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REVIEW 3 major objections 6 minor 87 references

Ad-Hoc Human-AI Coordination Challenge

T0 review · 3 major / 6 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read Human proxies make Hanabi AI benchmarking cheap and reproducible

desk verdict Useful benchmark infrastructure, but the proxies' human-likeness is not yet established; treat leaderboard scores as coordination with these specific policies, not with humans. read the letter →

arxiv 2506.21490 v2 pith:PJQDE7WY submitted 2025-06-26 cs.AI cs.HCcs.MA

Tin Dizdarević , Ravi Hammond , Tobias Gessler , Anisoara Calinescu , Jonathan Cook , Matteo Gallici , Andrei Lupu , Darius Muglich
show 2 more authors
Johannes Forkel Jakob Nicolaus Foerster
This is my paper
classification cs.AIcs.HCcs.MA
keywords human-AIcoordinationad-hocteamplayHanabihumanproxyagentsbehaviouralcloningregularisedreinforcementlearningzero-shotbenchmark
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

The paper is trying to establish that expensive, hard-to-reproduce human evaluation can be replaced by human proxy agents—policies trained on a large corpus of human Hanabi games that behave like the humans who supplied the data. If the proxies genuinely stand in for human partners, then AI coordination research gains a standardized, cheap, and reproducible evaluation protocol, which the authors deliver as the Ad-Hoc Human-AI Coordination Challenge (AH2AC2). The paper further claims that combining behavioural cloning with KL-regularized independent PPO yields proxies that are both stronger players than pure imitation and still human-like, and it provides baselines showing that current zero-shot coordination methods, human-data methods, population-based methods, and LLM agents all struggle on this benchmark.

What carries the argument

The central object is the human proxy agent trained with Human-Data-Regularised IPPO (HDR-IPPO). The procedure first trains an LSTM-based behavioural cloning policy on a large-scale human dataset (101,096 two-player and 46,525 three-player games from hanab.live following H-group conventions), then initializes from the best BC checkpoint and continues with IPPO while adding a KL regularisation term anchoring the policy to the BC action distribution, weighing the IPPO objective against KL divergence with a weight lambda.

What would settle it

A direct human-AI play experiment in which human participants play Hanabi with both the human proxies and with other humans, comparing scores and subjective coordination quality: if proxy-AI teams score systematically lower than human-AI teams (or proxy-human teams differ from human-human teams), the proxies do not stand in for real human partners.

Watch

Extended reading notes

Core claim

The central claim is that human proxy agents trained by behavioural cloning on a large-scale human dataset followed by KL-regularized independent PPO serve as robust, cheap, and reproducible human-like evaluation partners for ad-hoc human-AI coordination in Hanabi. The paper demonstrates that these proxies substantially improve self-play scores over pure BC policies—especially in three-player games, where zero-score games drop from over 70% to well under 1%—while retaining human-like behaviour, evidenced by cross-play compatibility with BC policies, comparable action-prediction accuracy on held-out human games, and nearly identical information-per-play and communicativeness metrics. On the resulting leaderboard, no baseline matches the human proxies themselves, and OBL, which uses no human data, outperforms methods that use the provided limited human data, highlighting the difficulty of efficient human-data use.

Load-bearing premise

The central assumption is that human proxy agents, trained on and validated against H-group hanab.live players, faithfully represent the humans an AI agent will actually coordinate with.

Editorial extensions

If this is right

  • AI coordination research gains a standardized, low-cost evaluation protocol for human-AI coordination in Hanabi, with 1,000-game evaluation runs through a controlled API instead of costly human studies.
  • The benchmark can track progress in data-efficient human-AI coordination, since participants receive only 3,079 open-source games while the proxies are trained on the full closed dataset.
  • The baseline results set a reference: OBL's strong zero-shot performance without human data suggests that existing methods underutilize limited human data, and the lagging performance of BC, BR-BC, FCP, OP, IPPO, and DeepSeek-R1 defines the current gap.
  • The action-prediction sub-challenge provides a complementary, replicable measure of human-likeness via teacher-forced cross-entropy loss on held-out human games.
  • If the proxies are accepted as human stand-ins, the leaderboard provides a meaningful ranking of human-AI coordination ability that can guide future algorithmic development.

