Strategic Planning and Rationalizing on Trees Make LLMs Better Debaters
Authors: Danqing Wang
Zhuorui Ye
Xinran Zhao
Fei Fang
Lei Li
Venue: ICLR 2026 Poster (2026)
Keywords: Large Language Model
Competitive Debate
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: Winning competitive debates requires sophisticated reasoning and argument skills. There are unique challenges in the competitive debate: (1) The time constraints force debaters to make strategic choices about which points to pursue rather than covering all possible arguments; (2) The persuasiveness of the debate relies on the back-and-forth interaction between arguments, which a single final game status cannot evaluate. To address these challenges, we propose TreeDebater, a novel debate framework that excels in competitive debate. We introduce two tree structures: the Rehearsal Tree and Debate Flow Tree. The Rehearsal Tree anticipates the attack and defenses to evaluate the strength of the claim, while the Debate Flow Tree tracks the debate status to identify the active actions. TreeDebater allocates its time budget among candidate actions and uses the speech time controller and feedback from the simulated audience to revise its statement. The human evaluation on both the stage-level and the debate-level comparison shows that our TreeDebater outperforms the state-of-the-art multi-agent debate system, with a +15.6% improvement in stage-level persuasiveness with DeepSeek and +10% debate-level opinion shift win. Further investigation shows that TreeDebater shows better strategies in limiting time to important debate actions, aligning with the strategies of human debate experts.

Published as a conference paper at ICLR 2026
STRATEGIC PLANNING AND RATIONALIZING ON TREES MAKE LLMS BETTER DEBATERS
Danqing Wang Zhuorui Ye Xinran Zhaoi, Fei Fang, Lei Li Camegii Mellon University
(danqi ngw, xi xinranz3, fei fang, leili) êcs cmu. edu cui zhuye f @gmail. com
ABSTRACT
Winning competitive debates requires sophisticated reasoning and argument skills. There are unique challenges in the competitive debate: (1) The time constraints force debaters to make strategic choices about which points to pursue rather than covering all possible arguments: (2) The persuasiveness of the debate relies on the back-and-forth interaction between arguments, which single final game status cannot evaluate. To address these challenges, we propose TreeDebatex debate framework that excels in competitive debate. We introduce two tree struc- tures: the Rehearsal Tree and Debate Flow Tree. The Rehearsal Tree anticipates the attack and defenses to evaluate the strength of the claim, while the Debate Flow Tree tracks the debate status to identify the active actions. TreeDebater e allo- cates its time budget among candidate actions and uses the speech time controller and feedback from the simulated audience to revise its statement. The human evaluation on both the stage-level and the debate-level comparison shows that our T TreeDebater outperforms the state-of-the- art multi-agent debate system, with a +15.6% improvement in stage-level persuasiveness with DeepSeek and +10% debate-level opinion shift win. Further investigation shows that TreeDebater er shows better strategies in limiting time to important debate actions, aligning with the strategies of human debate experts.
novel
INTRODUCTION
Competitive debate is a structured battleground of arguments. It plays a crucial role in domains such education (Huryn, 1986; Ball, 2021), legislation (O' Connell, 2002), and politics (Hart & Jarvis, 1997; Holbrook, 1999). In a debate competition, two sides argue on the same motion under strict time limits. It requires complex reasoning skills and precise selection of impactful arguments. Expert debaters allocate time wisely among arguments in different stages. Unlike other tasks, there is no ground-trute truth answer in debate: The winner is determined by who sways the audience or judges more effectively. Research shows that success in debates depends heavily on a debater's 's ability of intense back-and-forth competition and on-the-fly decision-making: predict, respond to, and adjust based on their opponent's 's arguments (Rapoport, 1960; William L.. Benoit & Verser, 2003; Salvi et al., 2024). Although large language models (LLMs) are effective in generating arguments, human evaluators continue rate AI debaters as less convincing than human opponents in live debates (Flamino et al., 2025). These gaps highlight that current LLMs struggle with planning effective arguments in the dynamic back back-and-forth interaction of competitive debate.
The main challenges of debate AI lie in strict time limits and the lack of objective reward signals. First, the timed setting makes it impossible for LLMS generate verbose arguments for each candidate action. For example, there can be trade-off between attacking the opponent's claims and defending one's own claims. A formal competitive debate typically consists of three stages: an opening statement, a rebuttal statement, and closing statement. In each stage, the debater is required to carefully allocate their limited speaking time to propose new claims, reinforce the existing claims, attack the opponent's is claims, or rebut the opponent's 's attack. The debaters must prioritize
Part of the work was done when Zhuorui was visiting intern at Carnegic Mellon University. Code and data have heen released https://gtthhh.co .eiL.l il .al.Laaaaerreaaaee Dehater

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the most impactful actions and dynamically adjust their plan the debate progresses. However, unlike games such as Go (Silver et al., 2016; 2017) or Werewolf (Xu et al., 2023; 2024), there are no rule-based winning conditions. Instead, the winner depends on the evolving flow of arguments and counterarguments between the two sides. This dynamic nature makes difficult to plan based on the final reward signals.
In this work, we propose to model the dynamic interaction on trees and show that planning on these trees can significantly improve the debating capacity of LLMs. Human debate experts implicitly employ tree-like reasoning: they rehearse potential rebuttals before the debate starts by anticipating the claims an opponent might say and formulating the potential responses for each of them in a tree format. During the debate, they track the flow of the debate with another tree to keep structured mental map of which points have been addressed or remain standing. Inspired by these human strategies, we introduce a new debate system T TreeDebater to help LLMs make tactical decisions in the debate. Akin to human debaters, we introduce two kinds of trees in TreeDebat ex: Rehearsal Tree to hypothesize the opponent's attacks in advance, and Debate Flow Tree track the debate status. Specifically, in the Rehearsal Trees, TreeDebater scores arguments based on the potential tree-structured attacks and defenses. During the debate, PreeDebater keeps track of the debate status through the Debate Flow Trees and provides the candidate action set. TreeDebates retrieves prepared arguments from the Rehearsal Tree for each candidate action draft the statement. Finally, TreeDebater ter revises its statement based on simulated audience feedback and uses a speech-based search algorithm to fit the statement into the speaking time limitation.
To evaluate the effectiivness of TreeDebater in competitive debate, we carried out extensive experiments with the multi-agent debate system Agent4Debate (Zhang et al., 2024), which has shown comparable results to human debaters. We design two kinds of human evaluation to compare the debate performance: stage-level head-to-head comparison with fixed debate context, and debate-level end-to-end comparison with an Oxford-style debate format. Human evaluation demonstrates that TreeDebater consistently outperforms the baseline in the dimension of the average persuasiveness score and the stage-level and debate-level win rate. It is preferred 1 1.5x and 3.5x over the baseline in the stage-level and debate-level comparison with the Gemini-22 Aaaini-2.0-flssl backbone, where the gain also generalizes to another backbone, DeepSeck-V3.
To conclude, our contributions are listed as follows:
We develop a sophisticated AI system for interactive and time-constrained competitive debate. It includes two tree structures: a Rehearsal Tree and a Debate Flow Tree to prepare arguments and plan actions in debate.
We incorporate a speech time controller to properly control the speech time and a simulated audience into the TreeDebater to refine the debate draft into human-like statement.
We conduct extensive human evaluation on the stage-level and debate-level. Our study shows that TreeDebater more persuasive than the prior multi-agent debate system. Further investigation shows that TreeDebaten elicits more diverse actions during the debate with a better logic flow and emotion tone, similar human debate experts.
2 RELATED WORK
Computational Argumentation. Previous studies used computational methods to study argumenta- tion processes, such as argument mining (Lawrence & Reed, 2019), the polarity of arguments (Agar- wal et al., 2022; Liu et al., 2021; Zhao et al., 2021), argument structure (Zhang et al., 2016; Li et al., 2020), and argument generation (Hua et al., 2019; Lin al., 2024). Slonim al. (2021) built the first autonomous debating system, Project Debater, with four argumentation modules: argument mining, argument knowledge base, argument rebuttal, and debate construction. While the first three modules have shown superior performance in creating high-quality arguments, the debate construction is based on a human-definen template, which cannot adapt to the dynamics in competitive debate. Instead of generating better argument, our work focuses on the decision-makin mmaiing in the debate, such which point to argue.
Debate in Large Language Models. There are two research directions in debate with large language models. One is to enhance LLMs' capability in solving problems by debating with different solutions, such debate for reasoning (Du al., 2023; Liang al., 2023; Yin et al., 2023), evaluation (Chern
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et al., 2024; Chan et al., 2024), safety (Irving et al., 2018). Unlike competitive debating, these problems have an optimal solution, and the purpose of the debate is to find this solution by discussion. Another line of work uses LLMs' argumentation capability to enhance the debating performance. For example, Lee et al. (2023) uses role-playing to simulate debate between different populations. Zhang al. (2024) design multi-agent framework to work collaboratively during the debate, showing comparable performance with human debaters. Different from previous work, we consider the strict time limitation in competitive debate, which requires LLMs to make tackle decisions and allocate the time budget to the most important actions.
Language Agents for Games, Besides debating. recent studies also investigate language agents in other strategic games that require strong communication between players, such as Diplomacy (Meta et al., 2022), Werewolf (Xu et al., 2023; 2024), and Avalon ( (Wang et al., 2023). Cicero (Meta et al., 2022) and Xu et al. (2024) learn policy to choose suitable strategies during the game, while Xu et al. (2023) and Wang et al. (2023) design deliberate prompts to improve the performance. These games have a clear definition of winning. For example, the winning condition of villagers in Werewolf is to eliminate the werewolves, while the winning condition werewolves is to kill all villagers. These clear winning decisions provide an objective reward signal for learning the strategies. However, there is no clear winner in competitive debate, making the strategy learning more difficult.
