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arxiv: 2604.16022 · v1 · submitted 2026-04-17 · 💻 cs.AI · cs.LG· cs.MA

Recognition: unknown

SocialGrid: A Benchmark for Planning and Social Reasoning in Embodied Multi-Agent Systems

Hikaru Shindo , Hanzhao Lin , Lukas Helff , Patrick Schramowski , Kristian Kersting
Authors on Pith no claims yet

Pith reviewed 2026-05-10 08:41 UTC · model grok-4.3

classification 💻 cs.AI cs.LGcs.MA
keywords LLM agentsmulti-agent systemssocial reasoningembodied AIplanningdeception detectionbenchmarkAmong Us
0
0 comments X

The pith

LLM agents achieve under 60 percent task accuracy and near-random deception detection in embodied multi-agent settings.

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

The paper introduces SocialGrid, an embodied multi-agent environment modeled on Among Us, to measure how well LLM agents handle planning, navigation, task execution, and social reasoning together. Even the largest open models complete tasks below 60 percent accuracy, often looping or failing basic movement, while deception detection stays at chance levels regardless of scale. An optional Planning Oracle separates navigation help from social evaluation, showing that assistance raises completion rates but leaves social reasoning as the unchanged bottleneck because agents use shallow cues instead of building evidence from behavior over time. This evaluation setup matters for turning LLMs into autonomous agents that must cooperate or compete in shared physical spaces.

Core claim

SocialGrid reveals that LLM agents show persistent shortfalls in both planning and social reasoning inside an embodied multi-agent environment, where task completion stays below 60 percent and deception detection remains near random chance even when navigation is assisted by a Planning Oracle, indicating reliance on superficial heuristics rather than accumulated behavioral evidence.

What carries the argument

SocialGrid, an embodied multi-agent environment inspired by Among Us that supplies an optional Planning Oracle to isolate social reasoning evaluation from planning and navigation deficits.

If this is right

  • Task completion stays low because agents enter repetitive loops or cannot handle basic obstacles in shared spaces.
  • Deception detection remains near random chance across all tested model scales, showing social reasoning does not improve with size alone.
  • Planning assistance raises overall completion rates but leaves social reasoning performance unchanged.
  • Automatic failure analysis and fine-grained metrics allow developers to pinpoint exact weaknesses in navigation versus social inference.
  • Elo-rated leaderboards from adversarial league play create a standardized competitive ranking for agent comparisons.

Where Pith is reading between the lines

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

  • Future agent systems may need dedicated components for tracking other agents' action histories and intentions rather than depending on single-turn heuristics.
  • The benchmark can serve as a testbed for training methods that jointly optimize embodied planning and social inference instead of treating them separately.
  • Results imply that purely text-based social evaluations may miss limitations that appear only when agents must act under physical constraints and real-time interactions.
  • The diagnostic tools in SocialGrid could guide creation of targeted training data focused on behavioral evidence accumulation in multi-agent settings.

Load-bearing premise

The Among Us-inspired environment together with the optional Planning Oracle isolates social reasoning deficits from planning and navigation problems without creating new behavioral confounds or task-specific biases.

What would settle it

A model that reliably detects deception at well above chance levels across varied scenarios in SocialGrid, even without the Planning Oracle, would falsify the claim of a persistent social reasoning bottleneck.

Figures

Figures reproduced from arXiv: 2604.16022 by Hanzhao Lin, Hikaru Shindo, Kristian Kersting, Lukas Helff, Patrick Schramowski.

