The authors ran it with Grobid for consistency with their other baselines.Source: §5.5 · pp. 7–8

6 Automated evaluation

The section evaluates generated review comments by aligning them with human-review comments and comparing overlap across methods. MARG-S leads on recall, but generates more comments and has lower precision and Jaccard scores.

6.1 Measuring overlap

  • Comment alignment uses a many-many candidate-matching stage followed by pairwise scoring; accepted pairs require medium or high relatedness and equal or greater generated-comment specificity.The many-many stage keeps pairs proposed in at least two of five GPT-4 passes.Source: §6.1 · p. 8

6.2 Metrics

  • Because comment alignments can be many-to-many, the evaluation defines directional intersections rather than relying on conventional set intersections.Precision and recall measure the fractions of generated and real-review comments aligned to at least one comment on the other side.Source: §6.2 · p. 10
  • Metrics are macro-averaged across reviews, while the human-review baseline compares each real review with other reviews of the same paper.The authors note this baseline is theoretically unbiased for recall but may have lower precision and Jaccard scores.Source: §6.2 · p. 10, §6.3 · p. 10

6.3 Results

  • MARG-S outperforms all baselines on recall, but its higher comment count accompanies lower precision and Jaccard scores.The authors prioritize recall because humans can recognize and ignore bad comments more easily than they can recover good comments that were omitted.Source: §6.3 · p. 10, Table 2 · p. 11
  • The simple SARG-B baseline has the lowest recall despite generating as many comments as MARG-S, and SARG-TP beats MARG-TP on recall despite their similar task prompts.The authors suggest multi-agent gains come from richer problem-solving structures using expert agents, not merely from adding agents.Source: §6.3 · p. 10

MARG-S ablations:

  • Among MARG-S sub-reviewers, the impact-focused model performs best; clarity-focused feedback struggles, while removing refinement reduces performance across all metrics.The experiment-focused model produces half as many comments as the impact model and has half its recall; clarity judgments are complicated by subjective judgments and missing visual layout.Source: MARG-S ablations · pp. 10–11

Effect of the matching thresholds

  • MARG-S loses relatively little recall as specificity thresholds rise, whereas LiZCa’s recall drops sharply when relatedness or specificity thresholds become stricter.MARG-S still loses recall when requiring high relatedness, as some matches do not express exactly the same sentiment.Source: Effect of the matching thresholds · p. 11, Figure 3 · p. 13

Cost

  • MARG-S has the best recall but generates roughly an order of magnitude more tokens than other methods, making it more expensive and taking roughly an hour longer to generate reviews.The reported token totals include internal discussion; the implementation runs one inference at a time, although the authors describe the process as highly parallelizable.Source: Cost · p. 13, Table 4 · p. 13

7 User study

The user study compared MARG-S with two baselines using comment-level and review-level ratings. MARG-S produced more good comments and more specific feedback, while review length and the limits of user ratings qualified the results.

Participants; Survey

  • Nine NLP and human-computer interaction researchers participated; the authors note the small sample but report many rated comments and analyses controlling for participant bias.The web survey rated comment specificity, accuracy, and helpfulness, as well as review length and overall helpfulness; methods were concealed and reviews randomized, but comment order was not.Source: Survey · pp. 13–14, Participants · p. 13

7.2 Total good comments

  • MARG-S generated more good comments per review than SARG-B (p=0.09) and LiZCa (p=0.003).Table 5 reports average counts by comment rating; LiZCa generated fewer comments overall, including fewer good and bad comments.Source: Table 5 · p. 15, §7.2 · p. 15

Review length

  • MARG-S reviews were rated “way too long” by 6 of 9 participants, while LiZCa reviews were often rated too short.SARG-B occupied a middle ground; despite a similar comment count to MARG-S, its comments were shorter, which the authors suggest may explain its more reasonable perceived length.Source: Review length · p. 15

7.3 Average comment ratings

  • MARG-S had the highest fraction of “good” comments; the per-comment difference was significant versus SARG-B (p=0.02), but not LiZCa (p=0.09).Participants rated MARG-S reviews 1.0 point higher for helpfulness on the 5-point scale; MARG-S also had the highest proportion of fully accurate comments, though differences were not significant.Source: §7.3 · p. 15, Figure 5 · p. 16, Figure 6 · p. 16
  • 71% of MARG-S comments were rated specific or very specific, compared with 40% for LiZCa; its very-specific rate was more than triple the other methods’ rates.The increase in very-specific comments was significant (p=0.002, per-user related-sample t-test).Source: §7.3 · p. 15, Figure 7 · p. 16

