Pith. sign in

REVIEW 4 major objections 3 minor 1 cited by

Cooperation in LLM multi-agent systems can be turned from an implicit ingredient of success into an observable, repairable process defined by four verifiable constraints.

Reviewed by Pith at T0; open to challenge. T0 means a machine referee read the full paper against a public rubric. the ladder, T0–T4 →

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

COOP2 formalizes cooperation in LLM multi-agent systems as verifiable constraint satisfaction over a cognitive–primitive trace, and adds a repair mechanism that predicts constraint failures and guides plan revision.

T0 review reviewed 2026-08-02 challenge →

load-bearing objection The COOP2 trace and constraint formalism are a useful step for LLM-MAS evaluation, but the repair claim in the abstract is untested and the observational results are noisy. the 4 major comments →

arxiv 2603.00349 v2 pith:S47C3MWK submitted 2026-02-27 cs.AI cs.MA

COOP$^2$: Defining, Observing, and Repairing Cooperation in LLM Multi-Agent Systems

Hanqing Yang , Narjes Nourzad , Shiyu Chen , Marie Siew , Jingdi Chen , Carlee Joe-Wong This is my paper
classification cs.AI cs.MA
keywords LLM multi-agent systemscooperation evaluationcooperative constraintsconstraint-guided repairmulti-agent planningcognitive-primitive interfaceMA-CrafterCUBE
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

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 argues that judging LLM-based multi-agent teams by final task success alone hides whether agents actually cooperated, how their interactions contributed to progress, or why they failed. To make cooperation itself observable, it defines a cooperative task as an environment task whose progress is guarded by four types of verifiable cooperative constraints — temporal, spatial, participation/capability, and dependency — and defines the cooperation process as the sequence of task states, participants, group capabilities, and constraint-satisfaction probabilities over the task's active span. A cognitive–primitive interface aligns the symbolic world where agents plan and talk with the primitive world where grounded actions change the environment. On that substrate, COOP2-Repair predicts which constraints a group's current plans will likely violate before execution and opens a targeted repair channel — naming the task, the group, and the failing constraints — so the team can replan. If correct, the framework gives a general way to diagnose why LLM teams fail and to intervene, improving task success and constraint satisfaction in exchange for measurable extra communication and decision time.

Core claim

The paper claims that cooperation in LLM multi-agent systems can be made a first-class, verifiable object rather than an implicit ingredient of success. Formally, a cooperative task is a state transition guarded by constraint satisfaction: each active task g carries probabilities p^c_{g,t} that the participating group satisfies each of four constraint types, and task progress evolves conditioned on those probabilities. The cooperation process is the trajectory of these cooperative states over the task's active span, with language-level activity logged and aligned to environment steps through a grounding bridge. Building on this, COOP2-Repair groups agents' committed plans by intended task, p

What carries the argument

The load-bearing machinery is the constraint-guarded task representation: for each active task g, a tuple (task state, participants, group capability, satisfaction probabilities p^c_{g,t}) over a constraint set {temporal, spatial/relational, participation/capability, dependency}, with task progress modeled as a transition conditioned on those probabilities. Around it sit the cognitive–primitive interface, whose grounding bridge aligns cognitive time (planning, messaging, interruption) with primitive environment steps and grounds symbolic tool operations into nondeterministic primitive action sequences; and COOP2-Repair's feasibility check, which reads each agent's step-aligned plan, computes

Load-bearing premise

The load-bearing premise is that cooperation in these systems is fully captured by the four hand-specified constraint types (temporal, spatial, participation/capability, dependency) and that the environments' injected requirements are faithful instances of those constraints; if a real cooperative mechanism — negotiation, information sharing, role emergence — escapes the set, the violation signals and repair targets track only the framework's own definitions, not cooperation a

What would settle it

Take a cooperative task whose success depends on a mechanism the four constraints do not encode — for example, two agents must negotiate and swap resources at a time and place not fixed by any task requirement. If COOP2 reports no violation deficits and COOP2-Repair never opens a repair channel while the team visibly fails to coordinate, the constraint set misses an essential cooperative requirement. Conversely, if the predicted-failure channel's accuracy against held-out actual violations is at chance, any score gain comes from added communication or cooldowns rather than from identifying the

Watch this falsifier. Get emailed when new claim-graph text bears on it.

