Report Arena Evaluation

• Report Arena is a dynamic reporting layer that converts pairwise judgments, forecast submissions, and benchmark traces into public leaderboards and comparative outputs.
• It standardizes inputs using statistical frameworks like Elo, Bradley–Terry, and rolling-window aggregation to uncover insights hidden by static benchmarks.
• By bridging model development and community judgment, it delivers continuously updated, evidence-based comparisons across diverse research domains.

“Report Arena” is an Editor’s term for arena-style evaluation infrastructures that function as reporting mechanisms for a research field. In this sense, the defining feature is not merely competitive testing, but the conversion of large volumes of pairwise judgments, forecast submissions, or benchmark traces into public comparative outputs such as leaderboards, ratings, rolling-window metrics, and aggregate analyses. Across generative 3D, conversational recommender systems, search-augmented LLMs, operational energy forecasting, and live time-series forecasting, these platforms replace or supplement static offline benchmarks with continuously updated evidence about comparative system behavior under standardized but evolving conditions (Ebert, 23 Jun 2025, Bernard et al., 2024, Miroyan et al., 5 Jun 2025, Kleinebrahm et al., 27 Apr 2026, Meyer et al., 23 Dec 2025). This suggests that a report arena is best understood as a reporting layer between model development and community judgment.

1. Definition and intellectual setting

The emergence of report arenas is tied to recurring failures of conventional evaluation. In generative 3D, image-based metrics such as PSNR and SSIM “evaluate only the final rendered output while ignoring the underlying 3D structure,” while geometric measures such as Chamfer Distance “fail to capture crucial aspects of 3D quality including perceptual appeal, aesthetic coherence, and mesh topology” (Ebert, 23 Jun 2025). In energy forecasting, the analogous problem is a “persistent comparability gap”: study-specific datasets, time periods, information sets, and scoring setups make reported gains non-comparable across papers (Kleinebrahm et al., 27 Apr 2026). In time-series foundation models, static historical benchmarks are further threatened by test-set contamination and by “illegitimate transfer of global patterns” across series affected by the same historical shock (Meyer et al., 23 Dec 2025). Search-augmented LLMs add another mismatch: existing datasets are small, single-turn, English-only, and mostly static fact-checking tasks, while real usage is multilingual, multi-turn, and intent-diverse (Miroyan et al., 5 Jun 2025).

Within this landscape, report arenas shift evaluation from one-shot benchmark release toward ongoing public reporting. Human-preference arenas do so by collecting pairwise judgments under anonymity and standardized presentation; operational forecasting arenas do so by enforcing ex-ante submissions and ex-post scoring on rolling windows (Bernard et al., 2024, Kleinebrahm et al., 27 Apr 2026). The result is an infrastructure whose main product is not a test set but a stream of comparative reports.

Not every “arena” in the literature is a report arena in this sense. Several systems use the term for simulators, toolkits, or experimental frameworks: the distributed MARL framework “The AI Arena” (Staley et al., 2021), the ROS-native navigation suite “Arena-Bench” (Kästner et al., 2022), the ROS 2 platform “Arena 4.0” (Shcherbyna1 et al., 2024), the MARL toolkit “Arena” (Wang et al., 2019), and the Unity-based platform “Arena: A General Evaluation Platform and Building Toolkit for Multi-Agent Intelligence” (Song et al., 2019). These systems are evaluation infrastructures, but their primary contribution is environment construction, orchestration, or agent training rather than a continuously updated public reporting layer.

2. Evaluation architectures

One major report-arena architecture is the anonymous pairwise battle. In 3D Arena, users inspect two anonymized image-to-3D outputs side-by-side, can rotate and zoom the assets, can switch between standard and wireframe views, and vote only before model identities are revealed (Ebert, 23 Jun 2025). CRS Arena adapts the same logic to conversational recommendation: a user interacts with two anonymous systems sequentially, marks each conversation as satisfaction or frustration, and then chooses a winner or a draw (Bernard et al., 2024). Search Arena extends the pattern to search-enabled LLMs by displaying two anonymous responses with inline citations and full multi-turn chat, followed by a vote of A, B, tie, or both bad (Miroyan et al., 5 Jun 2025). In all three cases, report generation begins from human preference data rather than from absolute rubric scoring.

A second architecture is the operational submission pipeline. Energy-Arena defines challenges through YAML configuration files specifying target variable, areas, time zone, horizon, submission deadlines, metrics, leaderboard windows, and ground-truth sources; submissions are made through a FastAPI backend and are scored after realized values arrive (Kleinebrahm et al., 27 Apr 2026). TS-Arena adds a stricter live-forecasting protocol: a past-to-present API exposes only the context window $[t_{p-c}, \ldots, t_p]$, a pre-registration API accepts forecasts for $[t_{p+1}, \ldots, t_{p+h}]$, and SCD2 historization with valid_from, valid_to, and created_at reconstructs exactly what was knowable at prediction time (Meyer et al., 23 Dec 2025). Here, the arena’s central report is not a human-preference score but a forward-looking record of forecast quality under operational constraints.