7.4 Relationships between factors

  • 97% of comments with major inaccuracies were rated bad, versus 30% with minor inaccuracies and 23% of accurate comments.Among accurate comments, 57% of specific comments were rated good versus 19% of non-specific comments; specificity was also positively associated with neutral ratings and weakly corresponded with higher accuracy.Source: §7.4 · p. 18

7.5 Compliments and ratings

  • Compliments appeared in 19% of MARG-S comments, 25% of SARG-B comments, and none of LiZCa’s; their presence was not associated with meaningful rating bias.The estimated compliment coefficient was 0.11 (p=0.76), and the authors caution that compliment detection was subjective.Source: §7.5 · pp. 18–19

8 Failure analysis

Although MARG-S performs well relative to other methods, the analysis reports many bad comments and low absolute automated precision and recall. It examines conversation logs to identify communication and refinement errors.

8.1 Scope

  • The analysis covered 30 main-stage conversations across 10 papers and one randomly selected refinement comment per 30 automated-evaluation papers; paper checks took 5–15 minutes each.The authors note that factual errors may have been overlooked.Source: §8.1 · p. 19

8.2 Main stage

  • 70% of main-stage conversations contained at least one logged error; missing context occurred in 53%, and misplaced SEND MESSAGE markers in 47%.Source: §8.2 · p. 19
  • Other main-stage errors included EITL (7%), FIE (17%), IRI (10%), and FR, SS, and LOOP (7% each).Source: §8.2 · pp. 19–20
  • Missing-context errors occurred in 33% of conversations; experts flagged 80% of those, and authors conjecture an early correction may serve as a one-shot example.No later missing-context errors occurred in logs where the leader initially omitted the summary.Source: §8.2 · p. 20

8.3 Refinement stage

  • Revising instead of pruning occurred in 30% of refinement cases; only 3% were pruned, and the leader often did not cross-check revised comments.Source: §8.3 · pp. 20–21
  • 47% of invalid comments were not pruned; many cases involved information the model ignored or information unavailable in the parsed paper.Source: §8.3 · pp. 20–21

9 Conclusion

The conclusion summarizes MARG’s multi-agent approach to generating scientific-paper feedback, including chunk-based coordination, internal discussion, and comment refinement, alongside example conversation logs. It reports that MARG-S produced more good comments than the baselines and was especially specific and detailed, while noting that most comments across methods were rated bad and 38–48% highly inaccurate.

9 Conclusion

  • MARG-S produced more good comments than the simple and contemporaneous GPT-4 baselines in both alignment-based evaluation and the user study.The user study also found its comments especially strong in specificity and more detailed than those of other methods.Source: §9 · p. 21
  • MARG uses a network of LLM agents that share information across paper sections and engage in internal discussion to generate review comments.Source: §9 · p. 21
  • Across all methods, a majority of comments were rated bad, while 38–48% were rated highly inaccurate, indicating substantial room for improvement.Source: §9 · p. 21
  • MARG-S costs substantially more in time and API usage than other methods; dynamically switching to faster, cheaper models for simpler tasks is proposed as future work.Source: §9 · pp. 21–22
  • Future work could incorporate background literature to support more informed critiques of related work and baseline choices.Source: §9 · p. 22
  • For very large papers, chunk-based communication can produce enough messages per round to overflow the input, motivating message compression or pruning.Chunking lets MARG handle papers beyond the base model’s input-size limit, but the messages themselves can constrain effective input capacity.Source: §9 · p. 22

MARG-S (experiments): Leader task prompt

  • The experiments prompt guides the leader to compare the paper’s claims with expected and actual evaluations, then request specific, justified fixes for shortcomings.It emphasizes experimental thoroughness and consistency between claims and evidence.Source: MARG-S (experiments): Leader task prompt · p. 26

