When Stories Evolve: Benchmarking LLM Storytelling Across Agent Architectures in Open-Ended World Simulations

Yuqi Chen; Sixuan Li; Yunfeng Cai; Xueai Li; Ka Man Yan; Ying Li

cs.CLAug 16, 2026

When Stories Evolve: Benchmarking LLM Storytelling Across Agent Architectures in Open-Ended World Simulations

Authors: Yuqi Chen, Sixuan Li, Yunfeng Cai, Xueai Li, Ka Man Yan, Ying Li

Organizations: The University of Hong Kong · Peking University · Beijing Institute of Mathematical Sciences and Applications (BIMSA) · Tsinghua University

Abstract

Large language models can write fluent stories, but open-ended storytelling requires more than local fluency. In evolving world simulations and AI-native games, models must preserve facts, relationships, causal dependencies, and character states as the world changes. We introduce WSE-bench, a process benchmark that separately evaluates sustained generation, canonical coherence, and meaningful development in dynamic LLM storytelling. Generation Coverage records the proportion of planned narrative steps produced; Consistency tracks when canon breaks; and Richness measures how meaningfully branching, player-shaped trajectories develop. Across frontier models, Consistency and Richness do not form a smooth trade-off: their empirical Pareto frontier is non-concave, with several non-dominated intermediate configurations that no positive linear weighting can select. Added structure can enrich trajectories, but it does not uniformly improve coherence and may shorten them. Model scale chiefly improves sustained generation, without producing reliable gains in canonical coherence or meaningful development. These results show that sustained generation, canonical coherence, and meaningful development are distinct and sometimes competing capacities. WSE-bench makes those dynamics visible by extending narrative evaluation from finished stories to the processes that create them.

Figures & tables

Figure 2: WSE-bench combines standardized episode construction, recurrent generation loop, and systematic comparison of agent architectures. Each episode contains one player-controlled character and four non-player characters. At each step, $B_{1}$ lets the Story Agent decide consequences directly, $B_{2}$ uses one Character Agent to propose for all non-player characters, and $B_{3}$ uses one isolated Character Agent per non-player character; the Story Agent remains the sole narrator. WMOD optionally organizes proposed actions into a pre-narration PlotPlan of at most 12 beats, whereas WMEM optionally records a structured post-narration world-state update. Crossing the three bases with the two binary modules yields 12 architecture conditions.

Figure 3: Model-by-architecture point estimates for the Qwen and Frontier panels. Columns index models, and rows index the 12 architecture conditions. The three panels show Generation Coverage $G$ , Consistency $C_{\mathrm{obs}}$ , and Richness $R$ ; the dashed rule separates the two analysis groups. Paired architecture contrasts below provide uncertainty intervals.

Figure 4: Consistency–Richness point estimates based on

C_{\mathrm{adj}}

, which sets the survival contribution to zero after a cell loses observational support. Shapes denote

B_{1}

(Story Agent only),

B_{2}

(one centralized Character Agent), and

B_{3}

(distributed Character Agents); white overlays mark WMEM, WMOD, or both. The upper-right boundary identifies non-dominated configurations, and supported configurations lie on its upper concave envelope.

Contrast	$\Delta G$	$\Delta C_{obs}$	$\Delta C_{adj}$	$\Delta R$
Qwen series
B2 – B1	+0.8 [-1.0, +2.5]	-9.2 [-14.5, -3.2]	-3.4 [-8.3, +3.3]	+4.0 [+2.6, +6.0]
B3 – B1	-1.1 [-3.0, +0.9]	-12.0 [-17.7, -5.4]	-8.4 [-10.5, -1.7]	+7.0 [+4.4, +9.0]
B3 – B2	-1.9 [-3.5, -0.3]	-2.8 [-8.5, +3.6]	-5.0 [-7.7, +0.4]	+3.0 [+0.2, +4.8]
WMEM on – off	-18.0 [-19.8, -16.1]	-5.6 [-10.2, -0.8]	-6.0 [-8.8, -0.6]	-1.3 [-2.5, +0.1]
WMOD on – off	-13.7 [-15.6, -11.7]	+4.7 [-1.0, +11.9]	-22.9 [-25.7, -16.0]	-5.9 [-7.2, -4.2]

Table 1: Architecture effects on the four reported outcomes. Each contrast averages over the other architecture factors. Values are mean differences across the evaluated models, weighted equally, with 95% hierarchical cluster-bootstrap intervals.

Figure 5: Per-model Consistency

C_{\mathrm{obs}}

across the three world families, with each model pooled over 12 architectures. Markers and whiskers show model-specific means and 95% hierarchical cluster-bootstrap intervals. The plotted profiles show within-model source-family variation; aggregate source-family contrasts give each of the 12 evaluated models equal weight.

Appendix figures & tables16 assets

Supplementary material from the paper’s appendix.

