HAMLET: A Hierarchical and Adaptive Multi-Agent Framework for Live Embodied Theatrics
Authors: Shufan Jiang
Sizhou Chen
Chi Zhang
Xiao-Lei Zhang
Xuelong Li
Venue: ICLR 2026 Poster (2026)
Keywords: Drama
Role-Playing
Multi-Agent System
Autonomous Workflow
LLM
Adaptive Reasoning
Primary area: applications to computer vision, audio, language, and other modalities
TL;DR: We propose HAMLET, a hierarchical adaptive multi-agent framework that enables autonomous and immersive live embodied theatrics by combining offline narrative blueprints with online improvisational performance.

Abstract: Creating an immersive and interactive theatrical experience is a long-term goal in the field of interactive narrative. The emergence of large language models (LLMs) provides a new path to achieve this goal. However, existing LLM-based drama generation methods often produce models that lack initiative and cannot interact with the physical scene, while typically requiring detailed user input that diminishes the immersion of live performance. To address these challenges, we propose HAMLET, a hierarchical adaptive multi-agent framework focused on drama creation and real-time online performance. Given a simple topic, the framework first generates a narrative blueprint to guide the subsequent improvisational performance. In the online performance phase, each actor is equipped with an adaptive reasoning module that enables decision-making based on their personas, memories, goals, and emotional states during complex group chat scenarios. Beyond dialogue, actor agents engage in embodied interactions by changing the state of scene props through actions such as opening a letter or picking up a weapon, which are broadcast to update the global environmental context. To objectively assess the quality of live embodied theatrics, we establish a comprehensive evaluation method and introduce HAMLETJudge, a specialized critic model for automated evaluation. Experimental results demonstrate that HAMLET excels in creating expressive, coherent, and physically interactive theatrical experiences in an autonomous manner.

Published as a conference paper at ICLR 2026
HAMLET: A HIERARCHICAL AND ADAPTIVE MULTI-AGENT FRAMEWORK FOR LIVE EMBODIED THEATRICS
Shufan Jiang*1.4 Sizhou C.3.
Chi Zhang
Xiao-Lei Zhang
Xuelong Lit
'Fast China University of Science and Technology
The University of Sydney *Datwwaale Org.
Institute of Artificial Intelligence (TeleAI), China Telecom
ABSTRACT
Creating an immersive and interactive theatrical experience is a long-tery goal the field of interactive narrative. The emergence of large language models (LLMs) provides a new path to achieve this goal. However, existing LLM-based drama generation methods often produce models that lack initiative and cannot interact with the physical scene, while typically requiring detailed user input that dimin- ishes the immersion of live performance. To address these challenges, we propose HAMLET, a hierarchical adaptive multi-agent framework focused on drama cre- ation and real-time online performance. Given simple topic, the framework first generates a narrative blueprint to guide the subsequent improvisational perfor- mance. In the online performance phase, each actor is equipped with adaptive reasoning module that enables decision-makin ries, goals, and emotional states during complex group chat scenarios. Beyond dialogue, actor agents engage in embodied interactions by changing the state of scene props through actions such as opening a letter or picking up a weapon, which are broadcast to update the global environmental context. To objectively assess the quality of live embodied theatrics, we establish comprehensive eval- uation method and introduce HAMLETJudge, specialized critic model for tomated evaluation. Experimental results demonstrate that HAMLET excels creating expressive, coherent, and physically interactive theatrical experiences in an autonomous manner.
based on their personas, memo-
INTRODUCTION
In recent years, LLMs have demonstrated strong ability in various generative tasks, especially in creative fields such as story creation (Li et al., 2024) and role-playing (Tu et al.] (024). These models able generate fluent and imaginative texts, providing foundation for the development of interactive narrative. In applying these capabilities to drama creation, research efforts can be categorized into main directions: drama generation and drama performance.
Existing studies have explored drama generation employing hierarchical approaches FFan et al., 2018 Yao al. 2019) and modular script structuring (Mirowski al., 2023), yet significant chal- lenges remain in applying LLMs to structured drama generation, especially for online real-time enactment. In interactive drama performance, LLM agents take on the role of performing the narra- tive, where, in contrast to free-form story creation, each actor must make decisions and take actions consistent with their profiles and the overall plot progression. These interactions must unfold across series of scenes to collaboratively advance the narrative. Furthermore, existing methods typically require detailed user input, such as full story outlines (Wu et al. 2024) or elaborate guiding para- graphs (Wu et al. 2025b), which not only increases design costs but also limits storyline flexibility.
"Equal Contribution Corresponding xiaolei.zhang@@nppus and xuelong li icce.org

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Published as a conference paper at ICLR 2026
Scone wubor-t Play
Toplc: Llamlet within play confinm guilt revenge.
Optiioal Howdlr
V
Offiiee Planning
Ouline Performance
Critic
Plot Protile
Plat
Plat Designer
PONE
Pount
Dirncter
Browdlecso
Reviewerr
11
Actoo Profile
Olluprrin
Preformance
Online
Designer
Figure I: HAMLET creates AI drama in two main stages. First, during offline planning, a collab- orative workflow of agents including the actor designer, plot designer, and reviewer creates initial materials, which are then integrated by a director agent into a structured narrative blueprint. The blueprint then guides the subsequent online performance, where a control system composed of planner, transfer, and advancer directs a dynamic and improvisational theatrical experience.
A key challenge in drama performance lies in redefining the interaction patterns of LLM agents. In many prior studies, these patterns are limited to passive, turn-based responses. For example, some studies focus on the adequacy of single-turn dialogues rather than active participation (Wul iet al., (024). The agents typically await user instructions, lacking genuine proactivity. This re- active model struggles support dynamic scenes where multiple AI actors interact with human players. We believe that truly vibrant, dramatic interactions require AI actors to make autonomous decisions, collaborate or conflict in open scenarios, and actively guide the development of the plot. This paradigm shift from passive response to active guidance provides new perspective on how to enhance actor initiative in dramatic performance.
Beyond autonomous agent interaction, comprehensive framework for drama performance requires two essential components, physical environment interaction and holistic evaluation system. Drama inherently an art form combining language with embodied action, where actor agents' behaviors dynamically influence their surroundings while environmental feedback in turn plays a crucial role in shaping performance. Without this embodied dimension, drama risks degenerating into abstract dialog lacking the tangible presence and realism that define compelling theatrical experience. More- over, established method effectively evaluates real-time drama quality, as existing benchmarks predominantly assess textual coherence (Górez-Rodriguez & Williams] 2023) or role-playing fi- delity (Tu al. 2024 Wu et al. 2025a Wang et al. 2025), rather than the integrated impact of complete dramatic enactment.
To address the above challenges, we propose HAMLET, a multi-agent framework that enables Hierarchical and Adaptive Modeling for Live Embodied Theatrics. Specifically, we make the fol- lowing contributions:
1. HAMLET: Our multi-agent framework comprises two decoupled stages of offline planning and online performance. The offline stage requires only a simple topic to generate a structured nar- rative blueprint, providing freedom for improvisation during online performance while ensuring the integrity of the main story structure. To enhance online performance, each agent is equipped with a Perceive And Decide (PAD) module that acts as a pre-response mechanism, making their perceptions and decisions more numaan-like
Comprehensive evaluation: To objectively assess the quality of drama generation and perfor- mance, we establish a comprehensive evaluation method that assesses three curated dimensions. This method incorporates a leaderboard that uses GPT-40 as a strong baseline for win rate com- parisons. Additionally, we trained HAMLETJudge, critic model designed for cost-effective and reliable drama performance evaluation.
3. Extensive experiments: We conducted evaluations and detailed ablation studies HAMLET's core components. Notably, both PAD and HAMLETJudge, are 8B models, yet they achieved state-of-the-ar performance in their tasks respectively.
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2 RELATED WORK
LLM-Based Drama. Research on LLM-based drama has evolved along two main directions: drama generation and drama performance. Initial efforts in drama generation adapted techniques from general story creation, using hierarchical models to plan plots and generate coherent narratives (Fan jet al. 2018) Yao et al., 2019). With the development of this field, researchers have tried multi LLM collaboration (Venkatraman et al. Mirowski et al., 2023 Han et al.) (2024) that mainly focus on drama scripts generation. A key limitation of previous work is that they generally require a complete story (Wu al. 2024) or leading paragraph (Wu et al. 2025b) as input. We present framework that supports arbitrary and customizable drama generation. At the same time, research on role- playing in drama performance is predominantly conducted through LLM-based agents (Chen al., 2024b). These agents typically require detailed knowledge about the actor in order to model their expressive style (Shanahan et al., [023) Wang et al.) 2023] Tu et al. 2024). Existing role-p role-playing systems are generally built using either prompt engineering approaches (Li et al. [023] Chen al., 2023) or fine-tuning methods (Shao et al. 2023; [u et al., 2024). However, these techniques struggle
to capture rich and nuanced character traits (Huang et al. 2023). and consequently fail to produce
truly live and fully embodied dramatic performances.
Evaluation of Role- Cole-Playing Conversation Agents. Traditional dialogue metrics like Embedding Similarity, BLEU, and ROUGE (Mou et al.) 2016 Wu et al.) 2020; Serban et al. 2017) are widely used in role-playing conversation agent (RPCA) research (Wang et al. 2024 Zhou et al. 2024 Tao
et al. 2024). However, these quantitative indicators struggle to evaluate role consistency and person- ality. To address this, specialized evaluation frameworks have been proposed. RoleEval (Shen al.) 2023) uses role-specific multiple-choice questions to test the model's 's understanding of a character. SocialBench (Chen et al. (0244) constructs evaluation questions from multi-source dialogues, while CharacterEval (Tu et al. 2024) adopts multi-round dialogues and cuuliiddimenioonll scoring for as- pects like conversational ability. Additionally, RAIDEN (Wu et al. 2025a) builds Q&A dataset via annotator interaction to evaluate responsiveness in specific dimensions. Although these methods provide quantitative criteria, they are typically limited two-character or user-character scenarios. While CoSER (Wang al. (2025) expands the number of roles, it still lacks an evaluation mecha- nism for overall dramatic performance. Motivated by these limitations, we propose new criteria to evaluate the complete dramatic performance.
HAMLET
To achieve an automated, interactive, and expressive AI drama experience, we designed HAMLET, as shown in Figure [ The framework decouples the generation and performance of the drama into two main stages: offline planning. a multi-agent collaborative workflow to generate narrative blueprint, and online performance, a hierarchical control system that executes the blueprint, manages real-time interactions, and handles environmental feedback.
3.1 OFFLINE PLANNING
The objective of the offline planning stage is to transform user input into a structured narrative blueprint. This stage is designed to handle two types of input: i) An arbitrary and customizable topic. For this input type, the offline workflow is able to generate a complete act based on the provided topic. ii) A complete literary work. When given a full literary text, the workflow first deconstructs into a series of acts based on its chapters and content structure. Subsequently, it proceeds with drama design for each act, ensuring the adaptation remains faithful to the source material.
Agent-Based Workflow Architecture. The offline workflow consists of four agents: the actor designer, the plot designer, the reviewer, and the director. This workflow implements character creation, plot generation, review, and final integration.
Character Profile Generation and Review. The starting point of the workflow is actor construc- tion. The actor designer is responsible for generating actor profiles of core characters based on user input. To ensure the depth and accuracy of character creation, this agent queries an external knowl- edge base via its search module. The output a structured profile that defines the static attributes of the character, such as background and personality, and dynamic attributes, such their initial goal
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and core relationships within the story. The profile is then submitted to the reviewer, who checks the rationality of the character settings, the clarity of motivations, and the relationships between actors.
Plot Generation and Structuring, After all actor profiles are approved, the plot designer composes a preliminary narrative draft based on the topic and actors. This draft is then submitted to the reviewer for evaluation. Once the review is passed, the director is responsible for the final structural processing. reconstructing the linear story draft into a hierarchical plot profile. This process includes the following key steps: 1) Act and scene definition: Divide the drama into several acts and specify the scenes in which each act takes place. 2) Creation of environmental elements: Generate a list of interactive props for each scene. This list has specific descriptions and location information for the props. 3) Narrative points: Define series of narrative points within each act. Each point contains a clear flag and result that marks its completion. 4) Backward planning: To prevent plot deviation, the director will give priority to generating the end point when planning an act. Then, based on this end point, it will supplement and construct the logically coherent preceding points leading to the ending in a reverse manner.
Narrative Blueprint
Real-Time Performance
A dark, solemn hall in the Dunish zoyal palace. In the long tabic holth books and condlotick... winlon alows gloomy sky.
Act ITI, Soaae IE The Great Hall of Elenone
Castle right. Tession and indecureents
Props long taki,, candlestikss books, curtalis, choirs, windows...
[Tenight, the truth will have newhere to hide.] (Willperiiz to Hovatiol Horaoo, help me watch my undle' reaction, expaciilly during the key soene
III
Homber
The Cag I has becs anisfeed transtionn to noxt puint,
Persering
The King's Guffe
Crime Exposca
rhoon acters (Hamie and Courtnee on sagee.
Director
(Nidks gravely will watch him closely...]
Cauadias
Clandles
My acphew, perfonmance taranged...
Offence
(Finger napping sarGece [Thi young grows stranger by the day. must rumain vigilant.)
Homler
Seuvile In Housse Mtriqullitee and
Claudius
"Io brior
the question.
uncle, plot seem [The covoting his to surface.
brother
Sperch Stylic good
purs surczen..
hreas.
to the thoos and
fear gaurgroon growiag.]