MARG-S (experiments): Expert prompt

  • The experiment expert is instructed to understand the paper’s goals, propose detailed setups and measurements, and explain why an existing procedure fails to meet those goals.The expert may ask the leader follow-up questions and compare the existing procedure with how they would conduct the experiments.Source: MARG-S (experiments): Expert prompt · p. 26

MARG-S: Refinement prompt

  • The refinement prompt recommends checking a comment’s validity and importance, then clarifying and rewording it to make it more specific.The prompt applies this process to supplied review comments.Source: MARG-S: Refinement prompt · p. 29

SARG-B: Task prompt

SARG-TP: Task prompt

MARG-TP: Leader system prompt

  • The multi-agent leader prompt assigns coordination across paper chunks, including planning, conveying context, checking responses, and following up to resolve misunderstandings.The prompt describes communication between the leader and workers as key to understanding the full paper.Source: MARG-TP: Leader system prompt · p. 30

MARG-TP: Worker system prompt

B Example multi-agent interaction

  • The example section presents conversation logs for MARG-S experiments, clarity, impact, and refinement, noting that only leader text after the SEND MESSAGE marker is sent to other agents.Messages containing the no-response indicator are omitted from the examples.Source: B Example multi-agent interaction · pp. 30–31

B.1 MARG-S (experiments)

The experiment overview describes DIGRAC evaluations on synthetic and real-world datasets across node clustering, node classification, and link-direction prediction. It reports stronger comparisons when ambient nodes are present and notes possible future extensions.

B.1 MARG-S (experiments)

  • The described DIGRAC framework clusters directed networks by maximizing flow imbalance between clusters, with a directed mixed path aggregation scheme (DIMPA).The overview says DIMPA builds node embeddings and a probability assignment matrix.Source: §B.1 · p. 31
  • The reported evaluation covers node clustering, node classification, and link-direction prediction, using ARI and accuracy.Experiments use both synthetic and real-world datasets.Source: §B.1 · p. 31
  • DIGRAC is reported to outperform competitors, especially when ambient nodes are present.The overview describes results across synthetic and real-world datasets.Source: §B.1 · p. 31
  • Node-clustering performance is described as especially strong with the “sort” loss function and additional label information.The overview also mentions semi-supervised learning and automatic cluster-count detection as possible improvements.Source: §B.1 · p. 31
  • The overview identifies more pairwise imbalance scores and regularization as possible future improvements.It also mentions adaptations for large-scale applications.Source: §B.1 · p. 31

3. The paper introduces a novel probabilistic imbalance loss

This section describes DIGRAC’s use of flow imbalance to cluster directed networks and reports experiments on synthetic and real-world data, including findings on the “sort” loss and supervised seed nodes. It also identifies missing component ablations and limited evaluation of label-informed use, and calls for tests of proposed extensions.

3. The paper introduces a novel probabilistic imbalance loss

  • DIGRAC achieves state-of-the-art performance on synthetic and real-world data across a wide range of network densities and topologies.The reported evaluations include synthetic and real-world data at different scales.Source: §3 · p. 33
  • The “sort” loss performed best among tested variants, and adding seed nodes with supervised loss enhanced performance.The paper infers that additional label information could further boost performance.Source: §3 · p. 33
  • Future work includes semi-supervised learning, detecting the number of clusters automatically, and selecting influential imbalance pairs or the value of β.The paper also identifies further analysis of how many pairwise imbalance scores to consider.Source: §3 · p. 33
  • The paper did not report ablations isolating the contributions of the probabilistic imbalance loss and directed mixed path aggregation.The review identifies this as a discrepancy between expected and conducted experiments.Source: §3 · p. 34
  • Although the paper reports improved performance using supervised seed nodes, reviewers request more detail and tests with additional label information.The feedback suggests comparing a semi-supervised approach using some labeled data with the unsupervised approach.Source: §3 · p. 35
  • The paper would benefit from ablations isolating the probabilistic imbalance loss and DIMPA to show each component’s contribution to performance.The suggested approach is to remove or modify one component at a time and observe the results.Source: §3 · p. 35
  • The paper’s proposed semi-supervised learning, automatic cluster-count detection, and large-scale adaptations lack experiments evaluating their potential.The reviewers call for experiments on these extensions to provide evidence of their potential and clarify future work.Source: §3 · p. 35

B.2 MARG-S (clarity)

This section describes MARG-S’s clarity-review process, in which agents summarize paper sections and a leader synthesizes and explains the paper, routes clarification questions to an expert, and finalizes feedback. Its DIGRAC example explains the probabilistic imbalance loss at a high level and identifies methodological details that remain unclear.

