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REVIEW 3 major objections 2 minor 1 cited by

Inclusion Arena: An Open Platform for Evaluating Large Foundation Models with Real-World Apps

T0 review · 3 major / 2 minor · reviewed 2026-08-05 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read In-app user votes can rank AI models stably

desk verdict The full text is an unrelated LEO satcom paper, so the abstract's Inclusion Arena claims are unverifiable from this submission; desk-reject the artifact, not the idea. read the letter →

arxiv 2508.11452 v2 pith:CBHC6H3T submitted 2025-08-15 cs.AI cs.CLcs.HC

Kangyu Wang , Hongliang He , Lin Liu , Ruiqi Liang , Zhenzhong Lan , Jianguo Li This is my paper
classification cs.AIcs.CLcs.HC
keywords InclusionArenaleaderboardBradley-Terryhumanfeedbacklargelanguagemodelsmodelrankingproximitysamplingplacementmatches
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper proposes Inclusion Arena, a live leaderboard that ranks large language and multimodal models using pairwise preference votes collected from users while they interact with real AI-powered applications. The idea is that evaluations should reflect actual usage, not static test questions or generic crowdsourced prompts. The paper claims that a Bradley-Terry rating model with two additions—Placement Matches for cold-starting new models and Proximity Sampling for choosing informative comparisons—produces rankings that are reliable, stable, more transitive than general crowdsourced data, and resistant to malicious vote manipulation. If true, this would give developers a continuous, deployment-grounded signal for improving and choosing foundation models.

What carries the argument

A Bradley-Terry pairwise-comparison model augmented with two innovations: Placement Matches, a cold-start mechanism that quickly assigns initial ratings to newly integrated models, and Proximity Sampling, an opponent-selection rule that prioritizes battles between models of similar estimated strength. These two devices carry the argument by making the vote-collection process self-correcting: they get new models into the rating system fast and concentrate measurement effort where it most reduces uncertainty about the global ordering.

What would settle it

Run a controlled blind preference study on a fixed set of models using the same tasks as the live platform, and compare the resulting pairwise order to Inclusion Arena's leaderboard; substantial disagreement on any comparable pair would indicate in-app voting is confounded. For the manipulation claim, simulate coordinated voting by a faction favoring a low-quality model and check whether that model's rating rises outside the platform's stated confidence bounds.

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Extended reading notes

Core claim

The paper's central claim is that a live leaderboard can rank LLMs and MLLMs from pairwise preference votes captured naturally inside AI-powered applications, rather than from static exams or general crowdsourced prompts. The ranking engine is a Bradley-Terry model that estimates each model's strength from votes, augmented by two devices: Placement Matches, which seed initial ratings for newly integrated models, and Proximity Sampling, which deliberately schedules battles between models of similar strength to maximize information gain. The paper reports that empirical analysis and simulations show the resulting rankings are reliable and stable, that the vote data exhibit higher transitivity

Load-bearing premise

The rankings are trustworthy only if the preference votes users cast during ordinary app interactions are a valid, unbiased, and sufficiently dense measure of model quality.

Editorial extensions

If this is right

  • Newly added models receive quick initial ratings through Placement Matches, so the leaderboard can stay current.
  • Proximity Sampling makes comparisons more informative by pairing similar models, tightening confidence intervals around ratings.
  • Higher transitivity in the collected votes means the global ranking is less likely to contain contradiction cycles.
  • If the manipulation-resistance claims hold, open leaderboards can trust in-the-wild votes without heavy moderation.
  • The platform provides a live, deployment-based complement to static benchmarks, so model quality can be tracked as applications evolve.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The full text accompanying this record is a separate manuscript about satellite semantic communication; the Inclusion Arena claims are therefore supported only by the abstract in this record, and readers should check the platform for the full methodology.
  • If in-app voting is indeed informative, the platform could become a continuous, always-on evaluation signal that complements periodic offline benchmarks, and the same vote-collection pattern could be reused in recommender-system A/B tests.
  • A natural next test would be cross-platform transitivity: compare the preference graph's consistency score against that of a general crowdsourced set on the same model pairs, under the same Bradley-Terry fit.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 2 minor

