Peer Review Argumentation Frameworks

• Peer review argumentation frameworks are computational formalisms that structure review content into propositions, arguments, and relational dynamics for clear evaluation.
• They integrate methods like argument mining, abstract and bipolar argumentation, and graph-based multi-agent reasoning to capture review and rebuttal interactions.
• These frameworks enable interpretable aggregation of conflicting judgments and automated meta-review synthesis for improved acceptance prediction.

Searching arXiv for the specified peer-review argumentation papers to ground the article in current literature. tool call: arxiv_search.query {"query":"PeerArg Argumentative Peer Review with LLMs (Sukpanichnant et al., 2024) OR Argument Mining for Understanding Peer Reviews (Hua et al., 2019) OR Conflict-Aware Meta-Review Generation via Cognitive Alignment (Chen et al., 18 Mar 2025) OR Dispute Resolution in Peer Review with Abstract Argumentation and OWL DL (Baimuratov et al., 18 Jul 2025) OR Exploring Jiu-Jitsu Argumentation for Writing Peer Review Rebuttals (Purkayastha et al., 2023) OR ScholarPeer (Goyal et al., 30 Jan 2026) OR ReViewGraph (Li et al., 11 Nov 2025)","max_results":10,"sort_by":"relevance"} I found relevant arXiv records matching the core literature on peer-review argumentation, including the PeerArg system (Sukpanichnant et al., 2024), peer-review argument mining (Hua et al., 2019), conflict-aware meta-review generation (Chen et al., 18 Mar 2025), rebuttal-oriented Jiu-Jitsu argumentation (Purkayastha et al., 2023), dispute resolution with abstract argumentation and OWL DL (Baimuratov et al., 18 Jul 2025), ScholarPeer (Goyal et al., 30 Jan 2026), and ReViewGraph (Li et al., 11 Nov 2025). Peer review argumentation frameworks are computational formalisms and system architectures that model reviews, rebuttals, and editorial synthesis in terms of propositions, arguments, support and attack relations, conflict resolution, and decision aggregation. In recent work, this label spans at least four closely related strands: argument mining over review text; formal abstract or bipolar argumentation for acceptability and dispute resolution; LLM-based pipelines that convert review content into interpretable argumentative structures; and multi-agent or conflict-aware systems for meta-review and automated reviewing. Taken together, these approaches treat peer review not merely as document classification, but as structured reasoning over evaluative claims, evidence, counterarguments, and venue-level decisions (Sukpanichnant et al., 2024, Hua et al., 2019, Baimuratov et al., 18 Jul 2025).

1. Historical and conceptual scope

A central precursor is argument mining for peer reviews. In "Argument Mining for Understanding Peer Reviews" (Hua et al., 2019), a review is modeled as a finite sequence of tokens

$R=(w_1,w_2,\dots,w_N),$

segmented into propositional units

$R=\{\pi_1,\pi_2,\dots,\pi_m\},$

with a type assignment

$t:\{\pi_1,\dots,\pi_m\}\to \mathcal T,$

where

$$\mathcal T=\{\mathsf{Eval},\mathsf{Req},\mathsf{Fact},\mathsf{Ref},\mathsf{Quot},\mathsf{NonArg}\}.$$

This formulation established a peer-review-specific argumentation view in which reviews are not monolithic judgments but structured compositions of evaluative, factual, and action-oriented propositions. The same work explicitly notes that its pipeline does not model argumentative relations such as support and attack among propositions, and identifies joint learning of segmentation, typing, and inter-proposition relations as an open problem (Hua et al., 2019).

Subsequent work expands the scope from proposition typing to explicit reasoning over relations, acceptance conditions, and decision making. "PeerArg: Argumentative Peer Review with LLMs" (Sukpanichnant et al., 2024) maps reviews into quantitative bipolar argumentation frameworks and uses gradual semantics to predict paper acceptance. "Dispute Resolution in Peer Review with Abstract Argumentation and OWL DL" (Baimuratov et al., 18 Jul 2025) formalizes reviewer-author exchanges as abstract argumentation frameworks and resolves them with OWL DL reasoners. "Conflict-Aware Meta-Review Generation via Cognitive Alignment" (Chen et al., 18 Mar 2025) introduces a conflict-aware architecture for meta-review synthesis, while ReViewGraph (Li et al., 11 Nov 2025) performs heterogeneous graph reasoning over LLM-simulated reviewer-author debates. ScholarPeer (Goyal et al., 30 Jan 2026) adopts a dialectical question-answering cycle over claims, baselines, and external literature, and Jiu-Jitsu rebuttal generation (Purkayastha et al., 2023) focuses on attitude root and theme-guided counterargument construction.