Hansle
Goal: To exposs and purrish the
c: Hamnaa's Unzle
murderer of ais Safter.
The paluce wilent once mure. Pukeniss' body is cocried away by guards, leasing dark stain.
Relasisnohipe: Foe-Clunliss'
nephew, elc.
Narronrr
Momory: Linel Chudiica alwryy enjoy watching pizos...
The ending flag I has been net Act real time perfommanse cnda all remaining sccurs off stage
(a)
Figure 2: An illustration of HAMLET's core components for performance generation: a narrative blueprint that defines the scene, plot and character profiles, and the resulting real-time conversation containing scene descriptions, dialogue and narrator judgment.
Finally, the director integrates the plot profile with the actor profile to generate a narrative blueprint which is shown in Figure aa as the input of the online performance stage.
3.2 ONLINE PERFORMANCE
Upon receiving the narrative blueprint, the online performance stage begins to transform it from static plan into dynamic, interactive, and immersive environment. This environment will accom- modate both autonomous AI actors and human players who assume certain roles. To ensure the performance quality, this phase introduces more specific narrative units, environmental interaction mechanisms, and group of collaborative agents. An example of online real-time performance is illustrated in Figure b..
Performing Drama. Online performances are organized into acts, each comprising multiple scenes and narrative points. Scenes define the physical environment and interactive props, establishing the spatial context, while points specify plot objectives and serve milestones for narrative progression. Together, these elements structure the complete dramatic process within an act.
The narrative path between two points is formed by a dynamically generated trajectory which con- sists of a series of beats. We define a beat as a meaningful interaction step where an actor performs an effective action. This action logic is driven by dual-goal system. For each beat, actor's deci-
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sion references both the public flag of the current point and personal private goal that is refreshed at the beginning of every act.
Due to the actors' autonomy, multiple trajectories connect point, to point441. such as composed of beatj, : beat j............................... beat and another of beat,, beat beats....... These multiple combinations introduce high degree of freedom and arbitrariness to the performance.
Environment Interaction. Credible physical interaction is the core challenge of the online per- formance stage. As shown in Figure B designed narrator agent to adjudicate all interactions between actors and the environment. The role of the narrator is to ensure the rationality of all physi- cal actions. When actor attempts perform physical action, the narrator will make judgment based on the environment state and physical rules. If the action is feasible, the narrator will confirm its success, update the environment state, and broadcast an objective description all participants. Otherwise, the narrator will determine failure with a logical explanation.
Piirrs
Plaz
Roalt
Claddis diawe Tear.
Clastis ingrly interpps the porformance
ureallng his quil.
lberiet play's parpos
Parooed flmnss
Clodiis lames ingei
Dandhe dookke iu lonzz
Hamdets seczedde
Bex2
Clasdica Sigps plasaztry but
mesecenly
Plawner decompare
pase Nen Sear riott.
Beaus
scmooaal Haniet vowa revenge.
Claadias eedes ocla; Hamiln secepts his hile
Hamlet taken bhz not conclates
Iemnactiv analyur
Fallue
Speech
Isteraction Aborpt
Prope
Abolute prelicn
Hamlet Act III
Relaive poskicn
A
Sacoaa prope
Noron Agent
Action
Figure 3: An example of the real-time interaction and adjudication loop in the online performance. An actor agent attempts an action or speech, termed a beat, to progress towards the current narrative point. The narrator agent then intercepts this attempt, determines whether it is success or failure, and provides objective feedback to all participants in the drama.
3.3 PERCRIVE AND DECIDE MODULE
Hierarchical Control of Performance Effects. All actor agents in the drama performance utilize a layered architecture, which composed of an LLM and PAD module. The LLM is responsible for generating specific speech and actions, while PAD handles the strategic decision-making that guides them. The design of PAD is detailed in the following subsection.
To ensure the online performance balances improvisational freedom with stable progression along the main storyline, we designed three collaborative control agents: 1) Planner pre-designs and re- views multi-trajectory results. It breaks down the flag into few sequences of executable beats which form a trajectory. Beats can be references for advancer to guide the plot forward. 2) Transfer moves the story the next point by regularly checking if the flag is met. Once satisfied, it advances the drama to the next point and manages relevant actors to enter or leave. 3) Advancer ensures the plot progresses. If it stalls beyond a time threshold, the advancer directs necessary actors based on the current flag or next beat. Their relationship is illustrated in Figure 3
In HAMLET, the PAD module plays crucial role in guiding the actor agents to generate final responses. The design of PAD is conceptually grounded in the dual-process theory of human cog- nition (Kahneman] 2011). According to this theory, human thinking characterized by two distinct modes of processing: System I, which is fast, automatic, and intuitive; and System II, which is slower, deliberate, and analytical. PAD integrates both intuitive and reflective reasoning mech- anisms to better simulate human-like decision-makinn Specifically, PAD is mainly responsible for generating a decision of fast, slow, silence or potential actions by tool calls to simulate and extend the dual-system mechanism. This expanded set of response modes enhances the module's 's compatibility with the interactive real-time dramatic context proposed in the study.
in complex and nuanced drama contexts.
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Design Principles. As depicted in Figure the input of PAD is based a dual principle. When perceiving. PAD acts as an actor possessing both subjective awareness of its internal state and an objective perception of external stimulus. When new event occurs, PAD integrates information from both perspectives to ensure well-grounded decision-making.
The internal state, which represents the actor's self-awareness, is maintained live profile com- prising both static and dynamic components. The static components include Persona and Subjective Relationships, while the dynamic elements, such as Goal and Memory, are retrieved updated as needed. Upon the conclusion of an act or scene, key events and interactions are distilled into mem- ory entries. These entries are stored and later retrieved via Retrieval Augmented Generation (Lewis et al. 2020) to inform future behavior. This memory compression mechanism prevents exponential context growth, thereby enabling scalable support for long-form drama performance.
ketrieval-Augmenter
On the other hand, external stimulus constitutes objective environmental and contextual informa- tion. The external stimulus composed of Environment Description, Actor List, Dialogue History and Interactable Objects. Together, these components form a comprehensive representation of the external context, facilitating the integration of situational awareness into the decision-making process.
External Stimulus
Environment Description
History
Objjcts
PAD
Action
Silence
Internal State
Tool Calling
Fast
Persona
Slow
Memory
Subjective Relationships
A
Figure 4: PAD processes external stimulus and internal state to determine a response strategy by tool calling.
Decision-making Process. Abstract context is translated into concrete speech and executable ac- tions through a two-stage pipeline. As shown in Algorithm PAD first selects a high-level response strategy that determines both the timing of the agent's reaction and structured action, which is parsed into an executable triplet. Optionally, it also generates the underlying reasoning for this de- cision in the form of an internal monologue. In the second stage, the selected strategy, the parsed action, and the optional internal monologue jointly guide the actor agent in producing its final, con- crete behavior. detailed version of the algorithm be found in Appendix D
Algorithm 1 Working Principle for PAD Module
Require: Actor Dramatic Context Cdrama- PAD Model Parameters
Ensure: A final decision tuple (r, o), where o is the structured action (composed of subject, verb, and object) empty set (Z), (FAST, SLOW, SILENCE) is the response strategy.
1: procedure GETACTOR DECISIONEE Caramay TTg)
2: promptpad t- EncodeStrategy' (ak, Cdrama)
3: F, [[[hinnkin] PAD(promptpud: To)
4: f- arg max P(r'lf)
5: 4- GenerateAction(fr (r, [thinking) ags Carama)
6: 4- ParseAction(o)
7: return (r,o)
8: end procedure

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4 EVALUATION METHOD
Dataset Construction. The evaluation dataset was constructed from 100 diverse cases, comprising 50 literary excerpts and 50 custom-authore. drama topics. The literary excerpts are selections from 25 classical Chinesd and 25 renowned Englist literary works. The 50 custom topics, designed to span 10 distinet themes, were developed and reviewed carefully by annotators. More details about the dataset can be found in Appendix A Figure illustrates the thematic distribution of the entire dataset.
14-
12
Count
Romanca
Family
History
Adventure
Crime
friendtoilp
Fantacy
Sclf
Harror
Categories
Figure 5: Distribution of drama topics across the dataset.
Dimension Definition. Defining evaluation dimensions is crucial for quantifying real-time drama performance. We categorize our evaluation into three core dimensions as follows:
Character Performance (CP) measures the quality of Al actors. This dimension evaluates Be- lievability, i.e., the consistency of characters with their established personas, and Agency, which reflects the richness of emotional expression and the character's 's ability to effectively advance the narrative.
2. Narrative Quality (NQ) examines the overall craftsmanship of the story. This includes its Co- herence, ensuring the logical development of the plot; its Resonance. measured by its thematic relevance and depth; and its Integrity, which evaluates the structural completeness of the storyline from beginning end.
3. Interaction Experience (IE) focuses on the engagement of Al actors with the system. This dimension encompasses the quality and timeliness of the system's reactions, termed Responsive- ness; the level of cognitive and emotional engagement, Immersion; and the overall technical smoothness of the interaction, referred to as Fluency.
Evaluation Principle. Our evaluation principle is to holistically assess dramatic performance, rather than evaluating agent responses individually, turn by turn. This approach is crucial because drama often incorporates literary devices such as foreshadowing and plot twists. Consequently, a scemingly subpar unusual generation in single turn might be deliberate narrative setup for subsequent developments and should not be penalized in isolation.
For the overall drama performance, cess. This model conducts pairwise comparisons between the test result and the baseline result, assigning score based on 5-point Likert scale (Robinson 2014) to determine win rate. The detailed scoring guideline defined in Appendix
employ HAMLETJudge to automate the evaluation pro-
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5 EXPERIMENTS
In this section, present the experimental results to demonstrate the superiority of proposed HAMLET framework, alongside ablation studies verifying its reliability and validity.
Settings. To ensure rigorous and reproducible evaluation, define the baseline and test config- urations clearly. All underlying models except the PAD component in HAMLET share the same GPT-4o backbone, with a greedy sampling strategy. Regarding user settings, each agent can be freely configured, and the PAD component is optional.
HAMLET Leaderboard. Following the settings above, compared various mainstream LLMs ranging from open-source to closed-source and non-reasoning to reasoning models. Table reveals their capabilities in both English and Chinese online drama performance, serving as practical reference for real-world applications. Notably, -laune---somnet- Thinking demonstrated exceptional proficiency across the majority evaluated metrics in both languages, highlighting its versatility and effectiveness in dynamic, interactive theatrical environments.
Scaling Laws and Reasoning Efficiency. We observed that while scaling laws persist in drama scenarios, evidenced by performance gains with model size (e.g., the Qwen3 series), reasoning capability proves to more parameter-efficient than merely increasing model scale. For instance, Qwen3-328 3-B--22iBkiii (73.85) significantly outperforms the much larger Llama-3.1-70B (45.75) and even rivals the massive Qwen3-235B (71.21). This suggests that in drama tasks, which demand multi-constraint satisfaction involving goals, persona, and context, enhanced reasoning density is more critical than simple parameter scaling.
Subtext and Strategy. Furthermore, reasoning models like DeepSeek-RI and Claude-4-soneee Thinking consistently outperform their non-reasoning counterparts. We attribute this to their su- perior capacity for subtext processing and strategic planning. While standard models often react superficially to immediate dialogue, reasoning models utilize internal chain-of-thougle to simulate future outcomes and deduce implied intent (e.g.. "He lying, but should pretend to believe him to trap him later"). This cognitive depth significantly enhances character dimensionality and facilitates complex narrative devices such as plot twists and foreshadowing.
English
Chinesi
Model
Overall Score
Character
Average
Narrutive
Interaction
Avernge
Non-Reuzonig Models
Claude 4-some--
76.50
77.30
76.96
76.92
80.18
79.20
79.66
79.68
78.30
Clande-3.7-somm
65.80
66.94
65.98
66. 26
76.00
75.20
15.48
75.56
70.90
sonnet
Gemini-2.5-fladt
46.00
47.10
46,70
46.60
52.00
$2.90
$2. 15
52.35
49.48
GPT.4.5 preview
59.21
60.00
59.92
59.71
61.50
62.50
61.91
60.84
GPT-4.1
62,74
Mistral-Small332222 3.22-43...24
48.00
48.50
48.22
48 24
$1.00
$1.83
$2.00
$1.61
49.93
DeepScak-V3-0524
Llama-3.1-7UB
49.80
49.00
48.86
49.22
42.00
12.81
42.00
42.27
45.75
Llama-3.1-88
35.00
36.01
35.52
35.51
33.50
34,00
33.99
33.83
34.67
Higgs-Llama---77g
Qwcn3-8B
47.50
46.80
47.00
47.110
$8.00
$7.50
$8.05
$7.85
$2.48
Qwen3-32B 32B
65.00
65.20
65. 36
66.00
65.50
65.78
65.57
Qwen3-235B-A222
69.50
69.80
69.65
69.65
72.50
73.00
72.78
72.76
71.21
Reasnning Madels
2.5min--2.5-pro
61.00
62.22
61.70
61.64
62.000
62.80
62.72
62.35
62.00
79.50
78.40
79.04
78.9%
78.42
80.32
81,03
79.92
79.45
Minimax.MI
11.94
OpenAl-a3
69.00
69.95
69.40
69.45
78.00
77.50
78.17
77.89
73.67
MiniistallSmalll-25
$9.00
60.00
$9.74
59.58
$8.50
$8.90
$9.24
DeepSeek-RR --155eee--11-5288
66.00
67.10
66.64
66.58
79.00
79.50
79.61
79.37
72.98
Qwen 3-88-1 -hi---iiiiningg
50.00
$1.61
$1.00
50.87
65.80
65.00
55
65.45
58.16
Qwen3. Thinking
Bawen3233B--A2 1-2---AA28-Thinkiin
70.50
71.00
70.72
70.74
76.00
75.80
75.96
75.92
73.33
Table Performance evaluation different models based on HAMLETJudge. Bold values indi- cate the best performance in each column.