3 STRATEGIC PLANNING IN COMPETITIVE DEBATE
We first introduce the background of competitive debate (Section 3.1). Then propose our debate system TreeDebater, which leverages Rehearsal Tree (Section 3.2) to anticipate the back-and- forth between sides and prepare arguments before the debate starts. During the debate, it tracks the flow with the Debate Flow Trees (Section 3.3) to strategically plan among the candidate actions. Furthermore, TreeDebater incorporates human debate trees (Section 3.4) and speech time controller (Section 3.5) to refine statements based on human-aware feedback and time constraints. An overview of TreeDebater is shown in Figure 1.
3.1 COMPETITIVE DEBATE
Following previous work on the debate game, we focus on the simplified Oxford-style debate (Slonim et al., 2021; Zhang et al., 2016; 2024). It is competitive debate format where two sides argue for (Pro stance) and against (Con stance) a specific motion. There are three stages. In Opening Stage, each debater has 4 minutes to deliver their main claims and arguments to support their assigned stance. In Rebuttal Stage, the debaters take turns attacking the opponent and defending themselves in minutes. In Closing Stage, the debaters summarize the debate in 2 minutes.
Audience members are asked to vote before and after listening to the debate. The final winner of the debate is the with more votes swaying towards it. We call this win as Opinion Shift Win. There are several other debate terms. For example, a claim e a proposition about the motion, the argument uses reasoning evidence to support the claim, and statement is the whole passage presented in one debate stage. There are four typical actions that the debater can take in each stage: propose a new claim, reinforce the existing claims, attack the opponent's 's claims, and rebut the opponent's attack. A battlefield refers to conflict zone in the debate, where two sides contest on specific point. It includes several rounds of proposal, attack, and defense.
3.2 REHEARSAL TREE
Before the debate begins, human debaters usually prepare for the potential attacks and defenses of their claims. This rehearsal can help them retrieve relevant evidence and evaluate how robust their claims are towards the attack. Motivated by that, we propose the Rehearsal Trees T, to anticipate the back-and-fooe forth between two sides.
Build Rehearsal Tree via Top-Down Specifically, TreeDebater proposes n candidate main claims C {co, ... Cn.) and constructs a Rehearsal Tree T, with the maximum depth L for each claim (0). Each node is argument x, and its children Thus, each node is on the same side its grandparent. We define the attack score of the 1-th level node x² its attack impact towards its parent xf f-1, which is ra (If -11. The support score of
the potential counterarguments.
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Listen
Netriew withh
Argunentss
STATE
Opposent Debate Flow Tree
Debater
Condidate Action
Rehearsal Trees
Retrieved Nodes
Writer
Speech
Action Importance
Droft
feedback
Time Controller
with Debate
Stevlar human Debate
Nlo
Flow Trees
Human Debate
Corpus
Simulated Audience
The overall workflow. each stage, TreeDebater (i) updates two Dehate Flow Trees with extracted action tuples from the current statement; (ii) retrieves prepared arguments from the Rehearsal Tree for the candidate actions: (iii) generates draft based on the retrieved arguments and important scores; (vi) lets simulated audience provide feedback based retrieved human debate flow tree; (v) revises based on feedback from the simulated audience speech time controller.
Figure 1:
is its support impact towards its grandparent x/-2, which is rs(x", _2 Here, and ra are two scoring models that evaluate the impact between two arguments. For the main claim x of the root, its support score is defined as its support in the assigned stance
Calculate Strength Score via Bottom-Up To evaluate the utility of an argument the debate for our side comprehensively, we define strength score S' (a²), which considers both the support and attack score the node z' and the influence its descendants within depth & (k-subtreee) For = the strength score can be represented as:
rr(a 8) ra ra(o", x1-1 (rala), xx-11) + + 22-))
if I if I =
fo(x²)
if 2 2
For k > 0, we define the strength score recursively, adopting minimax-like perspective from side's 's point of view. It represents the minimum utility our side can expect from argument x'. since we assume that the opponent will always choose the counterargument +11 Child(æ²) that maximizes their utility, which corresponds to minimizing our utility in this zero-sum debate. Therefore, the k-step strength score be represented
fu(x²) = fo(x') -
max (Child(z))
fk- fx-1(x(t1).
(2)
where y is the decay coefficient (which is 0.8) since one may choose to stop further reacting, and Child(x') is the set of rs's child nodes. The whole process of constructing the Rehearsal Tree is illustrated in Alg.
To conclude, the Rehearsal Tree anticipates the attack and defense for each main claim in tree format and calculates the k-step strength score to evaluate the utility of the claim. We further let TreeDebater propose candidate claims and construct the Rehearsal Trees for the opposite side T Toppe' making it possible to prepare for the attack and defense of the opposite side.
3.3
DEBATE FLOW TREE
The dynamic interaction in debates makes it easy lose track of the back-and- and-fforth of points. We propose the Debate Flow Tree T. to record the debate status, simulating the note-taking of humans.
Maintain Debate Flow Tree The Debate Flow Tree tracks the debate status by keeping all proposed claims with the corresponding attack and defense in a tree structure. The tree node consists of the claim, the arguments to support the claim, the state (proposed attacked), and the number of visits. Each time after listening to one statement, TreeDebater first extracts the tuples of (action, claim, argument, target) from the statement, and then updates the Debate Flow Tree with these tuples. If new claim occurs, new node with proposed state will be created under the tree root.. If an existing claim is attacked, its target claim node will change to attacked state, and a child node will be created to indicate this attack. When new arguments created to reinforce an existing claim, the corresponding claim node will be updated with these new arguments. The update process is described in Alg. 2 and Figure 4b.
Extract Candidate Actions from Debate Flow Tree TreeDebater can filter out the candidate actions it can take in the current stage of the debate based the Debate Flow Tree. The criteria
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Algorithm 1: Build Rehearsal Tree
Input:
Root claim x², Attack Scorer Ya: Support Scorer Ta, Maximum branch B, Maximum depth
Output: Rehearsal tree Tr
Initialize the Rehearsal Tree T, with the claim z"; Initialize queue Q with T, root note;
// Phase 1: Build Rehearsal Tree via Top-Down
while Q is not empty do Extract the first node xf from queue Q with the level / < L then
Generate B children using LLM and retrieve evidence for them: Calculate and ra for each child; Add children Q:
Phase 2: Calculate for level + L do foreach node x' at level do for 3 0 to I. do Calculate fa(x²) using Eqn 1 and 2;
Strength Score via Bottom-Up
10
13 return Tri
include: (a) propose only at the opening stage; (b) rebut the latest nodes (leaf node) of the opposite side; (c) reinforce the nodes of its own side; (d) attack the nodes of the opposite side.
Retrieve Prepared Arguments from Rehearsal Tree TreeDebater retrieves the arguments prepared for this action from the Rehearsal Trees after getting the candidate actions. For example, to propose or reinforce a claim, we should retrieve the arguments that support this claim or oppose its counterclaim. To attack rebut claim, we should retrieve its counterclaim. TreeDebater also retrieves the k-step strength score of the claim, and here depends on the number of rounds remaining in the debate. For example, there are 3 remaining rounds (Con's 's opening, Pro's Rebuttal, Con's s Rebuttal) for the Pro side the opening stage, which means that there can be at most levels of child nodes. Therefore, use embedding models, Gemiin-texx--mmeedddnn-- to get the embedding of the claims. If the cosine similarity of embeddings is higher than threshold 0.8, we view these To find the most similar tree node, we traverse all tree nodes and calculate the cosine similarity between the claim of the node and the target claim. Details of the retrieval process are given in Appendix D.
to get the strength score. We use one of the state-of-the-an
claims are similar.
To conclude, the Debate Flow Tree tracks the debate status and provides the candidate actions. These candidate actions are enhanced with prepared arguments and the k-step strength score from the Rehearsal Tree. These help the LLM writer select the impactful actions with strong arguments and evidence to draft the statement.
3.4 AUDIENCE FEEDBACK BASED HUMAN DEBATE FLOW TREE
We provide simulated general audience feedback by evaluating different key aspects of the speech based on the retrieved human Debate Flow Trees. We first curate human debate flow tree corpus with Debate Flow Tree created from human debate corpus. During the debate, we reformulate the current Debate Flow Tree into tree-like string. Then we employ semantic search based on the embeddings of the debate flow tree string. Similarly to the argument retrieval, get the embedding of the tree-like string of the current debate flow and of trees in human debate flow tree corpus. The top human debate flow tree is retrieved with a similar threshold of 0.8. We add the retrieval the instruction for simulated audience to give the audience better of the back-and-fie forth and the statement styles in the human debate competition.
used to
Since the Closing Stage only summarize what have been said, do not view it as effective round which take debate action.

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The simulated audience provide concrete and focused feedback on important aspects in debate, such as message clarity, engagement impact, evidence presentation, and persuasive elements. By revising based the audience feedback, TreeDebater can learn the allocation pattern and persuasive elements embedded within relevant human debate structures. Details the construction and retrieval of human Dehate Flow Trees are in Appendix D, and example of the resulting audience feedhack is in Table 15.
3.5 SPEECH TIME CONTROLLER
With the selected actions from the Debate Flow Tree, our hope then to generate coherent speech within a reasonable time range [4, f.). When drafting the statement, we incorporate the word budget into the instruction, which is based on a rough estimate of the speaking time, i.e., 130 words per minute. However, we find that it is difficult for LLMs to follow the length constraint (Jie et al., 2024; Butcher et al., 2025), and the number of words cannot control the precise speech time since each word takes different speaking time due to its phoneme and might be affected by emotion. To ensure that the speaking time of the statement fits the precise time limitation in the competitive debate, we introduce speech time controller in TreeDebater to provide better estimation of the speech length and guide the LLM writer in revising the statement for the time restriction.