This progression suggests a broadening of the field from local proposition analysis to end-to-end argumentative workflows: identifying what a reviewer says, formalizing how claims interact, and determining how those interactions should affect acceptance, rebuttal, or meta-review.

2. Representing review content as propositions, aspects, and latent concerns

At the textual level, peer review argumentation frameworks differ in what counts as an atomic argumentative unit. Hua et al. define propositions as contiguous spans with labels such as $\mathsf{Eval}$, $\mathsf{Req}$, and $\mathsf{Fact}$, casting segmentation as BIO tagging and, in the joint variant, extending the tag set to directly encode both boundaries and types (Hua et al., 2019). On an annotated subset of 400 ICLR 2018 reviews, they report 10,386 propositions, with type counts

$$\#\mathsf{Eval}=3982,\quad \#\mathsf{Req}=1911,\quad \#\mathsf{Fact}=3786,\quad \#\mathsf{Ref}=207,\quad \#\mathsf{Quot}=161,\quad \#\mathsf{NonArg}=339,$$

and high inter-annotator agreement for segmentation ($\kappa=0.93$) with acceptable agreement for type labeling ($\alpha_U=0.61$, Cohen’s $R=\{\pi_1,\pi_2,\dots,\pi_m\},$0 on matched spans) (Hua et al., 2019).

PeerArg uses a different decomposition. Each review is mapped to a three-level QBAF whose arguments are text arguments $R=\{\pi_1,\pi_2,\dots,\pi_m\},$1, aspect arguments

$R=\{\pi_1,\pi_2,\dots,\pi_m\},$2

and a single Decision argument (Sukpanichnant et al., 2024). Aspect classification is performed by a few-shot LLM, sentiment analysis assigns each sentence a score $R=\{\pi_1,\pi_2,\dots,\pi_m\},$3, and the polarity determines whether the sentence supports or attacks the tagged aspect. Base scores are set as $R=\{\pi_1,\pi_2,\dots,\pi_m\},$4 or default $R=\{\pi_1,\pi_2,\dots,\pi_m\},$5, with $R=\{\pi_1,\pi_2,\dots,\pi_m\},$6 and $R=\{\pi_1,\pi_2,\dots,\pi_m\},$7. This yields an explicitly interpretable pathway from review sentence to aspect strength to decision strength (Sukpanichnant et al., 2024).

Jiu-Jitsu rebuttal generation reparameterizes review content around latent motivational drivers. Each review sentence $R=\{\pi_1,\pi_2,\dots,\pi_m\},$8 is assigned an attitude root $R=\{\pi_1,\pi_2,\dots,\pi_m\},$9 and a set of attitude themes $t:\{\pi_1,\dots,\pi_m\}\to \mathcal T,$0, where the eight roots are Clarity, Substance, Soundness/Correctness, Motivation/Impact, Replicability, Originality, Meaningful Comparison, and Other, and the 13 themes include Introduction, Related Work, Methodology, Experiments, Results, and Overall (Purkayastha et al., 2023). A canonical rebuttal is then formalized as

$t:\{\pi_1,\dots,\pi_m\}\to \mathcal T,$1

mapping attitude root, theme, and rebuttal action to a template sentence (Purkayastha et al., 2023). This is still argumentative modeling, but oriented toward rebuttal generation rather than acceptance prediction.

ScholarPeer introduces yet another representation layer. The Summary Agent produces

$t:\{\pi_1,\dots,\pi_m\}\to \mathcal T,$2

where $t:\{\pi_1,\dots,\pi_m\}\to \mathcal T,$3 is the set of core claims, $t:\{\pi_1,\dots,\pi_m\}\to \mathcal T,$4 the methodology, and $t:\{\pi_1,\dots,\pi_m\}\to \mathcal T,$5 the evidence/results. The Sub-Domain Historian builds a chronological domain narrative

$t:\{\pi_1,\dots,\pi_m\}\to \mathcal T,$6

and the Baseline Scout produces a set of omitted baselines

$t:\{\pi_1,\dots,\pi_m\}\to \mathcal T,$7

The Multi-Aspect Q&A engine then constructs an interrogation log

$t:\{\pi_1,\dots,\pi_m\}\to \mathcal T,$8

where $t:\{\pi_1,\dots,\pi_m\}\to \mathcal T,$9 marks a discrepancy between self-answer and verified answer (Goyal et al., 30 Jan 2026). Compared with proposition typing or aspect graphs, this representation is explicitly oriented toward adversarial verification.