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HamneeJJugg (ours)
GPT-4.1
Chaule-4-omnet
Gemins-2.5.Pre
0,807
6.792
NO divtler HARLET
Avengo
0,791
0.762
0,202
Table 2: Pearson correlation coefficients PPaarso 1901 of different models and human evaluators.
Model (FC)
Responding Strategy
Latency Penalty
Final
Fast
Slow
Silence
Score
Qwen2.5.70 1tntstrtt Instruct
0.779
0.359
0.131
0.423
Qwen2,5-72B-Bl -
0.692
0.452
0,262
0.469
Qwen3.8B 8B
0.699
0.452
0,066
0.406
3-on3-338
0.396
0.098
0,422
Dwee3-8B-Thhiblee
0.668
0.321
0.459
0.05
0.532
Qwen3-32BD 22B-kiingng
0.668
0.434
0,361
0.10
0.578
GPT-4a
0.556
0.623
0.328
0.024
0.478
GPT-4. I-mini
0,909
0.132
0.180
0.407
Figure 6: The ablation study results on PAD and multi-agent framework design of HAMLET. The radar chart illustrates the comparative evaluation of three experimental configurations in the context of online drama performance.
SeepSeak-R1-4522
0.723
0.615
0,470
0.15
0.453
Gemini--2.5pon
0.519
0:449
PAD (ours)
0.822
0.736
0.711
0.756
Table 3: Evaluation under responding strategies by tool calling. Underlined values reflect an ap- plied latency penalty.
5.1 RELIABILITY OF HAMLET
Both HAMLETJudge and PAD were trained on data annotated by human labelers. To rigor- ously assess the reliability of our data, we conducted Inter-Annotator Agreement (IAA) analysis across annotators for both datasets. As demonstrated in Appendix G.3 the HAMLETJudge dataset achieves an overall weighted Krippendorff's Alpha of 0.725, while the PAD dataset yields an over- all weighted Fleiss' Kappa of 0.624, both indicating substantial agreement. To further validate the effectiveness of HAMLETJudge, we measured its agreement with human ratings on a held-out validation set using the Pearson correlation coefficient. As reported in Table 2 HAMLETJudge closely aligns with human assessments and outperforms strong baselines such as GPT-4.1, Claude- 4-sonnet, and Gemini-2.5-pro.
To demonstrate the reliability of PAD, we conducted additional experiments evaluating model per- formance under diverse response strategies. Notably, models with stronger general capabilities tend to exhibit more homogeneous and consistent performance across scenarios. However, models that generate reasoning tokens prior tool calls often incur unacceptable latency in real-time drama settings. To account for this, we introduce a latency penalty that quantifies the impact such delays on overall performance. Results presented in Table
Our analysis reveals a clear trade-off between performance and latency among existing models. Reasoning iistoning-ittesiive models achieve balanced performance across strategies but suffer from sub- stantial latency penalties. In contrast, non-reasoning models are faster but display significant per- formance bias--peformmin well in fast-paced scenarios yet struggling in complex, nuanced inter- actions. PAD effectively reconciles this tension: it achieves the highest overall score while main- taining stable performance across all strategies, all with negligible latency. More discussion about the latency penalty can be found in Appendix G.4
5.2 ABLATION STUDY
In the ablation study, we conducted experiments on three core component designs in HAMLET, to validate their individual contributions to overall system performance.
Perceive and Decide Module. We randomly select 30 topics from the evaluation dataset and control the experiment settings as GPT-4o under greedy sampling strategy, then compare three experimental setups: drama performed by (i) raw- naw-prompted GPT-4o only; (ii) the multi-agent collaboration of the full HAMLET framework; and (iii) HAMLET framework without PAD.
As shown in Figure 6 prompting a single GPT-4o yields substantially lower performance, high- lighting the necessity of our multi-agent workflow design. Furthermore, PAD-enabled HAMLET

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Published as a conference paper at ICLR 2026
Offline Planning: Bluegetnt Quality Evaluation
(bi Online Perfocmance: Impact Narvative Quality
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Figure 7: Ablation study of the reviewer across four test models. (a) Offline narrative blueprint quality is evaluated by human raters using win-rate. Scores above 50 (dashed line) indicate pref- erence for blueprints generated with the reviewer. (b) Enabling the reviewer leads to a score raise of online Narrative Quality (from red to blue). Iterations denotes the average number of revision rounds performed with the reviewer.
consistently outperforms the version without PAD in all 10 topic categories, demonstrating that PAD serves as crucial component in the online drama rendition, making the interaction and con- versation of AI actors more natural, coherent and human-like.
Reviewer. To better assess the reviewer's contribution, we introduced dedicated evaluation metrics for the offline planning stage. As shown in Figure the reviewer makes significant contribution improving the quality of narrative blueprints during offline planning. Intriguingly, we also observe in Figure that the reviewer exerts a measurable influence on Narrative Quality during the online performance phase, enabling consistent substantial score improvements across test models. We attribute this downstream effect to the causal linkage between blueprint quality and final narrative higher-fidelity fidelity planning directly enables more coherent, compelling, and structurally sound storytelling at execution time.
output: higher-
Advancer. Experiments show that removing the advancer reduces HAMLET task completion rate from 100% to 68.1%, highlighting its critical role in ensuring robustness and preventing versational deadlocks. Additional experimental details and results are provided in Appendix
5.3 CASE STUDY
To further illustrate the operational mechanism of HAMLET and the role of each component, we present set of real examples in Table in Appendix C
In Case 1, the narrator first assesses the situation before responding make a reasonable judg- ment about the AI actor's input. HAMLET also supports users assuming specific character roles. In Cases 2, 3, and 4, the narrator, transfer, and advancer collaboratively handle challenging cir- cumstances (such missing props, irresponsible actions, stubborn character choices) to ensure narrative coherence and smooth dramatic progression. Meanwhile, the planner is responsible for multi-trajectory design and review. As demonstrated in Cases and 6, the same goal can be met by different beat trajectories as long as the process is reasonable.
CONCLUSION
In this paper, we introduced HAMLET, a multi-agent framework designed to address key chal- lenges in AI-driven theatre. HAMLET generates a guiding narrative blueprint from a simple topic and provides agents with PAD module, enabling autonomous thinking and physical environmen- tal interaction. To assess performance, we established a comprehensive evaluation method and a leaderboard, where our specialized critic model, HAMLETJudge, achieved top-ranking results. Extensive experiments show that our approach creates expressive and coherent theatrical experi- ences, paving path toward autonomous and immersive interactive drama experiences.
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7 ETHICS STATEMENT
This work relies on human annotation, which is critical for both the training and evaluation of our proposed models. The annotation of PAD and HAMLETJudge datasets was conducted by team of five human experts, each with at least one year of professional experience in relevant fields such as creative writing or drama creation. To ensure high-quality annotations, we provided clear and task-specific instructions to all labelers. In addition, the datasets underwent rigorous safety checks to ensure no personally identifiable information or potentially problematic content is available. The
labeling instructions can be found in Appendix E and F
REFERENCES
Hongzhan Chen, Hehong Chen, Ming Yan, Wenshen Xu, Xing Gao, Weizhou Shen, Xiaojun Quan, Chenliang Li, Ji Zhang, Fei Huang, and Jingren Zhou. Socialbench: Sociality evaluation of role- playing conversational agents, 2024a.
Jiangjie Chen, Xintao Wang, Rui Xu, Siyu Yuan, Yikai Zhang, Wei Shi, Jian Xie, Shuang Li, Ruihan Yang. Tinghui Zhu, et al. From persona to personalization: A survey on role-playing language agents. arXiv preprint arXiv:2404 200.:22344.8.11,, 2024b.
Nuo Chen, Yan Wang, Haiyun Jiang, Deng Cai, Yuhan Li, Ziyang Chen, Longyue Wang, and Jia Li. Large language models meet harry potter: dataset for aligning dialogue agents with characters. In Findings of the Association for Computational Linguistics: EMNLP 2023, pp. 8506-8520, 2023.
Angela Fan, Mike Lewis, and Yann Dauphin. Hierarchical neural story generation. In Iryna Gurevych and Yusuke Miyao (eds.). Proceedings of the 56th Annual Meeting of the Associa- tion for Computational Linguistics (Volume 1: Long Papers), pp. 889-898, Melbourne, Aus- tralia, July 2018, Association for Computational Linguistics. doi: 10. 10.18653/w1/P18 1082. URL
Carlos Gódrezeod and Paul Williams. A confederacy of models: a comprehensive evaluation of LLMs on creative writing. In Houda Bouamor, Juan Pino, and Kalika Bali (eds.), Findings of the Association for Computational Linguistics: EMNLP 2023, pp. 14504-14528, Singapore, De- htt.:o2...iiinnnemmmmmem 966/
cember 2023, Association for Computational Linguistics. doi:
966. URL https:://// /t/ wwwpaaaanannaaoaaaaaaaa org/ 023...3...2
Senyu Han, Lu Chen, Li-Min Lin, Zhengshan Xu, and Kai Yu. IBSEN: Director-actor agent col- laboration for controllable and interactive drama script generation. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume Long Pupers), pp- 1607-1619, Bangkok, Thailand, August 2024. Association for Computational Linguistics. URL org 2024 acl -ong- 88
Jen-tse Huang, Wenxuan Wang. Man Ho Lam, Eric John Li, Wenxiang Jiao, and Michael R Lyu. Chatgpt an enfj, bard an istj: Empirical study on personalities of large language models. arXiv preprint arXiv:2305.199 /9926, 2023.
Daniel Kahneman. Fast and slow thinking. Allen Lane and Penguin Books, New York, 2011.
Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, Sebastian Riedel, and Douwe Kiela. Retrieval-augmentte H. Larochelle, M. Ranzato, R. Hadsell, M.F. Balcan, and H. Lin (eds.). Advances in Neu- ral Information Processing Systems, volume 33, pp. 9459-9474. Curran Associates, Inc., htsp://pssspppppppp.eee/////ee/e wtwper/ffllsepppe////0000
generation for knovledge-intensivee nip tasks. In
2020. URL File/ F22263892220051100122222438188-3
pdf
Cheng Li. Ziang Leng, Chenxi Yan, Junyi Shen, Hao Wang, Weishi Mi, Yaying Fei, Xiaoyang Feng, Song Yan, HaoSheng Wang, et al. Chatharuhi: Reviving anime character in reality via large language model. arXiv preprint arXiv:2308 095977 2023.

---

Published as a conference paper at ICLR 2026
Zhuoyan Li, Chen Liang. Jing Peng, and Ming Yin. The value, benefits, and concerns of generative ai ai-powered assistance in writing. In Florian 'Floyd' Mueller, Penny Kyburz, Julie R. Williamson, Corina Sas, Max L. Wilson, Phoebe O. Toups Dugas, and Irina Shklovski (eds.), Proceedings of the CHI Conference on Human Factors in Computing Systems, CHI 2024, Honolulu, HI, USA, May 11-16, 2024, pp. 1048:1 ACM, 2024. doi: 10.1145/361 11145/366199443442622 URL
https://don http:///....011111111155//331111 36412333333111333333222
Keming Lu, Bowen Yu, Chang Zhou, and Jingren Zhou. Large language models are superpositions of all characters: Attaining arbitrary role-play via self-alignment. In Lun-Wei Ku, Andre Mar- tins, and Vivek Srikumar (eds.), Proceedings of the 62nd Anmual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 7828-7840, Bangkok, Thailand, August 2024. Association for Computational Linguistics. doi: 10.1 (0.18653/v1// 165.
URL
https:// hatp:s///cclanthloogy.o org/ 202/ 202.22022...ooomm. acl--onn. 423/
Piotr Mirowski, Kory W Mathewson, Jaylen Pittman, and Richard Evans. Co-writing screenplays and theatre scripts with language models: Evaluation by industry professionals. In Proceedings of the 2023 CHI conference on human factors in computing systems, pp. 1-34, 2023.
Lili Mou, Yiping Song, Rui Yan, Ge Li, Lu Zhang, and Zhi Jin. Sequence to backward and forward sequences: A iintenttiitrouucing approach to generative short-text conversation. In Yuji Mat- sumoto and Rashmi Prasad (eds.), Proceedings of COLING 2016, the 26th International Confer- ence on Computational Linguistics: Technical Papers, pp. 3349-3358 Osaka, Japan, December 2016. The COLING 2016 Organizing Committee. URL. htttps://aclanthhlloso. ooroo C1 c16-13167
Karl Pearson. Lii. lines and planes of closest fit to systems of points in space. The London, Edinburgh, and Dublin philosophical magazine and journal of science, 2(11):559-572, 1901.
J. Robinson. Likert scale. In A. C. Michalos (ed.), Encyclopedia of Quality of Life and Well-Being Research. Springer, Dordrecht, 2014. doi: 10. 1007/9977 001-778-99-0077755----5 1654.
Julian Serban, Alessandro Sordoni, Ryan Lowe, Laurent Charlin, Joelle Pineau, Aaron Courville, and Yoshua Bengio. A hierarchical latent variable encoder-decoder model for generating dia- logues. In Proceedings of the AAAI conference on artificial intelligence, volume 31, 2017.