Appendix

Source	Initial world	Language	Opening snapshot	Seeds
ITP	Bianjing, 1100 CE	Chinese	Bianjing at the opening state in the year 1100 CE.	14
Smallville	25-agent Smallville	English	Base simulation at midnight on February 13, 2023.	14
DraCor	A Doll’s House	Dano–Norwegian	Act I, after the nurse brings the children upstairs.	2
DraCor	Uncle Vanya	Russian	Act I, just after Serebryakov, Yelena, and Sonya return from their walk.	2
DraCor	Three Sisters	Russian	Act I, after Andrei joins the guests at Irina’s name-day gathering.	2
DraCor	The Cherry Orchard	Russian	Act I, immediately after the family’s arrival in the nursery.	2

Appendix

Table 2: Initial worlds used to construct the 42 EpisodeSeed records. The snapshot identifies the shared canonical starting point, not a plot that generated trajectories are expected to follow.

Figure 7: Comparison of the two Consistency summaries across all 144 model–architecture cells. Panel (a) compares $C_{\mathrm{obs}}$ with $C_{\mathrm{adj}}$ . Panel (b) shows where they differ across architectures, and panel (c) relates that difference to Generation Coverage $G$ . The difference is concentrated in lower-coverage Qwen cells and changes no observed judgment or event location.

Figure 8: Human validation of the Judges. (a) Design-weighted Consistency classification on 120 trajectories. (b) One-vs-rest observed agreement across the same sample; the pairwise series average three comparisons. (c–d) Richness agreement and error on a separate 36-trajectory sample.

Figure 9: Horizon sensitivity for the two six-model groups. Panels (a–b) show equal-model mean generation and contradiction-free survival. Panel (c) reports the timing distribution of first contradictions observed by step 20. Panel (d) recomputes the normalized summaries at

h=5,10,15,20

Generation failures
Quantity	Qwen	Frontier
Centralized Character Agent failures	1.1%	5.4%
Distributed Character Agent failures	1.9%	12.8%
World Model failures	33.6%	6.9%
Story Agent failures	24.4%	4.8%
World Memory Agent failures	42.7%	24.8%

Appendix

Table 3: Two complementary error-propagation audits. Generation-failure rates use trajectories in which the component is enabled. Precursor rates use majority-inconsistent trajectories and report the plurality origin of conservative lexical matches to upstream content. These matches identify possible precursors, not causal effects or errors repaired downstream.

Figure 10: Architecture contrasts among paired conditions in which both trajectories reach the full horizon. Bars average the six model-specific estimates in each group. Positive values favor the first-named base or module-on condition. This analysis complements the primary analysis, which includes each trajectory up to its observed length.

Figure 11: Generation cost and coverage under four architecture contrasts. Bars average within-model percentage changes across the six models in each group. Positive values favor the first-named base or module-on condition. B3 invokes four isolated Character Agents per active step, and lower coverage can reduce later calls and tokens.

Figure 12: Generation outcomes by model across all 12 architectures. Each bar represents 504 planned trajectories. Full horizon indicates 20 completed narrative steps. Story ending denotes an accepted ending, such as the player’s death, permanent incapacity, or a genuinely closed story. Only four occur. Output-format and other technical failures reduce Generation Coverage but are not story outcomes.

Figure 13: Finite-horizon survival curves for the Qwen series. Panel (a) shows the probability of generating through step

t

. Panel (b) shows contradiction-free survival, with trajectories that stop for technical reasons right-censored. Dotted tails have fewer than 30 trajectories at risk. Curves end at the experimental horizon

H=20

, and their integrals yield the summaries in Figure 14 . Qwen3.5-397B-A17B is shown separately as an MoE model.

Figure 14: Qwen-series scaling under unequal trajectory coverage. Dense-model fits use log parameter count for Qwen2.5 3B–72B. Qwen3.5-397B-A17B is shown separately as an MoE model. Open markers have fewer than 30 trajectories at risk at step 20.

First-violation reason	Qwen: $n$ (%) / step	Frontier: $n$ (%) / step
Temporal conflict	131 (25.8%) / 8.0	144 (31.0%) / 11.0
World-fact conflict	52 (10.3%) / 10.0	88 (18.9%) / 13.0
Character-state conflict	70 (13.8%) / 7.0	38 (8.2%) / 9.0
Identity conflict	42 (8.3%) / 2.0	23 (4.9%) / 7.0
Relationship conflict	34 (6.7%) / 2.0	6 (1.3%) / 4.0
Knowledge conflict	2 (0.4%) / 10.0	4 (0.9%) / 11.5

Appendix

Table 5: Adjudicated reasons for the first inconsistency, excluding generation failures. A category requires agreement from at least two inconsistent Judges. Percentages use all majority-inconsistent trajectories in each model panel. The value after each slash is the median first contradictory step.