If this is right

  • Outcome scores alone cannot reveal whether cooperation happened: two runs with identical scores can differ entirely in which constraints were satisfied and when, and COOP2 traces make that distinction visible.
  • Distinct failure modes separate cleanly by constraint type: dependency violations track model strength — large models cut them by roughly an order of magnitude — while spatial violations persist at 36–43% even for the strongest backbone, indicating spatial coordination is its own failure mode.
  • More communication does not mean more cooperation: adding messages and interruptions helped weaker backbones but cut the strongest backbone's score by about two-thirds, exposing a planning-quality versus execution-opportunity tradeoff under wall-clock budgets.
  • Repairing predicted constraint failures shifts teams from low-level resource collection toward tool crafting and advanced resources, and across two environments and three communication structures it improves task success and constraint satisfaction.
  • The framework's process traces surface the cost of cooperation — decision time, messages, interruptions — alongside its benefits, letting designers compare communication structures by more than final score.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Beyond the paper: if the constraint set is meant to be generic, a natural test is porting COOP2 to a task domain whose cooperation demands negotiation or information trading; if repair channels then fail to improve outcomes while violation deficits stay at ceiling, the four constraint types are missing an operative cooperative mechanism.
  • Beyond the paper: the logged traces themselves are a supervised training signal — a learned predictor of constraint satisfaction could replace the lightweight heuristic estimator, which the paper itself flags as future work, directly improving the accuracy of predicted failing constraint sets.
  • Beyond the paper: the constraint-type breakdown gives system builders diagnostic telemetry — teams could choose communication structure per model family, favoring Centralized for weak coordinators and Individual for strong ones, instead of assuming more communication is better.
  • Beyond the paper: because the formalism separates constraint satisfaction from task completion, it invites a 'success through poor cooperation' metric that quantifies how much of a team's score came from individual effort rather than constraint satisfaction, sharpening comparisons across multi-agent frameworks.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 3 minor

Summary. The paper introduces COOP2, a framework for evaluating cooperation in LLM-based multi-agent systems. It couples a symbolic cognitive layer (plans, messages, interruptions) with a primitive environment layer through a grounding bridge, and formalizes cooperative tasks as constraint-guarded state transitions using four constraint types: temporal, spatial, participation/capability, and dependency. The framework is instantiated in two custom environments, MA-Crafter and CUBE, and used to run a systematic study varying LLM backbone, communication structure, team size, and environment, measuring task progress and constraint violations. On top of this, the paper proposes COOP2-Repair, a mechanism that predicts constraint failures from group plans and opens targeted repair channels to guide replanning. The abstract claims that COOP2-Repair improves task success and constraint satisfaction across two environments and three communication structures, but the manuscript contains no quantitative repair-vs-baseline comparison. The main body reports only baseline results (Table 2, Table 8, Appendix D) and one qualitative repair run (Figure 5).

Significance. If the formalization is taken as a scoping device rather than a complete theory of cooperation, COOP2 provides a coherent and reasonably clear process-level representation: the grounding bridge and MAEIL stages are sensible, the metric definitions are systematic, and the trace visualizations (Figures 4, 15-18) offer a useful complementary view to final-score evaluation. The experiment design covers 36 settings, which is a real strength. However, the paper's headline contribution, COOP2-Repair, is effectively unvalidated as reported: no controlled experiment, no aggregate numbers, no comparison against the no-repair baseline. Since the abstract and conclusion make a strong empirical claim, this gap is load-bearing. There is also a circularity risk in validating the framework only on two environments designed by the same authors around the same constraint types; the paper should either add an independent environment or explicitly restrict its claims to constraint-satisfaction cooperation.