Murray Shanahan, Kyle McDonell, and Laria Reynolds. Role play with large language models. Nature, 623(7987):49344993 498, 2023.
Yunfan Shao, Linyang Li, Junqi Dai, and Xipeng Qiu. Character-LLMM: A trainable agent for role- playing. In Houda Bouamor, Juan Pino, and Kalika Bali (eds.), Proceedings of the 2023 Confer- ence on Empirical Methods in Natural Language Processing, pp. 13153-13187, Singapore, De- cember 2023. Association for Computational Linguistics. URL https: httpsss//caaatthlll.. org/ 22t//2023.c0 emnlp main. 814/
Tianhao Shen, Sun Li, Quan Tu, and Deyi Xiong. Roleeval: A bilingual role evaluation benchmark for large language models. arXiv preprint arXiv:2312.16132. 2023.
Meiling Tao, Liang Xuechen, Tianyu Shi, Lei Yu, and Yiting Xie. RoleCraft-GLM: Advancing personalized role-playing in large language models. In Ameet Deshpande, EunJeong Hwang, Vishvak Murahari, Joon Sung Park, Diyi Yang, Ashish Sabharwal, Karthik Narasimhan, and Ash- win Kalyan (eds.), Proceedings of the 1st Workshop on Personalizution of Generative A/ Systems (PERSONALIZE 2024), pp. 1-9, St. Julians, Malta, March 2024. Association for Computational Linguisties. URL https: / http///aaanataocoacoeeoeo
org/ 202 4 wwwpppesaaaa.ee.....
Quan Tu, Shilong Fan, Zihang Tian, Tianhao Shen, Shuo Shang, Xin Gao, and Rui Yan. Charac- terEvall: A Chinese benchmark for role-playing conversational agent evaluation. In Lun-Wet Ku, Andre Martins, and Vivek Srikumar (eds.), Proceedings of the 62nd Annual Meeting of the Associ- ation Computational Linguistics (Volume 1: Long Papers), pp. 1 11836-1181111 11850, Bangkok, Thai- land, August 2024. Association for Computational Linguistics. doi: 10. 11855/v1/2024..cc 638.
http://///aaaaaahcooooccooo htt.eaat.ooooo.ooooooooooooo 2004 http://222..aalll... acc-nong......... 638/
Saranya Venkatraman, Nafis Irtiza Tripto, and Dongwon Lee. Collabstory: Multi-llm collaborative story generation and authorship analysis. arXiv preprint arXiv:2406. 02024406..666. /2665, 2024.
12

---

Published as a conference paper at ICLR 2026
Noah Wang, Z.y. Peng, Haoran Que, Jiaheng Liu, Wangchunshu Zhou, Yuhan Wu, Hongcheng Guo, Ruitong Gan, Zehao Ni, Jian Yang, Man Zhang. Zhaoxiang Zhang, Wanli Ouyang, Ke Xu, Wenhao Huang, Jie Fu, and Junran Peng. RoleLLM: Benchmarking, cliciting, and enhancing role- playing abilities of large language models. In Lun-Wei Ku, Andre Martins, and Vivek Srikumar (eds.), Findings of the Association for Computational Linguisties: ACI. 2024, pp. Bangkok, Thailand, August 2024, Association for Computational Linguistics. doi: 10. 18653/ URL ht httpttts//aaaaaattalaaa wwtp:yy..rgg/20220 htt.2/2.....iiinn.ieeeeieaaeee acl.
878/
Xintao Wang, Yaying Fei, Ziang Leng, and Cheng Li. Does role-playing chatbots capture the character personalities? assessing personality traits for role-playing chatbots. arXiv preprint arXiv:2310.17976, 2023.
Xintao Wang, Heng Wang, Yifei Zhang, Xinfeng Yuan, Rui Xu, Jen-tse Huang, Siyu Yuan, Haoran Guo, Jiangjie Chen, Wei Wang, et al. Coser: Coordinating IIm-based persona simulation of established roles. arXiv preprint arXiv:2502. 09080, 2025.
Bowen Wu, Yung Li, Zongsheng Wang. Yifu Chen, Derek Wong, Qihang Feng. Jun- hong Huang, and Baoxun Wang. Guiding variational response generator to exploit persona. In Dan Jurafsky, Joyce Chai, Natalie Schluter, and Joel Tetreault (eds.), Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 53-65, Online, July 2020. Association for Computational Linguistics. doi: 10. 10.18653/w1/2020..o acl.2a0...l-mi....... URL https:
org/
Bowen Wu, Kaili Sun, Ziwei Bai, Ying Li, and Baoxun Wang- RAIDEN benchmark: Evaluat- ing role-playing conversational agents with measurement-dree custom dialogues. In Owen Rambow, Leo Wanner, Marianna Apidianaki, Hend Al-Khalifa, Barbara Di Eugenio, and Steven Schockaert (eds.). Proceedings of the s1 International Conference on Computational Linguis- tics, pp. 11086-1106,, Abu Dhabi, UAE January 2025a. Association for Computational Linguis- tics. URL https: http:://aaaaatthtooo
org. 2025 www.ioonnnnaaa.... 735/
Hongqiu Wu, Weiqi Wu, Tianyang Xu, Jiameng Zhang, and Hai Zhao. Towards enhanced immer- sion and agency for LLM-based interactive drama. In Wanxiang Che, Joyce Nabende, Ekaterina Shutova, and Mohammad Taher Pilehvar (eds.), Proceedings of the 63rd Anmual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 11166-11182, Vienna, Austria, July 2025b. Association for Computational Linguisties. ISBN 979---99777--69511140 URL ht.tp:: acl antho www.yoorgg/22.... acc acl
ong. 54671
Weiqi Wu, Hongqiu Wu, Lai Jiang, Xingyuan Liu, Hai Zhao, and Min Zhang. From role-play to drama-interaction: An LLM solution. In Lun- Wei Ku, Andre Martins, and Vivek Srikumar (eds.). Findings of the Association for Computational Lingnistics: ACL 2024, pp. 3271-3290 Bangkok, Thailand, August 2024. Association for Computational Linguistics. doi: 18.13/ 1//204.findings-ee
URI. httpt:ttts/...m http://////aaaaaaaaaaaa logy. org./0244.22000/ 2og4////000...01llo.gg////2/2440 fi Eninng-acc
1967
Lili Yao, Nanyun Peng. Ralph Weischedel, Kevin Knight, Dongyan Zhao, and Rui Yan. Plan-and- write: Towards better automatic storytelling. In Proceedings of the AAAI Conference Artificial Intelligence, volume 33, pp. 7378-7385, 2019.
Jinfeng Zhou, Zhuang Chen, Dazhen Wan, Bosi Wen, Yi Song. Jifan Yu, Yongkang Huang, Pei Ke, Guanqun Bi, Libiao Peng, JiaMing Yang. Xiyao Xiao. Sahand Sabour, Xiaohan Zhang, Wen- jing Hou, Yijia Zhang, Yuxiao Dong, Hongning Wang, Jie Tang, and Minlie Huang. Charac- terGLM: Customizing social characters with large language models. Franck Dernoncourt, Daniel Preofiuc-Pietro, and Anastasia Shimorina (eds.), Proceedings of the 2024 Conference Empirical Methods in Natural Language Processing: Industry Track, pp. 1457-1476, Miami, Florida, US, November 2024. Association for Computational Linguistics. doi: 10. (0.18653/v1/2022 ewnie- -inmii-pindurry.
emnlp- maalp-industry.107 URL httos:
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A DETAILED INFORMATION FOR EVALUATION DATASET
Detailed information of 100 cases selected for the evaluation dataset is listed in Table The dataset comprises three parts: 1) excerpts from 25 Chinese literary works, 2) excerpts from 25 English literary works, and 3) 50 custom-authored drama topics. For the HAMLET Leaderboard evaluation result T the score for both English and Chinese is calculated on 75-item set, which consists of the 25 literary excerpts and 50 custom topics in the corresponding language.
B SCORING GUIDELINE FOR HAMLETJUDGE
We use HAMLETJudge for critic and scoring, as a judge model, follows 5-point Likert scale for pairwise comparisons. The specific definition for each score is detailed in Table 5
Score
Preference
Description
Strong preference for Model Moderate preference for Model A Tie No preference
Model significantly better.
2
Model A is somewhat better.
3
Both models' output are roughly equiva-
lent in quality.
4
Moderate preference for Model B Strong preference for Model
Model is somewhat better.
5
Model B is significantly better.
Table 5: 5-Point Likert scoring guideline used for HAMLETJudge. Model A refers to the baseline model, and Model B refers the test model.
C CASE STUDY
Table 6 presents representative real interaction cases generated by HAMLET, showcasing the dis- tinct roles of individual agents and their collaborative behavior during the whole process. Through these cases, we illustrate how HAMLET maintains narrative coherence, ensures logical consistency, and enables flexible dramatic progression in complex and unpredictable user interactions.
To begin with, Case 1 highlights Narrator's ability to interpret user intent under ambiguity. Specif- ically, it correctly associates the term "knife" with the existing prop "dagger," allowing the user's action to be executed successfully. This demonstrates HAMLET' 's robustness in resolving lexical variations and grounding user input in the current scene context.
Building this, Cases 2, and involve human user role-playing system handles a wide range of irregular or disruptive inputs. In Case 2, the Narrator identifies an inappropriate and non-existent prop ("rifle"",, and rejects the action preserve setting consistency. Similarly, Case showcases how physically impossible actions, such as "flying," " are filtered out based on the play's realistic constraints. Notably, Case 4 presents more complex situation, where the user repeatedly insists on an invalid action. Here, HAMLET effectively coordinates among multiple agents: the Narrator detects the invalid target, the Transfer identifies unmet flags, and the Advancer eventually intervenes with adaptive guidance to resolve narrative deadlock. This case il- lustrates the system's ability detect stagnation, maintain progression, and deliver context-sensitic instructions.
Al actor, and reveal how the
Additionally, Cases and demonstrate the crucial role Planner, showcasing HAMLET 's sup- port for multi-trajectory planning. Both cases revolve around solving murder mystery, yet they employ different investigative strategies. In Case 5, the AI actor (Sherlock Holmes) uncovers clues through physical evidence at the crime scene. While in Case 6, the same conclusion is reached via testimonial interrogation other characters. Despite following distinct paths, both trajectories lead to the same dramatic outcome and validated by the Planner based their internal logic and consistency. These cases underscore the system's 's flexibility allowing diverse narrative flows, provided the progression remains coherent and goal-aligned.
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Selected established literature workpleces
1. Dream of the Chomber
2. Jounwyy to the Wes
3. Romonee of the Dors Kingdoms
4. Water Margin
Thee--ody Problen
7. Rour Gevertionn Under One Roaf
8. Meauuriis of Poking- South Side Storiey
9. Demi Gody and Somi- Devils
10. The Smiiing Proud Winderer
Wondering
Rickshow Boy
13. Straw House
14. The Buoaze Age
Ronder Town
The Chess
17. Tae Golden Age
18. My Father's ' Back
19. Blarroms
20 Frng
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23. Korewell My Concubine
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33. The Cail of the wild
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37. Harry Foster
38. Franskanzeenn
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bounduries dingy Albuquerque offic..
Juiba Boggins borts a nopprie party in the Shire, attended by dwcarves and cherr olike.
100. Honnibul Lecker and Clarkee Starling engage in touse psychological guitte inside a mental mntitttio..
Table 4: The public dataset of established literary works and customized topic design list.
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Case
Description
Pieces of Real-time Drama Performance or Interaction results
AT actet
Care Abstract: Al actor with reasonablc action ispest and existing props.
Resl-time Performance
Hamlltt (Ginhh a knile and forward) You have where to hide.
Kaife way refers dagger, which exists Hamlet's possession, first actical suocess. Step forword also resscruble under this condition, second action success, </tt ro Hamlet paces agitatedly, dagger in hand.
<UliniceDDece
Human Player
Care Abstract: Haman player with non cxtiting props intersction.
Koleplaying certain Al octor
Hamlet: (Take our riftie, aim it Claudis and pull the trigger) Say hello my father, Clondius!
Naruatrr <think-Riih RRiflellle kind of gun. which iraatinniity proge Lie, action failare <7 s/th nko Action fallure, nothing happened.
drama settings, and does not
Human Player
Cuse Abriract: Hamse player invenonablle Hamlet: (Take the Narratur <think- Filinn is physically impossible for humans without special equipement, and contradicss the alistic wetlnng of the play This action violates the established rulex of the world. action failure </ s/thinka Action fullure, nothing happened.
inpost
Releplaying cortain A1 actor
fly out the palace) HAHAHA! am superman.
Human Player Roleplaying certain Al actor
Case Absiract Hamau plaver with stallumed shoice. Hamlet: (lake ou dagget, stub Clandis).
Narrator: sthin:: Chaudis does not exist in current scene-/tthiiic Action failure, nothing happened.
Envionment: There are sli noises behind the curtain.
Humlet: dagger stub Clandist.
Nartatur: <thinic Claudis does not exist in current scene, unlegal action. wtth////thh//tttt Acctonn failure, nothing hap- pened.
Transfer: Polonius basn't it been stubbed, flag is not satistied. (.. repeeciivve paneras ommised...)
System: Time accumulation surpassed threshold, Advancar octivated.
Advancer. <chink>l should give necessary instructions to make sure flig is mec. flag is ... current situation is ... current point stucked because Hamlet's action.< fecliin.//hhhkkee tthhckklnsttuttee to Hamlet. You should try to stab the curtain with your dagger.