3. Formal semantics: abstract, bipolar, and quantitative models

The most explicit formalization of peer review argumentation uses abstract or bipolar argumentation theory. PeerArg builds on bipolar argumentation frameworks and quantitative bipolar argumentation frameworks: $$\mathcal T=\{\mathsf{Eval},\mathsf{Req},\mathsf{Fact},\mathsf{Ref},\mathsf{Quot},\mathsf{NonArg}\}.$$0

$$\mathcal T=\{\mathsf{Eval},\mathsf{Req},\mathsf{Fact},\mathsf{Ref},\mathsf{Quot},\mathsf{NonArg}\}.$$1

where $$\mathcal T=\{\mathsf{Eval},\mathsf{Req},\mathsf{Fact},\mathsf{Ref},\mathsf{Quot},\mathsf{NonArg}\}.$$2 is a finite set of abstract arguments, $$\mathcal T=\{\mathsf{Eval},\mathsf{Req},\mathsf{Fact},\mathsf{Ref},\mathsf{Quot},\mathsf{NonArg}\}.$$3 is the attack relation, $$\mathcal T=\{\mathsf{Eval},\mathsf{Req},\mathsf{Fact},\mathsf{Ref},\mathsf{Quot},\mathsf{NonArg}\}.$$4 is the support relation, and $$\mathcal T=\{\mathsf{Eval},\mathsf{Req},\mathsf{Fact},\mathsf{Ref},\mathsf{Quot},\mathsf{NonArg}\}.$$5 assigns base scores (Sukpanichnant et al., 2024). It applies two gradual-strength semantics. Under DF-QuAD, the final strength of an argument $$\mathcal T=\{\mathsf{Eval},\mathsf{Req},\mathsf{Fact},\mathsf{Ref},\mathsf{Quot},\mathsf{NonArg}\}.$$6 is

$$\mathcal T=\{\mathsf{Eval},\mathsf{Req},\mathsf{Fact},\mathsf{Ref},\mathsf{Quot},\mathsf{NonArg}\}.$$7

with recursively defined aggregation $$\mathcal T=\{\mathsf{Eval},\mathsf{Req},\mathsf{Fact},\mathsf{Ref},\mathsf{Quot},\mathsf{NonArg}\}.$$8 and influence function $$\mathcal T=\{\mathsf{Eval},\mathsf{Req},\mathsf{Fact},\mathsf{Ref},\mathsf{Quot},\mathsf{NonArg}\}.$$9. Under the MLP-based semantics, the QBAF is treated as a 1-hidden-layer perceptron with support weights $\mathsf{Eval}$0 and attack weights $\mathsf{Eval}$1, iterating until convergence $\mathsf{Eval}$2 (Sukpanichnant et al., 2024).

A key construct in PeerArg is the aspect-to-decision linkage. After computing aspect strengths, each aspect $\mathsf{Eval}$3 is connected to Decision with strength

$\mathsf{Eval}$4

and it supports Decision if $\mathsf{Eval}$5, otherwise attacks it (Sukpanichnant et al., 2024). This creates a formally specified bridge from sentence-level evidence to binary acceptance prediction.

Baimuratov et al. instead model peer-review disputes as Dung-style abstract argumentation frameworks

$\mathsf{Eval}$6

with one special root argument $\mathsf{Eval}$7 representing the authors’ overall claim that the manuscript deserves acceptance (Baimuratov et al., 18 Jul 2025). Authors’ and reviewers’ remarks are treated as unstructured atomic arguments with metadata $\mathsf{Eval}$8. They define conflict-free, admissible, preferred, stable, complete, and grounded extensions in the standard way. Because attacks are asymmetric, connected, and round-ordered so that no later argument is attacked by an earlier one, the resulting review argumentation frameworks form a finite DAG and are therefore well-founded (Baimuratov et al., 18 Jul 2025). A stated consequence is that each framework has a unique extension under all major semantics, with linear-time computation $\mathsf{Eval}$9 (Baimuratov et al., 18 Jul 2025).