B.2 MARG-S (clarity)

  • The leader gathers section summaries, explains the combined account to an expert agent, routes questions to relevant workers, and finalizes feedback based on identified areas for improvement.The plan also calls for reviewing the comments before finalizing them.Source: §B.2 · p. 36
  • DIGRAC is a GNN framework for directed-network clustering that uses directionality and a probabilistic imbalance loss to optimize directed flow imbalance without label supervision.The summary also describes DIMPA as the framework’s directed mixed path aggregation scheme and reports tests on synthetic and real-world data, with superior ARI and imbalance scores.Source: §B.2 · p. 36
  • DIMPA builds node embeddings from local network information by aggregating neighbors across hops, and the method uses ARI, classification and link-direction accuracy, and normalized imbalance scores.The summary describes the objective as finding a partition that maximizes imbalance flow between clusters in a meta-graph, using a global probabilistic imbalance score.Source: §B.2 · pp. 36–37
  • The summary notes that DIGRAC can use the largest K Hermitian-matrix eigenvectors when node attributes are unavailable and discusses loss variants, supervised seed nodes, and future extensions.Future directions include semi-supervised use, estimating the number of clusters, choosing how many pairwise imbalance scores to consider, studying sparse regimes, and adapting the pipeline to extremely large networks.Source: §B.2 · p. 37

3. How does the paper define and calculate the Ad-

  • The expert asks for more detail on how the probabilistic imbalance loss is calculated, how DIMPA aggregates neighbor information, and how ARI and prediction accuracy are defined.The expert also asks how the number of eigenvectors is chosen and how seed nodes are selected and used.Source: §B.2 · pp. 37–38, §3 · p. 38
  • The expert also asks how DIGRAC might be used semi-supervised and what further research in the sparse regime entails.These questions follow the summary’s listed future directions.Source: §3 · p. 38
  • The self-supervised probabilistic imbalance loss, inspired by Cucuringu et al. (2020a), clusters nodes by maximizing normalized cut imbalance and scales the imbalance score to [0, 1].The response does not explain how this loss differs from similar functions.Source: §8 · p. 40

2. The paper introduces a directed mixed path aggre-

  • The review identifies DIMPA as part of DIGRAC but says it lacks details on the scheme or the significance of weighting information from neighbors within a fixed number of hops.Source: §3 · pp. 38–39

3. How does the paper define and calculate the Ad-

  • ARI and accuracy are named for node classification and link direction prediction, but the response gives no details on how either metric is defined or calculated.Source: §3 · p. 39
  • For papers without node attributes, the review notes use of the largest K Hermitian-matrix eigenvectors as features, but says the construction process and choice of K are unspecified.Source: §3 · p. 39
  • The review says the paper does not explain how supervised-loss seed nodes are selected or used, or how DIGRAC’s proposed semi-supervised setting would be implemented.Source: §3 · p. 39
  • The response says “sparse regime” is undefined in the paper and offers a graph-theory gloss: a sparse graph has edges close to the minimum needed for connectivity.Source: §3 · p. 39

8. My part of the paper does not provide detailed in-

This section identifies gaps in the paper’s reporting of methodological and experimental details, including metric calculations, model components, implementation choices, and experimental settings. It highlights unresolved questions about input selection, semi-supervised application of DIGRAC, and reproducibility.