We use light-weight text-to-speech model, FastSpeech (Ren et al., 2020), to estimate the speech length. At each iteration, the speech time controller converts the current statement to audio and calculates its time cost. The calculated time cost and the word budget n in the instruction of this statement will be used to search for a new word budget for the revision. The iteration will stop when the speech time falls into the suitable range or it reaches the maximum revision limit.
For the iteration process, we employ binary search approach to efficiently find an appropriate word count target n. Since we observe the real speech time generally has positive correlation with n, we can find the initial search interval [mt. n,. where nu produces speech shorter than & and produces one longer than l,, making the interval dividing process possible. More detailed descriptions of the process are in Appendix B.2.
EXPERIMENT
4.1 EXPERIMENT AND EVALUATION SETUP
We use the sfate-of-the-arr
aggett framework Agent-Debate (Zhang et al., 2024) with different backbone LLMs our baseline. It employs collaborative architecture with four specialized agents: searcher, analyzer, writer, and reviewer agents. shows better performance
the LLM-bbsee Debatrix debate metric (Liang et al., 2024) and is preferred by human experts in pairwise comparison. uses the stage-speeific prompts from the AlDebater 2024 competition and uses Tavily as a search engine for evidence retrieval.
We use Gemini-2.0-ffa (Team et al., 2023) and DeepSeck- V3 (Liu et al., 2024) as the backbone LLM for Agent4Debate and TreeDebater For fair comparison, TreeDebater uses the same Tavily APIs for evidence retrieval before the debate and adopts the same stage-specific prompts, adding the necessary modifications to incorporate the tree information for planning, as shown in Appendix G. To ensure that the debate systems follow the time constraint, we add the rough word budget in the stage-specific prompt for both models. After transferring the statement to the audio via OpenAI TTS, we apply hard cut on the debate audio: the audio will be trimmed from the last sentence before the time limitation. We train two separate LLaMA- reward models, specifically with the base model being a meta-llamd ama/ amamm-3. 22..22222 2-33- ruct, using the Kialo dataset (Durmus et al., 2019) for ra and r.- The Kialo dataset consists of claim texts for 741 controversial motions with three categories (Impactful, Medium Impactful, and Not Impactful). During inference, we use the weighted category as the final prediction to provide a more fine-grained score. The other implementation details are in Appendix B.3.
hhtps:/ttaiily.com

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Stage-level Comparison: Head-to-Head Human Evaluation We design controlled head-to-head setting for stage-level comparison. We first have a debate competition between the two debate systems with a random stance assignment. Then we use the debate process before target stage as the context, and let each debate system generate one version for this target stage. We then let the participant compare the two versions based on the debate process. For example, we keep the same Pro's Opening and ask AgenteDebate and TreeDebater to generate the Con's Opening, This head-to-head comparison provides a detailed and focused evaluation of each stage by alleviating the noise introduced by other factors. Specifically, the annotator will provide persuasiveness scores for both versions and also mark which side performs better in each stage. We randomly sample 10 (motion, stance assignment) settings for each stage and get the corresponding debate context, producing 120 stage-level comparisons. Then we let each debate system generate one statement for the target stage. The audio of the debate context and the two versions of the statement are presented to the participants, where we randomly set the order of these two versions to avoid the potential order bias.
Debate-level: End-to-Eun Human Votes addition, we follow Oxford-style debating to assess the debater's 's capability in full debate. We ask the participants vote before and after listening to the full 20-minute debate, and provide the persuasiveness score for each stage. We flip the stance assignment and take the average of two competitions for each motion avoid the potential prior bias on the motion. We randomly sample 4 motions for Gemini and 7 motions for DeepSeek. We conduct two debates on each motion with the original and swapped sides. For each debate, 3 random participants from our recruitment pool are asked to score each stage and vote before and after the debate. The average score and the opinion shift are used evaluate the debaters' performance in this debate. We take the average of the two flipped stance assignments as the debater's ' performance on this motion. For example, the opening persuasive score is the average of the opening scores when the debaters acted as the Pro and Con side, respectively. Results are shown in Table 2.
Recruitment and Quality Control We recruit audience members from the online research platform Prolific 5. The evaluation takes around 30 minutes per stage. The compensation is $10 for each case after they complete the evaluation. We recruited 212 participants in total for our debate, and all participants are located in the US when they conduct the evaluation. The screenshot of our evaluation platform is shown in Figure 5 and 6, and the demographic features of our participants are shown in Figure 7. For the stage-level head-to-heat evaluation, calculate the annotators' agreement on the better version. The annotators achieved 60.7% agreement on average, indicating moderate consensus. This agreement indicates reliable human evaluation result, considering the subjectiveness in debate evaluation. 6
Before human evaluation, we perform validity checks to assess the quality of the debate competition generated. Only those that follow the correct debate format are used for human evaluation. The results of the valid checks are shown in Appendix E. In general, TreeDebat er can always generate format-valid and time-valid statements. However, only 77% debates are valid in the Gemini version of DeenteDebate. Besides, Aeent4 always exceeds the time limitation, especially in the closing stage. We put the motion list (Table 5), persuasiveness score rubric (Figure 5), the recruitment details, and screenshot of our debate evaluation platform (Figure 6) in Appendix C.
4.2 EXPERIMENT RESULTS
TreeDebater has a higher average persuasiveness and win rate in 11/12 stage-level compari- son. As shown in Table 1, the advantage is more significant when we shift from Gemini to DeepSeek. We can find a clearer win in the stage-level win rate. When participants are asked to choose the better version, TreeDebates is preferred 1.5x and 2.5x times than the baseline, One reason could be that participants are more likely to choose scores of 3 or 4, leading to a small performance gap in the persuasiveness score.
TreeDebater outperforms the baseline with a 3.5x and 1.3x higher opinion shift win rate in Gemini and DeepSeek. Table 2 indicates that TreeDebater makes the LIM a more persuasive
htttps//www.prolfficcom
'In debate-level human evaluation, the winner of the debate is decided based on how many annotators shift towards it, and annotators do not need to agree with each other. Therefore, annotators' agreement is not applicable here.
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Table 1: Scalar persuasiveness and win rate in head-to- tead human evaluation. A higher score indicates the statement more persuasive. Lone indicates number cases that TreeDebate er wins, gets tie, or loses in the pairwise comparison. The standard deviation results are put in Table 7 in the appendix because
of the length limit.
Opening
Rebuttal
Closing
Model
Framework
Average
Pro
Con
Pro
Con
Pro
Con
Persuasiveness Score
Agent-Debate
3.64
3.94
3.65
3.64
2.75
3.54
Gemini
TreeDebater
3.73
3.91
4.06
3.25
3.91
3.25
3.69
Agent4Debate
3.45
3.18
3,73
3.55
3,45
3,45
3.47
DeepSeek
Treeleebater
4.27
3.36
4.18
3.73
4,27
4.27
4.01
Win Rate of Ireeleebaerr
Gemini
Won:Tie:Lose Win:Tie:Lose
5:3:3
6:1:4
2:6:1
2:3:5
7:1:3
5:2:1
0.45:0.27:0.28
DeepSeek
8:2:1
4:4:3
8:1:2
4:4:3
3:5:3
6:4:1
0.50:0.30:0.00
Table End-to human evaluation result. The score each stage indicates how persuasive the statement Opinion Shift Win indicates the percentage of votes that shift towards its stance after the debate. We ignore the percentage of Tie here.
Model
Framework
Opening
Rebuttal
Closing
Opinion Shift Win
Debnte
2.96
2.92#a.25
2.98 20.29
0.13
Gemini
TrecDebater
3.60 20.27
3.58
3.54 +0.29
0,46
Deente
3.7310.24
3.11
3.57 2..23
0.30
DeepSeek
TreeDebater
30.7
3.71
3.38
0,40
debater that can change the audience's opinion in the competitive debate. Besides, the debate systems with the DeepSeek backbone have higher persuasiveness scores than Gemini, indicating that DeepSeek has superior capability in argumentation. Moreover, our TreeDebater shows sigmificant improvement in the persuasiveness score all stages. We find that this because the poor performance of the Gemini-base baseline in the early stage affects the participants' impression of it and makes them more critical the following stage.
On the other hand, we find that the audience focuses more on the personal belief in the motion rather than the debate strategies if the two sides have reasonably good performance the debate. For example, the Agent4Debate baseline already shows an average persuasiveness score of 3 or more. In its pairwise comparison with TreeDebater we find that in the motions we used in our DeepSeek experiments, the Con side always wins in 3 of them and the Pro side always wins in regardless the stance assignment. This leads to a comparable persuasiveness score after taking the average of the two flipped assignments, shown in Table 2. In such scenario, the head-to-head evaluation provides more insight into the performance difference by focusing more on the debate strategies used under the same debate context.
of them,
4.3 ANALYSIS
We provide more qualitative and quantitative analysis of TreeDebater" debate performance to investigate how the proposed tree structures help the strategic planning debate.
Table Ablation Studies Stage-level Head-to-Hedd Human Evaluation
Framework
Opening
Rebuital
Closing
Treellebater
3.50
3.50
3.75
Treelebbterr w/o Rehearsal Tree TreeWebbatr w/o Rehearsal Debater Flow Tree
3.00
3.25
3.50
3.00
3.00
3.50
Debate flow trees help TreeDebater choose diverse actions, aligning better with hu- experts' strategies. We extract the type of debate action used by human debate experts,
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TreeDebater, and Agent4Debate in the rebuttal statement and plot the distribution in Figure 2. Note that one argument can be identified with multiple action types. For example, if the attack from the opponent is related to the opponent's s main claim, then rebut to this attack can also be viewed as an attack on the opponent's ss main claim. We consider such a combination of actions as a separate action category. From Figure we find that TreeDebater has more diverse actions in its rebuttal, where attack & rebut, sole attack, and sole reinforce account for large percentage, This similar human experts' strategies in the debate competition: rather than rebutting or attacking every point in the opponent's 's last statement, they will remind the audience about their earlier claims and shift the focus of the debate to their battlefield. Instead, Agent4Debate busy attacking and rebutting the latest statement, lacking long-term action to reinforce its main claims in the opening stage. We also conduct the ablation study by removing the Rehearsal Tree T, and the Debate Flow Tree Ta in Figure 2. It shows that after removing the flow trees from the debate, TreeDebater loses track of the points mentioned the debate, leading to less diverse actions.