Figure 1
Figure 1. Figure 1: SocialGrid Overview. Inspired by Among Us, SocialGrid is a controllable, embodied benchmark evaluating LLM agents in multi-agent, multi-objective environments. (Left) User Input: Enables systematic control of environmental complexity (e.g., map area, room count) and agent configuration. (Center) Environment: Agents operate under physical constraints; an optional Planning Oracle isolates social reasoning fr… view at source ↗
Figure 2
Figure 2. Figure 2: LLM agents struggle with spatial navigation in embodied settings. Comparison of crewmate performance on SocialGrid un￾der low (no assistance) and high (with planning oracle) conditions. 7 crewmates per episode; 20 episodes per model; error bars show SD. Green values indicate absolute improvement from low to high. Left: Task performance (completion rate). Middle: Planning success rate (tasks reached). Right… view at source ↗
Figure 4
Figure 4. Figure 4: Trust calibration hovers near random baseline. Trust metrics for crewmate models facing GPT-OSS-120B impostor. Left: Brier Score measures how well trust predictions match ground truth (lower is better); most models hover near the ran￾dom baseline (0.33, dashed). Right: Volatility measures how erratically trust changes between turns (lower is better); values around 0.33 indicate unstable assessments. 4 6 9 … view at source ↗
Figure 3
Figure 3. Figure 3: Detection accuracy reveals below-random perfor￾mance across all models. Heatmap showing crewmate detection accuracy across 36 matchups (30 cross-model league + 6 self-play diagonal). All models perform near or below the random baseline (33%, shown in colorbar), averaging 29.9% detection accuracy. The consistent near-chance performance indicates that impostor detection remains challenging regardless of mode… view at source ↗
Figure 6
Figure 6. Figure 6: Failure analysis reveals model-specific patterns. Each radar chart shows the distribution of six failure modes for a given model, normalized to the global maximum across all models. The percentage under each model name indicates total failure coverage (sum of all failure mode fractions). Failure mode abbreviations: D.S. = Door Spam (repeatedly toggling doors), P.P. = Position Ping-Pong (oscillating between… view at source ↗
Figure 7
Figure 7. Figure 7: RL training progression. Task Performance and Plan￾ning Performance of Qwen3-4B across training steps. RL training does not yield significant improvements in either condition, with or without the planning assistant. and MultiAgentBench (Zhu et al., 2025) evaluate collab￾oration at scale (Cui et al., 2025; Wang et al., 2025a). In contrast, SocialGrid targets adversarial, partially observable settings where … view at source ↗
Figure 8
Figure 8. Figure 8: SocialGrid Environment. The main game view showing the grid-based environment with multiple rooms connected by doors. Agents are represented as colored circles. The red block indicates a dead body. Task locations are marked throughout the map, and the agent’s limited field of view creates partial observability. Trust Score Dynamics ( [PITH_FULL_IMAGE:figures/full_fig_p013_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Trust Score Evolution. A temporal visualization showing how each agent’s trust beliefs evolve over an episode. Each line tracks one agent’s trust score toward other players over time, revealing patterns such as gradual suspicion accumulation, sudden trust drops after suspicious behavior, and the divergence between crewmate and impostor trust dynamics [PITH_FULL_IMAGE:figures/full_fig_p014_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Voting Phase. The voting interface is constantly activated by the environment. Agents submit natural language statements defending themselves or accusing others, followed by votes to eliminate a suspected impostor. The interface displays each agent’s statement, vote target, and the final tally. B. Prompting Strategy Movement System Prompt. The system prompt for crewmate movement is structured to provide c… view at source ↗
Figure 11
Figure 11. Figure 11: Effect of Planning Assistant on Failure Modes. Per-model change in failure percentage when the planning assistant is enabled. Positive values (green) indicate failures reduced by the assistant; negative values (red) indicate failures that increase. The assistant dramatically reduces passive failures (NOOP deadlock, task fixation) across most models, while active navigation failures show model-dependent ef… view at source ↗
Figure 12
Figure 12. Figure 12: Performance vs. environmental complexity. Top row: performance vs. map area (fixed 2×2 layout). Bottom row: per￾formance vs. number of rooms (fixed 10×10 room size). Columns show Task Performance (TP), Planning Performance (PP), Voting Accuracy, and Trust Brier Score (BS). Error bars indicate standard error across episodes. models specifically designed for complex inference tasks: DeepSeek-R1-70B and Phi4… view at source ↗
Figure 13
Figure 13. Figure 13: Head-to-Head Win Rate Heatmaps. Left: Overall win rate—each cell shows the win rate of the row model against the column model on the 10×10 2x2 pattern. Darker colors indicate higher win rates. The diagonal represents self-play and is set to 0.5. Right: Impostor win rate—complementary view showing impostor win rates for the same matchups. Colors range from gray (low) to red (high). The predominantly red co… view at source ↗
Figure 14
Figure 14. Figure 14: Impostors gained advantage by the navigation assistant. Comparison of winning score transition on SocialGrid . Terminal-filled variant (to reduce survivorship bias). After an episode ends at time τ , we fill the remaining trajectory with the terminal outcome: p˜crew(t) =    pcrew(t), t ≤ τ, 1, t > τ and crewmates win, 0, t > τ and impostors win. (13) The plotted curve is the mean of pcrew(t) (or p˜cr… view at source ↗
read the original abstract