8. My part of the paper does not provide detailed in-

  • Experiments use synthetic DSBM data and five real-world datasets, but the paper omits detailed experimental settings, hyperparameters, equipment, and material specifications.The named datasets are Telegram, Blog, Migration, WikiTalk, and Lead-Lag.Source: §8 · p. 39
  • The self-supervised probabilistic imbalance loss, inspired by Cucuringu et al. (2020a), maximizes normalized cut imbalance across clusters and yields an interpretable score in [0, 1] targeting directed flow imbalance.Source: §8 · p. 40
  • DIMPA forms a probability assignment matrix by aggregating information from neighbors across multiple hops, capturing both local and more global network information.Source: §8 · p. 40
  • The paper does not specify how ARI and accuracy are calculated for clustering, node classification, or link direction prediction.Source: §8 · p. 40
  • When node attributes are unavailable, the paper proposes using the largest K Hermitian-matrix eigenvectors as features, but does not specify K.Source: §8 · p. 40
  • The paper leaves seed-node selection, semi-supervised implementation, and further research in the sparse regime unspecified.Source: §8 · p. 40
  • The paper does not specify K for the largest-eigenvector input feature matrix, although K could affect performance and needs selection guidance.The passages suggest it may depend on application requirements or experimentation.Source: §8 · p. 41, §6 · p. 42, §4 · p. 42

5. Agent 4 noted that the paper does not provide spe-

  • The paper does not explain how seed nodes are selected for the supervised loss, despite their potential effect on method performance.Seed nodes are described as nodes with known cluster labels that guide clustering.Source: §8 · p. 41, §4 · p. 42

6. Agent 4 noted that the paper does not provide spe-

  • The review notes that DIGRAC’s possible semi-supervised use is raised without details on how it would be implemented.This is presented as a future-work possibility.Source: §8 · p. 41, §6 · p. 42, §4 · p. 42

8. My part of the paper does not provide detailed in-

  • The review says the paper omits hyperparameter, equipment, and material specifications, making the experiments difficult to reproduce.It also flags missing details on metric calculations and their importance for evaluating the method.Source: §8 · p. 41
  • The review requests definitions for CI vol_sum and DIMPA’s neighbor-weighting calculation, plus explanations of ARI and accuracy calculations.It also notes that the paper does not specify the factors determining DIMPA weights.Source: §8 · p. 41

1. The CI vol_sum is not explicitly defined in my

  • The section says CI vol_sum is undefined and that its own account lacks enough context to explain its calculation.It also describes DIMPA’s weighted neighbor aggregation and the ARI and accuracy calculations as insufficiently detailed.Source: §1 · p. 42

4. The paper suggests using the largest K eigenvec-

  • The paper leaves both the choice of K and the seed-node selection method unspecified, and does not detail how DIGRAC could be used semi-supervisedly.The passage suggests K may depend on application requirements or experimentation.Source: §4 · p. 42

7. The paper uses both synthetic and real-world data

The discussion identifies gaps in method and evaluation details, including information needed to reproduce the experiments, and outlines an iterative agent-review workflow.

7. The paper uses both synthetic and real-world data

  • The DIMPA description does not specify how neighbor weights are calculated, although row-normalized adjacency is used to aggregate local network information.The feedback asks for the weighting factors to be explained.Source: §7 · pp. 42–43
  • The paper uses ARI and accuracy for node classification and link-direction prediction, but the supplied discussion says their calculations are not detailed.The metrics are described as standard, without calculation specifics.Source: §7 · pp. 42–43
  • When node attributes are unavailable, the paper proposes the largest K Hermitian-matrix eigenvectors as input features, but does not specify K.The discussion calls for guidance on choosing K.Source: §7 · p. 43

5. The paper should provide more guidance on how

  • Seed nodes have known cluster labels and guide clustering, but the paper does not detail how they are selected.The feedback requests more guidance on seed-node selection.Source: §7 · p. 43, §5 · p. 43
  • The paper suggests semi-supervised DIGRAC as future work but does not explain how to implement that setting.The feedback also asks how seed nodes would be used with supervised loss.Source: §7 · p. 43, §5 · p. 43

6. The paper should provide more details on how DI-

  • The feedback requests clarification of how CI vol_sum is calculated in the imbalance-loss equation.The discussion identifies vol_sum as related to the loss function.Source: §7 · pp. 42–43, §6 · p. 43
  • The feedback requests hyperparameter, equipment, and material specifications to make the experiments easier to reproduce.The same request appears in the final feedback list.Source: §6 · pp. 43–44

B.3 MARG-S (impact)

  • For the impact review, agents summarize paper sections, an expert raises questions, and the agents respond in discussion until the expert finishes.The workflow includes compiling the summaries and relaying questions and responses.Source: §B.3 · p. 44

6. Based on the discussion with Agent 4 and the information

This section examines DIGRAC’s use of directed flow imbalance for unsupervised node clustering, including reported results across graph conditions and discussion of novelty, validation, scalability, and methodological scope. It also addresses questions about the imbalance score, evaluation, assumptions, and future extensions, with the response describing the score as normalized cut imbalance and citing experiments on synthetic and real-world data at different scales.