These architectures differ in surface form but share a common logic. A report arena standardizes inputs, constrains information flow, records interactions at scale, and then publishes comparative summaries that can be inspected by developers, researchers, and practitioners.

3. Reporting logics and statistical transformations

Human-preference report arenas typically translate pairwise comparisons into latent skill estimates. 3D Arena and CRS Arena use Elo-style updates. For model $A$ against model $B$, the expected score is

$E_A = \frac{1}{1 + 10^{(R_B - R_A)/400}},$

and the updated rating is

$R'_A = R_A + K(S_A - E_A).$

In 3D Arena, all models begin at 1200; in CRS Arena, all systems start at 1000 with $K=16$ (Ebert, 23 Jun 2025, Bernard et al., 2024). Search Arena instead fits a Bradley–Terry model,

$$\Pr(i \succ j) = \frac{\exp(s_i)}{\exp(s_i) + \exp(s_j)},$$

and then rescales the latent scores to an Elo-like leaderboard (Miroyan et al., 5 Jun 2025). These transformations are the core of the reporting process: raw wins, losses, and draws are converted into stable public rankings.

Operational arenas use rolling metric aggregation instead. Energy-Arena reports deterministic metrics such as MAE and RMSE and can also score probabilistic forecasts with CRPS and WIS over rolling windows of $1$, $7$, $[t_{p+1}, \ldots, t_{p+h}]$0, $[t_{p+1}, \ldots, t_{p+h}]$1, and $[t_{p+1}, \ldots, t_{p+h}]$2 periods (Kleinebrahm et al., 27 Apr 2026). TS-Arena uses MASE as its primary metric and adjusts leaderboard values by participation rate within the relevant scope, so selective participation worsens a model’s adjusted score (Meyer et al., 23 Dec 2025). In both systems, the report is a persistent leaderboard indexed by challenge, area, horizon, frequency, and time window rather than a single static test result.

This statistical layer is essential. Without Elo, Bradley–Terry, or rolling aggregation, an arena remains a collection of battles or forecasts. With those transformations, it becomes a report arena.

4. Representative platforms and reported outputs

Platform	Domain	Primary reported outputs
3D Arena	Image-to-3D generation	Elo, votes, win rate, format, format-level effects
CRS Arena	Conversational recommender systems	Elo, satisfaction rate, battle outcomes, dialog logs
Search Arena	Search-augmented LLMs	Bradley–Terry/Elo-like ratings, full traces, citation analyses
Energy-Arena	Operational energy forecasting	Rolling MAE/RMSE/CRPS/WIS leaderboards
TS-Arena	Live time-series forecasting	Pre-registered MASE leaderboards with participation adjustment

These platforms also differ in what they expose as raw evidence. 3D Arena publishes a continuously updated leaderboard plus detailed aggregate statistics, including number of votes, win rate, and representation type (Ebert, 23 Jun 2025). CRS Arena releases CRSArena-Dial, a dataset of 474 real conversations and 185 pairwise judgments, with per-dialog satisfaction labels (Bernard et al., 2024). Search Arena releases 24,069 conversations, 12,652 preference votes, and full system traces with cited URLs and metadata (Miroyan et al., 5 Jun 2025). Energy-Arena and TS-Arena expose challenge definitions, rolling windows, and forecast visualizations rather than full conversation or media traces (Kleinebrahm et al., 27 Apr 2026, Meyer et al., 23 Dec 2025).

Adjacent systems extend the reporting function into robotics and navigation. Arena-Bench computes up to 14 metrics, including success rate, collision count, time to goal, path length, movement jerk, curvature, roughness, and clearing distance (Kästner et al., 2022). Arena 4.0 adds a universal metrics recorder/evaluator node and benchmark pipelines for social navigation in generated worlds (Shcherbyna1 et al., 2024). These platforms broaden the arena idea, but their reporting layer is secondary to simulation, scenario generation, or deployment tooling.

5. Empirical insights produced by report arenas

Because report arenas gather large, behaviorally grounded datasets, they often reveal patterns that static benchmarks obscure. In 3D Arena, a May 30, 2025 snapshot included 123,243 pairwise preferences from 8,096 unique authenticated users across 19 models, with statistical fraud detection flagging 31 accounts and yielding 99.75% user authenticity (Ebert, 23 Jun 2025). The resulting analysis showed a 16.6 Elo advantage for Gaussian splats over meshes, a 144.1 Elo advantage for textured over untextured outputs, and a 78 Elo advantage of TRELLIS-3DGS over TRELLIS despite identical underlying generative models. The platform therefore reports not only who wins, but what forms of rendering and representation users reward.