ScholarPeer does not explicitly invoke a named formal argumentation calculus, but it provides a clear dialectical abstraction: $\mathsf{Req}$0 where $\mathsf{Req}$1, $\mathsf{Req}$2 when a question reveals a discrepancy in a claim, and $\mathsf{Req}$3 when evidence supports a claim (Goyal et al., 30 Jan 2026). The acceptance rule is operational rather than extension-based: a claim is marked verified if $\mathsf{Req}$4, otherwise it is flagged for critique (Goyal et al., 30 Jan 2026).

These models instantiate different semantic commitments. QBAF semantics quantify support and attack continuously; AAF semantics compute acceptability extensionally; ScholarPeer operationalizes a challenge-and-verify protocol without committing to a named semantics. A plausible implication is that “peer review argumentation framework” is now better understood as a family of related formalizations rather than a single canonical calculus.

4. Aggregation, conflict resolution, and meta-review synthesis

A distinctive difficulty in peer review is that multiple reviewers produce partially overlapping and often conflicting judgments. PeerArg addresses this through a pre-MPAF and two aggregation paths. Given $\mathsf{Req}$5 reviews $\mathsf{Req}$6, it defines

$\mathsf{Req}$7

where

$\mathsf{Req}$8

In the argumentation path, average strength is computed as

$\mathsf{Req}$9

then an MPAF is constructed and gradual semantics are reapplied to obtain $\mathsf{Fact}$0, thresholded at $\mathsf{Fact}$1 for accept/reject (Sukpanichnant et al., 2024). In the decision-vector path, each reviewer’s decision strength is mapped either to binary labels or to five levels,

$\mathsf{Fact}$2

then aggregated by majority or all-accept rules (Sukpanichnant et al., 2024).

CAF addresses the same broad problem from a meta-review perspective. A manuscript with $\mathsf{Fact}$3 reviews $\mathsf{Fact}$4 is mapped to a final decision $\mathsf{Fact}$5 and meta-review $\mathsf{Fact}$6 through a cognitive pipeline with review initialization, conflict-aware incremental integration, and dual-process cognitive alignment (Chen et al., 18 Mar 2025). CAF maintains a running cognitive state $\mathsf{Fact}$7 and detects conflict by

$\mathsf{Fact}$8

When conflict is detected, “Fast Thinking” quickly fuses sentiment trends and argument structures, while “Slow Thinking” performs deeper reconciliation by generating key points $\mathsf{Fact}$9; the slow cycle iterates until $$\#\mathsf{Eval}=3982,\quad \#\mathsf{Req}=1911,\quad \#\mathsf{Fact}=3786,\quad \#\mathsf{Ref}=207,\quad \#\mathsf{Quot}=161,\quad \#\mathsf{NonArg}=339,$$0 or a maximum of $$\#\mathsf{Eval}=3982,\quad \#\mathsf{Req}=1911,\quad \#\mathsf{Fact}=3786,\quad \#\mathsf{Ref}=207,\quad \#\mathsf{Quot}=161,\quad \#\mathsf{NonArg}=339,$$1 re-evaluations (Chen et al., 18 Mar 2025).

CAF also formalizes two bias measures. Anchoring is estimated by

$$\#\mathsf{Eval}=3982,\quad \#\mathsf{Req}=1911,\quad \#\mathsf{Fact}=3786,\quad \#\mathsf{Ref}=207,\quad \#\mathsf{Quot}=161,\quad \#\mathsf{NonArg}=339,$$2

with $$\#\mathsf{Eval}=3982,\quad \#\mathsf{Req}=1911,\quad \#\mathsf{Fact}=3786,\quad \#\mathsf{Ref}=207,\quad \#\mathsf{Quot}=161,\quad \#\mathsf{NonArg}=339,$$3 the anchoring coefficient, and conformity bias is quantified by

$$\#\mathsf{Eval}=3982,\quad \#\mathsf{Req}=1911,\quad \#\mathsf{Fact}=3786,\quad \#\mathsf{Ref}=207,\quad \#\mathsf{Quot}=161,\quad \#\mathsf{NonArg}=339,$$4

with $$\#\mathsf{Eval}=3982,\quad \#\mathsf{Req}=1911,\quad \#\mathsf{Fact}=3786,\quad \#\mathsf{Ref}=207,\quad \#\mathsf{Quot}=161,\quad \#\mathsf{NonArg}=339,$$5 indicating no conformity bias (Chen et al., 18 Mar 2025). This is not a classical argumentation semantics, but it is an explicitly conflict-aware and bias-aware aggregation framework.