As Large Language Models (LLMs) transition from text processors to autonomous agents, evaluating their social reasoning in embodied multi-agent settings becomes critical. We introduce SocialGrid, an embodied multi-agent environment inspired by Among Us that evaluates LLM agents on planning, task execution, and social reasoning. Our evaluations reveal that even the strongest open model (GPT-OSS-120B) achieves below 60% accuracy in task completion and planning, with agents getting stuck in repetitive behaviors or failing to navigate basic obstacles. Since poor navigation confounds evaluation of social intelligence, SocialGrid offers an optional Planning Oracle to isolate social reasoning from planning deficits. While planning assistance improves task completion, social reasoning remains a bottleneck: agents fail to detect deception at near-random chance regardless of scale, relying on shallow heuristics rather than accumulating behavioral evidence. SocialGrid provides automatic failure analysis and fine-grained metrics, enabling developers to diagnose and improve their agents. We also establish a competitive leaderboard using Elo ratings from adversarial league play.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 3 minor

Summary. The paper introduces SocialGrid, an embodied multi-agent benchmark environment inspired by Among Us, to evaluate LLM agents on planning, task execution, and social reasoning (including deception detection). It reports that even the strongest open model (GPT-OSS-120B) achieves below 60% accuracy in task completion and planning, with agents exhibiting repetitive behaviors and navigation failures. An optional Planning Oracle is provided to isolate social reasoning from planning deficits; while this improves task completion, deception detection remains near random chance across model scales. The work includes automatic failure analysis, fine-grained metrics, and an Elo-rated leaderboard from adversarial league play.

Significance. If the benchmark design and oracle successfully isolate social reasoning without introducing new confounds, the results would be significant for demonstrating persistent limitations in LLM agents' embodied social intelligence and for supplying a diagnostic platform with automatic analysis and a competitive leaderboard. The provision of reproducible metrics and adversarial evaluation setup are notable strengths that could support targeted agent improvements.

major comments (3)
  1. [§3 (Planning Oracle)] §3 (Planning Oracle): The headline finding that social reasoning is the bottleneck (deception detection near random even with oracle assistance) depends on the oracle cleanly removing planning/navigation confounds. No ablations on oracle variants, no controls for path-dependent observation effects (e.g., how oracle paths alter encounters with impostor behaviors), and no non-oracle baselines on purely social subtasks are reported, leaving open whether low performance reflects genuine social deficits or interactions with the environment's information structure.
  2. [§5 (Experiments and Results)] §5 (Experiments and Results): Specific performance claims (e.g., <60% task completion accuracy, near-random deception detection) are presented without details on number of trials per condition, run-to-run variance, statistical significance testing, or precise metric definitions, which prevents independent verification of the central empirical claims.
  3. [§2 (Environment Design)] §2 (Environment Design): The Among Us-inspired grid setup is described at a high level, but the paper does not analyze or control for potential task-specific biases, such as how limited visibility or grid navigation mechanics might systematically affect the availability of deception cues independent of agent reasoning.
minor comments (3)
  1. [Abstract and §4] Abstract and §4: The description of 'automatic failure analysis' would benefit from a concrete example or pseudocode in the main text to illustrate how failure modes are categorized.
  2. [Related Work] Related Work: Additional citations to prior embodied multi-agent benchmarks (e.g., extensions of AI2-THOR or other social simulation environments) would better situate the novelty of SocialGrid.
  3. [Figures] Figure captions: Some figures showing agent trajectories or failure cases lack scale bars or explicit legend explanations for the grid environment.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the thoughtful and constructive comments. We address each major point below, agreeing where revisions are needed to improve clarity and rigor, and providing our reasoning on the benchmark design.