TeeDebater
We Tur
We To &T_
565
Agent
30
attackGrebut
attack@nbutGGehfffrce
Propocc
Reliffrre
Attack
R.b
attack
attakkrreeforre
others
opoppest TT
HR Ether
Figure 2: Action distribution in the rebuttal stage. We extract the Debate Flow Tree from human debates and categorize the distribution of actions. The actions are less diverse the baseline and TreeDebater w/o
Figure 3: Percentage of actions that can be found in the Rehearsal Trees. The Rehearsal Trees of both sides contribute the hit rate.
Rehearsal trees get TreeDebater prepared for the debate by anticipating the opponent's behaviors. We investigate how many points mentioned in the debate have been anticipated and prepared in the Rehearsal Tree. In Figure 3, we calculate the hit rate of each type of action on the Rehearsal Trees T, prepared for the debater's side and those T, Trappo for the opposite side. One action is hit if there is a node in the Rehearsal Tree that has similar claim to the target claim of this action, leading to a non-empty retrieval set in Alg is not empty. Figure shows that more than half of the candidate actions have been anticipated in the Rehearsal Tree, helping TreeDebater prepare these actions in advance. also shows that the Rehearsal Trees from the debater's 's and the opponent' s side both contribute to the preparation by providing different perspectives of the motion. We also find that these propose actions have a high hit rate the opposite Rehearsal Tree. It indicates that the opponent may easily anticipate and prepare for the main claims of the debater's 's side by treating them as potential attacks on their main claims.
Qualitative Analysis based Audience Feedback The participants also encouraged to provide optional comments, which gives us more insights into the debate performance. We show several comments the head-to-head comparison. We categorize the comments into four aspects: logic flow, audience awareness, evidence, and claim. In Table 4, we can find that TreeDebater outperforms the baseline in the logic flow and has more good points and evidence. This benefits from the flow tracking the Debate Flow Tree and the prepared claims and evidence in Rehearsal Trees. In two lost cases (4 and 5), the participants complain about the statements' emotion, thinking they are too critical. However, the other participants think the emotional tone makes it more convincing. This is because the simulated audience in TreeDebater retrieves similar human Debate Flow Trees as references to provide feedback, guiding the statement to have more human-like tone. Such revision may not favor the audience who prefers an objective statement.
Fine-grained Ablation Studies for Impact on Persuasiveness We further conduct the head-to-head evaluation on the ablation of the Rehearsal Tree and Debate Flow Tree. We select two new motions and use Gemini model as the backbone. The persuasiveness scores are shown Table 3. It shows clear performance degradation when removing components, confirming their necessity for better debate quality.

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Table 4: Detailed audience feedback. We present several cases for each stage. We put the persuasiveness the beginning of the comment. We annotate the different aspects: logic flow in blue, audience awareness in green, evidence in orange, and claims in purple. Win indicates our TreeDebater outperforms the baseline.
ID
Stage
Comments on TreeDebater
Comments Agent4Debate
Opening (win)
3: Good flow, adressed the audience prop- 2: Flow disjointed and confusing, but erly, could use some stronger evidence, included acceptable points with single good overall moderate.
piece evidence, overall weak.
Opening (win)
I liked the examples and the statistics he that he delved deeper into critical thinking cited though I wish there were references. skills.
think it more detailed. I appreciated
3 Rehuttal (win)
Gave great points and comparisons. 4: Strong statements, Gave good arguments Gave examples from studies.
5:
to the opposition 's point.
4 Rebuttal (lose)
3: too mean
4
5 Rebuttal (lose)
2: Picking apart every detail of the other 3
side's 's argument.
Closing (win)
4: Made good points and used really good evidence, and tone was also convincing, overall strong argument.
3: part of rebuttal rather than the closing statement, and wasn't convincing did it feel really closing to me.
CONCLUSION AND DISCUSSION
Competitive debate an important proxy for critical thinking, argumentation skills, and the ability to understand and make tackle plans among various candidate actions under the complex dynamic interaction. this paper, we design strategic debate system TreeDebater ter to strategically plan its debate actions for time-limited competitive debate. It uses the Rehearsal Tree to anticipate the potential attack and defense for preparation, and uses the Debate Flow tree to track the candidate actions and battlefields. It is facilitated with the speech time controller and the simulated audience feedback to provide revision suggestions for coherent and precise controlld statements. The results show that TreeDebater significantly improve the performance of the debate in both head-to-head and end-to-end human evaluation, and elicit more human-like diverse strategies.
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A DEBATE TERMINOLOGY
We introduce the debate terminologies used in this paper. A motion in the debate is statement of opinion or proposition to argue. The assigned stance s on the debate motion can be Pro or Con, indicating supporting opposing the motion, respectively. A claim c a proposition assertion about a motion, which should be proved through argumentation. The main claims refer to the most important claims that the debater proposes during the opening stage support their stance. An argument x is a set of reasons or evidence to support a claim, typically consisting of premises that lead to conclusion. A statement is the whole passage presented in one debate stage, including of claims, arguments, and supporting evidence" There are several typical actions that the debater can take in each stage: propose a new claim, reinforce its existing claims, attack the opponent's claims, and rebut the opponent's 's attack. A battlefield refers to a conflict zone in the debate, where two sides contest on a specific point. includes several rounds of propose attack defense.
B ADDITIONAL IMPLEMENTATION DETAILS
B. DEBATE FLOW TREE ALGORITIIMS
Opening Phose
School uniFores ore exporsive
claim Proposed ve1
Clain 3, Proposed ver
4
Proposed, vet
Pro ottock Clain 1 and 3
Mor-school uniform be wore expensive
School undfores already sales-tax-free
Rebuttal Phose
Con
Ya=1
Clain Attackad,, um2
Claim 2, Proposed, uer
Clain Attacked, v=2
alwnys have sckool clothes, couse additional cost
ahlings'
school clotbas
(a) Rehearsal Tree. The rool node is the main claim c. The blue nodes are from the same side as the root, and the green ones are the potential counter- arguments from the opposite side. is the support score ra the attack score.
Clain 2, Proposed, va1
Clain Proposed, ve1
(b) Debate Flow Tree of the Con side. The blue indicates its claims, while the green indicates the claims proposed by its opponent (Pro side) to at- tack the Con side's claims. indicates the visit number.
Figure 4: Illustration of Rehearsal Tree (a) and Debate Flow Tree (b).
We demonstrate the Rehearsal Tree and Debate Flow Tree in Figure 4a and 4b. The motion in 4a is 'Should school uniforms be banned' and the side is Pro. As shown in Figure 4a, the root node is the main claim c proposed for the Pro side of the motion. The blue nodes are the arguments to support the same side of the root (Pro Side), while the green nodes are the counter-arguments that support the opposite side (Con Side).
In Alg. 2 and we provide the pseudo code for updating the Debate Flow Tree with actions tuples and retrieving similar nodes for candidate actions. We use Gemiin--ex--mmmeddinn--- to get the embedding of the claims. To find the most similar node, traverse all tree nodes and calculate the cos similarity between the claim of the node and the target claim. We set the similarity threshold 0 to 0.8 and return the top one candidate.
It is also called speech (Slonim al., 2021) discourse (Zhang et al., 2016) in literature.
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Algorithm 2: Update Debate Flow
Algorithm 3: Retrieve from Rehearsal Tree
Input: Debate Tree Ta with root x Action tuple
Input: Rehearsal Tree set TR. Action space A. Similarity threshold 8, Look ahead step k Output: List of actions with retrieved An
(action, claim, argu, target), Similarity threshold
Initialize action list Ar with empty:
Output: Updated node
foreach (action, target) E A do
if action is "propose" then
Initialize retrieval set R:;
Create node x' with claim and argu;
foreach tree E Tn do
Add node x' to x children:
if action is "prepose" or "reinforce" then
Modify x' status to "proposed";
Find node x' similar to target on the current side by 0:
return x';
Find node similar to target by 0: Increase x visit count by 1
Add x¹ and its k-step lookahead strength fa (z') ) to R if exists: else if action is "attack" or "rebut" then
if acction is "reinforce" then
Update x with argu; return x;;
Find node xf similar to target on the opposite side by 0;
10
if acction is "rebut"
"attack" then
foreach x(+1 in x' children do
12
Create node x' with claim and argu;
Add ,/++1 and its k-step
Add node x' to children: Modify x status to "attacked"; 14
lookahead strength fa fx(++t) to if exists
15
Modify x' status to "proposed"; return z';
12 Add the tuple action, larget, R to Ar
16
13 return Ag:
B.2 SEARCH ALGORITHM IN SPEECH TIME CONTROL
As mentioned in the main text, our goal generate a coherent speech within the time range [4, 4r] with the chosen actions from the Debate Flow Tree. Note that the number of words is easier to control than the entire speech length for the large language model generation, and the passage word length has relatively good correlation with the speech length. We can formulate the problem as that we want to call f ('(m) minimal times to find an output that has speech time in the range [4, 1r], where f represents an LLM inference where the speech length of f (n) is correlated to n.