The contrast between PeerArg and CAF is instructive. PeerArg externalizes conflict into support/attack structure plus aggregation semantics; CAF externalizes it into a cognitive-state update process and explicit conflict detection. This suggests two competing design idioms in the literature: argument semantics as the primary mechanism of aggregation, and conflict-aware orchestration as the primary mechanism of synthesis.

5. Multi-party debates, graph reasoning, and reviewer–author interaction

Recent work pushes beyond reviewer-only aggregation and models the interactional structure of reviewer-author discussion. ReViewGraph constructs a heterogeneous graph

$$\#\mathsf{Eval}=3982,\quad \#\mathsf{Req}=1911,\quad \#\mathsf{Fact}=3786,\quad \#\mathsf{Ref}=207,\quad \#\mathsf{Quot}=161,\quad \#\mathsf{NonArg}=339,$$6

with node types

$$\#\mathsf{Eval}=3982,\quad \#\mathsf{Req}=1911,\quad \#\mathsf{Fact}=3786,\quad \#\mathsf{Ref}=207,\quad \#\mathsf{Quot}=161,\quad \#\mathsf{NonArg}=339,$$7

and edge types

$$\#\mathsf{Eval}=3982,\quad \#\mathsf{Req}=1911,\quad \#\mathsf{Fact}=3786,\quad \#\mathsf{Ref}=207,\quad \#\mathsf{Quot}=161,\quad \#\mathsf{NonArg}=339,$$8

(Li et al., 11 Nov 2025). Reviewer-author exchanges are first simulated through LLM-based multi-agent collaboration, then relation triples are extracted, reviewer-side sentences are classified into four evaluation dimensions, and a Heterogeneous Graph Transformer performs reasoning over the resulting graph (Li et al., 11 Nov 2025).

The HGT uses heterogeneous mutual attention and message passing. For target node $$\#\mathsf{Eval}=3982,\quad \#\mathsf{Req}=1911,\quad \#\mathsf{Fact}=3786,\quad \#\mathsf{Ref}=207,\quad \#\mathsf{Quot}=161,\quad \#\mathsf{NonArg}=339,$$9, source node $\kappa=0.93$0, and relation type $\kappa=0.93$1, attention weights and messages are relation-specific and type-specific, followed by mean-pooling over node types and a 2-layer MLP for final accept/reject prediction (Li et al., 11 Nov 2025). The framework explicitly distinguishes fine-grained relation types where classical abstract argumentation would only distinguish attack, or BAF would distinguish only attack and support. The paper states that “attack” is split into reject/disagree, “support” into accept/agree/complement/progressive, with additional clarify, compromise, extend, and neutral edges (Li et al., 11 Nov 2025).

ScholarPeer also introduces multi-component interaction, though not as a typed interaction graph. Its agents perform contextualization, adversarial auditing, and claim verification with live literature. The dialectical protocol is defined as assertion, challenge, defense, and acceptance rule, and the Review Generator demotes claims under undefeated attack to weaknesses while highlighting claims with undefeated support as strengths or novelty (Goyal et al., 30 Jan 2026). In an example from the appendix, omitted baselines and methodological issues are surfaced through discrepancies in the interrogation log, then translated into weaknesses and suggestions (Goyal et al., 30 Jan 2026).

These systems complicate a common simplification that peer review argumentation is exhausted by static review text. In this strand of the literature, the relevant argumentative object may instead be a debate transcript, a multi-agent dialogue, or a cross-checked claim-evidence structure grounded in external literature.

6. Evaluation results, interpretability, and open problems

Empirical evaluation reflects the heterogeneity of tasks. For proposition segmentation on an 80-review test set, Hua et al. report $\kappa=0.93$2 for CRF, $\kappa=0.93$3 for BiLSTM-CRF, $\kappa=0.93$4 for CRF-joint, and $\kappa=0.93$5 for BiLSTM-CRF-joint; for proposition classification, CNN achieves $\kappa=0.93$6 on gold segments, and BiLSTM-CRF-joint reaches $\kappa=0.93$7 on predicted segments (Hua et al., 2019). They also note that peer-review segmentation is harder than essay segmentation because approximately 25% of sentences contain at least two propositions, versus 8% for student essays (Hua et al., 2019).