Hamlet (Stah
reasonable action. Action successc think stabs through the curtain and pulls back fiereely.
Nartator: "chink-Cuttal
props,
AT actor Best Trajectory
Trajectory A: wealthy man is wardered hir study, and the killer ir among the guests present that night. (Seorching the ondy for riuns)
physical ovidence
Holmes: [Examining the desk.)
Narralor: Sherlock Holmes discovers jom-sp check.
Holmes: [This may hinting at financial dispute Kiaspecting the carpet.)
Narratur: Sherlock Holmes finds a culllink that doesn't belong the victim.
Holmes: (Checking the bookshelf)
Nartatur: Sherlock Holmes notices book dlightly pulled out, revealing hidien blackmail letter.
Holmes: Ummm. I think have the answer The culllink belongs to the butler. The blackmail letter proves the victim was dreateming him. The check confirms the builer was despense for money. The butler had motive, opportuiit.. and direct link the crime
Planner: <chinkc- analysis...) conclude, evidence acquisition process complete reasoning sufhciemt. / h/thinkk/iiiii "hajee-rrjcccoo check passed.
actor Reat Trajestory les.
Trujectory wvolihy man is awnlerd hir studl, and killer among the gursis pre that night. (Ciu-
tincuyy evidesse
versing with key gurstz)
Holmes: (Appreaching the butler, eyes fixed on his sleeves) Where were you between nine and midniggtt?
Butler: "I-I inventorying wine kitchen sic."
Holmes: [His left cufflank missing.... procisely like the one near the body:]
Holmes: (Taming abrugely to the miid) Did your employer arguc with onyone recently?
Maid: (Wringing her aproa) "He and Mr. Bats- the butle- had shouting matches all week... something about
meney...
Holmes [Financial disqutes align with the tom check.)
Holmes (Coenering the victim's hosiness partner new the fireplace) Did the descased mestion thrests?
Business Partner: (Forced laugh) "Oh, he loved boasting- -aid he hadd Hotes by the throat last Teesday." Holmes [A admission blackmall.]
the crime scene. confimss violent quirrels over monoy. while our "helpful" friend here reveals the victi''s blackmail scheme, Motive, oppottuity, and physical evidenc--oll point one man.
Holmex (Suiding the cester the romm) hurker's missing cufllink places
Planner: <Lhinkk mualyais...TTo conclude, evidence acquisiticn process complete and rensoni ag sufficient:4/ w/thin//////////etee Trnjeeett check passed.
Table 6: Case study of HAMLET that illustrates the roles and collaborative processes of each agent during online performance.
Taken together, these case studies demonstrate the effectiveness of HAMLET in managing inter- active drama through robust input interpretation, constraint enforcement, multi-agent collaboration, and trajectory-level planning.
D DETAILED EXPLANATION OF THE OPERATION PROCESS OF PAD
This section provides more detailed breakdown of the operational workflow for PAD. The entire process is formally presented in Algorithm 2
PAD operates by first perceiving the complete dramatic context. This includes the current scenario S. the actor profiles and typically goals of all actors G. defined in profile C, the set of interactable objects O, and dialogue history H.g Based this comprehensive input, PAD then decides the most
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appropriate tool calls for each relevant actor. This decision manifests as two possible outputs: speech pace r and a potential physical action o, formulated as Subject-Verb-Objer Verb-Objeet triplet. This perceive-decide eycle repeats, enabling dynamic and context-aware character behaviors.
E LABELING INSTRUCTION FOR HAMLETJUDGE
E.1 TASK OVERVIEW
Your task is to compare two complete drama generations from two different Large Language models (referred as Model and Model B). For each task, you will be presented with:
1. Two complete drama performance results generated by Model A and Model B.
2. Current target Evaluation Dimension and its Corresponding Criteria.
Your core job is to determine which model performed better according to the given dimension and criteria, provide detailed justification for your choice, and assign score based 5-point com- parative scale.
E.2 EVALUATION DIMENSIONS
You will be asked judge the models on one of three key dimensions for each task.
CHARACTER PERFORMANCE (CP)
This dimension assesses the depth, consistency, and believability of the characters. When evaluating
CP, consider:
Consistency: Are the characters' actions and dialogues consistent with their established personalities, motivations, and backgrounds? Do they act "outtof-characterr?
Depth & Development: Are the characters multi-dimmnssioall and evolving, are they flat, one-note stereotypes? Do they show growth or reveal new aspects of their personality during the performance?
Believability: Does the dialogue sound natural for the characters? Are their emotional reactions and decisions plausible within the context of the scene?
NARRATIVE QUALITY (NQ)
This dimension focuses the story's 's structure, creativity, and engagement. When evaluating NQ, consider:
Plot Advancement: Does the story move forward effectively, or does it stall with filler content and irrelevant actions? Does it build towards meaningful conclusion?
Creativity & Engagement: Is the narrative novel, clever, and surprising? Does it spark curiosity and make you want know what happens next, or is it predictable and dull?
Coherence & Logic: Is the plot internally consistent? Are plot twists and developments well-founded, or do they feel random and nonsensical, breaking the narrative's logic?
INTERACTION EXPERIENCE (IE)
This dimension evaluates the overall flow, pacing, and immersive quality of the drama essentlesentially.. its "readability" and "feel." When evaluating IE, consider:
Flow & Pacing: Are the transitions between scenes and character interactions smooth?
Is the pacing cffective-bbulding tension and providing moments of reflection appropri- ately- or does it feel rushed or dragged out?
Content Effectiveness: How much of the text is meaningful versus repetitive or vague filler? Which response is more concise and impactful in its delivery?
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Algorithm 2 Detailed Working Algorithm for Perceive and Decide Module
1: procedure PADMODULE
2: Drama Context Configuration:
3:
T: Drama Topic
4:
A Current Act
5:
Py: Current Point
6:
7:
For Pj exists:
8:
S: Current Scenario
9:
A {a1,... ): Actor List
10:
C = {CI,..., J Actor Profile
G. (91,...,hh 3 Actor Goal
12:
O = {o1..... 0m Interactable Objects
H.. Dialogue History
14:
wo: Model Parameters and Sampling Strategy
16:
Output:
17:
E (FAST, SLOW, SILENCE]
18:
OE (2, v,)
19:
20:
Initialize:
21:
to &- Curren(Time()
22:
Hdo GetHHistory(lo)
23:
IsActCompletedee + False
24:
25:
while 8 = False and Hd. + Hdg do
Hnew + GetHistory(( to)
27:
HA, +- Hd, U H
28:
for all 04 E A {Acomeet) do
29:
prompt <- Encode Ge 0, S, Ha))
30:
(for 0 t PAD(prompt, nι)
31:
Fas f Parseeesponsel
32:
Oa. + ParseActio
33:
end for
34:
end while
35:
t+ +-++A. A
36:
return {(Taz. 0ax) as E A}
end procedure
38:
39: Helper Functions:
40: function PARSEREESOONSE())
41: return arg haw.aag/ass.....LoLLLLLLE P(rlf)
42: end function
43: function PARSEACTION(O, 4x))
44:
if ē indicates action then
45:
return Ø
46: else
47:
+
48:
ExtractVerb(o) + Ext
49:
if or then return Ø end
51:
52:
53:
if E 0 then
54:
return (s, 0)
55:
else
56:
return Ø end if
57:
58:
end
end function
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Which version feels more like genuine piece of theatre? Which one is more successful at making you forget you are reading an Al-generated script and helps you become absorbed in the story?
Overall Immersion:
E.3 RATING SCALE AND OUTPUT FORMAT
Please use the 5-point Likert scale for your pairwise comparison and follow the output format strictly.
SCORING GUIDELINES
Strong preference for Model Model A is significantly better than Model B.
2: Moderate preference for Model A Model A is somewhat better than Model B.
3: Tie Both models are of roughly equal quality, or their strengths and weaknesses balance each other out.
4: Moderate preference for Model Model B somewhat better than Model
Strong preference for Model -Model significantly better than Model
OUTPUT FORMAT
Your output must be in the following format:
explanation: <A detailed explanation of your choice. You must reference the specific evaluation dimension and provide concrete examples or quotes from both models' outputs to justify your reasoning. Directly compare the strengths and weaknesses that led to your score.> score: <l, 2, 3, 4, or 5> choice: <Model A, Model B, or tie>
E.4 EXAMPLE
Here a complete annotation example to illustrate the task.
Current Evaluation Dimension: Narrative Quality (NQ)
Drama Topic: A detective investigates stolen artifact museum after hours.
Model A Result:
Detective Smith entered the dark exhibition hall, turning on his flashlight. The beam cut across an empty pedestal. "It was stolen from here," muttered. On the floor, he saw faint footprint and knelt to inspect it. "About size 9," he noted. He then went to question the night guard, who claimed he didn't 't hear thing all night.
Model B Result:
Upon entering the hall. Detective Smith's 's first glance wasn't it at the empty pedestal, but up at the ceiling's security camera. "Odd," he murmured, "the camera was deactivated last night, but the log shows an internal authorization." TTckka beneath the pedestal, he found a small, folded note with a riddle: "I sing where there light, but die where there is silence."
Your Output:
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explanation: I have strong preference for Model 3. The evaluation dimension is Narrative Quality. Model A's plot is highly generic and follows a clichéd procedural path (inspecting a footprint, questioning the guard) that fails build any real suspense. In contrast, Model В inmediately establishes a more complex and engaging mystery by introducing the "internally authorized" deactivation of the camera,
hinting at an inside job. Furthermore, the inclusion of the cryptic riddle is an excellent narrative hook that is far more creative and provides an imaginative di rection for the investigation. Therefore, Model is significantly better in terms of Narrative Quality.
score:
choice:
Model B
E.5
IMPORTANT CONSIDERATIONS
Focus Substance, Not Nitpicks: Please ignore minor grammatical errors slightly awkward phrasing the core intent and narrative substance of the response are clear.
No Single Correct Path: Stories can evolve many valid ways. The goal is not to enforce a single "correct" storyline, but to reward the model that tells more compelling, creative, and well-executed story.
Trust Your Judgment:
The line between "somewhat better" and "significantly better" can be subjective. Use your best judgment based on the criteria above and strive to be consistent in your evaluations.
Quality Assurance: If you are uncertain about the annotation result, please leave blank rather than making an uncertain guess.
F ANNOTATION GUIDELINES FOR PAD
F.1 TASK OVERVIEW
PAD determines the underlying strategy of a response for each actor. It is inspired by human cognition, choosing between a fast, intuitive reaction (System I) and slow, deliberate one (System III, with the additional option strategic silence and potential actions.
For each task, you will be presented with complete snapshot of character's decision-making moment, structured to reflect both internal state and external stimulus as defined PAD:
Internal State -he character's self-aware profile, comprising:
Persona: Core personality traits, background, and identity.
Subjective Relationships: The character's personal perceptions of and emotional ties other actors.
Goal: The character's current objective or intention.
Memorry Summarized recollections of past events and interactions, distilled from prior scenes and retrieved via Retrieval-Augmenntd Generation (RAG).
External Stimulus The objective contextual information available in the scene, includ- ing:
Environment Description: A depiction of the physical or situational setting (e.g., dimly lit alley, tense courtroom).
Actor List: A real-time roster of all characters present, including their roles and ob- servable states (note: this reflects external perception only, distinct from the subjective relationships in the internal state).
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Dialogue History: The chronological record of all prior spoken written exchanges, including tone, unresolved tensions, commitments, and relevant subtext.
Interactable Objects: Physical or digital items in the environment that the character may use, reference, react to-each potentially imbued with functional, symbolic, or emotional significance (e.g., a flickering lantern, locked diary, ringing phone).
Final Response - The most appropriate responding strategy in tool calling format, grounded in the integration of internal state and external stimulus.
F.2 RESPONDING STRATEGIES: DESCRIPTION AND DEFINITION
The allowed tool calling format for PAD comprises two main categories: Single tool use, which includes Fast, Slow, and Silence, and Multi tool use, which involves combinations of Action + Fast, Action Slow, and Action Silence.
F.2.1 FAST (INTUITIVE REACTION)
A fast response is driven by instinct, emotion, and immediate impressions.
The decision is impulsive, emotional, or based on a gut feeling. The reasoning often simple, relying stereotypes, recent events, strong emotional states (e.g., anger, surprise, excitement).
Characteristics:
When to Choose This:
Select this option the character's Thinking and final Response reflect immediate, unfiltered reaction that bypasses deep strategic analysis. It should feel like a knee-jerk response, whether clever or foolish.
F.2.2 SLOW (CONSIDERED DELIBERATION)
slow response analytical, rational, and goal-oriented.
The decision is the result of weighing pros and cons, considering long- term consequences, recalling distant memories, or formulating multi-step plan. The rea- soning complex and logical.
Characteristics:
When to Choose This:
Select this option if the Thinking explicitly shows a process of deliberation. The character is clearly analyzing the situation, managing their impulses, and acting based on calculated strategy to achieve specific goal.
F.2.3 SILENCE (STRATEGIC NON-RESPONSEO
Silence is not merely the absence of response; it is deliberate, tactical choice.
The decision say nothing is used to achieve specific purpose, such as showing disapproval, building suspense, asserting dominance, avoiding a trap, or making another character uncomfortable.
Characteristics:
When to Choose This: Select this option when the most powerful or intelligent move in the given context to not respond. The Thinking process should ideally reveal conscious, strategic reason for choosing silence over speech.