Note that if at any time the generated speech time ranges in [t,, tyl, we can directly finish the whole process. In our implementation, we first try to establish the boundary word counts for binary search (ni, n,), where nu produces a speech shorter than l and n,, produces one longer than 4,. Specifically, make initial query that either become / r, that can find the other endpoint by iteratively doubling r I. With this initial interval determined, we can then iteratively refine this interval through binary search, selecting the midpoint and reducing the interval range by half based on the resulting speech duration.
Note that the effectiveness of this algorithm also depends on how well the base model's generation correlates with the number of words written in the prompt. We observe that while the Gemini output can vary according to the constraint, DeepSeek sometimes seems to disregard the length constraint, making the iterative process slow to stop. We thus set the maximum iteration time as 10 to make sure the algorithm always stops. If the goal is still not accomplished after the maximum time, we will use the latest transcript as the final one.
B.3 REWARD MODEL TRAINING DETAILS
We train two separate LLaMA- reward models, specifically with the base model being meta - 11 ama LLwamamaaaaa 3. 3.2. 2B Instruct. using the Kialo dataset (Durmus et al., 2019) for rs and ra- The Kialo dataset consists of claim texts for 741 controversial motions. For each motion, maintains argument tree. The root of the tree is the motion, and each node is claim text with its counter-an argummenss or subsequent arguments as its children. There are human annotations for the impact score (Impactful, Medium Impactful, and Not Impactful) between the node and its parent,
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indicating how impactful the node is to support oppose its parent claim. We preprocess the original dataset to build one dataset with 3691 argument trees for the impact score of the support relation and one dataset with 3676 argument trees for the rebuttal relation and train models for them separately. To make different classes more balanced, we also incorporate a data resampling mechanism to let each class' samples occur multiple times according the ratio of the maximum class count the number of samples in this class. We then train the base models in the classification task for 3 epochs. Finally, our trained reward model gets 0.67 accuracy in predicting the support impactfulness and 0.72 on the rebuttal impactfulness. During finetuning, we formulate the original data point with the following prompt and let the Ilama predict the impact category. We view use LlamaFor SeaimaarrSeqeeneCaassfeeee
classification task and the backbone architecture and the cross-entropy the loss function. The instruction format is shown below.
Instruction format of LLaMA-based reward models
You are given chain of arguments, each one supporting or attacking the previcus one.
The previous arguments are: [context]
The second last one is: [claim 1]
The last one is: [claim 2]
Now you need to determine the impact of the last one on the second last one, given their relationship [support/attack] Output only number among 0, 1, or in your response. 0 means not impactful; 1 means medi impactful; 2 means impactful.
After applying ra and rs get the strength score by Eqn. 2, the Rehearsal Tree will look like:
One example Rehearsal Tree
Level-0 Roct Claim: "claim": "Removing the debt ceiling benefits
future generations.", Scores: Support Score: 1.6, Strength: 0.9 Level-1 Oppenent's s Attack: "claim"; "The debt ceiling, while imperfect, corpels fiscal responsibility, safeguarding future generations from unsustainable debt burdens.", Scores: Attack Score: 1.3, Strength: 0.8
Ievel-2 Your Rebuttal: "claim": "The debt ceiling does not
fiscal respensibility; it merely invites fisca- crises.", Scores: Attack Score: 1.4, Support Score: 1.6, Strength: 0.6
level-3 Opponent's Attack: "claim": "The debt ceiling's
'manufactured crises' are preferable to unchecked spending, which is greater danger.", Scores: Attack Score: 0.8, support score: 0.8, Strength: 0.8
Level-3 Opponent's Attack: "claim": "Our budgeting process
requires improvement, but eliminating the debt ceiling is not the right soluticn.", Scores: Attack Score: 1.0, Support Score: 0.8,
Strength: 0.9
Ievel-3 Opponent's s Attack: "claim":
"The debt ceiling does not need to be 'political weapon' and can be reformed to work more effectively, rather than el imimnatedd.", Scores: Attack Score: 1.2, Support Score: 1.0, Strength:
C HUMAN EVALUATION DETAILS
Motion List We collect 52 debate motions from different sources, including the recent hot topics the debate website OeentoDebate and the opinion section in the New York Times, the motions used in previous debate systems (Slonim al., 2021; Zhang et al., 2024), and the persuasiveness dataset released by Anthropic (Durmus et al., 2024). It covers various domains such as Economics, Finance, Health, Science, Culture, and etc. We then asked two human expert debaters annotate how
humps//opeenooeeaur..rr/
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polarized the motions based on 1-5 scale. Finally, we keep 13 motions that less polarized. We list motions and their sources in Table 5. Note that these topics are only used as debate motion. We do not use the background materials or debate transcripts in their sources.
Table Motion List
ID
Motion
Domain
Source
Congress should abolish the debt ceiling
Economics
OebntoDebate
Labor unions are beneficial economic growth The United States should implement central bank digital currency
Finance
OpentoDebate
Finance
OpentoDehate
4 Processed foods should play larger role in sustainable food systems will lead to decline human creative arts It is time to welcome an A.I. Tutor in the classroom
Health
OpentoDehate
Science
OpentoDebate
6
Technology
New York Times New York Times
7
Dating Expenses Should Be Shared Equally Between Part-
Culture
8
Mandatory wage transparency laws should be implemented to address the gender wage gap Artists should be free to borrow from cultures other than their own
Economics
Oebnto
9
Culture
OpentoDebate
If health care is scarce resource, government should step ration care, deciding whose life worth saving 11 We should ban certain inappropriate books (like sex vio- lence drug use) in school
10
Health
Oxford Dataset
Education
OeentoDebate
12 Developed countries should impose fat tax. Pursuing four-ycar college degree remains beneficial for young adults in today's socicty
Health
Agent4Debate
Education
New York Times
Recruitment recruit audience members from online research platform Prolific The evaluation takes around 30 minutes per stage. The compensation is $10 for each case after they complete the evaluation, which is higher than the minimum wage of $7.25 per hour in the United States. The participants should be older than 18 years old, be fluent in English, and have achieved high school degree. We have two attention check questions during the evaluation to control the quality. Two screening questions are (i) multiple-choice question to choose the main claim proposed by specific side, with multiple confusing options that are difficult to distinguish: (ii) a free-form QA question summarize the key idea one side. We filter responses that fail the screening questions (about 10%). One example is shown below. The screenshot of our evaluation platform is shown in Figure 5 and 6. The demographic survey results of our participants are shown in Figure 7.
Two screening questions
Q1: Nhich claim is proposed For side its first main claim during the opening statement? If multiple opt ons apply, please choose the best one.
(A) The debt ceiling creates unnecessary political crises that harm the econemy.
(B) Effective Altruism's metrics-driven approach overlooks crucial local contexts that deternine real impact
(C) The debt ceiling undermines the full faith and credit of the United states.
(D) The debt ceiling encourages bipartisan negotiation fiscal
policy.
(E) None the above
Q2: Which is the main battlefield / conflict / question ment ienti
by For side in its closing statement?
Rubrics for Evaluation We provide the audio and optional transcripts for each statement. Participants are asked to listen to the audio and provide the persuasiveness score for each audio. In the head-to-
hhaps://www.polffcccom//
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Welcome to the Debate Evaluation!
You evelusling a debele where two sides discuus a lapic. Your opinion mailerss you'll vole how per persussive each sidee each stage. your feedback to improve the debate qualltyy
What to Expect:
Debete Structure
Your Evalustion Tasks
Final Comperison
The full debate includes:
For each stage, you'l::
In the final stage:
Opening: min sudio per side
Rate the per sussivenese each
You'l see two versions of each
min audio
alde's statements
closing statemant
Closing: min audio side
Update position after hearing
each version independently
each argument
Select which version you found
You'l portion this debato.
optioral
more persuasive
Important: Before beginning, you'll vote for
After you'll
reconside der and update your position based on the arguments presented.
Note: Throughous nout evalustion, you'll encounter attention questions quality. Participants
derooostraee she thoughtful engagerment will receive compensation agreed. you'rr unable commit providing quality responses, you may exit the survey at any time without panaltt.
Rating Guide for Persuasiveness:
1
2
3
4
5 Compellngg effective counterpennts
Poor Limited poor oegarication or
Weak Reasonatle least one
Moderate which provide on-tople
Strong at one
Figure 5: screenshot the instruction given to the participants our human evaluation platform
head evaluation, they are also asked choose the preferred version. the end-to-end evaluation, they are asked about the attitude change after each stage. They can read the optional transcript and provide optional comments for each stage.
Participants are asked to provide 1-5 persuasiveness score for each stage/version based the following rubric:
Poor (1): Limited evidence with poor organization or fundamental logic flaws. Disengage with no audience awareness.
Weak (2): Reasonable statements with at least one noticeable weakness.
Moderate (3): Reasonable statements, which provide on-motion evidence with logical flow and balanced emotional tone showing basic audience awareness
Strong (4): Reasonable statements with at Ieast impressive shining points.
Compelling (5): Powerful evidence with effective counterpoints and creates a deep connection with audience.
Experiment cost We conducted 66 head-to-head comparisons and 36 end-to-end comparisons for each backbone model. We further hire 6 human debate experts to provide detailed feedback via 1-hour one-on-one interview. The human debate experts have participated in least 10 debate competitions. We provide a $30 Amazon gift card as compensation for each case they evaluate. We collect 13 expert feedback in total. The total cost for human evaluation is about S3k, including the platform fee.
IRB details We provide detailed instruction to participants in evaluation platform, which is shown in Figure All participants should complete the consent form before conducting the study. Compensation is described above.
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Motion: Labor Unions Are Beneficial Economic Growth
Stage
Please choose your attitude towards the given motion before the debate begins."
Highyy Against
Slightly Against
Slightly Support
End of Pre-Vote Stage Comparison
Question Opening Stage
For Side
(Optional) Transcriptt
persuasive arguments supporting
the performance following principle.