Summary. The submission is labeled as arXiv:2508.11452 (cs.AI), 'Inclusion Arena: An Open Platform for Evaluating Large Foundation Models with Real-World Apps'. The abstract promises a live leaderboard that ranks LLMs/MLLMs from in-app human feedback, using a Bradley-Terry model with two innovations (Placement Matches and Proximity Sampling), and claims extensive empirical and simulation evidence for ranking reliability, higher transitivity than crowdsourced data, and robustness to malicious manipulation. However, the supplied full text is a different, unrelated paper: 'Importance-Aware Robust Semantic Transmission for LEO Satellite-Ground Communication' (running header arXiv:2508.11457v2 [eess.SP]), authored by Hui Cao, Rui Meng, Xiaodong Xu, Shujun Han, and Ping Zhang. The full text contains no description of Inclusion Arena, no Bradley-Terry model, no Placement Matches, no Proximity Sampling, no LLM leaderboard experiments, and no manipulation simulations. The central claims of the abstract are therefore entirely absent from the artifact under review.

Significance. If the Inclusion Arena paper existed as described in the abstract, its contributions could be significant: a live, application-integrated evaluation platform with cold-start rating mechanisms and information-aware pair selection would be of real value to the LLM evaluation community, and the claimed results on ranking stability, transitivity, and manipulation resistance would merit close scrutiny. However, none of that content is present in the submitted manuscript. The full text is a semantic communication paper for LEO satellite links. Consequently, the scientific significance of the submitted work cannot be assessed. There is no verifiable methodology, no experimental setup, no data, and no results corresponding to the abstract. This is not a case of a local weakness in an otherwise complete paper; it is a complete absence of the claimed subject matter.

major comments (3)
  1. [Full Text (Sections I–VII)] The full text is not the paper announced in the title and abstract. It is 'Importance-Aware Robust Semantic Transmission for LEO Satellite-Ground Communication', with an introduction on satellite-ground communications, a proposed IRST framework, and experiments on PSNR/SSIM/LPIPS for image transmission. None of the Inclusion Arena platform, Bradley-Terry rating model, Placement Matches, Proximity Sampling, or any LLM/MLLM leaderboard content appears anywhere in the manuscript. The abstract's central claims about ranking reliability, transitivity, and manipulation resistance are therefore unsupported by the submitted artifact.
  2. [Running header and references] The running header on the full text reads 'arXiv:2508.11457v2 [eess.SP] 15 Dec 2025', not the arXiv identifier stated in the submission header (2508.11452, cs.AI). The reference list contains only wireless-communications and image-transmission citations (e.g., Deep JSCC, Swin Transformer, remote sensing datasets) and contains no citations to LLM leaderboards or evaluation benchmarks such as Chatbot Arena or MMLU. This independently confirms that the submitted document is a different paper and not a draft of the Inclusion Arena work.
  3. [Abstract claims and evidence] The abstract asserts: 'Extensive empirical analyses and simulations demonstrate that Inclusion Arena yields reliable and stable rankings, exhibits higher data transitivity compared to general crowdsourced datasets, and significantly mitigates the risk of malicious manipulation.' No equations, algorithms, simulation protocols, datasets, or empirical results supporting these claims are present in the full text. As a result, none of the load-bearing premises—validity of in-app preference signals, effectiveness of Placement Matches and Proximity Sampling, or manipulation resistance—can be checked. The paper as submitted cannot receive a substantive scientific review.
minor comments (2)
  1. [Title/abstract metadata] The title, author list, and subject area of the submission metadata do not match the full text. This suggests a submission or packaging error that should be resolved editorially before any technical review.
  2. [Platform URL] The abstract gives https://www.tbox.cn/about/model-ranking as the public platform location. Even if the correct paper were supplied, a live URL is not a substitute for an archived experimental snapshot or detailed methodology in the manuscript.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity detected in submitted text; Inclusion Arena abstract is unverifiable because the full text is an unrelated LEO semantic-communication paper.