Reading between the lines

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

  • The proxies' validation is internal to one community (H-group on hanab.live); the paper itself defers direct human-play validation. If the goal is coordination with general humans, the proxies may over-represent a specific convention system, and leaderboard scores might not transfer to casual or differently conventioned players.
  • A direct testable extension would be a human study comparing human scores and subjective experience when paired with the proxies versus with real humans; if scores diverge, the proxies' role as a surrogate would need recalibration.
  • The KL-regularisation mechanism is quite general: the same BC-plus-regularised-RL recipe could create human proxies for other cooperative games or real-world human-AI collaboration settings where human demonstrations are available but live evaluation is expensive.
  • The near-identical behavioural metrics (IPP and communicativeness) between proxies and the human dataset are coarse summaries; finer-grained behavioural distributions (e.g., convention-following rates per game position) could reveal whether the proxies reproduce human strategy spread or only its average.
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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 / 6 minor

Summary. The paper introduces AH2AC2, a benchmark for ad-hoc human-AI coordination in Hanabi, and proposes human proxy agents as cheap, reproducible stand-ins for human partners. The proxies are trained by behavioral cloning on 147,621 games from the hanab.live H-group population, then refined with IPPO regularized by a KL term toward the BC policy. The authors open-source a limited dataset of 3,079 games, provide a controlled evaluation API and leaderboard, and report baselines including IPPO, BC, BR-BC, OBL, OP, FCP, and a prompted DeepSeek-R1 agent. The paper also defines an optional action-prediction challenge. Section 5 validates the proxies via self-play scores, cross-play with BC policies, action-prediction metrics on held-out games, and two behavioral statistics (IPP and Communicativeness); Section 7 explicitly defers direct human-AI play validation to future work.

Significance. If the central claim holds, AH2AC2 would be a valuable community resource: it is the first open-source human Hanabi dataset, it provides a controlled evaluation protocol, and it assembles a broad set of baselines that clarify the difficulty of human-AI coordination. The HDR-IPPO recipe, the hosted API, and the open baselines are concrete and reproducible contributions, and the paper gives credit where prior datasets were closed. However, the paper's headline claim is that the proxies are 'human-like evaluation partners', and the evidence for this is indirect: all validation strands are measured against the same H-group population used for training. The leaderboard therefore measures coordination with a learned proxy, not yet with humans. The benchmark artifact is useful even under that caveat, but the claims and the 'golden standard' label in Table 5 outrun the current evidence.

major comments (3)
  1. [Section 5.2, Table 3, Section 7] The load-bearing property of the central claim—that scores achieved with the proxies measure human-AI coordination—is not demonstrated. The three validation strands all measure agreement with the same H-group population used for training: action-prediction loss/accuracy on held-out games from that population, cross-play with BC policies trained on that population, and two aggregate behavioral statistics. None of these establishes that a candidate's score with a proxy predicts its score with a human, especially a human outside H-group conventions. Moreover, Table 3 shows that the HDR-IPPO refinement systematically degrades the direct human-likeness metrics relative to BC (accuracy −0.03 to −0.08; loss +0.05 to +0.08). The paper itself states in Section 7 that 'The ultimate validation of our human proxy agents requires direct human-AI play' and defers it. The Abstract and Section 4.2 claims that the proxies 'serve as robust, cheap, and reproducible human-like evaluation partners' therefore outrun the current evidence.
  2. [Table 5, Section 4.3] The leaderboard footnote labels the human proxies as a 'golden standard' because they are not constrained by game limits. This is circular relative to the paper's own caveat: the proxies are the proposed substitute for human evaluation, so their scores cannot serve as the criterion against which candidate agents are judged until the proxy-human correspondence is established. I recommend either adding a human-subject validation study or relabeling these scores as 'proxy-coordination scores' and removing 'golden standard' until such validation exists.
  3. [Section 4.3, Appendix A.7] The evaluation protocol is described inconsistently: the main text says registered participants receive access to 1,000 evaluation games, while Appendix A.7 states that the evaluation is 2,000 games total (1,000 in each of two- and three-player settings). Please disambiguate. Additionally, the text calls pre-registration 'the gold standard for empirical science'; pre-registration alone does not prevent iterative fitting to the hosted proxies across repeated evaluation requests, so the integrity guarantee needs a concrete statement about how many evaluation runs a participant may request and what controls exist.
minor comments (6)
  1. [Abstract and Section 4.1] The abstract says the proxies are trained on 'a large-scale human dataset', but the full dataset is closed and only 3,079 games are open-sourced; please make this asymmetry explicit in the abstract.
  2. [Section 4.1] There is a typo: 'gives them access to to 1,000 evaluation games' should read 'access to 1,000 evaluation games'.
  3. [Section 2] In the Dec-POMDP definition, the sentence 'i.e. sH is always a terminal state' lacks a period and is missing the preceding comma; please fix the punctuation.
  4. [Section 5.2] The phrase 'highlight the brittleness of BC policies in unfamiliar scenarios' and 'BC policies' poor single-player performance' use 'single-player' where 'self-play' is the intended term; please align the terminology with the rest of the paper.
  5. [Appendix A.6] The qualitative analysis of the proxy's convention-following is based on a single game; please state explicitly that this is illustrative rather than a systematic behavioral evaluation.
  6. [Appendix A.10] The LLM prompt templates contain several typos that may affect reproducibility, including 'your shop' for 'your chop', 'rnak clue' for 'rank clue', 'follwing' for 'following', 'Teamamte' for 'Teammate', and 'has not been discarded yet, where discarding'.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the proxy pipeline and benchmark are self-contained, and held-out human-action prediction provides independent evidence; direct human validation is explicitly deferred.