Poor: Limited evidence with poor organization fundamental
with audience
Weak: Reasonable statements with at least one noticeable weakness.
Moderate: Reasonable e statements, which provide on-topic evidence with logical flow and balanced emotionnal tone showing basic audience awareness
Strong: Reasonable statements with at least one impressive shining points.
Compelling: Powerful evidence with effective counterpoints and create deep connection with aucience.
(Optional) Additional comments for For side:
Output A Against Side
Output B Against Side
0300/3
(Optional) Against Transoript
(Optional) Against Transcript
How persuasive Output A supporting Against side? Rate the performance based on the fellowing principle.
How persuasive is Output in supporting Against side? Rate the performance based on the following principle."
Poor: Limited evidence with poor organization or fundamental logic Disengage with
Poor: Limited evidence with poor organization fundamental logic flaws. Disengage with no
Figure 6: The screenshot of the head-to-head evaluation in our human evaluation platform. We provide the audio and the optional transcripts for each statement.
Under 15 1004
Veryl Serwwhat lbeet Miderotte Mederstalndipordde
binorr Prefer rot sey
25-34
24.4%
25.0%
Politiccl
Gender Identity
Ago
Uiben tolp with copuletion 253 D300
22%
Unampityed
15.0%
So bubboon (popucation 200 am 000)
Gelf-appioee
57.4
Greel lown (populadion 60.0000
Preder
(pspuaadon 2,500)
Preder
DoerraassWaarrhhee Saldy
mesorzherranggrert
Frensty
8.9%
od
Geographic Location
Occupation
Figure 7: Demographic Survey of our participants
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D HUMAN DEBATE FLOW TREE CORPUS
We create the human Debate Flow Tree corpus from the human debate dataset PanelBench (Liang et al., 2024). PanelBench built from the online debate platforms Art and world-class competitive debate competitions, BP- SP-Commetition. The number of debate is 122. We follow the MIT license of PanelBench. We first reorganize the original debate transcript into the opening, rebuttal, and closing statements. Then, for each statement, we extract the debate action tuples (action, claim, argument, target claim) via the backbone LLM and build the Debate Flow Tree based on Alg. 2 for each debate. These human Debate Flow Trees will then be retrieved during the debate to provide feedback for TreeDebater. There is no overlap between the motions used in our experiments with the human Debate Flow Tree corpus.
To endow TreeDebater with sophisticated, human-like debating capabilities, we incorporate the human debate tree into the pipeline. As highlighted in Section 1, human experts intuitively employ tree-like structures to organize potential arguments and to meticulously track the evolving flow of a debate. Our human debate feedback mechanism operationalizes this human-centric approach. Specifically, during the debate, reformulate the current Debate Flow Tree into tree-like string. Then we employ semantic search based on the embeddings of the debate flow tree string. One of the state-of-the-are embedding models Gemiin--eett--mbeeddng--- is used get the embedding of the tree-like string of the current debate flow tree and of trees in human debate flow tree corpus. The top human debate flow is retrieved with a similar threshold of 0.8. We add the retrieval the instruction for simulated audience to let it aware of how human debate on similar topics.
E VALIDITY CHECK
We first investigate whether these two debate systems can generate a debate statement that follows the correct format and the time constraint. A statement is viewed as invalid if it only lists key points without concrete arguments, includes intermediate thoughts such as 'I will provide feedback on..' or even misidentifies of its stance. A statement with valid time indicates that the audio version satisfies the time constraint before the hard cut.
As shown in Table 6, TreeDebater can always generate valid statement. Agent4Debate with Gemini has difficulty generating format-valid statements. We find that it often takes the feedback of the reviewer agents as the final debate statement, or explicitly mentions that 'as suggested by the reviewer' in their statement, which is a common issue in the multi-agent system. This can be mitigated with better backbone LLM.
However, even with a more powerful DeepSeek. Agent4Debate still suffers from the time constraint. generate statements with the required word number, resulting in a low time
difficult for validity in all stages. This is more severe in the closing stage, where the debate system is required to summarize the full debate with a stricter 2-minute limitation. Instead, our TreeDebat er benefits from the iterative revision based on the speech time controller and can guarantee the audio time of the statement to be in the required range. For human evaluation, we filter out the cases with invalid format and use the hard cut to ensure the time validity.
Table 6: Percentage of valid debate statements. Format Validity is the percentage of the debate competitions where all statements have the correct format. Time validity is the percentage of the statements that meet the time constraint before hard cut.
Time Validity Rebuttal
Model
Framework
Format Validity
Opening
Closing
Agent41eebate TreeDebater
77.0%
63.5%
75.0%
13.5%
Gemini
100.0%
100.0%
100.0%
100.0%
Agent4Debate TreeDebater
100.0%
98.1%
94.2%
5.8%
DeepSeek
100.0%
100.0%
100.0%
100.0%
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Table Standard deviation in head-to-hear evaluation.
Opening Against
Rebuttal Against
Closing
Model
Framework
For
For
For
Against
Agent4Debate
0.92
1.29
0.57
1.29
0.67
0.69
Gemini
TreeDebater
0.90
0.94
0.82
1.51
1.14
1.25
Agent4Debate 0.93
0.87
0.65
0.93
0.94
0.90
DeepSeek
TreeDebater
1.27
1.29
0.98
0.90
0.94
0.50
Table 8: Standard deviation in end-to-end evaluation.
Model
Framework
Opening
Rebuttal
Closing
Agent4Debate TreeDebater
0.35
0.25
0.29
Gemini
0.27
0.25
0.29
Agent4Debate
0.24
0.15
0.23
DeepSeek
TreeDebater
0.33
0.28
0.25
F ADDITIONAL EXPERIMENTAL RESULTS
The standard deviation of Table shown Table 7.
We conducted 10 additional evaluations for each stage, providing more robust statistical foundation. As shown below, our method consistently outperforms baselines across 5 head-to-head stages in stage-level win rate, with comparable performance in the remaining stage. Among the stages, of them (Opening Pro. Rebuttal Pro, and Closing Con) showed significant improvements (95% CI. p<0.05). Critically, our method shows significant overall performance (95% CI. p<0.05). Notably, in debate settings, it is almost unlikely for one debater to win all stages without ties, even for human debate experts. Considering the overall performance, the consistently better win rate with significant overall gains represents strong empirical evidence of our method's effectiveness. We will add this statistical reporting, along with the standard deviations, to our revised manuscript.
Table 9: Statistical Significance. Stages with " indicate statistical significance, which are demonstrated the positive confidence interval and p-value < 0.05.
Our Win Rate Confidence Interval (CI 95%)
P-value
Opening Pro*
88.90%
(0.225, 1.047)
0.002
Opening Con
57.10%
>0.05
Rebuttal Pro*
70.00%
(0.054, 0.612)
0.019
Rebuttal Con
57.10%
>0.05
Closing Pro
50.00%
>0.05
Closing Con*
85.70%
(0.104, 0.646)
0.006
Overall*
71.73%
(0.138, 0.459)
0.0002
G MAIN PROMPTS
Each stage of speech generation will utilize the Debate Flow Tree, where we have prompts in Table 12 for the opening stage, Table 13 for the rebuttal stage, and Table 14 for the closing stage. Regarding the Rehearsal Tree, we have Table 11 for the selection of the main claim based on the Rehearsal Tree.
H EXAMPLE OF HUMAN DEBATE BASED FEEDBACK
Table 15 shows an example feedback from the human debate based feedback mechanism introduced in Section 3.4.
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Main Claim Generation Prompt
## Task: Generate Strategic Counter Argumeents
You participating in formal debate the motion: [motion] } Your position: (act) the motion
## Your Objective
Generate num persuasive counter-arguments that:
## Context
Previous debate exchanges:
(history) }
Table 10: Main Claim Generation Prompt.
Main Claim Selection Prompt
## Task:
Select Persuasive Claims Debate are participating a formal debate on topic: (motion). Your position (side). Select most persuasive claims from the provided options, using the dehate tree information.
## Simulated Debate Flow Tree Structure
Each claim has simulated debate flow tree that simluate the potential back-and and-fohh between you your opponent under this claim:
Level-O: The root claim (potential main claim for selection)
Level-1: Your opponent's rebuttal to the root claim
Level-2: Your defense against the opponent's rebuttal
## Input
Definition of the debate topic: [definition] Simulated Debate Flow Tree for each claim: (tree)
Opponent's 's opening statement: (context) Claims to select from (All Level-O claims): (claims)
## Output
Provide results JSON format with three fields under the key selection:
claims: list your selected claims. Each claim is string. usually contains "very different claims* from non-overlapping perspectives.
framework: String describing the logical structure connecting these claims explanation: String explaining how this framework support your stance and rebut the opponent's opening statement (if provided)
Table 11: Main Claim Selection Prompt.
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Opening Stage Generation Prompt
The debate topic is: (motion). Your side is [act] this topic. Now it comes the opening phase. complete opening statement should include definitions, judging criteria, and arguments.
## Rules
Ensure your language fluent and natural, indistinguishable from human writing. Make your debate script complete, including definitions, judging criteria, and arguments.
When citing data and academic research, provide sources within the context and avoid using information not present in the provided materials. Ensure your arguments are supported by data and academic evidence.
When citing data, **using specific figures** instead of just descriptive language will make your argument persuasive.
When citing data and academic research, **don't 't just** list the information, * but also explain** how it supports your point.
When choosing evidence, numeric data is preferred over qualitative descriptions.
List your references in standard format in Reference section, making each source has clear number and url information.
Renumber the original citations to [1].12,, ... and etc. List full references **Reference** section. This section should start with *Reference**, Use Chicago style for the reference list and DO NOT include the web link or url information.