full rationale

The supplied full text (arXiv:2508.11457v2, 'Importance-Aware Robust Semantic Transmission for LEO Satellite-Ground Communication') is not the Inclusion Arena paper described in the abstract. The abstract claims that 'Extensive empirical analyses and simulations demonstrate that Inclusion Arena yields reliable and stable rankings, exhibits higher data transitivity compared to general crowdsourced datasets, and significantly mitigates the risk of malicious manipulation,' but the accompanying text contains no Bradley-Terry model, no Placement Matches, no Proximity Sampling, no ranking simulations, and no analysis of transitivity or manipulation resistance. There is therefore no derivation chain present to audit for circularity: no equation, fit, or self-citation exists in the artifact that could be shown to reduce to its own inputs. Per the hard rules, I do not speculate that the missing analyses would have been circular; the mismatch is a verification failure rather than a demonstrated circular step. The IRST text itself, considered separately, is a self-contained systems paper that compares against external baselines (WITT, Deep JSCC, SS-DDPM) and does not exhibit self-referential derivation. Accordingly, no circularity step can be identified, and the score is 0. This should not be read as validating the abstract's empirical claims, which cannot be checked against the submitted full text.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

The abstract discloses no equations, so free parameters of the ranking or simulation pipeline cannot be enumerated; any simulation hyperparameters are undisclosed. The central claims rest on two domain assumptions: the validity of naturally occurring user feedback as a quality signal, and the adequacy of the Bradley-Terry model for the preference data. A third, submission-specific axiom is that the claimed empirical analyses and simulations exist and are correctly executed, since none appear in the provided document.

assumptions (3)
  • domain assumption Human feedback passively collected during natural app usage is a valid and unbiased signal of model quality.
    The entire ranking rests on the validity of in-app user preference data as ground truth. Stated in the abstract ('human feedback collected directly from AI-powered applications', 'ensuring evaluations reflect practical usage scenarios'), with no evidence in the provided text about user-selection bias or interaction confounds.
  • domain assumption Pairwise model preferences can be represented by a Bradley-Terry model with a single latent strength per model.
    Bradley-Terry is invoked in the abstract as the ranking backbone; its transitivity and independence assumptions are not discussed in the provided material.
  • ad hoc to paper The simulations and empirical analyses claimed in the abstract are correctly executed and representative.
    The abstract asserts 'extensive empirical analyses and simulations demonstrate...' but the body contains none of this content, so the claim rests on an unverifiable premise specific to this submission.

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Cite this review

Pith. "Pith review of Inclusion Arena: An Open Platform for Evaluating Large Foundation Models with Real-World Apps." pith.science (2026). https://pith.science/paper/CBHC6H3T

@misc{pith2026250811452,
  author       = {Pith},
  title        = {Pith review of: Inclusion Arena: An Open Platform for Evaluating Large Foundation Models with Real-World Apps},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/CBHC6H3T}},
  note         = {Machine review of arXiv:2508.11452}
}
read the original abstract

Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs) have ushered in a new era of AI capabilities, demonstrating near-human-level performance across diverse scenarios. While numerous benchmarks (e.g., MMLU) and leaderboards (e.g., Chatbot Arena) have been proposed to help evolve the development of LLMs and MLLMs, most rely on static datasets or crowdsourced general-domain prompts, often falling short of reflecting performance in real-world applications. To bridge this critical gap, we present Inclusion Arena, a live leaderboard that ranks models based on human feedback collected directly from AI-powered applications. Our platform integrates pairwise model comparisons into natural user interactions, ensuring evaluations reflect practical usage scenarios. For robust model ranking, we employ the Bradley-Terry model augmented with two key innovations: (1) Placement Matches, a cold-start mechanism to quickly estimate initial ratings for newly integrated models, and (2) Proximity Sampling, an intelligent comparison strategy that prioritizes battles between models of similar capabilities to maximize information gain and enhance rating stability. Extensive empirical analyses and simulations demonstrate that Inclusion Arena yields reliable and stable rankings, exhibits higher data transitivity compared to general crowdsourced datasets, and significantly mitigates the risk of malicious manipulation. By fostering an open alliance between foundation models and real-world applications, Inclusion Arena aims to accelerate the development of LLMs and MLLMs truly optimized for practical, user-centric deployments. The platform is publicly accessible at https://www.tbox.cn/about/model-ranking.

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Pith tools

Reviewed August 5, 2026 · model on record in the stance chip above.