full rationale

The paper is an empirical benchmark contribution, not a derivation whose conclusion is algebraically forced by its inputs. The proxy agents are trained by behavioral cloning on a large human dataset followed by KL-regularized IPPO (Eq. 2); the KL term uses the BC policy as a reference, but this is a training objective, and the paper does not define 'human-like' as 'close to this BC policy.' Human-likeness is then assessed on held-out games excluded from training (Table 3), which is a genuine out-of-sample prediction, together with cross-play against BC policies and aggregate behavioral metrics (Table 4). The cross-play check is partly internal—BC policies are themselves derived from the same H-group data—so it is not independent evidence of human equivalence, but it does not reduce a prediction to a fitted parameter. The paper explicitly acknowledges that 'The ultimate validation of our human proxy agents requires direct human-AI play' (Section 7), i.e., the human-likeness claim is an assumption awaiting external validation, which is a limitation rather than circularity. Self-citations to Hu et al. and Bakhtin et al. support the regularized-RL methodology, but the paper provides its own experiments (Tables 2-4, Appendix A.8), so the citations are not load-bearing. No uniqueness theorem, ansatz smuggled via citation, or renaming of a known result is present. Therefore no circular step is substantiated under the quoted-evidence standard.

Assumptions & free parameters 3 free parameters · 3 assumptions · 1 invented entities

The benchmark rests on the assumption that proxies trained on hanab.live H-group players faithfully represent human partners. The KL weights and open dataset size are hand-chosen design parameters that shape all reported leaderboard scores.

free parameters (3)
  • KL regularization weight lambda (human proxies) = 0.3, 0.2, 0.1, 0.15 for theta1..theta4
    Chosen per proxy (Table 9) to balance human-likeness against robustness; directly shapes the trade-off between imitating humans and improving self-play score.
  • Entropy coefficient (IPPO) = 1e-5, 1e-4, 1e-4, 1e-4
    Hand-tuned per proxy (Table 9); influences exploration and determinism of proxy policies.
  • Open dataset size per setting = 1,000 training games plus validation (858 two-player, 221 three-player)
    Design choice to encourage data-efficient methods; affects the difficulty of all data-limited baselines.
assumptions (3)
  • domain assumption H-group conventions on hanab.live constitute a meaningful distribution of human Hanabi play.
    Section 4.1 assumes the dataset 'naturally contains a variety of playstyles' despite all games adhering to H-group conventions; proxies are only validated against this population.
  • domain assumption Behavioral cloning supervised on human actions, then refined by KL-regularized self-play, yields human-compatible policies.
    Section 4.2 builds on prior works (Bakhtin 2022, Hu 2022, Jacob 2022, Cornelisse 2024) without re-deriving; the paper's own ablation (A.8) shows lambda matters but does not prove human equivalence.
  • standard math Standard PPO/IPPO convergence and the environment implementation are correct.
    Used throughout (Appendix A.2, A.4) as unexamined background machinery.
invented entities (1)
  • Human proxy agents as evaluation partners
    purpose: Serve as standardized stand-ins for human partners in the AH2AC2 benchmark, accessed via a controlled API.
    Their human-likeness is only evidenced by held-out action prediction from the same hanab.live population, cross-play with BC policies from the same population, and two aggregate metrics; the authors state direct human-AI play remains future work (Section 7).