Search Arena shows a different layer of perceptual bias. Its dataset spans 24,069 conversations, 12,652 preference votes, 71 languages, and 22.4% multi-turn conversations (Miroyan et al., 5 Jun 2025). In a feature-augmented Bradley–Terry analysis, citation count has a positive coefficient $[t_{p+1}, \ldots, t_{p+h}]$3, supporting citations have coefficient $[t_{p+1}, \ldots, t_{p+h}]$4, and irrelevant citations have coefficient $[t_{p+1}, \ldots, t_{p+h}]$5. Tech/coding citations have coefficient $[t_{p+1}, \ldots, t_{p+h}]$6, community platforms $[t_{p+1}, \ldots, t_{p+h}]$7, social media $[t_{p+1}, \ldots, t_{p+h}]$8, and Wikipedia $[t_{p+1}, \ldots, t_{p+h}]$9. The reported implication is that users reward citations and certain source types even when those citations do not actually support the attributed claims.

CRS Arena similarly reports a divergence between offline metrics and interactive preference. Its Elo rankings across open and closed crowdsourcing are highly correlated, with Pearson $A$0 and Spearman $A$1, while satisfaction scores show Pearson $A$2 and Spearman $A$3 across settings (Bernard et al., 2024). Yet Elo correlates strongly with satisfaction ($A$4) and weakly negatively with Recall@10 ($A$5). This suggests that report arenas can surface end-to-end properties of interaction that conventional offline metrics systematically miss.

Operational arenas yield a different kind of empirical insight. Energy-Arena’s shift from retrospective backtesting to ex-ante submission and ex-post evaluation is designed to prevent information leakage and retroactive tuning, while TS-Arena’s moving temporal frontier ensures that evaluation targets “do not exist yet” at forecast time (Kleinebrahm et al., 27 Apr 2026, Meyer et al., 23 Dec 2025). A plausible implication is that report arenas are especially valuable in domains where the main threat is not annotation mismatch but temporal contamination.

6. Limitations, governance, and future directions

Report arenas inherit domain-specific limitations. 3D Arena acknowledges representation bias, prompt-selection bias, and likely user-base skew toward general enthusiasts rather than professional 3D artists (Ebert, 23 Jun 2025). CRS Arena notes demographic uncertainty, domain restriction to movie recommendation, and noisy unfiltered conversations (Bernard et al., 2024). Search Arena documents demographic skew, search-engine dependence, short interaction depth, and the fact that its citation-attribution pipeline is applied only to a subset and itself relies on LLM judgments (Miroyan et al., 5 Jun 2025). Energy-Arena and TS-Arena both enforce deadlines but cannot fully verify participants’ exogenous inputs; information-set transparency is encouraged through metadata rather than hard enforcement (Kleinebrahm et al., 27 Apr 2026, Meyer et al., 23 Dec 2025).

A second class of limitation is governance. “The Leaderboard Illusion” identifies undisclosed private testing, selective disclosure, biased sampling, asymmetric deprecation, and unequal data access on Chatbot Arena (Singh et al., 29 Apr 2025). The paper reports 27 private LLM variants tested by Meta in the lead-up to the Llama-4 release, estimates that Google and OpenAI received 19.2% and 20.4% of all Arena data respectively, and states that 83 open-weight models combined received 29.7% of the total data. It further shows that access to Arena-like data can produce relative performance gains of up to 112% on the arena distribution. In parallel, “Gaming the Arena” argues that arena-style evaluation is entangled with attention capture, commercialization, and “model battling,” and describes five themes of arena-ization: critique of benchmarks, limits of expertise, scaling of scoring, seeking user attention through participation, and model battling (Hind, 17 Dec 2025).

These critiques have produced explicit reform proposals. 3D Arena recommends multi-criteria assessment, task-oriented evaluation, and format-aware comparison, including separate topology-focused and visual-preference Elo scores (Ebert, 23 Jun 2025). The Leaderboard Illusion recommends prohibiting score retraction, publishing all tested variants, imposing transparent limits on private variants per provider, using fair sampling, and making deprecation rules auditable (Singh et al., 29 Apr 2025). Energy-Arena and TS-Arena embody a different corrective strategy: they move evaluation into live operational time, where pre-registration, rolling windows, and explicit deadlines reduce the scope for hidden retrospective optimization (Kleinebrahm et al., 27 Apr 2026, Meyer et al., 23 Dec 2025).

Taken together, report arenas are best understood as powerful but non-neutral evaluation infrastructures. They can expose perceptual, operational, and interactive properties that static benchmarks miss, but they can also become sites of bias, asymmetry, and strategic optimization. Their long-term value therefore depends not only on scale and statistical modeling, but also on transparent governance, scope-aware reporting, and explicit acknowledgment of what their leaderboards do—and do not—measure.