For accept/reject prediction, PeerArg evaluates on PRA, PeerRead, and MOPRD using Macro F1 on binary accept/reject, with raw accuracy also shown in figures. The best variant, $\kappa=0.93$8sentiment-base, DF-QuAD, 5-level, majority$\kappa=0.93$9, achieves 0.766 on PRA, 0.662 on PeerRead(default), 0.765 on PeerRead(ratings), and 0.681 on MOPRD, outperforming the end-to-end few-shot Mistral-7B baseline on all three datasets listed in the paper’s table (Sukpanichnant et al., 2024). The paper attributes three strengths to the framework: transparency, modularity, and empirical gains, while identifying error propagation, hyperparameter sensitivity, and equal weighting of the seven aspects as limitations (Sukpanichnant et al., 2024).

CAF evaluates on PeerSum, approximately 15k papers with reviews and meta-reviews from NeurIPS 2021–22 and ICLR 2018–22, using FacetEval for sentiment consistency and ROUGE-1/2/L for content consistency (Chen et al., 18 Mar 2025). Reported improvements relative to the best non-CAF baseline include sentiment consistency gains up to 19.5% for Llama3-8B and content gains up to 12.9% in ROUGE-2 for GPT-4o. The anchoring coefficient $\alpha_U=0.61$0 drops from 0.255 to 0.221 for GPT-4o, and conformity $\alpha_U=0.61$1 rises from 0.125 to 0.444 for GPT-3.5 (Chen et al., 18 Mar 2025). The authors explicitly note that no formal statistical significance tests were reported (Chen et al., 18 Mar 2025).

ReViewGraph reports an average relative improvement of 15.73% in Macro-F1 over the strongest baseline across ICLR 2023/2024/2025, with $\alpha_U=0.61$2 (Li et al., 11 Nov 2025). Baimuratov et al. evaluate on 88 manually reconstructed peer-review argumentation frameworks from the MDPI Open Peer Review Corpus and report that papers are ultimately acceptable in 53.4% of cases, with average JSON-to-OWL DL conversion plus reasoning time $\alpha_U=0.61$3 s (Baimuratov et al., 18 Jul 2025). Jiu-Jitsu argumentation reports best theme prediction Micro-F1 $\alpha_U=0.61$4, a binary SciBERT classifier with Macro-F1 $\alpha_U=0.61$5 for suitability as canonical rebuttal, and end-to-end canonical rebuttal generation results such as BART $\alpha_U=0.61$6-1 $\alpha_U=0.61$7, $\alpha_U=0.61$8-2 $\alpha_U=0.61$9, $R=\{\pi_1,\pi_2,\dots,\pi_m\},$00-L $R=\{\pi_1,\pi_2,\dots,\pi_m\},$01, BERTScore $R=\{\pi_1,\pi_2,\dots,\pi_m\},$02 under full fine-tuning (Purkayastha et al., 2023).

Two recurring themes emerge. First, interpretability is repeatedly foregrounded. PeerArg states that each step is interpretable; Baimuratov et al. emphasize a transparent, math-based dispute model; ScholarPeer decomposes outputs into auditable agent traces; ReViewGraph ties arguments to evaluation dimensions and typed relations (Sukpanichnant et al., 2024, Baimuratov et al., 18 Jul 2025, Goyal et al., 30 Jan 2026, Li et al., 11 Nov 2025). Second, current systems still rely on upstream extraction quality, domain-specific annotation, or LLM prompting heuristics. The stated future directions include learning aspect-weighting schemes, incorporating cross-review attacks and supports, uncertainty-aware semantics, automating argumentation mining for OWL-DL dispute resolution, integrating rebuttals, and extending beyond ML-centric corpora (Sukpanichnant et al., 2024, Baimuratov et al., 18 Jul 2025, Chen et al., 18 Mar 2025).

A common misconception is that peer-review automation is necessarily a black-box accept/reject classifier. The recent literature instead includes proposition-level tagging, aspect-scored QBAFs, finite DAG dispute models with unique extensions, conflict-aware meta-review orchestration, typed reviewer-author interaction graphs, and attitude-guided rebuttal generation. Another misconception is that support and attack alone are always sufficient. ReViewGraph explicitly argues for more granular relation types, while ScholarPeer’s interrogation logs and CAF’s cognitive alignment show that conflict handling can also be framed procedurally rather than purely semantically (Li et al., 11 Nov 2025, Goyal et al., 30 Jan 2026, Chen et al., 18 Mar 2025).