read point-by-point responses

  1. Referee: [§3 (Planning Oracle)] §3 (Planning Oracle): The headline finding that social reasoning is the bottleneck (deception detection near random even with oracle assistance) depends on the oracle cleanly removing planning/navigation confounds. No ablations on oracle variants, no controls for path-dependent observation effects (e.g., how oracle paths alter encounters with impostor behaviors), and no non-oracle baselines on purely social subtasks are reported, leaving open whether low performance reflects genuine social deficits or interactions with the environment's information structure.

    Authors: We agree that further validation of the oracle would strengthen the isolation claim. In revision, we will add ablations comparing perfect oracle, noisy oracle, and no-oracle conditions, along with analysis of observation histories to control for path-dependent effects. We will also introduce non-oracle baselines by evaluating agents on isolated social subtasks (e.g., deception detection from fixed observation logs without navigation). These additions will help rule out confounds while preserving the current finding that social reasoning remains near chance even with planning assistance. revision: yes

  2. Referee: [§5 (Experiments and Results)] §5 (Experiments and Results): Specific performance claims (e.g., <60% task completion accuracy, near-random deception detection) are presented without details on number of trials per condition, run-to-run variance, statistical significance testing, or precise metric definitions, which prevents independent verification of the central empirical claims.

    Authors: We acknowledge the need for greater transparency on experimental details. The revised manuscript will specify the number of trials (50 episodes per model per condition), report means with standard deviations across runs, include statistical significance tests (e.g., paired t-tests), and provide explicit definitions for all metrics including task completion accuracy and deception detection rate. This will enable full independent verification of the reported results. revision: yes

  3. Referee: [§2 (Environment Design)] §2 (Environment Design): The Among Us-inspired grid setup is described at a high level, but the paper does not analyze or control for potential task-specific biases, such as how limited visibility or grid navigation mechanics might systematically affect the availability of deception cues independent of agent reasoning.

    Authors: The grid and visibility mechanics are core to creating embodied social scenarios analogous to Among Us. We will add a dedicated subsection in §2 that explicitly discusses these potential biases, explains the randomization of starting positions and impostor behaviors used to mitigate systematic effects, and analyzes how visibility constraints influence cue availability. This analysis will clarify that the benchmark intentionally tests integrated planning and social reasoning rather than isolating them artificially. revision: yes

Circularity Check

0 steps flagged

No circularity: purely empirical benchmark with no derivations or fitted predictions

full rationale

The paper introduces SocialGrid as a new embodied multi-agent benchmark inspired by Among Us and reports empirical performance of LLM agents on task completion, planning, and social reasoning tasks. No mathematical derivation chain, equations, or first-principles results are claimed. Results rest on direct observation of agent behaviors in the environment, with the Planning Oracle presented as an optional experimental control rather than a fitted or self-referential component. No self-citations, ansatzes, or renamings of known results appear as load-bearing steps. The work is self-contained as an empirical evaluation.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The contribution is the creation and initial use of a new benchmark environment rather than any derivation from axioms or fitting of parameters; no free parameters, axioms, or invented entities are described.

pith-pipeline@v0.9.0 · 5485 in / 1112 out tokens · 27335 ms · 2026-05-10T08:41:38.949390+00:00 · methodology

discussion (0)

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Reference graph

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