major comments (4)
  1. [8.2, Table 2, Table 8, Appendix D] The abstract's central claim that COOP2-Repair 'improves task success and constraint satisfaction across two environments and three communication structures' is not supported by any quantitative comparison in the manuscript. Table 2 and Table 8 report only baseline structures; Appendix D contains additional traces, not repair-vs-no-repair results. Figure 5 is a single qualitative centralized GPT-5.4-mini run with no baseline, no repetition, and no aggregate numbers. Please add a controlled comparison (same environments, backbones, communication structures, and team sizes, with and without COOP2-Repair) reporting Score, constraint violations, overhead, and variance, or remove/qualify the abstract and conclusion claims.
  2. [5, Eq. (9)-(10)] COOP2-Repair is under-specified. Eq. (9) defines group plans and Eq. (10) defines the repair channel, but the predicted constraint likelihoods bpc_{g,t} are introduced without any algorithm for computing them from plans. The thresholds tau_c in the definition of predicted failing constraints are never assigned values. The text only says 'lightweight heuristic estimators' and defers a learned predictor to future work. This makes the mechanism non-reproducible and its claimed benefits untestable. Please specify the estimator and threshold choices, and include a sensitivity analysis.
  3. [4/6, Eq. (6), Table 1] The framework defines cooperation as satisfaction of four hand-specified constraints, and both validation environments are authored by the same group with these constraints built into the task design. This is a legitimate scoping choice, but the paper also calls the constraints 'general' and reports results as 'cooperation' broadly. The current validation therefore shows mainly that the framework can diagnose and guide repairs for its own definitions. Please either add a third-party cooperative environment or explicitly restrict claims to 'constraint-satisfaction cooperation' and discuss which cooperative phenomena (e.g., negotiation, information sharing, role emergence) are outside the model.
  4. [7, Table 2] The quantitative analysis relies on five runs per setting, and many standard deviations are very large relative to the means (e.g., 3-agent GPT-5.4 Individual Score 809.0±405.8; 6-agent GPT-5.4-mini Centralized 141.8±89.2). Claims such as 'stronger models progress better' and 'communication is not always cooperation' would benefit from significance testing, effect sizes, or a larger number of runs. At minimum, report confidence intervals rather than only mean±std.
minor comments (3)
  1. [A.3, Eq. (13)] The satisfaction score sc(eg) used in the violation deficit definition is not formally defined. It is referenced to Sec. 6, but the satisfaction score for a combined constraint (e.g., participation AND spatial AND temporal) should be written explicitly.
  2. [Table 2] The table legend says 'bold marks the best value within each agent-model setting,' but the table as rendered contains no bold entries. Please fix the formatting or remove the legend.
  3. [Throughout] Notation is inconsistent: 'COOP2' and 'COOP 2' are used interchangeably, and 'MA-CRAFTER' alternates with 'MA-Crafter'. Please standardize.

Circularity Check

0 steps flagged

No significant circularity; the Repair improvement claim is unsupported but not circular.

full rationale

COOP2's derivation chain is definitional rather than circular: it defines cooperation as constraint satisfaction (Eq. 6; Sec. 4), instantiates four constraints in MA-Crafter and CUBE (Table 1; Eqs. 14-23), and then measures constraint violations via Eq. (13). These are stipulated formal choices, so there is no step where an input is reconstructed as a prediction. The Repair mechanism in Sec. 5 introduces bpc and tau_c without specifying how bpc is computed or what tau_c values are used, so the manuscript does not actually exhibit a prediction that could reduce to a fit. The paper itself says 'COOP2-Repair instantiates this idea with lightweight heuristic estimators. Learning a general constraint-satisfaction predictor is a promising direction and is left for future work.' Section 8.2 contains only a single qualitative trace (Fig. 5) with no baseline or aggregate comparison; the abstract's claim that Repair 'improves task success and constraint satisfaction' is therefore unsubstantiated, but absence of evidence is not circularity. Self-citations to MA-Crafter and CUBE (Yang et al. 2025a,b) establish environments, not a theorem, and no uniqueness result is imported from those papers. No step in the derivation is equal to its own input by construction.

Axiom & Free-Parameter Ledger

5 free parameters · 4 axioms · 3 invented entities

The framework rests on the assumption that cooperation is fully captured by four hand-specified constraint types and that the authors' own environments instantiate them faithfully; all empirical validation is internal to these constructs. The repair-specific parameters (thresholds, estimator) are undeclared, and the episode scores depend on hand-set rewards.

