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arxiv: 2605.01783 · v1 · submitted 2026-05-03 · 💻 cs.AI

Runtime Evaluation of Procedural Content Generation in an Endless Runner Game Using Autonomous Agents

Rishabh Kar This is my paper

Pith reviewed 2026-05-10 15:20 UTC · model grok-4.3

classification 💻 cs.AI
keywords procedural content generationruntime evaluationautonomous agentsendless runnerplayabilitywave function collapsegame development
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The pith

Autonomous agents can evaluate and validate procedurally generated content in real time within an endless runner game.

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

This paper shows that procedural content generation and its evaluation can be combined into a single runtime loop in a game called Momentum. Instead of checking generated levels separately after creation, two autonomous agents—an aerial scanner and a ground-traversal agent—move ahead of the player to inspect the upcoming terrain using ray casting and physics sweeps. This approach aims to catch issues like blocked paths or unbalanced content before they affect gameplay. The work also develops a framework to measure the generated content along axes of playability, diversity, controllability, and performance, while deriving bounds on the generation process itself.

Core claim

The central discovery is that by integrating terrain generation, object spawning with constraint mechanisms like Wave Function Collapse, and agent-based evaluation into one gameplay loop, problematic scenarios can be identified and reported at runtime, unifying what are usually separate offline processes.

What carries the argument

The pair of autonomous evaluation agents—an aerial scanner for geometric inspection and a ground-traversal agent for navigational validation—using ray casting, volumetric sweeps, and obstacle filtering to check the path ahead.

Load-bearing premise

The agents using ray casting, volumetric sweeps, and obstacle filtering will detect all problematic generated scenarios before the player encounters them.

What would settle it

A test run where the player encounters an unplayable or blocked section of generated terrain despite the agents having scanned ahead would falsify the claim that the evaluation pipeline reliably prevents bad content from reaching the player.

Figures

Figures reproduced from arXiv: 2605.01783 by Rishabh Kar.

Figure 1
Figure 1. Figure 1: High-level architecture of the Momentum system, organised into the UI layer, evaluation layer, character physics, agent system, and environment. The concept is intentionally minimal so that the focus of the work remains on procedural generation and runtime evaluation. The player can move laterally and jump, while the ground and objects are generated ahead of the player. This makes the game a suitable test … view at source ↗
Figure 2
Figure 2. Figure 2: Momentum during gameplay: the player advances along the procedurally streamed terrain while the aerial evaluation agent inspects the corridor ahead. The on-screen overlay exposes the runtime sliders for forward speed, side speed, and spawn density. 4.3.1 Fields and Inspector Exposed Constants The [SerializeField] variables (script fields exposed to the Unity editor while remaining private to other code) ar… view at source ↗
read the original abstract

Procedural Content Generation (PCG) enables game content to be created algorithmically without direct manual level-design effort, but it introduces a serious evaluation problem: generated content may become unbalanced, blocked, repetitive, or technically unsolvable. This paper presents Momentum, an endless-runner game that integrates runtime terrain generation, environment object spawning, and autonomous agent-based evaluation into a single gameplay loop. Ground tiles and environmental objects are generated dynamically as the player advances, object placement follows a constraint-driven mechanism inspired by Wave Function Collapse (WFC), and the runtime navigation surface is rebuilt asynchronously to remain consistent with the streamed environment. Two autonomous evaluation agents move ahead of the player and inspect the generated path: an aerial scanner that examines the corridor geometrically, and a ground-traversal agent that validates the same region from a navigational perspective. The evaluation pipeline combines ray casting, volumetric physics sweeps, obstacle-layer filtering, and structured crash reporting to identify problematic generated scenarios before they reach the player. The work demonstrates how generation and validation can be unified within the same runtime loop, rather than treating evaluation as a separate offline pass. Around this implementation, the paper formulates a measurable evaluation framework along the canonical PCG axes of playability, diversity, controllability, and runtime performance, derives a structural saturation bound on the spawner from its own placement constraints, and quantifies the per-segment scanning cost of the agents from first principles.

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 / 2 minor

Summary. The manuscript presents Momentum, an endless-runner game that unifies procedural content generation (PCG) of terrain and objects with runtime evaluation using two autonomous agents: an aerial scanner employing ray casting and volumetric sweeps, and a ground-traversal agent performing navigation surface checks and obstacle filtering. The generation uses constraint-driven mechanisms inspired by Wave Function Collapse, with asynchronous navigation surface rebuilding. The paper claims this integration allows detection of problematic content (unbalanced, blocked, repetitive, unsolvable) before it reaches the player, and around the implementation formulates an evaluation framework on playability, diversity, controllability, and runtime performance, derives a structural saturation bound from the spawner's constraints, and quantifies per-segment scanning costs from first principles.