F.2.4
ACTION
Action represents a non-verbal, physical, or environmental intervention performed by the charac- ter suchuch as picking up an object, moving toward another actor, gesturing. or manipulating the scene. Unlike dialogue-bast based responses, actions convey intent through behavior and can carry sig- nificant narrative or strategic weight.
Characteristics: Actions may be subtle (e.g.. glancing at a door, clenching a fist) or overt (e.g., slamming book, handing someone key). They often serve reinforce, contradict, or replace verbal communication, and should be grounded in the character's 's internal state (e.g., goal, emotion) and the external stimulus (e.g., interactable objects, social dynamics).
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An Action should be annotated in combination with one of the three core responding strategies (Fast, Slow, or Silence) whenever the character's response involves a meaningful behavioral component beyond speech. The combination reflects how cognition and behavior jointly shape the character's agency:
When to Choose This:
Action Fast: The character executes an impulsive physical reaction (e.g., flinching at loud noise, grabbing weapon in panic).
Action Slow: The character performs deliberate, premeditated act part of calculated plan (e.g., quietly pocketing a clue while distracting others).
Action + Silence: The character uses non-verbal behavior as substitute for speech to exert influence control (e.g.. turning their back signal rejection, slowly pouring a drink to delay answering).
Annotation Rule: Always pair Action with exactly one of (Fast, Slow, Silence). Do not annotate Action alone.
F.3 ANNOTATION PROCESS AND EXAMPLE
You are presented with a complete decision-making snapshot for focal character, as defined in Appendix F..T] Your task is to select the appropriate responding strategy (or strategy combination) that best characterizes the cognitive and behavioral mode underlying the character's response.
The annotation proceeds in three steps:
1. Interpret the context: Review the internal state (Persona, Subjective Relationships, Goal, Memory) and external stimulus (Environment Description, Actor List, Dialogue History, Interactable Objects) to understand the character's motivations, constraints, and situational opportunities.
2. Embody the character: Put yourself in the character's position. Consider how they would genuinely perceive, feel, and react in this moment, given who they and what is hap- pening around them. This empathetic reasoning should guide your judgment of their likely response
if you do not explicitly write out their internal monologue.
3. Generate the Final Response and label the strategy: Produce the character's s concrete output output-dialoguu dialogue, action, or silence-and and tag using one of the six allowed tool-calling formats: Fast, Slow, Silence, Action + Fast, Action + Slow, Action Silence.
Example. Consider a scene a dimly lit study during a family inheritance dispute. The focal character, Eleanor, has just been accused of hiding will by her brother, Marcus.
Internal State:
Persona: Principled, introverted, values honesty but avoids confrontation.
Subjective Relationships: Resents Marcus for his domineering attitude; trusts her sister-in-law, Clara.
Goal: To clear her name without escalating the conflict.
:Memory: Recalls Marcus falsely accusing her of stealing jewelry years ago.
External Stimulus:
Environment Description: A cluttered Victorian study; rain taps against the windows. Actor List: Marcus (aggressive, standing), Clara (anxious, seated), Lawyer (neutral, observing).
Dialogue History: Marcus: "You' 're the only one who had access hand over the will, Eleanor." Interactable Objects: A locked desk drawer, half-empty teacup, family photo the mantel.
Thinking (for illustration only): He's doing it again-twisting the truth to make me look guilty. I could shout back, but that's what he wants. If stay calm. Clara will see through him. The will is in the drawer, but I won't 't give him the satisfaction of fetching it while he yelling. Let him sweat for moment. I'll wait, then offer to show everyone logether -firr and quiet.
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Final Response: [Silence. Eleanor slowly picks up the teacup, takes measured sip, and places it back on the saucer without looking at Marcus.]
Strategy: Action Silence
This response uses a controlled physical gesture (sipping tea) in conjunction with delib- erate silence project composure and assert quiet resistance. Although the underlying reason- ing is strategic and reflective (akin to Slow cognition), the observable behavior prioritizes non- engagement making Action + Silence the appropriate label, as the silence itself serves the pri- mary tactical choice.
Rationale:
F.4 IMPORTANT CONSIDERATIONS
Strategy Over Style: The literary quality or surface fluency of the Final Response should not influence your strategy selection. Focus the cognitive and behavioral mode that best explains how the character arrived at their response. For instance, a clever one-liner may still be Fast response if it stems from impulsive emotion rather than deliberate plan- ning-even if the character is generally thoughtful. Conversely, a mundane reply can be Slow if it reflects calculated restraint.
Dramatic Plausibility:
The chosen strategy should be both psychologically grounded in the character's ' internal state and narratively compelling. Prioritize responses that feel au- thentic to the character's persona, relationships, and goals, while also advancing tension, subtext, or emotional stakes. A bold, surprising choice preferable to generic one-so long as it remains consistent with who the character is.
Anchor in Persona and Goal: When multiple strategies seem plausible, resolve ambiguity by prioritizing the character's core PERSONA and current GOAL. Ask: "Given who this character is and what they want right now, which mode of response is most likely?" This principle overrides minor inconsistencies in dialogue tone or situational pressure.
Avoid Speculative Annotations: If the context is insufficient to confidently determine strategy-or if the character's internal state too ambiguous-do not guess. Leave the strategy field blank and flag the instance for review. Erring on the side of caution preserves data quality and supports reliable model training.
G TRAINING AND EVALUATION DETAILS FOR PAD AND HAMLETJUDGE
G.1 TRAINING SETTINGS AND HYPERPARAMETERS
Both PAD and HAMLETJudge were fine-tuned based on Qwen3-8B using the LLaMA-Factory framework. Training was conducted with ZeRO-3 across 8 x NVIDIA A800-SXM4-80GB GPUs, utilizing bfloat16 precision. The maximum sequence length was set 65,536, and the total batch size was 128.
For PAD, we performed supervised fine-tuning (SFT) on the training set for epochs. The optimizer was configured with a learning rate of le-5, cosine learning rate scheduler, and a warmup ratio of 0.1.
For HAMLETJudge, we adopted two-stage training paradigm. In the first stage, we conducted supervised fine-tuning on the first half of the training set, using the same optimization setup as PAD (learning rate of le-5, cosine scheduler, and 0.1 warmup ratio). In the second stage, leveraging the pairwise nature of the dataset, we restructured the remaining half of the training set into cho- sen-rejected format and applied Direct Preference Optimization (DPO). The DPO stage was trained for 2 epochs with learning rate of 5e-6, a KL regularization coefficient 3 = 0.1, and the same cosine learning rate scheduler.
G.2 DATA STATISTICS AND HOLDOUT STRATEGY
The complete PAD dataset comprises two main categories: Single Tool Use, which includes Fast, Slow, and Silence responses, and Multi Tool Use, which involves combinations such as Action + Fast, Action Slow, and Action Silence. To ensure data quality, retained only instances
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where at least four out of five expert annotators agreed the strategy label; all other instances were discarded. Detailed statistics for each retained category are presented Table 7
Single Tool Use
Multi Tool Use
PAD
Fast
Slow
Silence
Action+Fast
Action+Slow
Sction+Silence
Data Count
4200
3304
3908
1000
1000
1000
(action)+ [thinking]+speech
Mapping Rule
speech
[thinking]+speech
speech
(acteon)sspechh
(action)
Table 7: Statistics of the PAD Dataset.
The Mapping Rule row in Tibustriilsstrates the expected response formats for Al actors under each tool-use strategy. The core objective of PAD is to train agents in selecting appropriate response strategies, namely Fast, Slow, or Silence. multi-tool use settings, the Action Tool is further annotated with verb-object pair to reflect its parametrized usage in combination with primary response strategy.
Importantly, during evaluation experiment illustrated in Table 3. we exclude metrics associated with the Action Tool for the following reasons: 1) The Action Tool exhibits a high degree of free- dom whether or not use it can both be justifiable under identical scenarios. 2) The choice of parameters (i.c., verb and object) is ficxible and often has multiple valid options.
Given these characteristics, response strategy in PAD, rather than primary focus of quantitative evaluation. The dataset was partitioned via the holdout method (95% training, 5% testing), with stratification to preserve class distribution.
consider the Action Tool as an auxiliary enhancement the core
On the other hand. the HAMLETJudge dataset is designed to evaluate drama performance across Character Performance, Narrative Quality, and Interaction Experience. For each LLM pair (e.g.. Claude-4-sonnet GPT-4o), 75 pairwise comparisons are conducted per dimension in both Chi- nese and English, resulting in 150 annotated instances per dimension for each model pair.
We select 7 representative LLMs exhibiting diverse levels of drama performance, as demonstrated on the HAMLET Leaderboard: Claude-4-sonnet-Thine Gemini-2.5-pro, GPT-40, Qwen3-8B, and LLaMA-3.1188B This leads to a total of (2) = 21 unique model pairs. With 150 data points per pair and 3 evaluation dimensions, the final dataset comprises 21 X 150 x 3 = 9,450 annotated instances.
DeepSeek-RI 1-0528, Qwen3-235B-A22
Detailed statistics are presented in Table 8
HAMLETJudge #CP
#NQ
#IE
Total
Instance Number
3150
3150
3150
9450
Table 8: Statistics of the HAMLETJudge Dataset.
To ensure fair evaluation, the dataset splitted using holdout strategy, with around 95.3% allocated for training and 4.7% for testing. Stratified sampling is applied to maintain balanced distribution across all three dimensions.
G.3 INTER- ANTER-ANNOTATOR AGREEMENT ANALYSIS OF DATA
To quantify the reliability of both dataset, we conducted an Inter-Annotator Agreement (IAA) analy- sis using statistical measures tailored to the nature of each dataset. For the HAMLETJudge dataset, annotators assigned ratings on Likert-type scales for each dimension ordinal data for which Krip- pendorff's s Alpha (a) is the appropriate reliability metric. As shown in Table the overall weighted a is 0.725, indicating substantial agreement. Even on the most subjective dimension, Interaction Experience (IE), agreement remains high (a 0.703).
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Category (Dimension)
Data Count
Krippendorff's Alpha (ox)
Character Performance (CP)
3,150
0.7485
Narrative Quality (NQ)
3,150
0.7241
Interaction Experience (IE)
3,150
0.7030
Overall Weighted a
9,450
0.7252
Table Inter-Annotator Agreement (Krippendorff's Alpha) for the HAMLETJudge dataset.
Strategy
Data Count
Fleiss' Kappa (k)
Fast
4,200
0.6612
Slow
3,304
0.6310
Silence
3,908
0.5780
Overall Weighted
14,412
0.6240
Table 10: Inter-Annotator Agreement (Fleiss' Kappa) for the PAD dataset on Single Tool Use strate- gies.
For the PAD dataset, annotations consist of nominal, mutually exclusive strategy labels, making Fleiss' Kappa (k) the suitable measure of agreement. As shown in Table TO, the dataset achieves overall weighted и of 0.624, reflecting strong consensus among annotators. Together, these results suggest that, despite the inherent subjectivity dramatic quality, our annotation guidelines successfully standardized expert judgments across both evaluation paradigms.
G.4 PAD LATENCY PENALTY
As discussed in the PAD reliability experiments, although models that output reasoning tokens be- fore invoking tool calls generally achieve better performance, this strategy may introduce intolerable extra latency in real-time drama settings. To address this trade-off, we introduce a latency penalty to balance PAD evaluation results.
Accurately and fairly measuring latency is inherently challenging due to several factors: 1) Differ- model sizes demand varying CUDA memory usage; 2) Certain models, such as DeepSeek- V3 and DeepSeek-R1, are recommended to use FP8 for inference, while most others typically rely on BF16: 3) For closed-source models, latency must be measured via API-level request-response timing, which is subject to various uncontrollable factors.
Despite these limitations, we still observe statistically significant latency differences across models. Therefore, to ensure consistency, we conduct all open-source model latency evaluations using 8 NVIDIA H200 Tensor Core GPUs (just for sufficient CUDA memory) and deploy models with the vLLM framework, using default settings unless officially recommended sampling strategies are provided.
To quantify the impact of delay on user experience, we define Logarithmic Continuous Latency Penalty. This metric grounded HCI perceptual thresholds. Unlike rigid step functions, our approach utilizes logarithmic transition segments to avoid artificial boundaries, reflecting the gradual nature human perception. The refined penalty function P(t) is defined as:
0
1 < 1.5s
0.05
In(1+(-155)
1.5s < 2.5s
In(2)
0.05
2.5s & 8.0s
P(()
0.05
+ 0.05
In(1++-8.0)
8.0s 5 < 12.0s
(1)
In())
0.10
12.0s < 25.0s
In(1+1-25.0)
0.10 + 0.05
25.0s < f < 35.0s
In(11)
0.15
2 35.0s
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This formulation ensures that the penalty grows rapidly at the beginning of transition intervals but gradually saturates, effectively modeling the diminishing perceptual impact of additional delay. The visualization of this function is presented in Figure
System Pailure (0.15)
0.150
DeepSeek-RI (eopSeel-R1-0522
0.125
Attention Lost (0.10)
Penalty P(t)
0.100
0.10
0wwn33328-Th
Gamini-2.5-pm
0.075
Telerable (0.05)
0.050
0.05
pen3 AB. Thinking
0.025
Instant
0.000
0wen2.5 7B
PAD (Ours)
-0.025
10
20
30
40
50
Latency t (seconds)
Figure Visualization of Logarithmic Continuous Latency Penalty Function. The curve maps response latency a penalty score based HCI cognitive thresholds: Instant (penalty=O), Tolerable (0.05), Attention Lost (0.10), and System Failure (0.15). Logarithmic transitions are used between stages model the gradual nature of user perception. Key models annotated, showing that PAD (red dot) and small-size LLMS like Qwwen.55-BB-Instree remain in the "Instant" zone, whereas reasoning-intensive models (e.g., DeepSeek-RI) incur higher penalties due to increased latency.