free parameters (5)
  • repair thresholds τc = not reported (τc ∈ [0,1])
    Thresholds below which a constraint is predicted to fail; chosen by hand, values omitted (Sec. 5, after Eq. 9).
  • repair heuristic estimator = unspecified
    The 'lightweight heuristic estimators' for bpc_g,t are not defined; their parameters, if any, are unknown (Sec. 5).
  • task scores r(g) = wood=1, stone=10, coal=25, iron=25, diamond=80
    Hand-set scores define episode reward and therefore all headline Score comparisons (Table 5).
  • participation thresholds and tool prerequisites p(g) = see Table 4 (e.g., coal: 2 agents + wood_pickaxe)
    Hand-designed cooperative requirements per resource; they are the ground-truth constraints the framework measures, so results depend on these choices.
  • wall-clock budget T̂ = not reported in main text
    Episode termination uses min{T, κ(T̂)}; the value of T̂ affects Steps and all timing-sensitive metrics (Sec. 6, reward definition).
axioms (4)
  • domain assumption LLM agents' symbolic plans can be parsed into tool operations and grounded into primitive actions via Γ.
    The whole cognitive–primitive interface (Sec. 3, Eq. 3) assumes a reliable grounding bridge; the paper does not quantify grounding failures.
  • ad hoc to paper Cooperation reduces to satisfaction of the four constraint types (temporal, spatial, participation, dependency).
    This is the paper's definition of cooperation (Sec. 4, Eq. 6); if real cooperation includes negotiation or information sharing not captured by these constraints, the framework measures something narrower.
  • standard math The probability p^c_g,t of satisfying constraint c is well-defined and evolves by Eq. (7).
    The formal model assumes these probabilities are meaningful quantities that can be estimated from traces; no consistency or identifiability argument is given.
  • domain assumption Environment-defined requirements (Table 1, Table 4) are valid instances of the four constraint types.
    The empirical validation treats the hand-injected requirements as ground truth for cooperation.
invented entities (3)
  • COOP2 trace (cognitive–primitive interface with MAEIL stages R/W/X/I) no independent evidence
    purpose: Formal object connecting symbolic cognitive activity to grounded primitive execution for diagnosis
    A modeling construct; its usefulness is demonstrated only on the authors' own environments.
  • Four cooperative constraint types (temporal, spatial, participation, dependency) no independent evidence
    purpose: Define verifiable cooperation requirements and violation metrics
    Novel taxonomy introduced by the paper; not anchored to external or theoretical benchmarks.
  • Repair channel Rg,t no independent evidence
    purpose: Targeted communication/replanning loop to fix predicted constraint failures
    The claimed benefit (improved task success) is not quantified in the paper; only a single illustrative run in Fig. 5.

reviewed 2026-08-02 · how reviews work

0 comments
Cite this review

Pith. "Pith review of COOP$^2$: Defining, Observing, and Repairing Cooperation in LLM Multi-Agent Systems." pith.science (2026). https://pith.science/paper/S47C3MWK

@misc{pith2026260300349,
  author       = {Pith},
  title        = {Pith review of: COOP$^2$: Defining, Observing, and Repairing Cooperation in LLM Multi-Agent Systems},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/S47C3MWK}},
  note         = {Machine review of arXiv:2603.00349}
}
Share X Bluesky LinkedIn Reddit HN
abstract

Many complex tasks require extended effort, diverse capabilities, or coordinated actions beyond what a single agent can provide. However, simply adding more agents does not guarantee better performance, as effective cooperation depends on how agents interact with each other and with task structure to satisfy evolving constraints over time. This challenge is amplified for LLM-based multi-agent systems (LLM-MAS): plans, messages, and revisions occur in natural language, whereas task progress depends on grounded environment actions. Current evaluations mostly treat cooperation as an implicit ingredient of final task success, leaving both cooperation and the effect of multi-agent interaction on task dynamics difficult to study. We introduce COOP$^2$, an evaluation framework that grounds high-level agent cooperation dynamics in LLM-MAS within task progress in the environment. COOP$^2$ then defines cooperative tasks with verifiable cooperative requirements, allowing us to analyze how cooperation unfolds over time with respect to task progress, as well as where and why cooperation breaks down. Building on this framework, we develop COOP$^2$-Repair, which predicts constraint failures from group plans and opens targeted repair channels for guided revisions. Across two environments and three communication structures, COOP$^2$-Repair improves task success and constraint satisfaction while exposing the additional decision overhead and communication load required for repair. The project web page can be found at: https://happyeureka.github.io/coop2.

Figures

Figures reproduced from arXiv: 2603.00349 by Carlee Joe-Wong, Hanqing Yang, Jingdi Chen, Marie Siew, Narjes Nourzad, Shiyu Chen.