Significance. If the agents provide complete coverage of failure modes, this approach would represent a significant advance in runtime PCG validation for games, moving evaluation from offline to integrated runtime processes. The first-principles cost quantification and saturation bound offer measurable insights, though the absence of empirical validation limits the immediate impact. The work addresses a key challenge in PCG by attempting to ensure playability dynamically.

major comments (3)
  1. [§4 (Structural Saturation Bound)] §4 (Structural Saturation Bound): the bound is derived directly from the spawner's own placement constraints, rendering it partially self-referential and limiting its utility as an independent measure of generation capacity.
  2. [§5 (Evaluation Pipeline)] §5 (Evaluation Pipeline): the description of the aerial scanner and ground-traversal agents assumes complete detection of all problematic scenarios (blocked paths, unsolvable jumps, repetitive segments, balance issues) without providing a coverage argument, false-negative analysis, or results from exhaustive test cases to support this assumption.
  3. [§6 (Results and Evaluation Framework)] §6 (Results and Evaluation Framework): no quantitative results, error analysis, or performance benchmarks are reported for the agent-based evaluations or the overall system, despite the formulation of a measurable framework; this leaves the claims about runtime performance and the unification benefit unverified.
minor comments (2)
  1. The abstract and introduction could more clearly distinguish between the implemented system and the proposed evaluation framework.
  2. [Notation] Ensure consistent use of terms like 'saturation bound' and 'per-segment cost' across sections.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their constructive and detailed feedback on the manuscript. We address each major comment point by point below, indicating the revisions we will make to strengthen the paper while remaining faithful to the presented work.

read point-by-point responses

  1. Referee: [§4 (Structural Saturation Bound)] the bound is derived directly from the spawner's own placement constraints, rendering it partially self-referential and limiting its utility as an independent measure of generation capacity.

    Authors: We agree that the structural saturation bound is derived from the spawner's placement constraints and is therefore system-specific rather than a fully independent general metric. Its value lies in providing a theoretical upper limit on achievable diversity under the exact generation rules used, which helps characterize the capacity of this particular PCG approach. We will revise §4 to explicitly acknowledge the self-referential aspect, clarify the bound's intended role as a system-specific analysis, and discuss its limitations as an independent measure. revision: yes

  2. Referee: [§5 (Evaluation Pipeline)] the description of the aerial scanner and ground-traversal agents assumes complete detection of all problematic scenarios (blocked paths, unsolvable jumps, repetitive segments, balance issues) without providing a coverage argument, false-negative analysis, or results from exhaustive test cases to support this assumption.

    Authors: The agents target specific, common failure modes via geometric ray casting, volumetric sweeps, navigation surface checks, and obstacle filtering. We do not claim exhaustive coverage of every conceivable scenario. We will revise §5 to add an explicit discussion of the failure modes addressed, a coverage argument based on the implemented checks, and an analysis of potential false negatives, while noting that comprehensive exhaustive testing is left for future work. revision: partial

  3. Referee: [§6 (Results and Evaluation Framework)] no quantitative results, error analysis, or performance benchmarks are reported for the agent-based evaluations or the overall system, despite the formulation of a measurable framework; this leaves the claims about runtime performance and the unification benefit unverified.

    Authors: The manuscript centers on the design of the unified runtime loop, the formulation of the evaluation framework, and first-principles derivations of the saturation bound and scanning costs. The implementation serves to demonstrate feasibility rather than to supply full empirical benchmarks. We acknowledge that quantitative results would strengthen the claims and will incorporate performance measurements, runtime cost data, and basic error analysis from the agent pipeline in the revised manuscript. revision: yes

Circularity Check

1 steps flagged

Structural saturation bound reduces to spawner's own placement constraints by construction

specific steps
  1. self definitional [Abstract]
    "Around this implementation, the paper formulates a measurable evaluation framework along the canonical PCG axes of playability, diversity, controllability, and runtime performance, derives a structural saturation bound on the spawner from its own placement constraints, and quantifies the per-segment scanning cost of the agents from first principles."

    The saturation bound is stated to be derived directly from the spawner's placement constraints; therefore the reported bound is a calculable consequence of those same constraints rather than an independent prediction or external validation of the evaluation pipeline.

full rationale

The paper's core demonstration of unifying PCG generation and agent-based validation inside a single runtime loop stands as an independent engineering contribution and does not reduce to its inputs. The only load-bearing step that exhibits self-reference is the derivation of the structural saturation bound, which the abstract explicitly ties to the spawner's placement constraints. No other equations, uniqueness theorems, or self-citations are shown to collapse the central claims. The per-segment cost quantification is presented as first-principles and does not trigger the circularity criteria. This yields a moderate score reflecting one localized self-definitional element without compromising the overall framework.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 2 invented entities

Only the abstract is available, so the ledger is necessarily incomplete. The paper relies on standard game-engine assumptions and introduces two new agent roles without external validation of their completeness.

axioms (2)
  • domain assumption The underlying game engine supports consistent asynchronous rebuilding of the navigation surface in sync with streamed terrain.
    Invoked to maintain consistency between generation and agent evaluation.
  • domain assumption Constraint-driven object placement (WFC-inspired) can be executed at runtime without violating real-time performance.
    Required for the spawner to operate inside the gameplay loop.
invented entities (2)
  • Aerial scanner agent no independent evidence
    purpose: Geometric examination of the generated corridor via ray casting and volumetric sweeps.
    New role introduced specifically for pre-player validation.
  • Ground-traversal agent no independent evidence
    purpose: Navigational validation of the same region from a ground-level perspective.
    New role introduced specifically for pre-player validation.

pith-pipeline@v0.9.0 · 5544 in / 1492 out tokens · 46462 ms · 2026-05-10T15:20:09.436845+00:00 · methodology

discussion (0)

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Reference graph

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