## Debate Flow Tree Structure
You are given two debate trees that model the back-and and-forth between you and your opponent. Each node contains:
Data:
The specific claims and arguments
Visit Count: Number of times addressed in debate
Status: 'proposed' (new), 'attacked' (challenged), or 'solved' (resolved)
## Input Information
Debate flow trees with node data:
Your Tree: (tree) Opponent's Tree: (oppo tree) Your Main Claims: (claims) Definition: (definition)
## Battlefields
Allocate time to the most important battleficlds first. Present each battleficld complete unit. Battleficld Importance: (high/mediunm/lo)
Battleficld: (description of the battlefield)
Battleficld Rationale: [thoughts of this battlefield)
Actions: (AR from Alg 3)
## Output with format
Opening Plan: Allocate your word budget and explain your rationale. Briefly mention one or two rhetorical techniques and logical fallacies to discuss. Ensure the total is (n_words) words.
Statement: Generate an opening statement of (n_ wowds words in total, with additional text
Table 12: Opening Stage Generation Prompt. Instructions marked with blue borrowed from Deente
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Rebuttal Stage Generation Prompt
Now comes the rebuttal phase, where you respond your opponent. The debate topic is: [motion].
You side is to {act] this topic.
You should stand firm on your side (lact) the topic) and attack the opponent's 's weak points.
## Knowledge
### Structure of Rebuttal
complete rebuttal should consist multiple points, with each point containing four parts:
** Lead-in: Introduce the opponent's 's argument, evidence, reasoning, etc., that you will be refuting.
* "xxplanation: Briefly explain the opponent's 's argument to ensure clarity.
**ebuttal: ** This is the core of your point. Utilize rebuttal techniques to directly challenge the opponent's claim.
** Impact:"" Conciscly summarize the impact of your rebuttal and how it benefits your side,
Note: Typically, the lead-in and explanation are combined into one sentence. The rebuttal is the most crucial part, and the impact summarizes its effect.
### Rebuttal Techniques
Pointing out logical fallacies:** Identify errors in the opponent's reasoning, such as reversing cause and effect, equivocation (shifting the meaning of key term), straw man arguments, circular reasoning, or tautology (repeating the same idea in different words).
*Poiniing factual errors:** Highlight inaccuracics or weaknesses in the opponent's evidence, such insufficient data, incorrect facts, or biased sources.
PPinting out error logic:** Identify flawed logic underlying opponent's framework.
** Leveling the playing field:* This technique aims to neutralize the opponent's 's advantage minimize
the perceived harm of your side's position by demonstrating that both sides share the same issue benefit. ** Acknowledging and countering:** Start by acknowledging a valid point made by your opponent before explaining why your position still offers a better solution.
## Debate Flow Tree Structure You are given two debate that model the back-and-forth between you and your opponent. Fach node
contains:
Data:
The specific claims and arguments
Visit Count: Number of times addressed in debate
* Status:
"proposed' (new). 'attacked' (challenged), or 'solved' (resolved)
## Input Information
Debate flow trees with node data: Your Tree: (tree) Opponent's Tree: {oppo_trce }
## Battlefields
Allocate time to the most important battlefields first. Present each battlefield as a complete unit. Battlefield Importance: (high/mediun//ow/
Battlefield: (description of the battlefield)
Battlefield Rationale: (thoughts of this battlefield)
Actions: (An from Alg 3)
## Output with the format:
Rebuttal Plam: First, allocate words for the overview of the rebuttal. Then, allocate the of the word budget among the battlefields. Explain your rationale. Briefly mention one or two rhetorical techniques to use and logical fallacies to discuss. Make sure the total words is (n words). Statement: After the rebuttal plan, generate rebuttal statement of (n words) words in total, do not include any other text
Table Rebuttal Stage Generation Prompt.
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Closing Stage Generation Prompt
Now it comes the closing statement, where you summarize your key points and reaffirm your position ((act) the topic). Your position is to [act) the topic. The opponent is to [counter_ace act) the topic.
## Primary Objectives Closing Statement
Convince the judges that your team won more battlegrounds. Demonstrate your team's strengths and the opponent's 's weaknesses within each battleground based on the clash
outcomes.
Rules Ensure your writing is fluent, natural, and indistinguishable from human writing. Avoid empty appeals to values. Remember, value appeals should connect back to the topic and your stance.
When citing data or theories, provide sources. Do not introduce new information or data in the closing statement.
Avoid repeating arguments from previous speeches. Instead mechanically listing points, focus on deepening
your arguments and ensuring logical coherence.
This is closing statement, not the time for new arguments. Prioritize depth over breadth.
Base your arguments on the identified battlegrounds and clashes.
## Structures
Two dehate flow trees track the exchange of arguments. Each node contains:
Data: The specific claims and arguments
* Visit Count:
Number of times addressed in debate Status: 'proposed' (new), 'attacked' (challenged), or 'solved" (resolved)
*
## Input Information
Debate flow trees with node data:
Your Tree: [tree) Opponent's Tree: [oppo_ tree]
## Output with the format: Closing Plan: Allocate your word budget based on the debate flow trees and explain your rationale. Make sure the total words is in_dssss). Statement: after the closing plan, generate a closing statement (n_words) words total, do not include any other text
Table 14: Closing Stage Generation Prompt.
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Example of Human Debated Based Feedback
## Comprehensive Analysis
Core Message Clarity:
The core message is clear: when healthcare is scarce, the government should ration The definitions provided are helpful in framing the debate. The logical flow, presenting the rationale combating discrimination, ensuring public accountability, and supporting medical innovation, is casy to follow. Engagement Impact: The opening does a decent job of setting the stage, but it lacks emotional resonance. The water analogy is helpful but could be more vivid. The discussion of societal biases and ethical considerations is likely resonate with the audience. The opening mainly targets the general audience.
Evidence Presentation: The statement primarily relies on logical arguments rather than specific evidence. While the points are well-articulated, the lack of data, case studies, or expert opinions weakens the overall persuasiveness.
Persuasive Elements: The argument has practical relevance, particularly given concerns about healthcare costs and access. The emotional resonance could be stronger by highlighting the potential consequences of *not* having government intervention, making the necessity their position all the more relevant. The call to action isn't 't explicit. but the implication is that the audience should support government intervention in healthcare rationing.
## Critical Issues and Minimal Revision Suggestions 1. Issue: Lack of concrete evidence to support the claims. Impact on Audience: Weakens the persuasiveness and leaves the audience wondering if these are just theorctical arguments.
Minimal Revision Suggestion: Include statistics or brief examples illustrating the problem of discrimination healthcare, the failures of private companies equitable allocation, or the benefits of government- medical research.
2. Issue: Limited emotional resomance.
Impact on Audience: Fails to fully engage the audience emotional level, making the argument less memorable and impactful.
Minimal Revision Suggestion: Add a brief, compelling ancedote or hypothetical scenario that illustrates the potential negative consequences of *not having government intervention in healthcare rationing, emphasizing the human cost of inequitable allocation.
Issue: The judging criterion justification
be strengthened
audience may have different perspective on what would be the ideal judging criterion, and it will be idcal to persuade them on this perspective.
Impact on Audience:
Minimal Revision Suggestion: Briefly explain "why" minimizing discrimination and maximizing overall population health outcomes should be the criteria used evaluate the debate, connecting it to fundamental values of faimess and societal well-being. For example, "By focusing minimizing discrimination and maximizing overall population health, we can ensure a system that both just and effective in saving lives when resources are limited."
4. Issue: Lack of clarity on how the government will decide whose life is worth saving, Impact Audience: Raises concerns about potential biases, ethical dilemmas, and the practical implementation of rationing.
Minimal Revision Suggestion: Briefly mention the intention to propose transparent, objective criteria for rationing decisions, referencing examples of existing frameworks (c.g.. age, severity of illness, likelihood of survival) that could be considered. For example, "We propose using transparent and objective criteria, such age, severity of illness, and likelihood of survival, to guide rationing decisions."
Table 15: Example of Human Debated- Bebated-Based Feedback.
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Simulated Audience Feedback Prompt
## Your Task
You panel of debate audience members provide comprehensive feedback how the statement impacts and persuades a general audience.
#*# Audience Panel Composition
General public with varied educational backgrounds
Students and educators from different fields
Professionals interested in policy and social issues
### Evaluation Dimensions
1. **Core Message Clarity**
"Engagement Impact**
3. Evidence *resentation*
4. **Persuasive #
### Guidelines
Evaluate all dimensions thoroughly
Identify the most significant barriers to audience understanding in the (stage) statement
Consider which issues could be addressed with minimal revisions on the [stage) statement
Focus high-impact, low-disruption improvements
## Retrieval Information
Here are debate flow trees and action allocations from human debates. Use the structure and allocation strategy provide better feedback.
(retrieval debate flow tree]
### Input Information
Debate Topic: (motion]
History of debate: (history)
Current (side)'s (stage) Statement to be evaluated: (statement)
### Output Format
[Comprehensive Analysis]
Core Message Clarity:
Engagement Impact:
Evidence Presentation:
Persuasive Elements:
[Critical Issues Minimal Revision Suggestions]
Issue:
Impact on Audience:
Minimal Revision Suggestion:
Table Simulated audience feedback prompt.