6. Based on the discussion with Agent 4 and the information

  • Motivated by directionality as a flow signal even without edge-density differences, DIGRAC targets clusters with strong imbalanced flows using self-supervised clustering and directed mixed-path embeddings.The paper introduces an imbalance score to evaluate clustering by flow imbalance.Source: §6 · p. 45
  • DIGRAC is reported to attain state-of-the-art directed graph clustering results across varied noise, sparsity, graph structures, and topologies, and to perform especially well with ambient nodes.The summary also says it performs well across network densities and noise levels.Source: §6 · p. 45
  • The paper says DIGRAC complements existing approaches and extends applicability beyond digraph inputs, but Agent 4 asks how this extension is achieved.Source: §6 · p. 45, §1 · p. 46
  • Future work includes semi-supervised clustering, automatic cluster-count detection, and adaptations for large-scale industrial applications.Agent 4 asks whether the paper offers preliminary evidence for industrial-scale performance and how DIGRAC might improve on existing methods for these extensions.Source: §6 · p. 45, §1 · p. 46

1. The paper introduces a new measure, the imbal-

  • Agent 4 asks how the imbalance score is calculated and justified, which benchmarks validate the performance claim, how efficiency compares, and what ambient nodes means.Source: §1 · pp. 45–46
  • Agent 4 also asks what assumptions or limitations accompany the self-supervised loss and directed mixed-path embedding methods.Source: §1 · p. 46
  • The questions request evidence for the imbalance score’s novelty and effectiveness, including benchmarks, datasets, state-of-the-art comparisons, and computational efficiency.Source: §1 · p. 46

3. Could you clarify what is meant by "ambient nodes"?

  • The questions seek clarification on large-scale applications, how DIGRAC extends beyond digraph inputs, methodological limitations, semi-supervised settings, and automatic cluster-count detection.Source: §3 · p. 46

1. The imbalance score is a novel contribution of this

  • The imbalance score is a normalized form of cut imbalance across clusters, justified with synthetic and real-world experiments at different scales.Source: §1 · pp. 46–47

2. The paper does not provide specific benchmarks

The reviewed material reports DIGRAC’s performance across varied graph conditions, outlines aspects in which it complements existing approaches, and identifies areas not covered in the reviewed section.

2. The paper does not provide specific benchmarks

  • DIGRAC is reported to attain state-of-the-art clustering across noise and sparsity levels and graph structures, though the reviewed passage names no specific benchmarks.The passage notes that the reviewed section does not provide specific benchmark or dataset names.Source: §2 · p. 46

3. The term "ambient nodes" is not mentioned in my

  • The reviewed section says ambient nodes are not mentioned and provides no preliminary results or discussion of DIGRAC for large-scale industrial applications.These are boundaries of the material described in the passage, not claims about the full paper.Source: §3 · p. 46

5. DIGRAC complements existing approaches in var-

  • DIGRAC is described as detecting alternative patterns such as meta-graphs, incorporating node features or labels, and using an imbalance-based objective.Its self-supervised loss maximizes normalized cut imbalance, while DIMPA implements the framework by aggregating information from neighbors across hops.Source: §5 · pp. 46–47

7. The paper does not provide information on exist-

The passages describe DIGRAC’s experimental settings and methods, report a strongest-performing loss variant, and note open questions about evaluation and future extensions.