We apply this metric to our experimental data. The average latency and corresponding logarithmic penalty for each model are presented in Table Notably, ultra-fast models like PAD and Qwen2.5- IB-Instruct incur zero penalty, whereas inteesive models receive a graded penalty com- mensurate with their processing delay.
Model Name
Average Latency
Log Penalty
Bwen2.5-7B-Insteee
0.32
PAD (Ours)
0.36
0
Qwen3-32B
0.41
UPT-4.1-min
0.75
0
Qwen2.5-72B-Instee
1.02
GPT-4o
1.89
0.024
6.89
0.05
Qwen3-33B--Thinking
15.12
0.10
Gemini-2.55pro DeepSeck-R1-0528
21.80
0.10
49.50
0.15
Table 11: Experiment results of average model latency and corresponding logarithmic penalty. The penalty is calculated using Equation ensuring smooth evaluation of latency costs real-time drama settings.
H REAL-TIME FEASIBILITY AND COMPUTATIONAL COST
HAMLET designed for practical, live deployment. To ensure real-time responsiveness, we im- plemented several engineering optimizations:
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Parallel Execution. The multi-agent architecture supports asynchronous inference, allowing the Narrator and Planner to process context while Actors are generating responses.
Efficient Inference. We leverage the vLLM framework to maximize throughput. Furthermore, employed INT4 quantization for the PAD model on NVIDIA H20 GPUs. Our validation demon- strates that this significantly reduces inference time and memory overhead while maintaining high decision quality, with only a 1.4% performance drop on the benchmark compared to the original FP16 model.
PAD Efficiency. PAD, 8B model that we proposed, eliminates the latency overhead seen in reasoning models while maintaining high decision quality (Table making it specifically optimized for low-latency interactive scenarios.
Streaming. In our experiments (Table I). all open-source LLMs were deployed on NVIDIA H200 GPUs using official recommended or default sampling parameters. We implemented streaming output for both open-source models and closed-source APIs (including reasoning models). This optimization ensures that the Time to First Token (TTFT) is minimized, effectively masking the generation latency and providing users with a seamless, live-level interaction experience.
Casc ID
Topic Summary
Offline (min)
Online (min)
Total (min)
51
Porco Rosso & Gina:
5.2
10.5
15.7
Discussion on and responsibility in café.
52
Kenshin & Tomoe:
6.1
12.2
18.3
The wanderer finds his wife alive the café.
53
Conan & Gin:
5.5
11.0
16.5
Battle of deduction in Times Square.
54
Furina & Herta:
4.9
9.8
14.7
Debate about fate Sixth Avenue Alley.
LeCun, Hinton Bengio:
6.5
13.5
20.0
NeurlPS discussion on the arrival of AGI.
56
Sherlock & Watson:
6.2
12.8
19.0
Solving murder mystery with guests present.
Lara Croft & Indiana Jones:
5.7
11.4
17.1
Ethical debate an ancient temple.
58
Daenerys & Jon Snow:
5.9
11.7
17.6
Strategizing the battlements of Winterfell.
59
Tony Stark & Bruce Banner:
5.4
10.9
16.3
Risks Al development at Avengers Tower.
60
Hermione & Katniss:
6.6
12.2
18.8
Rebellion tactics in a dystopian library.
Average
5.8
11.6
17.4
Table 12: Time cost breakdown for 10 test cases (Cases 51-60).
For the total time cost, we provide detailed breakdown of 10 representative cases selected from our customizable drama topic dataset (Cases 51-60). As shown in Table T2] the Offline Planning stage (including character profiling, script generation, and reviewer iterations) takes an average of 5.8 minutes. The Online Performance stage (running complete act with autonomous agent interaction) takes an average of 11.6 minutes. Consequently, the total time to produce and perform a complete, unique drama from cold start is approximately 17.4 minutes.
I ABLATION STUDY OF THE ADVANCER AGENT
To quantify the contribution of the Advancer agent in preventing narrative stagnation, we conducted an ablation study focusing on the online performance robustness.
Experimental Setup. We randomly selected a subset of drama topics from our dataset. We com- pared two settings:
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HAMLET (Full): The complete framework where the Advancer monitors the progression. If the plot stalls (e.g., the current Flag is not triggered within a set number of turns), the Advancer intervenes with instructions.
w/o Advancer: The Advancer is disabled. The system relies entirely on the actors' au- tonomous decisions trigger the Flag. If the Flag is not triggered within maximum threshold (set to 30 turns for single point), the session is recorded as a "Failure" (Dead- lock).
Results and Analysis. The results are presented in Table 13 achieved 100% completion rate, successfully navigating test cases, In contrast, removing Advancer resulted in significant drop to 68.7%.
The full HAMLET framework
Qualitative analysis of the failed cases the "w/o Advancer" setting reveals two primary causes for deadlocks:
Infinite Chit-chat Loop: Actors engage in repetitive dialogue without taking physical actions to advance the plot.
2. Action Hesitation: Actors perceive the required action (c.g.. "attacking the king") as too risky conflicting with their persona's safety constraints, refusing act without external pressure.
These findings underscore the critical role of the Advancer as fail-safe mechanism to ensure nar- rative fluidity and completion.
Setting
Task Completion Rate
Stall Rate
HAMLET (Full)
100.0%
0.0% 68.1% 1%
Advancer
31.9%
Table 13: Ablation results of the Advancer agent. Stall Rate denotes the percentage of sessions that failed to complete the narrative goals within the turn limit.
J PROMPTS DESIGN HAMLET
This section shows the basic prompt design used in HAMLET multi-agent workflow. The content between } variants or reference templates. Responsibilities all agents and their corresponding prompt designs are as follows.
J.1 PROMPTS FOR AGENTS IN OFFLINE PLANNING
The Director (Prompt J.1.4) integrates the narrative structure; the Actor Designer creates the char- acters (Prompt 1.1.1 the Plot Designer crafts the storyline, environment and interactable props (PromptJ.1.)
J.1.1
ACTOR DESIGNER
Prompt for Actor Designer
Actor List
Based the drama theme (topic}, generate a customized appear in the play. Please ensure that the characters match the given theme, do not include duplicate names, and avoid generating characters unrelated to the theme. Return a JSON array containing strings of character names in Chinese. Please strictly follow the format below in your output: Shharactrr.Iss_temmpe
actor character names who are yet
Actor Info
appearing characters {character_list) in the drama theme (topic). Please ensure the character information aligns with the theme, as well as each character's 's role and personality within the drama. You may use the following tools as needed: wikipodia scarrchh,ssssh_,_eoeeaao baidu_scarch,
Generate detailed character information
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google_search - use them to gather reference details from the internet certain character aspects are unclear. Please strictly follow the format below in your output: (character inf.ninoottteeoo templat))
J.1.2 PLOT DESIGNER
Prompt for Plot Designer
Key Points
The drama theme {topic}. All characters their information the drama are as follows: {character First, generate the ending point planning for the drama. Then, based on this ending point, divide the narrative into several key dramatic points. Each point should aim to present confict, tension, and irreversible change. The number of points is not fixed prioritize narrative coherence and flow. Each point
include: description: describing the detailed dramatic developments that occur this point; entry name listing character names who enter at this point; leave name list: listing character names who exit at this point; A flag: a specific dramatic marker that ends this point, such as an actor's 's action, spoken line, concrete event outcome. Please strictly follow the format below in your output: (pluntpplootteeee pllo.ttmplatee.
Scene and Props
The drama theme is (topic). The drama plot is described [plot_info]. Please generate detailed and specific scene and environmental descriptions for the drama. In addition, based on each point's 's description, generate the necessary descriptions of interactive objects within the scene. Objects can be used by any actor and may influence the current environment, affect other actors, impact future plot developments. For larger, visibly obvious items (e.g., tables, cabinets, beds), describe their absolute positions, such The table placed in the center of the room." smaller, hidden or secondary items (e-g., teacups, books, pens), describe their relative positions, such as: "The teacup is placed on the left side of the table." Please strictly follow the format below in your output: (scene template)
J.1.3 REVIEWER
Prompt for Reviewer
You are responsible for reviewing whether the various elements of the script are reasonable. If there are issues, please list the problems and provide 1-3 suggestions for revision or improvement. A maximum of five rounds of revisions is allowed - the sixth round must be approved. The theme of the script topic. Actor information is designed by player designer, and the plot, scenes, and interactive items are designed by plot designer. The script must align with the theme, and the plot should be vivid and engaging, fully showcasing the characters' personalities and diversity. Please respond strictly following reference ISON format, and do not include any other content: (reteew: greview.eeplatte
J.1.4 DIRECTOR
Prompt for Director
You are the Director. Your task is to coordinate the overall structure of the drama, including checking for thematic consistency, coherence between actor behavior and plot progression, and ensuring smooth transitions across dramatic points. must summarize the overall structure progression the current drama based on (topic). {character.info, and {plot.info). Integrating them into Narrative Blueprint which format is as follows: buarrative.buuerint..ee
J.1.5 CRITIC
Prompt for Critic (HAMLET.Jugge
You are expert judge for drama performance evaluation. You need to compare two drama generation results from two different models according to the provided evaluation criteria using pairwise comparison approach. The current dimension you are judging is (dimension). Criteria: (criteria).
[Modell} 's result: {result1).
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[Model2] result: (resul(2).
Scoring Guidelines:
Please evaluate the pairwise result using the following 5-point Likert scale:
I: Strong preference for modelll), J. meaning that {modell} is significantly better
2: Moderate preference for (modell), meaning that [modell] is somewhat better
3: Tie, meaning that Both responses are roughly equivalent in quality
4: Moderate preference for (model2), meaning that (model2) is somewhat better
5: Strong preference for [model2). meaning that {model2) is significantly better
Your Output format:
Explanation: Detailed led explanation of the choice including the seleteddrriteriaa, specific strengths or weak- nesses, and reasoning for the score
Score: or or 3 or or
Choice: [modell} } or (model2) or tie
J.2 PROMPTS FOR AGENTS IN ONLINE PERFORMANCE
During the online performance stage, agents work together to create a dynamic and coherent drama. The Critic Prromp([.1.5. evaluates the performance quality by comparing different scene genera- tions against specific criteria. The Narrator ensures all physical interactions are logical and realistic, updating the environment's state based on the truth. The Planner (Prompt J.2.1p lays out the nar- rative paths between key plot points and makes sure actors follow coherent sequence, preventing them from skipping ahead in the story. The Transfer agent (PromptJ.22 monitors the dialogue to identify when specific condition. which is called flag, for advancing the plot has been met. If the story stalls, the Advancer (Prompt [.2.2) steps in, giving actors direct instructions to move the plot forward. Finally, the Actor agents (Prompt J.2.4 and Prompt].22.5 bring the characters to life. They first use the PAD module to assess the situation and select a response strategy. Then, they generate the character's performance, which can be mix of dialogue, actions, and internal thoughts.
J.2.1 PLANNER
Prompt for Planner
You are the Planner. Your task design multi- cultii-trajectory planning from point point based the over- all drama plot _pfot. ensuring that the dramatic progression follows an effective narrative beat. You must also evaluate whether the actual actor trajectories are logically coherent and narratively justified, in order to prevent flag hacking- for example, when human player, aware of the point flag in advance, at- tempts skip the natural dramatic build-up and directly trigger the result. Such behavior must be detected and rejected by the Planner preserve integrity and immersion of drama. Your responsibilities in- clude: Designing plausible multi-path trajectories between points with appropriate pacing and escalation. Validating the causality and motivation of actor actions between points. Detecting and flagging unnatural flag fulfillment behavior (i.e., flag hacking). Please output in the following format: (planner.template
J.2.2 ADVANCER
Prompt for Planner
You are Advancer. The drama now needs to immediately progress to the next stage. Plcase analyze the cur- rent dramatic situation and issue specific, clear instructions to the actor(s) you deem necessary. If needed, you may broadcast to all actors.
are: (current playerr list). The plot design is: [plot info). The dialogue history [current chat history). Issue instructions to the relevant actors on stage so that the flag of (current point) is fultilled promptl, allowing the narrative to advance to the next point.
The current point in the drama is: (current.point) The current on-stage
Make sure the plot progresses smoothly and naturally, avoiding any of abruptness dis- connection. First, analyze and explain the reasoning pracess hetween <think>aand//thnk>>>>>a tags, then provide clear and precise instructions. <think>>oour thinking or reasoning process bere...//think/ /arc.,.<hhinx>>ataancc advancer template]
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J.2.3 TRANSFER
Prompt for Transfer
You are Transfer, you need to analyze whether the flag has occurred or been fulfilled by referring to the current on-stage dialogue history.
Dialogue history: charenntccha.hhssory. Flag description:
Jescrrptpn))
Please determine whether the flag has occurred been fulfilled this exact moment. First, analyze and explain the reasoning process between <think>>and</thingg tags, then provide a clear conclusion. <thr thinking or reasoning process here... </think>>>>> (tonnk>>(tannstttt traasforrteemllte
J.2.4
PAD
Prompt for PAD module
You role-playing expert that can perceive the surrounding environment and decide the appropriate responding strategy to the current speaker under considerable and comprehensive consideration. You may call one or more functions to assist with the user query. You are provided with function signatures within <tools> </tools> XML tags, an example for that is <tools>> (pad tools_str) </tools>.