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Cite this review

Pith. "Pith review of Ad-Hoc Human-AI Coordination Challenge." pith.science (2026). https://pith.science/paper/PJQDE7WY

@misc{pith2026250621490,
  author       = {Pith},
  title        = {Pith review of: Ad-Hoc Human-AI Coordination Challenge},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/PJQDE7WY}},
  note         = {Machine review of arXiv:2506.21490}
}
read the original abstract

Achieving seamless coordination between AI agents and humans is crucial for real-world applications, yet it remains a significant open challenge. Hanabi is a cooperative card game featuring imperfect information, constrained communication, theory of mind requirements, and coordinated action -- making it an ideal testbed for human-AI coordination. However, its use for human-AI interaction has been limited by the challenges of human evaluation. In this work, we introduce the Ad-Hoc Human-AI Coordination Challenge (AH2AC2) to overcome the constraints of costly and difficult-to-reproduce human evaluations. We develop \textit{human proxy agents} on a large-scale human dataset that serve as robust, cheap, and reproducible human-like evaluation partners in AH2AC2. To encourage the development of data-efficient methods, we open-source a dataset of 3,079 games, deliberately limiting the amount of available human gameplay data. We present baseline results for both two- and three- player Hanabi scenarios. To ensure fair evaluation, we host the proxy agents through a controlled evaluation system rather than releasing them publicly. The code is available at \href{https://github.com/FLAIROx/ah2ac2}{https://github.com/FLAIROx/ah2ac2}.

Figures

Figures reproduced from arXiv: 2506.21490 by the authors.

Figure 1
Figure 1. Ad-Hoc Human-AI Coordination Challenge (AH2AC2) and thus rely on others to give interpretable hints on which cards to play or discard. It is designed for 2-5 players, but we restrict ourselves to two- and three- player settings in this work due to the availability of human gameplay data for these configurations. The maximum score in the game is 25, but it cannot be achieved for every shuffling of the deck. If the te… view at source ↗
Figure 2
Figure 2. Cross-play performance matrices for human proxy agents and corresponding BC policies in two-player and three-player set￾tings. Strong cross-play performance between two policies indi￾cates that policies converged to compatible conventions. Each element (i, j) represents the average score achieved by different team configurations, over all possible permutations of player posi￾tions. For the three-player setting team … view at source ↗
Figure 3
Figure 3. Cross-play performance matrix for two-player agents, comparing a human proxy agent (π HP θ2 ), its corresponding BC policy (π BC θ2 ), and a non-regularised HDR-IPPO agent initialised from the same BC policy, π BC θ2 , but where λ = 0. Each matrix element represents the average score achieved by a team, averaged over both player orderings. Averages are calculated based on 15,000 games per team permutation. Consider … view at source ↗
Figures from the paper (4 more)
Figure 4
Figure 4. Figure 4: Cross-play performance matrix for three-player setting, with scores for a human proxy agent (π HP θ4 ), its corresponding BC policy (π BC θ4 ), and a non-regularised HDR-IPPO agent initialised from the same BC policy, π BC θ4 , but with λ = 0. Each matrix element (i, j…
Figure 5
Figure 5. Figure 5: Cross-play performance matrix. Each element (i, j) represents the average score achieved by a team comprising of Agent j and Agent i, averaged over possible permutations of player positions. The average score for each team is calculated based on 25,000 games per permut…
Figure 6
Figure 6. Figure 6: KL divergence throughout training for all trained HDR-IPPO policies [PITH_FULL_IMAGE:figures/full_fig_p026_6.png]
Figure 7
Figure 7. Figure 7: KL divergence throughout training where λ ≥ 0.08. 26 [PITH_FULL_IMAGE:figures/full_fig_p026_7.png]

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Reference graph

Works this paper leans on

87 extracted references · 76 canonical work pages

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