Imbalance score

  • The imbalance score is described as a normalized form of cut imbalance across clusters, and the passages call for comparison with existing measures.The score is presented as a novel contribution whose effectiveness is supported by the paper’s experiments.Source: §7 · p. 47

Evaluation scope

  • The evaluation is described as using synthetic Directed Stochastic Block Models with varying density and noise, plus real-world Telegram, Blog, Migration, WikiTalk, and Lead-Lag datasets.The passage says computational efficiency was not directly compared with existing methods.Source: §7 · p. 47

Ambient nodes

  • Ambient nodes are nodes outside meaningful clusters and the graph’s main directional signal, treated as background noise.The passages also report that DIGRAC is particularly effective when ambient nodes are present.Source: §7 · p. 47

Loss and aggregation scheme

  • The paper presents its self-supervised clustering loss and DIMPA as novel contributions, but the reviewed passages state no specific assumptions or limitations for them.The loss maximizes flow imbalance; DIMPA aggregates neighbor information within a specified number of hops.Source: §7 · p. 47

Experiments on real-world data

  • On real-world data, the “sort” loss variant performed best; the experiments also examined the effect of adding seed nodes.The passages do not name the specific real-world datasets in this experimental account.Source: §7 · p. 47

Future directions

  • Semi-supervised settings, automatic cluster-count detection, and adaptation to large-scale industrial applications are identified as future directions.The passages also describe adapting DIGRAC to extremely large networks, potentially with sampling or mini-batches, as worth exploring.Source: §7 · p. 47

1. The imbalance score is a novel contribution of this

This section presents DIGRAC as an imbalance-driven directed-clustering approach and identifies its probabilistic imbalance loss and DIMPA as proposed components. It credits the approach’s flexibility while noting gaps in validation, comparisons, methodological detail, and ablation-based evaluation of the components’ individual contributions.

1. The imbalance score is a novel contribution of this

  • The novel imbalance score is a normalized cut-imbalance measure, evaluated on synthetic and real-world data, but reviewers request comparison with existing measures.Synthetic data vary in network density and noise; real-world datasets include Telegram, Blog, Migration, WikiTalk, and Lead-Lag.Source: §1 · p. 47
  • DIGRAC is tested on directed stochastic block models and five real-world datasets, but the paper does not directly compare its computational efficiency with existing methods.The real-world datasets are Telegram, Blog, Migration, WikiTalk, and Lead-Lag.Source: §1 · p. 47

1. Regarding the imbalance score, it’s good to know

  • Reviewers seek an account of ambient-node handling and comparisons with semi-supervised or automatic cluster-count methods, while the proposed methods’ assumptions and limitations are unspecified.Ambient nodes are described as background noise outside meaningful clusters.Source: §1 · pp. 47–48

1. The imbalance score is a novel contribution of this

  • DIGRAC can detect alternative patterns such as meta-graph structures and incorporate exogenous node features or labels, complementing existing approaches.The paper frames the imbalance-based objective as another aspect of that complementarity.Source: §1 · p. 48
  • The method introduces a self-supervised loss that maximizes imbalance flow rather than intra-cluster similarity and inter-cluster dissimilarity, alongside directed mixed path aggregation.The aggregation scheme takes a weighted average of information from neighbors within a specified number of hops.Source: §1 · p. 48

B.4 Refinement Stage

  • The refinement-stage comment proposes ablations that remove or modify the probabilistic imbalance loss and DIMPA to assess each component’s contribution to overall performance.The refinement plan says to check whether such studies are already present before finalizing the comment.Source: §B.4 · p. 49
  • The self-supervised probabilistic imbalance loss uses a global score derived from pairwise normalized-cut imbalances to find partitions maximizing imbalance flow.Volume normalization penalizes partitions that assign most nodes to a single cluster.Source: §B.4 · p. 50
  • DIMPA implements DIGRAC as a digraph extension of KernelGCN, building node embeddings from local network information.The description includes row-normalizing the adjacency matrix, adding weighted self-loops, and defining separate source and target feature mappings.Source: §B.4 · p. 50
  • DIGRAC introduces a probabilistic imbalance loss and DIMPA, but no ablation studies isolate their individual contributions to overall performance.The review recommends removing or modifying each component one at a time and comparing results, including testing alternative path aggregation schemes.Source: §B.4 · pp. 50–52
  • The proposed method includes a probabilistic imbalance loss and a directed mixed path aggregation scheme (DIMPA).The review comment describes both as novel contributions.Source: §B.4 · p. 52
  • The paper lacks ablations isolating these components; suggested tests remove or modify each one and compare loss variants or alternative path aggregation schemes.Such experiments would help assess each component’s importance and contribution to overall performance.Source: §B.4 · p. 52
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