Agreed reason	Initial World	Earlier Narrative	Current Narrative	Unresolved tied	Total
Temporal	21	82	17	155	275
World fact	2	63	2	73	140
Character state	5	54	9	40	108
Identity	48	0	1	16	65
Relationship	39	0	0	1	40
Knowledge	0	3	1	2	6

Appendix

Table 6: Candidate source of the canonical commitment violated at the first inconsistent step, pooled across both model panels. Sources are localized from the Judges’ stated prior commitment. Ambiguous and tied cases are grouped as unresolved. The table identifies where a commitment was recorded, not which agent caused the conflict.

Figure 15: Leave-one-DraCor-play-out sensitivity for the 12-model

C_{\mathrm{obs}}

contrasts. Each point compares DraCor with a simulation-native control after omitting one play. All DraCor–Smallville contrasts remain positive; one DraCor–ITP contrast is slightly below zero (

-0.27

points).

Consistency: estimate [95% bootstrap CI]
Measure	ITP	DraCor	Smallville
$C_{\mathrm{obs}}$	82.1 [78.4, 86.1]	83.5 [79.1, 87.9]	78.1 [75.7, 84.0]
$C_{\mathrm{adj}}$	80.3 [77.3, 82.8]	83.5 [77.1, 87.8]	75.2 [72.4, 77.0]
DraCor contrast: difference [95% bootstrap CI]
$C_{\mathrm{obs}}$	+1.4 [-4.6, +6.9]	—	+5.4 [-2.2, +9.9]
$C_{\mathrm{adj}}$	+3.2 [-3.8, +8.3]	—	+8.3 [+1.8, +13.5]

Appendix

Table 7: Source-family Consistency and trajectory timing. Cells report equal-model means with 95% hierarchical bootstrap intervals. DraCor contrasts subtract ITP or Smallville from DraCor.

Qwen series
Generator	Contrast	$\Delta G$	$\Delta C_{\mathrm{obs}}$	$\Delta C_{\mathrm{adj}}$	$\Delta R$
Qwen2.5-3B	B2 – B1	$+1.8\,[-0.8,+4.4]$	$-37.6\,[-64.2,-8.4]$	$+0.1\,[-24.2,+33.3]$	$+5.8\,[-1.1,+15.0]$
	B3 – B1	$+0.0\,[-1.7,+1.3]$	$-38.9\,[-62.0,-9.8]$	$-20.0\,[-24.3,+6.8]$	$+10.8\,[-2.3,+20.2]$
	B3 – B2	$-1.8\,[-3.7,-0.3]$	$-1.3\,[-29.1,+31.6]$	$-20.0\,[-30.6,+3.2]$	$+5.0\,[-10.0,+13.9]$
	WMEM on – off	$+0.7\,[-1.1,+2.3]$	$-9.3\,[-33.8,+10.6]$	$-25.1\,[-33.5,+5.6]$	$+1.2\,[-5.3,+8.0]$
	WMOD on – off	$-0.8\,[-2.5,+0.7]$	$-1.2\,[-25.0,+27.1]$	$-20.0\,[-29.9,+9.0]$	$-4.9\,[-10.4,+3.5]$

Appendix

Table 8: Per-model counterpart to main-text Table 1 for both six-model groups. Each block reports the same five marginal contrasts as point estimates with 95% hierarchical bootstrap intervals. Models are fixed and are not resampled.

Qwen series
Generator	Conditional contrast	$\Delta G$	$\Delta C_{\mathrm{obs}}$	$\Delta C_{\mathrm{adj}}$	$\Delta R$
Qwen2.5-3B	WMEM at B1	$+0.4\,[-3.2,+2.7]$	$-11.9\,[-47.5,+0.0]$	$-44.4\,[-49.5,+7.5]$	$-1.1\,[-12.1,+10.9]$
	WMEM at B2	$+0.1\,[-4.6,+3.9]$	$-25.1\,[-70.0,+28.2]$	$-30.7\,[-58.6,+13.5]$	$+4.1\,[-11.3,+15.7]$
	WMEM at B3	$+1.5\,[-0.1,+3.0]$	$+9.0\,[-55.1,+64.5]$	$-0.1\,[-11.9,+9.2]$	$+0.5\,[-10.7,+15.8]$
	WMOD at B1	$-0.4\,[-3.9,+1.4]$	$-11.9\,[-47.5,+0.0]$	$-39.4\,[-45.0,+10.0]$	$-2.8\,[-11.0,+11.8]$
	WMOD at B2	$-1.9\,[-6.2,+1.7]$	$-5.4\,[-38.7,+37.9]$	$-18.8\,[-44.4,+22.8]$	$+1.4\,[-9.7,+12.4]$

Appendix

Table 9: Per-model module effects within each base for both six-model groups. Each module is compared on versus off while averaging over the other. Entries are point estimates with 95% hierarchical bootstrap intervals. Models are fixed and are not resampled.

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