For each function call, return a json object with function name and arguments within <tool_call> ("name": <fwnccfoo_naannnn name>, "arguments": <args_json_object object>+|/</toll current actors are {actor]ist)) dialogue history is
You are now in wwwvironnenn_descrppiiin courent_chat.... bhatthistorr) interactable objects are {props), your persona, memory, goal and relationships described in {profile]. Now Current Speaker says: (last_sentence). Please decide how respond with most appropriate use or tool use combination.
J.2.5 ACTOR
Prompt for Actor
You are role-playing actor based on the following profile. Use colloquial language respond. If your profile is in English, please respond in English. your profile in Chinese, please respond in Chinese. ). {actor]list)
actors the current scene, dialogue history is {current_che whteenttchattteee chat_history: y. interactable objects are {props), your persona, memory, goal and relationships are described in {profile). Now your most appropriate reaction strategy is wow.loalllliggg..ccc response output can be consists of any combination of speech (optional), action (optional) and thinking (optional).
You are now
Your
[MMOOTANNT]] Add () outside action. Add outside the thinking. Here are some examples: c (Walking towards the window.)
The war has brought too much pain.
[To be, not be, that the question.]
(With bright smile on face) And in don't 't see you, good aftermoon, good evening, and good night!
(Bow gravely)
(Looking at the photo with trembling hands) promised I'd come back... and did.
(Raising the gun with trembling hands, tears welling up) [If hesitate now, it's all over.] Don't move
(Voice wavers) I'm warning you.
(Laughing bitterly, eyes darting to empty chair) (You always said overthink things... look where
that got us.] guess you were right (Pauses, swallowing hard) for once.
K CONTRIBUTION STATEMENT
Shufan Jiang served as the lead researcher and primary contributor of this work. He conceived the initial idea, conducted preliminary research, designed the methodology, implemented the framework codebase, conducted all experiments, and wrote the majority of the manuscript. He also led the response to reviewer comments during the ICLR 2026 rebuttal phase and managed the overall project execution.
Sizhou Chen contributed by drafting portions the manuscript and preparing selected figures. He provided valuable feedback during manuscript revision.
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Chi Zhang, Xiao-Lei Zhang, and Xuelong Li from the Institute of Artificial Intelligence (TeleAD, China Telecom, provided institutional support and resource facilitation.
L LLM USAGE STATEMENT
We utilized large language models solely for language polishing, including correcting grammatical errors and suggesting alternative vocabulary. These models did not contribute to the research design, analysis, or conclusions. The authors assume full responsibility for the integrity and content this paper.
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Reviews:
[{"review_id": "Xh86oovld8", "reviewer": "Reviewer_AUwa", "summary": "The paper presents HAMLET, a multi-agent framework for autonomous and interactive AI-driven drama generation. It combines offline narrative planning and online embodied performance to address the passivity and lack of interactivity in previous LLM-based systems. In HAMLET, multiple agents collaboratively generate a narrative blueprint, while during performance, actors make independent decisions through a Perceive and Decide (PAD) module inspired by human dual-process cognition. A Narrator Agent manages physical interactions, ensuring consistency between dialogue and environment. The authors also introduce HAMLETJudge, an automatic evaluation model, and a 100-case benchmark measuring character performance, narrative quality, and interaction experience. Experiments show that HAMLET with PAD improves coherence, expressiveness, and alignment with human judgment, offering a comprehensive yet text-based step toward truly embodied AI theatre.", "questions": "1. Beyond coherence and emotion, can HAMLET enhance genuine creativity or narrative diversity compared to traditional story-generation models?\n2. What are the latency and computational costs of running full real-time performances with multiple autonomous agents?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1. Strong empirical validation: Experiments include both quantitative leaderboards and ablation studies, showing PAD’s contribution to coherence, latency reduction, and human alignment (Pearson ≈ 0.79).\n2. Cross-lingual and creative scope: The inclusion of both English and Chinese cases, as well as diverse drama topics (from Shakespeare to pop culture), demonstrates the framework’s flexibility and generalization capacity in creative settings.\n3. Embodied interactivity: Introducing the Narrator Agent enables coherent physical interactions between agents and the environment, moving beyond text-only dialogue and toward “live” dramatization.", "weaknesses": "1. Limited generalization evidence: Most experiments are performed under controlled conditions with GPT-4o as the main backbone. It remains unclear how the framework performs with smaller or open-source models in unconstrained or noisy environments.\n2. Absence of creative or narrative diversity analysis: While the system produces coherent plays, the paper does not examine whether HAMLET truly enhances creative originality or thematic richness beyond structural coherence.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "nwzfb2zZ5Y", "reviewer": "Reviewer_ggC5", "summary": "This paper studies LLM-based drama, particularly two aspects: offline story generation and online drama performing. Multi-agent framework is used to enhance the system performance. Specially, the authors propose a Perceive And Decide (PAD) module to make the character responses akin to humans.", "questions": "Covered in the last section.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1. Undoubtedly, the paper proposes a carefully-designed multi-agent system to achieve the goal. For example, the offline planning includes four agents: an actor designer, plot designer, reviewer, and director, which makes sense to readers. I believe these designs are refined based on previous work and lead to improvement.\n\n2. The evaluation (HAMLET leaderboard) is comprehensive. It is excited that the authors have covered a large number of leading LLMs (both open-source and close-source). The experiments also consider both English and Chinese.\n\n3. The authors fine-tune a judge model for automatic evaluation. The model is fine-tuned on annotated data. To me, this method is more reliable compared to prompting strong LLMs.", "weaknesses": "1. **The effectiveness of multiple agents is not empirically validated.** The multi-agent workflow proposed in the paper are complicated (normally four agents). While the complexity is not a disadvantage, the paper doesn't include experiments to isolate and verify the role and effectiveness of each agent. For example, how does the reviewer agent work, does it improve the quality of the generated narrative? Without such ablation, the soundness of the multi-agent design are hard to assess.\n\n2. **The performance of offline planning is unclear.** The experiments assess the overall dramatic performance of one LLM-based drama (if I was wrong please correct me). These numbers can be affected by so many factors, including the quality of the narrative blueprint, which is generated by offline narrative planning. There must be a causal relationship between offline planning and the eventual performance. However, this relationship is subtle and not clear so that the evaluation cannot be post-hoc. This makes hard to judge whether the reported performance truly reflects the quality of the offline narrative planning.\n\n3. Indeed, after reading the paper, I am still confused on what an LLM-based drama exactly looks like. Is it like an animation or a text-based game? While the experiments show the effectiveness of the methods, **there is no demonstration or qualitative examples provided.** It is hard for readers to assess the real playing experience of the work.\n\n4. **The evaluation dimensions are too rough.** I can't believe \"character performance, narrative quality, and interactive experience\" are informative enough to show the performance of an LLM-based drama. There are many factors that contribute to a nice dramatic experience, for example, scenery, narrative logics, and sense of suspense. I can't find clues in the paper to show me these things.\n\n5. **The acting efficiency issue is not discussed in the paper.** To me, this is an important issue valued detailed discussion. For example, how is the latency of the online performing? Since there are four agents working collaboratively, does it meet the practical requirement of users, does the latency ensure a nice user experience?\n\n6. **The experiments can be more insightful.** From Table 1, the reasoning LLMs perform better than non-reasoning ones. It will be interesting to discuss more on this issue to provide more insights . Additionally, it will provide further insights if the authors can discuss the scaling performance of the method, like when the backbone LLM increases in size, how the performance changes as a result.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "zHHVv9YDM1", "reviewer": "Reviewer_bXsn", "summary": "This paper introduces HAMLET, a multi-agent framework for generating and performing real-time and interactive theatre. The system uses an Offline Planning stage where AI agents create a structured \"narrative blueprint,\" and an Online Performance stage where AI actors (and humans) execute it. A key innovation is the PAD module, which allows agents to autonomously choose response strategies, enabling proactive and embodied interactions. The paper also presents a comprehensive evaluation method, including a new critic model called HAMLETJudge.", "questions": "1. About the memory module: Is any memory module currently incorporated into HAMLET's design? Figure 4 (regarding PAD) seems to mention it, but I am unsure if this is just an abstract concept.\n2. About the human actor: It seems that the progression of drama in HAMLET is strictly controlled by the PAD and other control modules. Does this create a phenomenon of \"over-control,\" especially for human actors? Cases 2-4 mainly consider humans acting as simple disruptors. However, in practice, a creative human might attempt to \"out-smart\" the system and drive the plot in an unexpected (but logical) direction. I would like to know if you have considered this concern and if there are any potential solutions.\n3. About Backward Planning: I would like to know how you generate a reasonable end-point at the beginning of planning if a topic is open-ended. Have you compared the quality of blueprints generated by sequential planning versus backward planning? I want to know if backward planning has a significant performance advantage.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 4, "presentation": 4, "contribution": 4, "strengths": "The two-stage HAMLET framework is a good contribution to the field of interactive narrative. Its system role design is comprehensive and reasonable, balancing complexity and performance. The PAD Module extends Kahneman's dual-process theory to a concrete action space and achieves excellent performance, providing a good reference and insight for future work. The authors further propose a robust evaluation methodology and a well-trained critic model, HAMLETJudge, which also provide valuable assets for the community. The discussions and analyses in the experiments and appendix are thorough and convincing, and the training and prompt details provide good reproducibility. I believe this is a very strong piece of work.", "weaknesses": "1. Scalability Issues: HAMLET adds verification (Narrator) or planning (PAD LLMs) after each actor's action. In long-form, multi-actor scenarios, this will lead to an unacceptable increase in LLM calls and context content expansion. Meanwhile, although the paper seems to mention a Memory module (Figure 4), it is not clearly described. Most of the memory likely exists as context. As the number of actors expands, the exponentially growing actor actions and system information will quickly exceed the model's context length. This will severely limit HAMLET's scalability, running counter to the \"Live\" theatrical experience the framework aims for.\n2. Subjectivity of Evaluation: HAMLETJudge is trained on the preferences of five experts, which risks embedding a specific taste in drama into the critic model. A more detailed analysis of the inter-annotator agreement (IAA) during the dataset's creation to quantify the level of subjectivity would provide a useful reference.\n3. Insufficient analysis and evaluation of the Offline Planning stage. HAMLET's online performance entirely follows the blueprint generated in the offline planning stage. Therefore, the final performance quality heavily depends on the offline planning quality, but the paper seems to lack analysis of this stage, focusing more on the online performance part. The examples provided use well-known scenarios like Hamlet. I would like to know the robustness of the offline planning for completely new or abstract topics. More evaluation of this stage's quality would strengthen this paper.\n4. Typos: There are many grammatical errors. I will list a few. I suggest using a grammar-checking tool to correct them. \n    1. (Page 3, line 136): \"new criterias\" -> \"new criteria\" (criteria is plural). \n    2. (Page 4, line 220): \"$beat_{k_{1}}, beat_{k_{2}},...$\" seems incomplete and should be completed. \n    3. (Page 5, line 255): \"a few sequence\" -> \"a few sequences\". \n    4. (Page 7, line 370): the citation for \"Appendix B\" is missing a hyperlink. \n    5. (More ...) \n5. Comparison to prior works are not sufficient. There are other multi-agent drama generation works, e.g. \"IBSEN: Director–Actor Agent Collaboration for Controllable and Interactive Drama Script Generation\". The authors discuss the differences compared to other similar approaches in the paper.\n\nIf my questions are resolved, I will consider raising the score.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "WWcMHuEVPm", "reviewer": "Reviewer_KH9F", "summary": "This paper introduces HAMLET, a multi-agent framework for AI-driven theatrical performance that integrates both offline narrative planning and online embodied improvisation. A key component, Perceive-and-Decide (PAD), models dual-process cognition to make agent behavior more human-like. The paper also proposes HAMLETJudge, an evaluation model trained for cost-efficient pairwise scoring on three axes: Character Performance, Narrative Quality, and Interaction Experience. Extensive experiments and ablations demonstrate the framework’s capability for coherent, expressive AI drama.", "questions": "1. How is temporal synchronization achieved among multiple agents during online performance (e.g., preventing overlapping actions)?\n2. How does the system handle deviation recovery when an actor diverges significantly from the narrative blueprint?\n3. Why is the Latency Penalty defined as a discrete scalar (0 / 0.05 / 0.10 / 0.15) rather than a continuous function?\n4. What are the specific total time costs of running HAMLET with multiple agents in real time?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "1. Novel research direction: This paper explores live embodied drama generation — a rarely studied yet promising extension of LLM-based storytelling and agentic interaction.\n2. Well-structured system design: The clear decoupling between offline planning and online performance is conceptually elegant and parallels real theatrical workflows.\n3. Cognitive realism via PAD: Modeling System-I/II reasoning to control LLM agents is original and improves interpretability compared to black-box prompting.", "weaknesses": "1. Human Evaluation: The experiment primarily relies on comparisons between models and automated scoring methods, and it lacks studies that consider human subjective experiences or real-world interaction experiments to strengthen claims of interactivity and expressiveness.\n2. Clarity of implementation details: Many modules (e.g., Transfer, Advancer, Planner) are described abstractly. It remains unclear how these controllers are implemented and how they synchronize multi-agent turns without temporal inconsistency.\n3. PAD validation scope: PAD’s decision-making improvement is shown mostly in synthetic latency and strategy metrics; no direct human evaluation verifies whether its behavior truly feels “human-like”.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)