Figure 1
Figure 1. Figure 1: Overview of COOP2 . COOP2 models LLM-MAS through a cognitive–primitive interface (top) that couples a symbolic cognitive layer, where agents plan, communicate, interrupt, and replan through the Multi-Agent Environment Interaction Loop (MAEIL, bottom-left), with a primitive layer, where joint actions drive environment states and task-state transitions (sec. 3). Cooperative tasks, instantiated in MA-CRAFTER … view at source ↗
Figure 2
Figure 2. Figure 2: Decoupling cognitive and environment clocks in multi-agent execution. At each cognitive step, an agent maintains an internal state consist￾ing of its current interaction stage and its commit￾ted symbolic plan. Plans may be newly generated (green), resumed (yellow), or terminated (red, indi￾cating success or failure). COOP2 models LLM-based multi-agent sys￾tems through two coupled levels: a cognitive layer … view at source ↗
Figure 3
Figure 3. Figure 3: Two cooperative environment in￾stantiations. CUBE: agents push weighted blocks where heavier blocks require more participants in spatial alignment. MA￾Crafter: agents collect resources whose difficulty scales with prerequisites, tools, and required participants. Agents aim to maximize the total score of completed tasks within a bounded episode, which terminates when either the environment-step budget T or … view at source ↗
Figure 4
Figure 4. Figure 4: COOP2 process traces for MA-Crafter with 3-agent teams, averaged over five runs. Columns compare backbones, and rows compare communication structures. The blue heatmap in each cell shows the number of agents whose active plans target each resource or tool at each environment step. The black curve shows cumulative team score with shaded 95% confidence intervals across runs, and the red dashed line marks the… view at source ↗
Figure 5
Figure 5. Figure 5: COOP2 -Repair process trace for a centralized 3- agent GPT-5.4-mini run. Gold markers indicate steps where COOP2 predicts an upcoming constraint failure and opens a targeted repair round. The top rows show observed satisfac￾tion across the spatial, temporal, and dependency constraints [PITH_FULL_IMAGE:figures/full_fig_p009_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: 10-agent running example. 15 [PITH_FULL_IMAGE:figures/full_fig_p015_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: MA-Crafter can host 100 agents, and more! [PITH_FULL_IMAGE:figures/full_fig_p016_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: CUBE is a grid world where teams push weighted blocks into a goal zone while respecting [PITH_FULL_IMAGE:figures/full_fig_p016_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Timeline visualizations for the three communication structures considered in this work. [PITH_FULL_IMAGE:figures/full_fig_p018_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: MA-Crafter [PITH_FULL_IMAGE:figures/full_fig_p020_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: MA-Crafter Experiment C.1.1 Actions Although the symbolic action set is compact, these actions remain expressive because coordination emerges through agents’ choices of where to move, when to wait, and which block to push, all under embodied constraints from collisions, alignment, and quorum requirements. 20 [PITH_FULL_IMAGE:figures/full_fig_p020_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: CUBE At its base level, CUBE is a grid-world environment built on PettingZoo’s parallel API [Terry et al., 2021] and modified from the multi-agent block￾pushing environment introduced in [Yang et al., 2025b]. Compared to the original formulation, we simplify the environment design while retaining its cooperative block-pushing objective. Agents must cooperate to push square blocks into a designated goal re… view at source ↗
Figure 13
Figure 13. Figure 13: CUBE Experiment C i n (g, t) = capi (t), (19) C i ∆ℓ (g, t) = min g∈Gg ∥§i,t − g∥ ≤ 1, (20) C i ∆t(g, t) = ai,t = push(b(g)), (21) N(C)(g, t) = X i∈I I [PITH_FULL_IMAGE:figures/full_fig_p027_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: Illustration of chains: a chain succeeds or fails depend￾ing on whether available agents meet the block’s weight require￾ment and the maximum force ex￾erted on the block face. Block chain. A block B(g) ∈ B of weight wb(g) may occupy one or more grid cells. When another block lies directly in front of Bj along a push direction d ∈ {↑, ↓, ←,→}, the blocks form a block chain. The chain behaves as a composite… view at source ↗
Figure 15
Figure 15. Figure 15: COOP2 process traces for MA-Crafter with 6-agent teams, averaged over five runs. The traces show active task focus, observed constraint satisfaction at collection attempts, and cumulative team score across backbones and communication structures [PITH_FULL_IMAGE:figures/full_fig_p031_15.png] view at source ↗
Figure 16
Figure 16. Figure 16: COOP2 process traces for MA-Crafter with 3-agent teams, single run. The traces show how agents’ active task focus, constraint satisfaction, and cumulative score evolve over time, providing an additional view of cooperation dynamics in a spatial coordination environment. 31 [PITH_FULL_IMAGE:figures/full_fig_p031_16.png] view at source ↗
Figure 17
Figure 17. Figure 17: COOP2 process traces for CUBE with 3-agent teams, single run. The traces show how agents’ active task focus, constraint satisfaction, and cumulative score evolve over time, providing an additional view of cooperation dynamics in a spatial coordination environment. 32 [PITH_FULL_IMAGE:figures/full_fig_p032_17.png] view at source ↗
Figure 18
Figure 18. Figure 18: COOP2 process traces for CUBE with 6-agent teams, single run. The traces show how agents’ active task focus, constraint satisfaction, and cumulative score evolve over time, providing an additional view of cooperation dynamics in a spatial coordination environment. D.2 Annotated Communications We provide annotated examples of agent communication under different structures and difficulty levels, illustratin… view at source ↗
Figure 19
Figure 19. Figure 19: Communication messages of LLM-MAS operating under centralized communication [PITH_FULL_IMAGE:figures/full_fig_p033_19.png] view at source ↗
Figure 20
Figure 20. Figure 20: COOP2 bridges the cognitive layer, where agents perform reasoning, planning, and communication, and the primitive layer, where plans are grounded into primitive actions. The figure depicts a centralized communication structure under COOP2 : the leader broadcasts messages to workers, interrupting workers’ ongoing execution and triggering replanning or resumption. 34 [PITH_FULL_IMAGE:figures/full_fig_p034_… view at source ↗
Figure 21
Figure 21. Figure 21: Individual communication structure [PITH_FULL_IMAGE:figures/full_fig_p035_21.png] view at source ↗
Figure 22
Figure 22. Figure 22: Chain communication structure. 35 [PITH_FULL_IMAGE:figures/full_fig_p035_22.png] view at source ↗