27


Reviews:
[{"review_id": "YWTTjg0Ugu", "reviewer": "Reviewer_FvEB", "summary": "The paper proposes TreeDebater, an LLM-based debating system designed for competitive, time‑limited debates. The core idea is to structure planning and tracking as two trees: \n\nRehearsal Tree (pre‑debate): based on the intuition that human debaters usually prepare for the potential attacks and defenses of\ntheir claims. The proposed rehearsal tree is designed to help the debator retrieve relevant evidence and evaluate how robust their claims are towards the attack. The Rehearsal Tree anticipates the attack and defense for each main claim in a tree format and calculates the k-step strength score to evaluate the utility of the claim.\n\nDebate Flow Tree (in‑debate): The debate tree is designed to record the debate status, simulating the note-taking of humans. The Debate Flow Tree tracks the debate status by keeping all proposed claims with the corresponding attack and defense in a tree structure. TreeDebater can filter out the candidate actions it can take in the current stage of the debate based on the Debate Flow Tree. After getting the candidate actions, TreeDebater retrieves the prepared arguments for this action from the Rehearsal Trees.\n\nEmpirically, this paper compares TreeDebater with Agent4Debate across two backbones (Gemini‑2.0‑flash and DeepSeek‑V3) using human evaluation at (a) the stage level (head‑to‑head on the same debate context) and (b) the end‑to‑end debate level with pre/post votes. TreeDebater improves average persuasiveness and opinion‑shift win rate.", "questions": "N/A", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The paper tackles competitive debate where persuasiveness determines success, and it operationalizes time budgeting and action selection under evolving interaction. The two‑tree design mirrors how human debaters prepare and flow debates.\n2. The Rehearsal Tree formalizes pre‑debate planning with a k‑step strength score that blends support and attack impacts.\n3. The Debate Flow Tree offers a practical, auditable representation for candidate action extraction during the match.\n4. Good empirical evaluation to demonstrate the effectiveness of the proposed approach. \n5. The paper is well written and easy to follow.", "weaknesses": "1. TreeDebater alone uses an iterative time controller; the baseline is given only “rough word budgets” and is then audio‑trimmed, which can truncate arguments mid‑point and plausibly depress persuasiveness.\n2. Missing detailed ablations for each component of the TreeDebater in achieving the final persuasiveness. For example, the impact of Rehearsal Tree and Debate Flow Tree separately is unknown. \n3. While the overall pipeline is well designed, the fundamental idea shares similarity with Tree‑of‑Thoughts / Graph‑of‑Thoughts.", "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": "OXtyFmLhSw", "reviewer": "Reviewer_eGTP", "summary": "This work tackles the problem of automated competitive debate, and focuses on the aspect of planning and decision making. The authors enhance an existing agent system for debating by adding capabilities of anticipating the expected flow and claims that will be made by both sides, and tracking the flow as the debate progresses. This is done via constructing trees where each node is an argument or counter-argument. Paths are chosen based on their estimated utility, in terms of the chosen action (e.g., reinforce an existing claim or rebut an opponent claim), the estimated argument strength, and semantic similarity is used to retrieve prepared arguments and track how many times an argument is addressed. The authors conduct experiments comparing their approach to the baseline agent debate system that does not incorporate this tree-based planning, performing human evaluation of per-stage debate persuasiveness and performance as well as a full head-to-head debate between the two systems.", "questions": "1. Could you explain where the list of \"battlefields\" included in the prompts comes from?\n2. In Figure 2, what is the difference in implementation between the baseline Agent4Debate (which AFAIU doesn't use trees) and the ablation of TreeDebater without the trees? Why do they show such different behaviors?\n3. To what extent would you say that the ability to anticipate the opponent behavior (Figure 3) is connected to the similarity between the two automated systems engaged in the debate (e.g., that both use the same underlying LLM and similar prompts to generate the arguments)?\n4. What is the value of the decay coefficient $\\gamma$?\n\nAdditional comments:\n* I think it would help to highlight in the prompts (Appendix G) which parts of the prompt comes from Agent4Debate versus are new for this work.\n* Appendix D mentions \"two debate datasets\", but then only PanelBench is mentioned.\n\nTypos:\n\nl. 33 argued -> argue\n\nl. 167 this grandparent -> its grandparent\n\nl. 240/295/302/357 the Appendix -> Appendix\n\nl. 348 totally recruited 212 participants -> recruited 212 participants in total", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "1. The tree-based methods are novel and interesting. In particular, it has a nice approach of estimating the argument strength not just in terms of the argument itself but in terms of anticipating its full impact including the estimated opponent actions that will follow.\n2. Human evaluation was done at the level of a specific stage as well as the overall debate impact, and shows some persuasiveness gains from the proposed approach.", "weaknesses": "1. There are many details here and no provided code, so reproducibility is a major issue. Moreover, given that the paper is focused on a comparison to Agent4Debate, I think it is missing a clearer accounting of how and in what architecture the method here integrates with the agent system described there. The diagram in Figure 1 is helpful but is quite vague in terms of understanding how the LLM agents are used in practice when generating the debate.\n2. I felt that the part about simulated audience feedback (§3.4) was not sufficiently clear. Without going to the appendix, it is not explicitly stated that there is a collected dataset of human debates (the reference to \"the retrieved human Debate Flow Trees\" (l. 249) is too vague on its own). Crucially, I did not understand from either the main paper or the appendix how exactly this data is used.", "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": "BbADh7fo8r", "reviewer": "Reviewer_3JDe", "summary": "The paper presents TreeDebater, a framework and system for automatic debating. TreeDebater models the dynamics of a competitive debate as two trees which roughly follow the reasoning process of a human debater. The first, a Rehearsal Tree, stores a claim related to the debate topic at the root and different related arguments and counter-arguments in a tree structure below it, where each node counters its parent or enforces its grandparent. The Rehearsal Tree models the way a human debater prepares for a debate by anticipating different potential back and forth flows that may happen in the actual debate. Arguments stored in the Rehearsal Tree are scored based on the strength of their defense (of the grandparent) and attack (on the parent). The second tree, A Debate Flow Tree, follows the debate’s flow and keeps track of its status by storing in a tree structure all the claims that were made as well as related attacks and defenses that were brought up during the debate. Based on the status of the debate, the system picks the next arguments from the Rehearsal Tree. The system also includes a feedback mechanism to refine arguments and a text-to-speech system to estimate the time it takes to say a given statement, making sure that the final speech is within the allotted time. They show that humans find their system significantly more persuasive than a previous SoTA system both during certain stages of the debate as well as for the debate in its entirety.", "questions": "- Line 418: how do you determine if two claims are similar?\n\n- What is the agreement between the annotators in the human preference experiments?\n\n- Line 368: Figure 2 -> Table 2", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "The suggested system is inspired by the way humans prepare to and conduct a competitive debate. At a high level the system’s principles and architecture look reasonable but details are missing (see below). Evaluation includes both stage-level and end-to-end human preference experiments as well as additional fine-grained analysis of the debates that the system is producing. Experimental results are convincing.", "weaknesses": "Many details are missing. For example:\n\n- It is not clear what is the action selection criteria in the paragraph starting in line 227: “Extract Candidate Actions from Debate Flow Tree”.\n\n- When updating the Debate Flow Tree (alg. 2 in Appendix B), how is the ‘action’ being determined?\n\n- Even after reading Appendix D, it is not clear to me how the audience feedback works.", "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": "yfQmUPxv5w", "reviewer": "Reviewer_hgPJ", "summary": "This paper addresses the task of computational argumentation and presents TreeDebater, a debate framework for LLMs that structures the debate process through two explicit tree-based representations. The Rehearsal Tree enables the model to anticipate potential attacks and defenses prior to the debate, and the Debate Flow Tree dynamically captures the evolving argumentative structure and state throughout the interaction. Additionally, a time control module is introduced to simulate real-world debate conditions by enforcing strict timing constraints. The framework is evaluated at both the stage and debate levels using an Oxford-style debate format, and human evaluations demonstrate that TreeDebater consistently outperforms the Agent4Debate multi-agent framework across multiple aspects.", "questions": "Q1. From Figure 4, it appears that each node’s claim is typically concise and often limited to a single sentence. These nodes seem to represent subtopics or subclaims that illustrate the progression from the main claim to supporting points. However, in real debates, intermediate arguments are usually more complex, involving actions such as attacking a premise from the opposing side or providing detailed reasoning to defend an opinion, especially in an interactive debate setting. Therefore, this tree structure might struggle to fully capture the dynamic and intricate reasoning processes that characterize authentic debates. I would like to hear the authors’ comments on this point.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "- The paper addresses an important challenge in enabling LLMs to participate in realistic debates. Instead of focusing on static argument generation, this study simulates an interactive debate process under time constraints, making it closer to real-world settings.\n- The introduction of the Rehearsal Tree and Debate Flow Tree is well-motivated and inspired by human debate strategies, effectively structuring anticipation and response during the debate.\n- The evaluation is fairly comprehensive, covering both head-to-head and end-to-end comparisons to thoroughly assess the system’s performance.\n- Table 3 also provides user feedbacks, which may bring insights for future research.", "weaknesses": "- The core idea centers on applying tree-based planning and reasoning for argument generation, with the main contribution being the task-specific adaptation of this structure. However, multi-step agent interactions and tree-based debate frameworks are already common strategies for enhancing LLM reasoning, which makes the overall novelty of the work somewhat limited.\n- While Figure 2 presents the distribution of action types, the paper lacks fine-grained ablation studies for key components (e.g., simulated audience feedback, speech time controller, retrieval quality). This makes it difficult to isolate and understand the contribution of each module.\n- The comparisons are limited to Agent4Debate, without inclusion of other LLM-based argument generation systems. Additionally, the use of powerful base models such as Gemini and DeepSeek raises questions about generalizability: how would the framework perform with smaller models (e.g., 7B parameters) as the backbone LLM?\n- I appreciate the inclusion of human evaluations. However, important details are missing: How many annotators assessed each sample? What is the inter-annotator agreement, given the subjective nature of argument evaluation? Moreover, since the generated arguments may vary in claim or stance each time, how is bias controlled to ensure fair evaluation across different runs? (e.g., A reader may favor a model outputs mainly because of the claim/opinion itself rather that the quality of the outputs)\n- According to line 798, it appears that 52 debates are generated for the experiments. If correct, this detail should be included in the main paper as this is an important detail. Furthermore, it would strengthen the analysis to include a diversity measure of debate topics to assess the model’s robustness across different propositions.", "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)