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Is Inter-Seed Cross-Play Enough? Evaluating the Robustness of Zero-Shot Coordination Algorithms to Implementation Details

    cs.AI 2026-08 conditional novelty 5.0

    For Other-Play in Yokai, agents trained with different implementation details coordinate across implementations about as well as across seeds, supporting inter-seed cross-play as a proxy for cross-implementation evaluation.

Reference graph

Works this paper leans on

11 extracted references · cited by 1 Pith paper

  1. [1]

    Satisfy prerequisites (tools and materials)

  2. [2]

    Each agent observes all previously generated messages

    Chain: During reasoning, each agent broadcasts messages to all other agents. Each agent observes all previously generated messages

  3. [3]

    "" FOLLOWER_ROLE =

    Centralized: For n agents, one agent acts as a leader and the remaining agents are followers. During reasoning, the leader broadcasts a message to all followers and waits for their responses. Each follower waits for the leader’s message, generates a response, and forms a plan. If a follower is interrupted by a new leader message during execution, it decid...

  4. [4]

    Navigate to resources usingnavigate

  5. [5]

    Use thecollectaction once nearby

  6. [6]

    Be cooperative: help others when needed and ask for help when necessary

  7. [7]

    Your collaborators should not include yourself

  8. [8]

    Work with no more agents than needed. C.3 CUBE Figure 12: CUBE At its base level, CUBE is a grid-world environment built on PettingZoo’s parallel API [Terry et al., 2021] and modified from the multi-agent block- pushing environment introduced in [Yang et al., 2025b]. Compared to the original formulation, we simplify the environment design while retaining ...

    2021

  9. [9]

    AllWagents must issue push/move commandsINTOthe block simultaneously

  10. [10]

    If successful, both the block and pushing agents move forward

  11. [11]

    up”, “down

    Block is delivered when any part reaches the goal column Actions move(direction, num_steps): Move in a direction fornum_steps. –direction: “up”, “down”, “left”, “right” –num_steps: 1–10 push(block_id, num_steps): Push a block by moving into it. –block_id: ID of the target block –num_steps: How many steps to push – Direction is automatic based on agent pos...

This paper was first reviewed by deepseek-v4-flash on August 2, 2026.